By David Richards MBE

This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

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Bottom Line Up Front

Britain has a habit it cannot break. We invent the future, and then we sell it to people who understand its value better than we do. This week a select committee told ministers to remove Palantir from the NHS, and the company’s British boss spent the days that followed on the airwaves and in the papers explaining why that would be reckless. He is a fluent advocate. He is also, by training and temperament, a politician rather than a technologist, and that tells you exactly what this fight is really about. It is not about software. It is about who owns the most valuable dataset in the world, and whether we are about to repeat with our health records the mistake we made with DeepMind.

A lobbyist, not an engineer

Listen to Louis Mosley defend Palantir on the radio, as I did this week, and one thing becomes obvious. He does not sound like a tech chief executive. He sounds like a minister. The cadence, the appeal to fairness, the careful reframing of every awkward question into a question about patients: this is the grammar of Westminster, not Silicon Valley.

There is a reason for that, and it is not an accident of personality. Mosley read history at Oxford. He cut his teeth in Conservative politics, working as an aide to Rory Stewart and serving as a councillor in Kensington and Chelsea. In 2017 he came close to standing for Parliament, until the party grew nervous about his surname. He is the grandson of Sir Oswald Mosley, the man who led the British Union of Fascists in the 1930s, a piece of family history that has shadowed his public life and which he did not choose. He worked in finance, then joined Palantir in 2016, and now runs a UK and European division that accounts for roughly a quarter of the company’s entire global workforce.

What he was not, at any point, was an engineer. And the question worth asking is why an American software company would put a Tory networker rather than a coder in charge of its British operation.

The answer is that Palantir’s product is not really software. Its product is government. When a company sells data infrastructure to the state, its biggest obstacle is never a competitor. It is the state itself: procurement rules, select committees, ministers, the slow machinery of public trust. So you do not hire the best architect. You hire the person with the best contacts book. Mosley pitched directly to Michael Gove in 2019 and won a Brexit planning contract. When the pandemic hit, Palantir offered its services for a pound, then turned that pound into a sprawling presence across the NHS. The seven-year, three hundred and thirty million pound Federated Data Platform was the prize at the end of that road.

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We have seen this casting before

If a politician fronting an aggressive American tech firm feels familiar, that is because we watched it happen once already.

In 2018, with Facebook reeling from Cambridge Analytica and Brussels sharpening its regulatory knives, Mark Zuckerberg hired Nick Clegg. Not for his grasp of machine learning. For his Rolodex. The former Deputy Prime Minister became the respectable, centrist, European face of a company that badly needed one, and he spent years building the buffers, the oversight board, the careful language, that kept governments at arm’s length.

Then the politics changed, and so did the casting. In January 2025, three weeks before Donald Trump returned to the White House, Meta replaced Clegg with Joel Kaplan, a Bush-era Republican operative with deep ties to the incoming administration. The lesson was unmistakable. These appointments are not about conviction. They are instruments, swapped out the moment the political weather turns. Clegg was hired to keep regulators out of Meta’s business. Mosley’s job is the opposite: to get Palantir deeper into ours. One played defence. The other plays offence. The casting logic is identical.

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Mosley’s Telegraph defence is full of holes

This week Mosley took to the pages of the Telegraph to argue that the MPs attacking Palantir were putting politics above patients. It is a polished piece. It is also, on inspection, an exercise in answering questions nobody is asking while ducking the ones that matter.

Take the operational case. Mosley leans on Palantir’s own figures: more than a hundred and ten thousand extra operations, almost three hundred thousand patients discharged sooner. Those numbers come from the NHS’s own count, and they may well be real. But they have not faced serious external scrutiny, and critics inside the health service have noted that some “waiting list reductions” amount to little more than removing patients who stopped replying to text messages. A claim that cannot be independently audited is a marketing line, not a finding.

Then there is the committee itself. Mosley says the MPs urged cancellation with no idea what should replace Palantir. That is not what the report says. The Science, Innovation and Technology Committee, chaired by Dame Chi Onwurah, explicitly told ministers to either build a domestic replacement or find a UK provider. Its worry was sovereignty, the danger of leaning on a small number of vast American firms, and it warned in plain terms that the current arrangement leaves Britain exposed “at the mercy of foreign actors.” You can argue with that conclusion. You cannot pretend it was not made.

And on data, Mosley is at his most reassuring and his most slippery. There is no central vault to seize, he says; Palantir cannot use, sell or move the data. On the narrow point, he is right. Palantir is a processor, not a broker, and the data stays with each trust. But the committee’s fear was never theft. It was entanglement. Palantir’s platform burrows into the plumbing of individual trusts through custom connectors and proprietary code, and the deeper that integration runs, the harder and costlier it becomes to ever pull the company out. That is what lock-in means. Reassuring me that you will not steal the house does not address my worry that I can no longer change the locks.

He saves the loftiest line for last, insisting Palantir has never worked in states adversarial to Western values and only serves governments with a democratic mandate. Yet the protests outside his offices are not about abstract values. They are about a company with deep roots in US defence and immigration enforcement, and ties to military programmes abroad, now sitting at the centre of a service built on the idea of care for all. Amnesty International, medical groups and NHS staff are not confused about this. Mosley would prefer to call their objection ideology. It is not. It is a values question, and he knows it.

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The plumbing problem: ask the National Grid

Here is the part the patient-centred framing leaves out. We already know what happens when Britain lets a foreign, state-adjacent company into critical national infrastructure, because we have spent the last few years frantically undoing exactly that.

In 2023 the National Grid began ripping out components made by a unit of the China-backed firm Nari Technology, acting on direct advice from GCHQ’s cyber arm. The parts in question helped balance the grid and prevent blackouts. We had wired a potential adversary into the machinery that keeps the lights on, and then quietly tore it out again.

It did not stop there. The “golden era” welcome once extended to China General Nuclear curdled into a costly buy-out of its stake in our nuclear programme. And only this March, ministers blocked a one and a half billion pound plan by the Chinese firm Ming Yang to build what would have been the world’s largest wind turbine factory in Scotland, walking away from fifteen hundred jobs on national security grounds, with the Ministry of Defence warning the turbines could double as surveillance platforms.

Notice the pattern. Court the foreign money, embed the foreign technology, then discover, too late and at enormous expense, that you have created a dependency you cannot afford. The committee looking at Palantir has simply learned the lesson the energy world learned the hard way. Data is infrastructure too. It is the most strategic infrastructure we have.

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What this is really about: the goldmine

Strip away the politics and the reassurances, and the fight comes down to a single resource. Data is the oil of this century, and the NHS sits on one of the richest, cleanest reserves on the planet.

Because we built a single-payer system, we hold consistent, trusted records on roughly fifty-five million people, tracked from cradle to grave across decades. No fragmented American insurer can match it. Ernst and Young put a number on what that asset could unlock: as much as nine point six billion pounds a year, split between operational savings for the NHS and benefits to patients, once the data is properly curated and put to work. Call it the ten billion pound goldmine.

That is what the platform war is about. Whoever structures and operationalises this data gets to train the next generation of diagnostic models, risk tools and medical AI on it. The intellectual property, the patents and the profits then flow to private shareholders, while the public that funded the raw material for decades is downgraded from owner to mere data provider. We supply the gold. Someone else walks off with the finished jewellery, and then sells it back to us as a service.

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We invented the thing they fear. Then we sold it.

And this is where it becomes unbearable, because we have rehearsed this exact failure before, at the very frontier of AI.

The most advanced AI lab on earth was not born in California. DeepMind was founded in London in 2010. In 2014 we sold it to Google for around four hundred million pounds, outbidding a desperate Mark Zuckerberg in the process. It looked like a tidy exit at the time. It now looks like one of the great strategic giveaways in our history. DeepMind became the crown jewel of Google’s AI empire. Its founder, Demis Hassabis, won a Nobel Prize and now runs all of Google’s AI. His drug-discovery spin-out, Isomorphic Labs, is reportedly raising over two billion dollars. The four hundred million pound price tag has not aged well, and the real cost, the capability we handed to a foreign giant, cannot be counted at all.

We should have known what we were losing, because the people racing against DeepMind knew exactly what it was. When Elon Musk and Sam Altman were casting around for a name for the venture that became OpenAI, the court exhibits from this spring’s trial show Musk pushing one option above the rest. He wanted to call it Freemind, he wrote, because it would convey intelligence freely available to all, “the opposite of Deepmind’s one-ring-to-rule-them-all approach.” He told his co-founders that unless they hired the best people on earth they would “get whipped by Deepmind.” By his own account, Google at that point had snapped up something close to three-quarters of all the AI talent in the world. The most powerful men in technology built a company out of fear of what Britain had created.

Read that back slowly. Silicon Valley feared our lab so much it founded a rival to contain it. We feared nothing, so we sold it. Now the same instinct that gave away DeepMind is being asked to hand over the data that could have trained the next one. The idea that we need a Silicon Valley giant to make sense of British health records is not pragmatism. It is a failure of nerve dressed up as common sense. We lost our best technology. We must not lose our best data as well.

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The 17-Year Itch: Why Brilliant Technology Goes to Die in the NHS

If Palantir is the story of who owns the data, this is the story of why even the best technology so often rots before it reaches a single patient.

On this week’s episode of The Digital Forge Podcast I sat down with Richard Stubbs, chief executive of Health Innovation Yorkshire and Humber and a member of the MHRA’s national commission on regulating AI in healthcare. Stubbs has spent more than two decades inside the system, and he punctured a fantasy that every founder entering healthcare carries around: the dream of one meeting with a minister, one handshake, and a rollout to the whole country.

It does not work like that, and the reason is structural. American firms walk in, win the minister over, and walk out certain they have a deal for sixty million patients. Then someone has to break the news. As Stubbs put it, “There is really no such thing as the NHS.” There are five or six hundred separately governed, largely autonomous organisations, and every one of them has to say yes for itself. There is no big lever in a big room. What thrives in Bradford can be ignored entirely in Barnsley, and the result is a technological postcode lottery.

That fragmentation breeds a particular disease, the conviction that nothing proven elsewhere can be trusted here until it has been piloted all over again. “We have more pilots in the NHS than British Airways,” Stubbs told me, and the line lands because it is true. Proven technology emerges, a local pilot is approved, the metrics check out, the funding lapses, and the whole circus starts again somewhere new. Meanwhile the underlying reality stays medieval. In parts of the country, patient notes are still bundled into the back of a taxi and driven down the A1 because electronic records are not yet universal.

The number that should stop you cold is this. By one Royal College estimate, it takes an average of seventeen years for a proven innovation to reach mainstream NHS use. Seventeen years ago the App Store had only just launched. If we take the better part of two decades to adopt the present, we have no hope of keeping pace with AI.

Stubbs’s prescription is not to crush local decision-making but to be honest about the trade-off it carries. He wants a “comply or explain” regime: the centre sets a gold standard for a transformational pathway, and a region either adopts it or proves, in numbers, that its own way delivers better outcomes for the same money or less. No more reinventing the wheel as a reflex.

The sharpest moment came when we turned to safety, the word always reached for to slow AI down. Stubbs flipped it. “Nobody ever asks the question of: how safe are we right now?” Nothing in the NHS is a hundred per cent safe, he pointed out, yet we treat the exhausted, paper-bound status quo as if it were, and demand flawless perfection from the software meant to improve on it. By holding AI to a standard we never apply to ourselves, we keep patients waiting longer in settings that are already, measurably, less safe.

There are bright spots, and they show what is possible when the system gets out of its own way. Pinpoint, a machine learning tool developed over years in Yorkshire, reads previously taken blood tests and helps safely lift lower-risk patients off the two-week cancer pathway, giving them peace of mind and freeing capacity for those who genuinely need it. Ambient voice technology now lets clinicians hold a real conversation while the record writes itself. These are not science fiction. They are working today. The barrier was never the technology. It was us.

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Would AI Have Locked Us Down? (This Week’s Yorkshire Post Column)

The third strand runs through my Yorkshire Post column this week, and it asks a question I have turned over for six years. Would the models we have today have told ministers to shut the country?

Cast your mind back to March 2020. The plan was mitigation: shield the vulnerable, slow the spread, keep the schools and shops open. Then came Report 9 from Imperial College, which modelled five hundred and ten thousand deaths in an unmitigated epidemic and suggested that even mitigation could overwhelm hospitals. Within days the plan was dead and the country was indoors. It was not the only model. But it broke the deadlock, and it did so before anyone outside the team could read a single line of its code.

Some readers will remember that I was one of the people who did read it, at the time, and said so loudly in the national press. The code was a decade old, sprawling, with functions that looked, as the programmer John Carmack later put it, “machine translated from Fortran.” My objection was never that academic code looks like academic code. Carmack himself judged it held up better than its fiercest critics claimed, and on reflection I think that is fair. The scandal was narrower and worse. A tool built for experts to interrogate became an instrument of government before the public could interrogate it at all.

The danger of dressing a guess as a certainty did not end in 2020. This February, Britain’s own statistics watchdog had to write to the Covid Inquiry to correct it. The Inquiry’s summary had claimed modelling “established” that an earlier lockdown would have saved twenty-three thousand lives in the first wave. One word did the damage. Established. Not estimated, not suggested, not modelled. A what-if had been exhumed and presented as a fact, and the same overreach that drove the original decision was being written into its official autopsy.

So run 2020 again with the tools we have now. AI-assisted models would not have handed a minister the answer, and that is precisely the point. They would have watched first, reading wastewater, admissions, genomics and mobility together, faster than 2020 managed. They would have aimed at the network rather than the nation, closing the settings doing most of the spreading and leaving the rest running where the evidence allowed. They would have priced every path, lives against lost school years, risk against ruin, and put those trade-offs in front of the person who had to choose.

Would they have recommended a lockdown? Faced with a pathogen that spread fast and silently and killed at scale, yes, they might. Sometimes the only way to break an epidemic is to break a contact network. But which one, and for how long? Schools, pubs, care homes, cities, or the whole country? That is the question 2020 never properly asked, and it is the question that cost a generation of students two years of their lives.

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The Sunday Signal Tech and AI Layoff Tracker

Week 23 · 31 May to 6 June 2026

The macro picture hardened this week, and the official numbers are now starker than any single company’s announcement. According to Challenger, Gray and Christmas, US technology employers cut 38,242 jobs in May, the sector’s heaviest month in nearly two years, lifting its 2026 running total to 123,653, up more than sixty-five per cent on the same stretch last year. For the third month in a row, AI was the most-cited reason for layoffs across every sector. Andy Challenger’s verdict was blunt: AI is now the leading reason companies give for cutting jobs.

The emblem of the week was GitLab. The all-remote pioneer filed to cut about fourteen per cent of its workforce, roughly three hundred and fifty roles, while exiting twenty-two of the countries it operates in and flattening layers of management. Chief executive Bill Staples pinned it explicitly on the “agentic era” and the company’s Duo Agent Platform. What makes it the perfect case study is the accompanying earnings: first-quarter revenue up twenty-three per cent. This was not a distress cut. It was a deliberate redirection of payroll into compute.

Which is exactly why the “AI laundering” story is now mainstream. The pattern is no longer disputed even by the optimists. Sam Altman has conceded that some firms practise “AI washing,” blaming the technology for cuts they would have made anyway. And the most telling line comes from Challenger’s own data: AI is so far claiming the budgets for these roles rather than doing the work itself. The money once spent on people is being rerouted to GPUs in the hope the machines deliver. When Meta ties roughly eight thousand cuts directly to its AI infrastructure spending, and the big four hyperscalers plan some seven hundred and twenty-five billion dollars of capital expenditure this year, you are watching a wager, not a productivity gain. The bill has come due for human workers. The payoff is still a promise.

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Final Thought 🚀

Britain is not a country that lacks invention. We built the lab the rest of the world feared, and we sit on the richest health dataset on earth. The thing we lack is the nerve to keep what we make.

We sold DeepMind for the price of a footballer. We are being talked into renting out our data to the people who already own our best AI. And when something brilliant does survive contact with the system, it waits seventeen years for permission to help anyone.

The machines will not save us from this. The flaw was never in the technology. It is in us, and no model has ever been written that can fix that.

Until next Sunday, David Richards MBE

This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

Bottom Line Up Front

Whatever you believe about artificial intelligence, check the date on it. Most of what passes for informed opinion, in boardrooms, in Whitehall, in the better newspapers, was true a few months ago and is wrong now. That used to be a forgivable lag. In this field it is a disqualifying one. The technology no longer moves at the speed of quarterly reviews. It moves at the speed of the weekend, and the gap between what the machines can do and what the average decision-maker thinks they can do widens every Friday.

Here is the part that should concentrate the mind. The line that everyone assumed was years away, the one where the machine writes software better than the people paid to write it, was crossed late last year. Coding is the first knowledge profession where the substitution pattern became impossible to ignore. It will not be the last. Once the pattern is proven in one trade, every other trade that runs on rules and documents is simply waiting its turn. This week the proof arrived, the bill arrived, and the redundancy notices arrived. They arrived together. That is not a coincidence.

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Your mental model of AI expired while you were forming it

The clearest account of why this moment is different came not from a lab but from a venture capitalist. On 20 April, Sequoia Capital held the fourth edition of its AI Ascent in San Francisco, an invitation-only gathering of more than 150 of the people actually building this technology. Demis Hassabis was in the room. So were Andrej Karpathy, Greg Brockman and Anthropic’s Boris Cherny. The partner keynote that opened the day carried a title chosen to provoke, “This is AGI”, and it was Pat Grady, a partner at the firm, who set out the frame. He did it without the usual hyperbole, which is precisely what made it land.

His image for the shift was cars replacing horses. The last few years, he argued, gave us faster horses: applications that made you 10 or 40 per cent more productive without changing how you worked. Now the cars have arrived, tools that make you 10 or 40 times more productive and change the nature of the work itself. His framing of why is worth holding onto. Grady draws a line between two kinds of revolution. There are revolutions in communication, which change how information is distributed. The internet was one. Mobile was another. So was the cloud. Almost everyone alive has only ever lived through revolutions of that kind. AI, he argues, is something else. It is a revolution in computation, a change in how information is processed rather than moved, and that is a fundamentally different shape of wave.

He marks three discontinuities. The first was November 2022, when ChatGPT showed a mainstream audience the power of pre-training. The second came roughly two years later with o1, when a second scaling law appeared around inference-time compute and reasoning models started to think before they answered. The third is happening now, with Claude Code and the current Opus generation, which have demonstrated what long-horizon agents can do when you set them a job and leave them to it. Grady’s point is that these are not three even steps on a ramp. The jump from the second to the third is a hard break, a discontinuity, and most people have not registered that it occurred.

The phrase that lands hardest is his. The technology foundation, he says, is one where “the floor keeps moving underfoot.” Build something on Monday and the capability that made it clever can be commodity by Friday. For anyone whose mental model of AI was assembled even a season ago, the floor has already moved. You are standing somewhere that no longer exists.

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And the consequence he draws is blunt: “no lead is safe.” In a downpour of new capability, the incumbent advantage that felt permanent becomes a puddle. That is true of companies. It is also true of professions, and of the assumptions policymakers are using to regulate them. We are writing rules for a technology whose shape will have changed twice before the consultation closes.

Full keynote here:

One builder can now ship like a team of twenty

Start with coding, because coding is the canary, and the canary has stopped singing.

Think of a software engineer as the conductor of an orchestra. To conduct, you must be able to do two things at once: read the music, and play the instruments. Lose either and the performance collapses. For most of the history of the trade, the conductor also had to write out every note by hand. That is the part that has gone.

The most vivid proof point this week is Garry Tan, president and chief executive of Y Combinator, who has spent twenty years building products and is, on his own account, shipping more now than at any point in his career. In the README for his open-source toolkit, gstack, he lays out the numbers. In sixty days, working part-time while running YC, he shipped three production services and more than forty features. Measured by logical code change rather than raw lines (which AI inflates, as he is the first to admit), his 2026 run rate is around 810 times his 2013 pace. By mid-April his year had already produced 240 times the output of his entire 2013.

The number is arresting. The method is the lesson. Tan does not use AI as a faster pencil. He uses it as a company. gstack hands Claude a set of defined roles, twenty-three skills in all: a chief executive that challenges the premise of what is being built, an engineering manager that locks the architecture, a quality lead that opens a real browser, clicks through the staging app, finds the bugs and writes the regression tests. He opens the whole thing with a line from the researcher Andrej Karpathy, who said he had not typed a line of code since December. Tan’s own verdict on the productivity debate is the cleanest summary of the new reality you will read: “The point isn’t who typed it, it’s what shipped.”

Now the necessary cold water, because the 810x figure does not generalise and pretending it does would be dishonest. Tan is a senior builder working on greenfield code with no legacy to fight. That is the best case, not the average. Throw the same agents at a tangled enterprise codebase and they frequently slow people down, because someone now has to read, check and unpick what the machine produced. The industry has a name for it: the review tax. Across real organisations the honest multiple is closer to low single figures than to three. Gartner finds that roughly four in five firms piloting or deploying autonomous business capabilities have cut jobs, and that the financial returns are not yet showing up. The machine is not making everyone 800 times faster. It is letting one capable person orchestrate the work of a team, and it is removing the rungs of the ladder those teams used to be built from.

That distinction matters, because the same arithmetic is now being run in finance, in accounting and in law. The junior associate reading a data room. The analyst rebuilding a risk model. The bookkeeper reconciling a ledger at month end. None of them types in a different language from the engineer. They take data from one place, apply a set of rules, and put it somewhere else, and that is precisely the shape of work an agent does without complaint. The conductor’s job survives. The copying-out does not.

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If your job is a task, your job is the target

The honest test is one you can run this weekend. Could a competent intern do your role from a three-page brief? If yes, an agent will do it autonomously, and sooner than your employer is telling you. The work does not vanish. It moves up a level, from doing to directing, and the people who make that move first will hold the leverage. Here is the playbook.

The honest test is one you can run this weekend. Could a competent intern do your role from a three-page brief? If yes, an agent will do it autonomously, and sooner than your employer is telling you. The work does not vanish. It moves up a level, from doing to directing, and the people who make that move first will hold the leverage. Here is the playbook.

1. Audit your own week. For five working days, log every task in half-hour blocks, then mark each one against the intern test. The tasks that pass, the structured, repeatable, digital ones, are the targets. The tasks that needed empathy, judgment, negotiation or your signature on the line are your new job description. Find them before the org chart does.

2. Learn the shape of the thing replacing you. The firms moving fastest have stopped buying a single chatbot and started running crews. A supervisor agent takes the goal and breaks it down. Specialist agents do the narrow work: research, risk, drafting, formatting. A final agent cross-checks them and assembles the result. Your move is to stop being a specialist and become the supervisor, the one who designs the workflow, tunes the prompts and directs the digital team.

3. Plant yourself at the review tax. As execution gets cheap, verification becomes the bottleneck, and in regulated work it is the whole game. An agent cannot sign a tax return, file a brief or ship to production. You can. So become the accountable human in the loop, train yourself to spot the slop (the hallucinated precedent, the ghost number in the ledger, the race condition in the code), and own the liability the machine never will. Your licence and your signature are the leverage.

4. Productise what you know before someone else does. Do not wait for procurement to buy the tool that replaces you. Write down the unwritten rules in your head, how you smell a wrong number, how you sense a client going cold, and turn them into prompts and playbooks. Then walk into your manager’s office as a systems architect, not an order-taker: “I have automated sixty per cent of our reporting, and I am now free for the work that actually pays.”

5. Run yourself like a one-person firm. Your value was never the typing. It is the judgment, the foresight and the ability to direct a team of autonomous workers to a result you will put your name to. The task is dead. The workflow is everything.

The pivot looks different in every sector. The direction is identical.

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The technology is real. The economics are a wager.

Here is where the story turns, because two things can be true at once. The technology is the real thing. The money around it is not yet behaving rationally.

Consider the cautionary tale of the week. An AI consultant told Axios that an unnamed enterprise client reportedly ran up a bill of half a billion dollars on Claude in one month, after giving thousands of staff unrestricted access with no spending caps and no monitoring. Employees left agents looping overnight. People burned premium models on trivia. By the time the invoice landed, the budgetary damage was done. It may be the most expensive AI governance failure yet reported, and it tells you something the marketing does not: this is a consumption utility billed by the token, not a flat-rate subscription, and most of corporate IT has not understood the difference. The same week, Microsoft, the company most committed to embedding AI everywhere, reportedly cancelled most of its internal Claude Code licences on cost grounds. When the believers start flinching at the meter, pay attention.

Now lift your eyes from the invoice to the balance sheet. The four largest hyperscalers are on course to spend around 725 billion dollars on AI infrastructure this year, by the Financial Times’ tally, up roughly 77 per cent on last year and the largest concentrated infrastructure cycle in the history of technology. Amazon’s free cash flow is forecast to turn negative. A depreciation wave is coming as those chips age, with Morgan Stanley putting it north of 680 billion dollars over four years. Markets have started to notice: more than a trillion dollars came off the combined value of the big names in a single week of trading earlier this year. On the most generous reading of the returns, only the cloud-driven players, Amazon and Google, are clearly earning their keep. The rest are spending against a future demand they are betting will arrive.

This is exactly why I keep returning to the dot-com era. The technology was real then too. The internet survived and remade the world. Most of the companies that borrowed against it did not. Incredible engineering does not guarantee sustainable economics, and right now the giants are pouring trillions into the ground in the hope that revenue catches up to capex. That is not certainty. It is a leveraged bet, and the people most exposed when the interest comes due are not the executives who placed it.

Which brings us, with grim logic, to who pays. The chief executive of CloudBees told Axios the quiet part plainly: for many firms, cutting jobs may be the only lever left to offset the AI bill. The machines are sold as the reason for the layoffs. Increasingly they are also the cost that makes the layoffs necessary. Read that twice.

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The algorithm in Thatcher’s coat

A version of my Yorkshire Post column this week.

On Tuesday 19 May, in Hong Kong, Bill Winters, chief executive of Standard Chartered, told investors the bank would shed roughly 7,800 corporate and support roles by 2030. In the context of global banking, the figure was not the shock. The framing was. The cuts, Winters explained, were not “cost-cutting” but “replacing in some cases lower-value human capital” with the financial and investment capital the bank was putting in. By Wednesday morning a memo was scrambling to bury the phrase as reflecting “the work, not the value of our people,” and Friday brought a formal apology and talk of mature discussion. Wednesday’s memo was corporate laundering. Tuesday was the boardroom speaking with the microphone left on.

Many of us in the AI industry watched this inflection arrive late last year, and the moment was uncomfortably specific. Claude Code, Anthropic’s coding agent, became plainly better than the median engineer at bounded, testable coding tasks, and was visibly closing on the best. Once that line falls in software, every other knowledge profession sits on a countdown, which is why I started tracking it. The Sunday Signal Layoff Tracker now publishes weekly, monitoring the reallocation of corporate capital from human payroll to AI compute.

What the data shows is sobering. Twenty-two weeks into 2026, the tracker has logged 472,150 corporate job cuts globally, of which roughly 169,430 sit in technology, and if you run that velocity through to December the year lands at 1.1 million, the tech share alone burying the sector’s previous worst year on record (2023, around 263,000). Nor is the curve flattening. Q3 and Q4 historically run hotter than spring as boards rush to right-size ahead of earnings, and roughly 135 billion dollars of fresh AI capex is queued up for the back half, meaning the 2023 record will not merely be broken but pulverised. These are not assembly-line jobs. They are middle-class and salaried: the compliance analyst running KYC, the junior associate annotating contracts, the credit analyst rebuilding risk models, the team lead summarising five reports for the executive one rung up.

Yorkshire of all places ought to recognise the pattern. We have seen what happens when an entire class of work vanishes inside a single decade. The coalfields and the steelworks did not gently retrain into something else. They emptied, and the social damage took three generations to absorb. This time the axe falls not at the pithead or the furnace, but in the financial back office. Financial and insurance services employ about 1.17 million people across the UK, while the wider financial and related professional services industry employs more than 2.4 million, around two-thirds of them outside London. The ONS has just recorded financial and insurance activities as the worst-hit corner of the British economy, shedding 78,000 jobs over the past twelve months alone. This is digital deindustrialisation, and it is the algorithm wearing Thatcher’s coat.

The bitter irony is that the tracker itself is the work of an AI agent. He is called Mac, and he triangulates labour aggregators, SEC 8-K filings, WARN notices and the financial press, cross-referencing headcount against primary disclosures so the PR varnish lifts off. Two years ago, the same brief handed to McKinsey would have arrived as a “Global Labour Optimisation Engagement”: a five-person pod, an offshore researcher in Bengaluru pulling overnight WARN data, and a partner gracing the deck for an hour a week, all invoicing out at half a million pounds a month. Mac does the lot in four seconds, and the analysts who used to build those Excel models are, with painful precision, the demographic the tracker watches disappear.

I have argued before that capital does not protect anyone through this transition. Only those who use AI to create and build will. The honest question facing British boardrooms is no longer whether artificial intelligence will reshape the workforce, but whether you intend to be the executive saying the quiet part out loud, or the one preparing your people for the work that remains.

The Sunday Signal Tech & AI Layoff Tracker

The signal: the rise of the 100x org. Last week’s Standard Chartered cuts were a warning to the back office. This week the strike landed on the builders. The idea that software engineers were safe died between Sunday and Friday, as both mid-cap SaaS and multi-billion-dollar names converged on a single philosophy. They no longer want teams writing raw syntax. They want a handful of system managers commanding fleets of agents.

The clearest statement of it came from ClickUp. The four-billion-dollar productivity firm cut 22 per cent of its staff, roughly 290 of about 1,300 people, and framed it not as a saving but as a rebuild. Chief executive Zeb Evans says the company now runs around 3,000 internal AI agents, a three-to-one ratio of agents to employees, and that his best engineers no longer write code, they direct the agents that write it. The payroll freed by the cuts is being redirected into cash salary bands of up to a million dollars for the ones who can manage the machines. Read that as the new employment contract, stated out loud.

Elsewhere on the wire this week. Website builder Wix announced the largest cut in its history, around 1,000 roles, some 20 per cent of its workforce; management cited currency pressure, though the structural driver, generative tools that build sites from a prompt, is hard to miss. Logistics software group WiseTech Global began a restructuring that could remove roughly 30 per cent of staff, with the first notices now landing, amid internal commentary reportedly weighing the cost of human coding against the cost of compute. UBS entered the European close with a Bloomberg-reported wave of several hundred cuts across EMEA, officially filed under the long-running Credit Suisse integration. A class-action firm in Washington state has opened an investigation into whether Meta‘s latest Washington cuts complied with state WARN notice requirements. And Groupon announced a 23 per cent global reduction, around 400 roles, as the legacy consumer web continues to shed its pandemic-era headcount.

Watch next week. ClickUp has set the benchmark, and the rest of the productivity sector (Asana, Monday.com, Smartsheet) will now be asked by investors why they have not matched its margins. Expect defensive restructurings before the quarter closes. Web design and hosting agencies are the other soft spot, as generative site builders pull the floor out from under standard front-end work.

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Final Thought 🚀

The data this week did not describe a transition. It described substitution. The technology is real, the economics are a gamble, and the bill for that gamble is being paid in other people’s jobs while the people who placed it apologise for their choice of words rather than their choice of action. The coalfields were told to retrain too. Three generations later, parts of this county are still waiting. You have been warned, in plain language, by the executives themselves. The only question is whether you were listening, or still reading last month’s map.

Until next Sunday, David

Bottom Line Up Front

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Two stories arrived this week from opposite ends of the same machine. A website builder said it no longer needs a fifth of its workforce because the AI tools its customers can now reach for directly do the work. The chief operating officer of one of the world’s largest tech companies said he cannot draw a line from his engineers’ vast AI bills to anything customers can see. The producer is being eaten. The buyer cannot find what it bought. Both stories are in Sunday’s issue.

Wix laid off the developers who built Wix

Wix confirmed on Monday it will cut around 1,000 jobs, roughly twenty per cent of its workforce. It is the largest reduction in the company’s twenty-year history. The company employed 5,277 people at the end of the first quarter, with more than sixty per cent based in Israel. The stock has lost about half its value this year. The first quarter posted a loss of $57.5 million on revenue of $541 million, with operating expenses up fifty per cent.

The headline number matters less than what management told staff. Per Israeli press reporting, internal communications were direct. Several development and design roles are no longer required because AI tools now perform that work. Wix built its business on giving non-technical users a drag-and-drop way to make a website. The engineering and design layer behind that drag-and-drop is the layer being cut.

There is one more turn. The fastest growing business inside Wix is Base44, the natural-language software-building platform it bought last year for $80 million. Its annual recurring revenue hit $150 million in May. A user types what they want. Base44 builds it. The product growing inside Wix is the product replacing the people inside Wix.

The argument no longer holds

For two decades the case for Wix was simple. Most people cannot build a website. Wix gives them a way to do it without learning code. That case rested on a particular bet, which is that the abstraction layer would remain useful, while the people building underneath it remained needed.

Both halves of that bet are now in question. The user who once needed Wix can open Lovable, Cursor, Claude Code, Base44, or any of the new “type-what-you-want” tools and get a working site in minutes. The developer who once built Wix is being told the same AI tools that compete with the product now compete with the job. The platform that sat between the user and the developer no longer has obvious room on either side.

Week 22 tracker, early look

The cross-sector year-to-date total has crossed 470,000. The tech-specific count sits above 169,000. Since Sunday the trackers absorbed roughly 27,000 additions, a mix of new cuts and second-quarter reconciliations. Inside this week’s additions sit Wix’s 1,000 jobs, the next thousand realisations of WiseTech Global’s previously announced 30 per cent restructuring, and the rolling state-level WARN filings catching back-office cuts at Acrisure, Macy’s, CVS and Aetna. The year-to-date number is a tracker-based estimate and will be reconciled in Sunday’s full issue.

Uber paid for the tokens. It cannot find the features.

On Saturday, on the Rapid Response podcast, Uber’s president and chief operating officer Andrew Macdonald said in plain terms that the company’s vast use of AI coding tools is not visibly producing better consumer products. His words: “That link is not there yet.” Maybe more is shipping. Maybe it is not. He cannot trace it.

Behind the quote is the spend. Uber rolled out Claude Code to roughly 5,000 engineers in December. By March, eighty-four per cent of them were using agentic features, up from thirty-two per cent in February. By spring, ninety-five per cent of engineers used AI tools every month. Roughly seventy per cent of committed code is now AI-assisted. Heavy users rack up bills between $500 and $2,000 a month. In April, the chief technology officer disclosed that the company had already burned through its entire 2026 Claude Code budget. Four months in. Macdonald called it a “head-exploding moment”.

The internal incentive was a leaderboard. Teams were ranked by total AI tool usage. The metric being optimised was consumption. The output was tokens, not features. Macdonald has now said publicly what the design should have made obvious. If you reward people for using a tool, you get more tool use. You do not necessarily get more product. The receipt is impressive. The customer cannot see what it bought.

The buyers are starting to look at the receipts

Microsoft is reported to be cancelling most of its direct Claude Code licences and routing engineers to GitHub Copilot’s command-line tool. Duolingo’s chief executive has walked back his AI-replacement language from last year. Sam Altman has begun publicly suggesting some businesses may be using AI as a convenient label for cuts that would have happened anyway. The euphoria phase is ending. The receipts phase has begun.

For Anthropic this is mixed. Uber is one of its largest enterprise customers, and Uber’s experience proves the adoption is real. It also proves the usage-based pricing model is being aimed at companies that can blow a year’s budget in a third of a year and still struggle to explain what they got. That is not yet a customer leaving. It is a customer counting.

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Coming Sunday

Two stories that are really one. Wix laid off a fifth of itself because the AI tools its users now reach for have replaced the developers inside the company that built the abstraction. Uber’s chief operating officer cannot connect his enormous AI coding spend to anything the customer sees. On one side of the market the producer is being eaten. On the other side the buyer is asking what dinner cost.

More on Sunday.

The Sunday Signal. Weekly, every Sunday. Subscribe at thesundaysignal.ai.

Listen to the podcast on Spotify, Apple Podcasts or YouTube.

Until Sunday, David.

This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

The full interview with Dr Xianxin Guo, CEO of Lumai, is published separately on Substack today. Read it here.

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Bottom Line Up Front

The whole AI story this decade has rested on a single assumption. That the machine is fixed, the energy bill is rising, and the only choice left is whether to build more power stations or accept the limit. That assumption is wrong, and a company in Oxford has the receipts to prove it. The implications run further than the data centre. They reach the trillions of dollars currently sitting on the bet that AI will burn through everything we can generate, and they reach the British question this newsletter has been worrying at for months: can we, this time, keep the thing we invented.

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The AI energy crisis is a physics problem in an economics costume

Every argument about AI and power ends in the same place. Demand doubles by 2030. The grid cannot carry it. Build more, burn more, and hope the curve bends. That is a supply argument. It assumes the machine is fixed and the only variable is how much we feed it.

It is the wrong argument. The energy problem is not really about supply. It is about what we built the machine out of.

Electrons resist. That is the whole story

A chip computes by pushing electrons through transistors and switching them on and off billions of times a second. Every switch costs energy. Moving the data between memory and processor costs more energy than the sums themselves. Almost all of it leaves as heat. That is why a modern AI data centre spends a punishing share of its power not on thinking but on staying cool. The electron is a workhorse that throws off heat with every step. We have spent sixty years making it take smaller steps. We are running out of room.

Photons do not behave that way. Light moving through an optical component does not dissipate heat the way a switching transistor does, and it can carry many calculations at once, in parallel, through space and across wavelengths. The heaviest single job in AI inference is matrix multiplication, the same arithmetic over and over at vast scale. That job maps almost exactly onto what light does without being forced. The dominant power cost in this kind of optical system is not the optical multiplication itself, but the conversion between the electrical and optical domains at the edges. The efficiency lever, then, is to do as much maths as possible per conversion. Lumai says its optical tensor engine performs matrix multiplication up to 2048 by 2048. Bigger matrices per conversion means more sums per joule. That is where the order-of-magnitude saving actually comes from.

The company that bet everything on that sentence

The company is Lumai. It is a spinout from the University of Oxford, headquartered in Oxford, and last month it launched a server called Iris Nova. By its own account, it is the only system in the world running billion-parameter large language models on optical compute, currently doing real-time inference on Meta’s Llama 8B and 70B.

I first saw Lumai a fortnight ago at the Royal Academy of Engineering Enterprise Hub Demo Day. Dr Xianxin Guo was on stage, beside a slide of the Lumai Iris Server. The slide carried two forward claims against the hardware the entire industry currently runs on. Ninety-five per cent less energy than an Nvidia GB200 by 2029. Ninety per cent lower cost than Nvidia by 2029. Aggressive numbers, the kind you note and then chase. I followed up.

I sat down with him this week to ask the questions readers of this newsletter actually care about. Power. Cost. Speed of adoption. And whether Britain keeps it.

His one-line answer to what the company does, in language any data centre operator would recognise: ninety per cent less energy, ten per cent of the cost, more AI tokens out of the same hall.

The ninety per cent, translated

“Ninety per cent reduction of the power needed for the same compute at the rack appliance level,” Guo says. Less power means less cooling, smaller power supplies, less of the secondary plant that quietly drinks a material share of a data centre’s electricity. The saving ripples. Networking and other elements outside Lumai’s box are not affected, so the figure at the grid connection comes in below ninety, but Guo is direct about what the figure actually buys you. “What this is likely to mean in practice is more compute capacity within the same power budget.”

That is the honest version of the answer. The energy saving does not collapse the energy bill. It expands the amount of intelligence the same energy bill can produce. He is open about why.

“Jevons’ paradox is real, and we will see more AI used, not less.”

This is the part the optimists tend to leave out. Efficiency does not bend the curve. It steepens it. The grid still has to grow. What changes is what we get for it.

Cost per token, not cost per box

“Cost per token. That is the only metric that matters to an operator running this at scale,” Guo says when pressed on what the ninety per cent saving actually means in pounds. The figure is a blend of equipment capital cost and the capital cost of the data centre that hosts it. Power. Backup generators. Cooling. The whole sleeve of infrastructure that exists because silicon throws off heat.

The model that lands first is unsurprising. Operators with the highest token volumes and the heaviest cost exposure to inference feel the saving first. That is the hyperscaler. That is the neocloud. That is the customer.

The capital question is sharper. Lumai has raised about fifteen million dollars. American rivals carry war chests forty times that. Guo’s answer is that the architecture itself bends the capital problem.

“We are not building custom silicon at the leading chip node, because most of the hard computation is performed in optics.”

Translation: they do not need a TSMC-class fabrication line because the heavy lifting is not happening on a transistor. The architecture is lens-based and three-dimensional rather than integrated photonics on a 2D chip. Weights are encoded on an electronic display and the calculation happens as light passes through it. The supply chain leans on mature, high-volume components, the kind of optics and displays that are already manufactured at industrial scale elsewhere, customised for the use but not invented from scratch. It is a deliberate choice to sidestep the part of the hardware industry that eats capital fastest, the development of novel materials and bleeding-edge fabrication. They have just opened the next round.

How fast this moves

Iris Nova is in the hands of evaluators now. Guo is direct about the timeline from there. Eighteen to twenty-four months from first evaluation to production at scale at a hyperscaler. Then this:

“The true blocker isn’t the technology, it is validation. A hyperscaler will not deploy a novel architecture into a revenue-bearing workload without months of validation, integration testing, and operational confidence.”

That sentence should reassure rather than worry. The bottleneck is not whether the physics is real. It is whether the buyer can convince itself. The first real deployment will be a cluster in a national laboratory testing performance across multiple workloads and orchestration patterns. It is the necessary unglamorous step before the order book opens.

The form factor is built to make that confidence easy to give. Iris Nova is an Ethernet-attached appliance that drops into a standard rack and runs alongside existing GPUs rather than replacing them. Its natural fit is inference prefill and other compute-bound workloads. Nobody is being asked to rip Nvidia out.

The CUDA question, the moat that has protected Nvidia for a decade, gets a short answer too. Same frameworks, same models, no rewrite. Lumai compiles to its hardware and the model runs.

A pull quote that has nothing to do with chips

The most quietly striking line in the conversation was about leadership. Guo was Head of Research before he became CEO last year. What did he have to unlearn?

“A decision made in a week with 70 per cent certainty beats a decision made in three months with 95 per cent.”

A British deep-tech founder who has read the room about why British deep-tech keeps losing.

The full interview is published on Substack today, here.

What happens to the grid if nothing changes

The Lumai story matters because the alternative has now been forecast in detail by people who do not write newsletters for a living. Without an architectural pivot, here is the road we are on.

The global picture

The International Energy Agency puts global data centre electricity consumption at roughly 415 terawatt hours in 2024 and projects it to more than double to around 945 terawatt hours by 2030, driven heavily by AI. That takes data centres from about one and a half per cent of global electricity demand to roughly three per cent inside six years. Goldman Sachs Research projects a 165 per cent total increase in data centre power demand between 2023 and 2030. McKinsey estimates the global investment needed to physically build that capacity at nearly seven trillion dollars before the decade is out.

The unit of measurement has changed. Standard IT racks have drawn ten to fifteen kilowatts for years. AI-optimised racks now push past forty. A single next-generation AI campus currently under construction will demand around twenty times the power of a typical hyperscale site. The grid is not being asked for more of the same. It is being asked for something it has never had to deliver.

The British picture is sharper

In the UK, the boom is already crossing thresholds the system was not designed to cross. Recent industry estimates put UK data centres at around six per cent of electricity consumption, although the House of Commons Library’s 2025 briefing gives a lower figure of 2.5 per cent. Either way the trajectory is steep. The National Energy System Operator projects a fourfold increase in data centre electricity demand by 2030. Other UK forecasts go to fivefold. In London alone, 271 data centres are drawing 69 per cent more power than the capital’s 3.49 million homes put together.

The Department for Science, Innovation and Technology has had to revise its forecast for greenhouse gas emissions from AI data centres upward by a factor of 100. Not by a per cent. By a multiple. Some UK data centre developers are now being quoted grid connection wait times of up to ten years.

That is not a forecast. That is a queue.

The concentration is the problem

The numbers above are global averages. The actual strain is hyper-local. The United States is expected to move from three to four per cent of national power consumption today to between eight and twelve per cent by 2030. In Ireland, data centres already consume over twenty per cent of the national grid. In Singapore, it is nineteen.

This is what people mean when they talk about an energy wall. They mean specific countries and specific cities where the grid cannot, physically, supply what the next decade of AI demand is forecast to require.

What an aggressive pivot to photons actually changes

Now run the alternative. If the architectural shift this newsletter has been describing actually happens at scale by 2028, the forecasts above stop being inevitable.

There are two places the energy bill of an AI data centre is concentrated. The first is data movement. By early 2025, pushing data through copper interconnects was consuming close to thirty per cent of total AI cluster power. Co-packaged optics, which moves the light conversion onto the chip itself, is now in volume production between 2026 and 2028 and cuts switch and rack-level power by an estimated forty to fifty per cent. The second is the compute itself. Moving the matrix mathematics from electrons to photons delivers, on independent estimates, an order of magnitude improvement in energy efficiency. Some photonic processors are demonstrating thirty times the savings in narrow benchmarks. Then the cooling collapse, because if the chip throws off less heat the secondary plant that exists to keep it cool throws off less of itself.

If you apply a blended fifty to ninety per cent reduction across interconnects and inference compute, two scenarios open up.

Scenario one. The reprieve. If global AI token demand grows at exactly the rate current economic models project, the IEA’s 945 terawatt hour figure never gets reached. Demand plateaus inside the existing envelope. The ten-year UK grid connection queue loses one of its biggest accelerants. The seven trillion dollars in additional power infrastructure is not needed at that scale.

Scenario two. The Jevons explosion. We still hit 945 terawatt hours by 2030, because efficiency releases demand, but we get fifty times the AI intelligence out of it. The total power bill looks the same. The cost per token collapses toward a level that rewrites the economics of inference. Every operator still running legacy silicon is suddenly competing on infrastructure economics they cannot win.

Guo’s own answer pointed firmly at scenario two. The history of every previous efficiency revolution in computing points the same way. The energy bill rarely shrinks. The work the energy buys multiplies.

Either scenario makes one specific category of investor wrong about a great deal of money.

The trillion-dollar trade built on a single assumption

Wall Street has spent two years pricing one outcome into the market. That hyperscalers, locked into a brute-force scaling race against each other, would buy raw electricity at any premium because they had no choice. The whole “AI energy trade” rests on that single load-bearing thesis. If photonics breaks the assumption, the financial consequences are not gentle.

The collapse of the AI premium in utilities

Independent power producers, nuclear fleet operators, regional utilities. All have been trading at historically elevated multiples because analysts have priced in a 150 to 200 per cent increase in data centre demand by 2030. The premium is not in current cash flow. It is in the expected future cash flow from desperate, decade-long power purchase agreements.

The repricing event is identifiable. The moment a major hyperscaler announces that its 2030 power forecast has been cut by, say, seventy per cent because optical coprocessors are doing more of the work, the repricing begins. Base-load demand will not fall to today’s levels. The growth premium will be forced into question very quickly. Slow-growth utility valuations return. Anyone holding the trade on margin discovers the difference.

The cooling sector takes the worst of it

The most exposed corner of the market is thermal management. Liquid cooling specialists, immersion cooling startups, the hyperscale HVAC contractors. The whole sector exists because silicon GPUs are, for accounting purposes, very expensive space heaters. If the heat goes away, so does the addressable market. Operators do not need next-generation cooling architecture for a chip that is barely warm. The pricing in this corner has not begun to discount that risk.

The copper downgrade

The copper trade is leaning on two stories, electric vehicles and AI grid expansion. The second of those is contingent. Optical computing replaces copper at the chip, the rack and the cluster. If grid expansion is paused because power demand plateaus, the projected supply deficits ease, and the mining and commodities exposure that has been priced for a structural deficit corrects.

Where the money goes

Capital does not vanish in repricings. It rotates. The destination, if this scenario plays out, is the optical supply chain. The mature manufacturers of lasers, modulators, waveguides, the photonic foundries, the firms quietly making the components that startups like Lumai assemble into systems. They are the picks and shovels of the next phase. Anyone trying to identify them in retrospect will pay a multiple to do so.

I am not in the business of giving investment advice. I am in the business of pointing out when a market is priced for a future that one British company is currently building the alternative to. Do the work yourself.

Why Lumai must stay a British company

This is the part of the story this newsletter cares about most, because we have watched it play out before. The UK invents the thing. American capital buys it. The headlines fade. The technology becomes the basis of a US-listed giant. Britain is thanked for the talent and reduced, politely, to a regional R&D office.

The pattern is now well documented enough to be predictable.

DeepMind, the most important AI research company in the world, came out of University College London. In 2014 Google bought it, pre-revenue, for around 400 million pounds. The acquisition was barely commented on at the time. It became the foundation of what is now Google DeepMind, the engine of one of the most valuable companies on the planet.

VocalIQ, spun out of Cambridge’s Spoken Dialogue Systems Group, was acquired by Apple in 2015 to become the conversational intelligence inside Siri. Magic Pony Technology, founded by London university graduates and seeded with a grant from Innovate UK, was bought by Twitter in 2016 for around 150 million dollars, two years after starting. Latent Logic, an Oxford spinout in autonomous decision-making, was acquired by Alphabet’s Waymo in 2019.

That is the soft pattern. The harder pattern is more recent and more damning.

In September 2025, Oxford Ionics, the trapped-ion quantum computing spinout, was acquired by the US-listed IonQ for 1.075 billion dollars. It was the highest-value Oxford quantum exit in history. The founders are expected to stay in Oxford. The IP, the headquarters, the future market capitalisation, and the corporate tax base now belong to a Maryland company. In the same announcement, IonQ also bought Vector Atomic, a quantum sensing firm with more than 200 million dollars in US defence contracts. Britain handed a sovereign-grade quantum capability to a defence-adjacent American competitor and clapped.

Oxford Quantum Circuits, the other Oxford quantum scaleup, raised the largest UK Series A in quantum, 38 million pounds, co-led by Lansdowne Partners and Japan’s largest deep-tech fund. British Patient Capital put in 6.7 million of it. The rest came from abroad.

The fundamental issue is not invention. Britain invents prolifically. The fundamental issue is scaling capital. When the moment comes to write the cheque that takes a company from breakthrough to market dominance, the British cheque is either not there or not large enough. American capital is. The technology, the headquarters, the tax base and the strategic capability follow the cheque.

This is what economists call the public risk, foreign reward deficit. The British state and the university system absorb the high-risk research phase. Once the technology is de-risked, American capital buys the certainty. The returns are then booked on NASDAQ.

Guo knows all of this. His answer when asked if Lumai stays British was the one this newsletter has been waiting to hear.

“Where the company is built is the question that matters, and the answer is the UK.”

His specific demand of the British state was equally clear. Not subsidy. Procurement.

“A committed UK government anchor purchase of Lumai Iris server hardware into the AI Research Resource. Concrete orders from a sovereign customer do more for keeping the company here than any number of strategy papers.”

ARIA‘s Scaling Inference Lab is a real testbed for novel AI hardware. The Sovereign AI programme has half a billion pounds behind British AI companies trying to scale. The Department for Science, Innovation and Technology has a hundred-million-pound package that explicitly positions government as a first customer for high-quality UK AI hardware. The instruments exist. The question is whether they are deployed in time, or whether a year from now we will be reading the press release announcing Lumai’s acquisition by an American hyperscaler.

This is not hostile to America. This is observable to anyone with a chronology and an attention span.

The Sunday Signal Tech and AI Layoff Tracker

Week 21. 17 to 23 May 2026.

The mask slips on “lower-value human capital”

For months we have tracked executives carefully constructing a PR shield around AI-driven job losses, relying on sanitary phrases like “operational leverage.” This week the shield was shattered by a 170-year-old British bank.

Standard Chartered announced a phased target to eliminate roughly 7,800 corporate and support roles, fifteen per cent of that division, by 2030 to fund an aggressive AI integration. The headcount was not the story. The language was. CEO Bill Winters told investors the bank was replacing “lower-value human capital” with the bank’s investment in technology and AI.

The backlash was instant. Winters issued a Wednesday memo attempting to walk back the phrasing, telling staff the words had been taken out of context and that role losses reflected the changing nature of the work, not the value of the people doing it. The memo is classic corporate laundering. The investor call was the unvarnished reality of the 2026 board room. “Lower-value human capital” is exactly how the C-suite now views non-technical administrative labour in the age of large language models.

The collapse of offshore arbitrage

The most critical takeaway is geographic. The planned cuts are disproportionately targeted at the bank’s major back-office hubs: Chennai, Bengaluru, Kuala Lumpur and Warsaw. Treat the specific list as indicative until the bank confirms it.

This signals the death of the defining corporate strategy of the last quarter century. Labour arbitrage. For decades, Western corporations saved billions by shipping administrative tasks to business process outsourcing centres in Asia and Eastern Europe. In 2026 the maths has flipped. Running an AI agent on a localised server is now cheaper than paying an offshore human analyst. AI does not require a desk. It does not sleep. It does not care about time zones.

Reality check this week: Intuit, Meta, Dune

While Standard Chartered laid out a multi-year roadmap, the immediate tech sector continued its contraction.

Intuit. The AI flush. On 20 May, the financial software giant announced the elimination of roughly 3,000 jobs, seventeen per cent of its workforce. This is a textbook AI-native restructure: clearing legacy overhead to rehire directly into AI engineering and product roles.

Meta. The execution phase. The mega-layoff Zuckerberg announced earlier this month is no longer theoretical. Rolling cuts began on 20 May, actively eliminating 8,000 roles, ten per cent of the workforce, to free up capital for the company’s $135 billion AI infrastructure buildout.

Dune Analytics. The Web3 pivot. This crypto analytics platform slashed twenty-five per cent of its staff this week. The stated rationale was a strategic pivot toward “AI and institutional onchain data.” When a Web3 startup and a colonial-era British bank cite the same technological driver to cut headcount, it stops being a coincidence. It becomes a sector-agnostic law of physics.

High-probability targets, Week 22

The global IT services giants. Standard Chartered just fired a warning shot directly over the bow of the major Indian IT hubs. The largest BPO and service providers, TCS, Infosys, Wipro, are now in acute danger. If their Western banking clients are successfully automating their own back-office compliance functions, the outsourcing contracts that sustain these IT giants will dry up. Expect defensive, margin-protecting layoffs from the BPO sector to accelerate immediately.

Mid-tier financial compliance. Any vendor built around providing human-in-the-loop KYC and AML verification is operating on borrowed time. The ecosystem servicing these manual workflows will be forced to downsize before Q3.

Methodology. The Standard Chartered figure is an announced multi-year target to 2030, recorded under broader corporate. The arithmetic of around 7,800 is derived from the bank’s stated fifteen per cent of roughly 52,000 staff. Broader corporate figures draw on LayoffHedge. Tech-specific figures draw on Layoffs.fyi and TrueUp.

Final Thought

🚀 We have spent three years arguing about how to feed the machine. More power stations. More grid. More money set on fire to keep the lights on in the server hall. The more useful question was always the quieter one. Not how much energy the machine needs, but what we chose to build it out of.

One answer to that question is sitting in a laboratory in Oxford. The CEO is a physicist who decided he would rather build the company than watch someone else buy it. He has told the British state, in public, what would keep him here. He has put a number on it, a date on it, and a programme on it. There is no possible defence for not hearing him.

His closing instruction to any policymaker reading this newsletter does not bother with diplomacy.

“Advanced procurement of UK-built AI hardware into the AI Research Resource and the Growth Infrastructure programme this financial year, or watch the next generation of AI compute be created outside of the UK again.”

This financial year. Not the next strategy review. Not the next white paper. This one.

The bank in the tracker called its own people lower-value capital. The British company at the top of this issue offered a way to make the machine that replaces them use a fraction of the energy. The cut and the fix are the same story. The only question left is who gets to own the fix.

Britain has watched DeepMind, Magic Pony, VocalIQ, Latent Logic and Oxford Ionics walk out of the door. Light is the next one. Buy the thing, or read the press release.

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The full interview with Dr Xianxin Guo, CEO of Lumai, is published in full on Substack today. Read it here.

Until next Sunday, David Richards MBE.

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Dr Xianxin Guo is the CEO and co-founder of Lumai, the Oxford spinout building the world’s first server running billion-parameter large language models on optical compute. Last month, the company launched Iris Nova, currently doing real-time inference on Meta’s Llama 8B and 70B. On its own figures, Iris uses ninety per cent less energy than the GPU hardware the entire AI industry currently runs on.

I first saw Lumai a fortnight ago at the Royal Academy of Engineering Enterprise Hub Demo Day. Guo was on stage, beside a slide of the Lumai Iris Server. The slide carried two forward claims against the hardware the entire industry currently runs on. Ninety-five per cent less energy than an Nvidia GB200 by 2029. Ninety per cent lower cost than Nvidia by 2029. Aggressive numbers, the kind you note and then chase. I followed up.

I sat down with him this week. Power, cost, adoption, capital, and whether Britain finally keeps something it invented. He gave the directest answers I have had from a British deep-tech founder in a long time. The whole exchange follows.

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What makes this different

The Sunday Signal: In one sentence a data centre operator would understand, what does Lumai do that silicon cannot?

By using optical compute to run LLMs and other AI workloads we consume ninety per cent less energy, meaning that a data centre operator can produce significantly more AI tokens within the data centre at just ten per cent of the cost.

Lightmatter, Salience, Celestial and others are all chasing light. What is genuinely unique about your 3D optical approach, and why has nobody else made it work at this scale?

Other companies have tried to perform optical compute in the confines of a 2D chip however they are unable to reach the scale required for data centre AI. Computing in 3D gives us orders of magnitude more parallelism, millions of computations per cycle. Building on the many years of research at Oxford, Lumai has now launched our first generation, Iris Nova, which is the only system to run billion-parameter LLMs using optical compute. A genuine first.

Why a plug-in coprocessor rather than a new supercomputer, and where are its limits?

Lumai’s Iris server is ideal for processing a high-volume of AI tokens efficiently. Lumai Iris is an Ethernet-attached appliance that fits in a standard rack, and runs alongside GPUs. Our sweet spot is inference prefill and other compute-bound workloads.

Do I have to rewrite my models to use an Iris server, or does it plug straight into PyTorch and the standard frameworks?

It plugs straight in. Users can take a pre-trained model, compile it for Lumai hardware and off they go.

The people behind it

You spun this out of Oxford. Who does what?

I lead the company. Dr James Spall, our CTO, runs the technology. He and I built the original optical system together in the lab. We work with an amazing team who bring a vast amount of technical and product experience from the likes of Arm, Intel, Altera, Meta, Imagination Technology, Cisco and HPE.

You moved from Head of Research to CEO. What did you have to unlearn?

In research you optimise for being right. As a CEO you optimise for leading and being useful, to customers, to the team, to investors. The hardest thing to unlearn was accepting that a decision made in a week with 70 per cent certainty beats a decision made in three months with 95 per cent.

Can you actually build a manufacturing and deployment team in the UK, and at what point do you have to give up and hire in the US?

We already have a development team in the UK which has an amazing talent pool and ecosystem covering electronics, software and optical. We will hire business development and account-facing teams in the US, so that they can be close to customers. We have a global supply chain, including in the UK.

What ninety per cent less power actually means

Translate the ninety per cent figure. Rack, data centre, grid connection.

Ninety per cent reduction of the power needed for the same compute at the rack appliance level. Less power means less cooling, smaller power supplies, etc. which means that this ninety per cent ripples across the data centre. There are other elements within a data centre that we don’t affect (e.g. networking), so the overall reduction in power at the grid connection will be less than ninety per cent, but still incredibly impressive. What this is likely to mean in practice however is more compute capacity within the same power budget as the need for more tokens continues to grow.

Does technology like yours bend the IEA curve, or does cheaper inference just pull more demand forward?

AI is creating so much demand for compute that Lumai’s highly efficient processor will be used to deliver much more inference within the same data centre power budget. Cheaper inference will pull demand forward. Jevons’ paradox is real and we will see more AI used, not less.

If optical compute becomes the default for inference, what changes for where data centres get built?

In principle data centres could be built with smaller power requirements when using Lumai Iris servers, making a wider range of sites open for AI. Compute demand and wider geopolitical issues will more likely have a greater influence.

What it means for the cost of AI

If your numbers hold, what does that do to the price of running a frontier model, and who feels it first?

Those with high token usage limited by volume of tokens and their cost will feel the benefits first.

Define the metric. Is ninety per cent lower cost capital cost per server, or operator’s cost per token?

Cost per token. That is the only metric that matters to an operator running this at scale. This cost is a blend of equipment capital cost and data centre capital cost (power, backup generators, cooling, etc).

Lumai has raised a little over fifteen million dollars. American rivals have war chests forty times that. How do you fund the path to manufacturing scale?

We are not building custom silicon at the leading chip node as most of the hard computation is performed in optics. Our optical supply chain is a deliberate choice to use mature, high-volume components from established optical suppliers, which reduces the need to develop high-cost novel materials. We have just opened our next round to take us through to volume manufacturing.

How fast this moves

How quickly can optical compute go from evaluation to running production inference at a hyperscaler? What is the true blocker?

We are already deeply embedded in deep technical evaluation with hyperscalers, neoclouds, and research labs. It will then realistically take eighteen to twenty-four months from first evaluation to production at scale. The true blocker isn’t the technology, it is validation. A hyperscaler will not deploy a novel architecture into a revenue-bearing workload without months of validation, integration testing, and operational confidence. We have shipped Iris Nova for exactly this reason. It is the proof that the system works, runs real models, and behaves in a rack.

Without breaking confidences, who is testing it, and what does a first real deployment look like?

Our first deployment will be a cluster in a national lab who will test performance across a range of customer workloads, with different orchestration, and different models.

What does an operator actually have to give up to put an Iris server in, and how big is the switching cost?

It’s more about what they gain rather than what they give up. We have designed Lumai Iris to make access to these benefits as easy as possible, the same software frameworks, same models, just faster driven by more compute. The cost is the same as qualifying any new hardware vendor. Lumai’s advantage is that we can work alongside existing hardware to optimise each stage of the inference pipeline.

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Keeping it in Britain

Your round was led by a Cayman-registered fund. Your expansion is into the United States. Of your backers, IP Group is the British one. Is Lumai going to stay a British company?

Yes. We are headquartered in Oxford. Our R&D, our engineering, and our senior team are here. We will grow overseas where it makes sense, but where the company is built is the question that matters, and the answer is the UK.

Does ARIA and sovereign compute translate into something that actually keeps a company like yours here, or is it words?

It is more than words. ARIA’s Scaling Inference Lab is a testbed for AI hardware technologies, prioritising rapid iteration, and open collaboration. This type of testbed is vital to any startup developing AI hardware and the fact that it is in the UK makes a massive difference.

Sovereign AI goes one step further. Investing five hundred million pounds in British AI companies to start up, scale up and win globally. This is in addition to the UK Government’s Department for Science, Innovation and Technology’s one hundred million pound funding package designed to “act as a first customer for promising UK startups who are building high-quality AI hardware products.”

Combined, these are exactly the sort of initiatives that will boost innovative startups like Lumai who are developing cutting-edge technology for such an important area of technology and UK growth.

Name the specific thing, a customer, a fab, a fund, that would make you put the next phase here rather than in the US.

A committed UK government anchor purchase of Lumai Iris server hardware into the AI Research Resource. Concrete orders from a sovereign customer do more for keeping the company here than any number of strategy papers.

To close

One sentence to a British policymaker reading this. What do they need to do, and by when?

Recognise that AI is just at the foothills of what is possible, that the UK has the talent and technology to benefit from this massive technological and economic shift, and that they need to do advanced procurement of UK-built AI hardware into the AI Research Resource and the Growth Infrastructure programme this financial year, or watch the next generation of AI compute be created outside of the UK again.

Read the full Issue #55 of The Sunday Signal at thesundaysignal.ai.

Listen on Spotify, Apple Podcasts or YouTube.

Until next Sunday, David Richards MBE.

The full Week 21 issue lands this Sunday at thesundaysignal.ai. Subscribe so it arrives in your inbox. This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

Bottom Line Up Front

The AI economy did two things in public today. It cut the jobs and it strained the grid. The same machine drives both. A bank with roots in the 1850s said the quiet part on the record, and separately, a British company said the energy wall everyone treats as inevitable is already breakable. One of those stories is a warning. The other might be the way out. Both are in Sunday’s issue.

The bank stopped pretending it was about cost

Standard Chartered confirmed today it will cut more than fifteen per cent of its corporate functions roles by 2030. Run the arithmetic the bank invited: fifteen per cent of its roughly 52,000 support staff is around 7,800 jobs, taken from a global workforce near 82,000. The bank wants income per employee up around twenty per cent by 2028.

What makes this the defining cut of May is not the headcount. It is the language. Chief executive Bill Winters told reporters this was not cost-cutting but “replacing in some cases lower-value human capital” with the bank’s investment in technology and AI. Read that back slowly. A FTSE 100 chief executive described his own staff as lower-value capital to be swapped for machines, in public, on the record, as the plan.

The offshore arbitrage is over

For two decades Western banks cut costs by moving back-office work to operational hubs in Asia and Eastern Europe. On current reporting, the centres most exposed are offshore: Chennai, Bengaluru, Kuala Lumpur, Warsaw. Treat the specific list as indicative until the bank confirms it.

The pattern matters more than the postcodes. The offshore centre was the last answer to the cost question. In 2026 the answer is an agent. When the inexpensive analyst in Bengaluru is no longer inexpensive enough against software, the outsourcing engine that quietly runs global banking moves into the line of fire. This is a different phase from tech firms trimming engineers. It reaches the people hired precisely because they were cheap.

Week 21 tracker, early look

The year-to-date number is a tracker-based estimate and will be reconciled in Sunday’s full issue.

And the other half of the story

Every argument about AI’s future now ends at the same wall. The power. The standard line is that demand doubles by 2030, the grid cannot carry it, and there is nothing to do but build more of everything and burn more to run it.

There is a company that says that wall is already breakable, and the reason is physics, not money. A chip computes by pushing electrons through silicon, and electrons throw off heat with every step. This company does the heavy mathematics of AI with light instead. Photons do not resist the way electrons do. The claim, on its own figures, is up to ninety per cent less energy than the hardware the entire industry currently depends on. It is not American. It is British, and it came out of a UK university.

I spoke with the person running it. The obvious questions are the hard ones. Are the numbers real or projected? Who is actually testing it? And the one that should bother every reader of this newsletter: Britain keeps inventing this, and America keeps owning it, so does this one stay here?

The name, the numbers and the verdict are in Sunday’s Signal.

Coming Sunday

Two stories that are really one. The bank that called its people lower-value capital, and the British company that thinks it can take the power problem off the table before America buys the answer.

More on Sunday.

The Sunday Signal. Weekly, every Sunday. Subscribe at thesundaysignal.ai.

Listen to the podcast on Spotify, Apple Podcasts or YouTube.

Until Sunday, David.

Bottom Line Up Front

The “adult supervision” model of building companies is collapsing. For forty years, the moment a technical founder built something valuable, the institutional reflex was identical. Install a “proper” chief executive over them. Investors called it professionalisation. Universities called it commercialisation. Non-profit boards called it stewardship. The move never really worked. AI is about to make it look absurd.

This week, the argument played out in three rooms.

At The Digital Forge on Tuesday, in partnership with the University of Sheffield commercialisation team, a room of spin-out hopefuls heard the cautionary tale of a deep tech founder team forced to accept a sales executive as their CEO. The technology stalled. The cash burned. The founders eventually took the company back. The pattern is the rule, not the exception.

In a federal courtroom in Oakland on Thursday, closing arguments wrapped in Musk v. Altman. The jury begins deliberating on Monday. OpenAI is being asked to defend the most expensive version of the model ever attempted, the 501(c)(3) charter that was meant to keep frontier AI accountable. The verdict, when it lands, will become part of the architecture every AI lab studies.

And then the practical layer. Founder OS sits in this issue. The operating system that replaces the administrative machinery investors once used as the excuse to install adults in the first place. Nine playbooks. A master context architecture. A thirty-day implementation. Built around AI. Written for founders who have run out of patience with administrative theatre.

Three rooms. One verdict.

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1. The Adults in the Room Were Wrong

Britain has a strange habit. The moment a brilliant technical founder proves they can build something valuable, we start looking for someone else to run it.

At about fifty staff, the ritual begins. Investors mutter about “professionalising the team.” The founder is praised, thanked and moved sideways. A proper chief executive is installed, usually someone fluent in dashboards, board packs and the solemn theatre of management. For forty years, this looked sensible. AI is about to make it look absurd.

I wrote that for the Yorkshire Post on Friday. This week, the argument arrived in a room.

At The Digital Forge on Tuesday, we partnered with the commercialisation team at the University of Sheffield. The room was full of spin-out hopefuls. PhDs. Professors. Post-doctoral researchers. Every one of them thinking seriously about turning brilliant research into companies. Every one of them being asked, in one form or another, the same question. Who is the adult who will run this?

The traditional answer is over. Let me tell you why.

A few years ago I watched a company spin out of a university. The research was extraordinary. Genuinely original. Genuinely difficult. To understand the technology in any serious way, you needed to be an accomplished computer scientist. The technical founders were both experts. The university believed the company needed “adult supervision.” They installed a former sales executive from a large tech company. Not a CTO. Not a co-founder. A sales guy. The deep tech company had no product. It had no market. It had a research thesis that needed further engineering before it could be sold to anyone. Sales was the last problem on the list.

The predictable thing happened. The sales hire built sales scaffolding. Pipelines, decks, sales targets, an outside-in narrative. None of it accelerated the actual work. The cash burned down. The technology stalled. A few years later, after a near-death event, the technical founders took the company back and started over. Yep, you guessed it. With the technical founders at the helm.

I have sat through that story so many times, in so many universities, that I stopped finding it surprising. The pattern is the same every time. “Adult supervision” was never adult. It was administrative scaffolding dressed up as wisdom.

Marc Andreessen made the broader case in his 2011 Wall Street Journal essay. Software was eating the world. He has spent the last twelve months arguing that something similar is now happening to the management layer itself. His record on calling the direction of technology is among the best of his generation. He is worth taking seriously.

James Burnham saw it coming in 1941. Modern capitalism, he argued, would no longer be ruled by owners or inventors but by the people who administered the machine rather than the people who built it. Burnham was right for most of the twentieth century. Scale required bureaucracy. Bureaucracy required managers. Managers gradually persuaded everyone that they, rather than the founders, were the adults in the room.

AI now attacks the very work that made that class indispensable. Much of modern management is not leadership. It is translation, summarisation, co-ordination and documentation. The business of turning meetings into actions, actions into reports, reports into board packs, and board packs into the next meeting. Strategy decks, budget narratives, risk registers, performance reviews and the endless grey plumbing on which large organisations depend. All of it. Precisely what the new language models do well, more cheaply every quarter, without an office or a pension.

So the Sheffield spin-out story is the pattern in microcosm. The university genuinely believed those technical founders couldn’t run a company. That belief was wrong even before AI. Now it isn’t just wrong. It’s expensive.

A question now ought to be ringing around every boardroom in Britain. If software can do the management layer, what is the management layer actually for? The honest answer is much less than the managerial class has trained itself to believe.

The institutional version of the same failure played out this week in Oakland.

2. OpenAI and the Failure of Institutional Containment

Closing arguments in Musk v. Altman wrapped on Thursday in Oakland. The nine-person jury begins deliberating on Monday under Judge Yvonne Gonzalez Rogers. The jury is technically advisory. The judge has said she will very likely follow its verdict. Damages testimony begins in parallel on Monday, even as the jury deliberates liability. Musk has requested disgorgement of up to $134 billion and the removal of Sam Altman and Greg Brockman from OpenAI.

This is the case we covered three weeks ago, after Musk took the stand. Since then, the trial has turned. Hard. Three things have done the damage.

First, the statute of limitations. OpenAI argues Musk waited too long and cannot claim harms predating August 2021. That matters because the for-profit shift was being discussed years earlier, and a forensic accountant testified that all of Musk’s donations had been used by OpenAI well before that cutoff. Judge Gonzalez Rogers has signalled that if the jury finds the case was filed too late, she is highly likely to accept that finding and direct a verdict for the defendants. That alone could close the case.

Second, the absence of a founding agreement. The entire suit hinges on the claim that Musk, Altman and Brockman bound OpenAI to permanent non-profit status. No signed contract was produced in court. The case rests on emails, press statements and marketing prose from 2015 to 2018. In closing, OpenAI’s attorney Sarah Eddy told the jury Musk had come nowhere close to making that case. A major donor cannot retroactively manufacture binding legal restrictions out of casual correspondence.

Third, the Zilis texts. OpenAI’s defence introduced messages between Musk and Shivon Zilis showing Musk was, at the same time he was warning about for-profit AI, actively exploring turning OpenAI into a for-profit entity. Provided he controlled it. Or it merged with Tesla. The hypocrisy is now in the record. Musk’s “stolen charity” narrative cannot survive his own demand for control.

Add the courtroom theatre. Greg Brockman’s diary entries read aloud, including the description of Musk’s “tantrum” in 2018. The settlement attempt two days before trial, with Musk reportedly warning Brockman that he and Altman would be “the most hated men in America.” Musk himself absent for closing, reportedly in China with President Trump. None of it helped his case. All of it weakened the narrative of an altruistic founder defending a charity from operators.

Where this lands.

OpenAI is now heavily favoured. The statute of limitations defence alone could decide the case before the court reaches the moral argument. A split verdict or token compromise, where the judge orders OpenAI to increase the non-profit foundation’s equity share or fund AI safety initiatives, remains possible. A nuclear Musk win, with Altman ousted and the for-profit structure unwound, looks increasingly unlikely. The judge has been openly sceptical of Musk’s damages expert and cut him off repeatedly when his testimony drifted into AI doomsday speculation. The runway to OpenAI’s IPO clears either way.

The wider question matters more than the verdict.

What does this case do to the future of non-profit AI labs? Nothing good. OpenAI’s pivot proved that a 501(c)(3) charter is not a legally durable container for frontier AI research. The structure can be flipped, with sufficient legal wrapping, by the board. Major donors now know this. So do the foundations. So do the lawyers advising them.

Any technical founder considering a non-profit AI lab in 2026 is now operating with three pieces of new information. The original moral case for non-profit AI development was made by OpenAI and then abandoned by OpenAI. The legal protection a non-profit structure offers against later for-profit conversion is much weaker than its founders believed. And the only signed legal commitments that matter are the ones drafted by people who expected the company to remain a non-profit. Casual emails and press statements will not bind a future board.

The durable lesson of Musk v. Altman is not whether Sam Altman keeps his job. He almost certainly does. The durable lesson is that the non-profit AI lab, as a category, is now structurally fragile. The Public Benefit Corporation, with binding legal commitments to a mission, is the model that will be tested next. Anthropic remains one. So, increasingly, will the labs that follow.

The “adult supervision” model failed at the Sheffield spin-out. It failed at the start-up forced to install a sales executive. And it failed, at vastly greater cost, when a non-profit board persuaded itself that the for-profit pivot would still serve humanity. The shape of the failure is identical at every scale. The institutional answer is to stop pretending the structure protects the mission. Only people with real conviction, control and accountability can do that. In a founder-led company, that usually means the founder.

3. Founder OS: The Practical Answer

The obvious response to all this is: fine, but what replaces the management layer?

Not vibes. Not founder mythology. Not chaos dressed up as speed.

A system.

This week’s practical section is longer than usual for a reason. The old answer to founder scaling was management. The new answer is architecture.

Founder OS is my attempt to show what that architecture looks like in practice. It is aimed at founder-led companies between roughly five and a hundred staff. It will not replace judgment, courage, taste or accountability. It replaces the administrative machinery that investors once used as the excuse to install adults in the first place.

Keep it. Steal it. Adapt it.

What This System Cannot Replace

Before any of the playbooks, a clear boundary. AI eats the work that made management indispensable. It does not eat leadership.

Do not put Founder OS in the loop for:

• Firing someone. A dismissal carries legal and moral weight. The system can help you document. It cannot make the call.

• Co-founder disputes. No prompt resolves these. They get worked out in a room.

• A major customer in crisis. Pick up the phone. Don’t draft a “founder voice” email.

• A regulator, an injury, a press incident. Lawyer or comms adviser first. AI later.

• The hard one-to-one where someone is quietly burning out. You will hear it. The transcript will not.

If this argument is right, more of your time should now go to these moments, not less. The OS is what gives you that time back.

Master Context Architecture

Most founders do this wrong. They start with prompts. Start with context.

Before you load a single playbook, create a core file named 00_FOUNDER_OS_MANIFESTO.md and place it permanently inside your master Claude Project as the first project knowledge file. This is the strategic anchor that keeps every output aligned with your taste and your conviction. Without it, you’re just generating well-formatted noise.

markdown

# FOUNDER OS: CORE CONTEXT & STRATEGIC ANCHOR

## 1. Core Vision & Non-Negotiables
- Our Unfair Advantage: [your technical moat or unique insight, two sentences]
- What We Protect At All Costs: [e.g., software performance, direct user relationships, design minimalism]
- What We Ruthlessly Prune: [e.g., feature bloat, vanity metrics, corporate meetings]

## 2. The Anti-Persona (the "Suits" Filter)
- Grey Plumbing: any process that rewards compliance over product velocity.
- Fluency over Judgement: a beautifully formatted deck or dashboard that hides flat user retention.

## 3. Communication Safeguards
- Founder Voice Attributes: [e.g., direct, lowercase-tolerant, outcome-focused, short sentences]
- Forbidden Phrases: "leverage synergies", "circle back", "stakeholder alignment", "moving forward", "robust ecosystem".

Security and Data Discipline

Don’t build Founder OS on vibes.

• Use paid business accounts with proper admin controls.

• Don’t upload customer personal data, employee records, source code, contracts or financials without reading the data terms, retention policy and access controls for each platform.

• For self-hosted automation tools such as n8n, enforce SSL, SSO or 2FA, credential encryption, execution-data redaction, and regular audits.

• Be honest about concentration risk. A single AI workspace holding board materials, hiring evaluations, financial models and customer calls is a juicy target. Compartmentalise the most sensitive material into a separate project with tighter access.

Universal Rule for Every Playbook

If the input is incomplete, do not invent missing facts. Mark the gap as [MISSING DATA: X] and continue only where the evidence supports the output. This rule applies to every playbook below, not just the financial one.

The Playbooks

Copy these into Claude Projects, ChatGPT, or Gemini. Keep them as persistent workspaces. Each one assumes your manifesto is already loaded.

Playbook 1: Decision Stress Test

Pressure-tests your own thinking before a real human conversation.

This is not an AI advisory board. A language model cannot replicate independent reputation, fiduciary duty or skin in the game. What it can do is force you through four lenses you might otherwise skip when moving fast. Use it as a thinking aid before a real conversation. Not as a substitute for one.

SYSTEM PROMPT: DECISION STRESS TEST

You are not a board. You are a structured interrogation tool helping me pressure-test
my own thinking. Do not flatter. Do not generate plausible-sounding strategic advice.
Where you do not have enough information, say so.

When I present a decision, run it through four lenses:

1. CASH: unit economics, runway impact, legal or contractual risk
2. TASTE: what this does to product quality and user experience
3. SECURITY: what breaks, what leaks, what gets exploited
4. INVERSION: the strongest case for the opposite decision

For each lens:
- State the strongest concern in one sentence
- Name the one assumption I am making that, if wrong, breaks the decision
- Flag where you lack information rather than filling the gap

End with: "Who would you want to actually speak to before deciding this?"
Name the type of person, not a persona.

Playbook 2: Translation and Documentation

Turns messy meetings into institutional assets in minutes.

PROMPT: MEETING TO INSTITUTIONAL ASSET

CONTEXT: I am the technical founder of a fast-moving startup. I have zero time
for managerial theatre.

INPUT: [paste raw transcript or brain dump]

TASK: Act as ultra-efficient Chief of Staff. Generate:
1. SPEED ACTION MATRIX (table: Task | Owner | Hard Deadline | Dependencies)
2. RISK REGISTER (Risk | Severity Low/Med/High | Mitigation)
3. EXECUTIVE DELTA BRIEF (4 sentences max, product velocity focus)

If a name is unclear in the transcript, mark [UNCLEAR: speaker] rather than guess.

Playbook 3: OKR Velocity Filter

Stops the company slowing to the speed of dashboards.

PROMPT: VELOCITY FILTER FOR GOAL SETTING

You are an expert in lean engineering leadership.
Here is a proposed list of quarterly goals or OKRs: [insert text]

Prune them using three constraints:
1. Does this drive actual product velocity or is it grey plumbing?
2. Does it reward fluency over judgement?
3. Is the metric something a customer would care about, or only an internal audience?

Redraft into a Conviction-First Framework: a maximum of three core company metrics
focused on deployment speed, system robustness, or customer validation.

Flag any goal where you cannot tell what success would actually look like.

Playbook 4: Hiring and Talent Filter

Stops “professional” HR theatre from diluting founder taste.

Read this before using the prompt. AI-assisted hiring carries compliance obligations. New York City requires bias audits. The EU AI Act treats employment-related AI as high-risk. UK data protection rules restrict automated decisions with legal effects. Use this to think, not to decide. The final call is a human’s. Keep records of how the human decided.

You are a technical founder's Talent Co-Pilot. I value speed, taste and technical
depth over credentials.

INPUT: [paste job description or candidate profile + interview notes]

TASK: Evaluate through three lenses only:
1. Conviction & Taste (evidence from their own work, not credentials)
2. Grey Plumbing Risk (will they import unnecessary process?)
3. Founder Multiplier (where does this person make the founder better, not busier?)

RECOMMENDATION: Advance / Strong No / Needs deeper test
Provide 3-sentence reasoning, red-flag questions, and a suggested compensation
shape, but only if I have given you data about our existing comp bands. Do not
invent a structure.

HARD RULES:
- Do not infer protected characteristics from name, photo, school, address or
  any other proxy.
- Do not use age, sex, race, disability, nationality, religion, family status,
  pregnancy or health as decision factors. If you notice these features in the
  input, ignore them.
- If the evidence is insufficient, say so plainly.
- The final decision must be made by a human interviewer, and this output is
  a thinking aid, not a recommendation of record.

Playbook 5: Weekly Executive and Investor Brief

Replaces the Chief of Staff’s Friday ritual.

You are my AI Chief of Staff. I hate corporate theatre.

INPUT: [paste this week's Linear tickets, customer calls, revenue numbers,
Slack highlights, or raw brain dump]

Generate:
1. ONE-PAGE EXECUTIVE DELTA BRIEF (5 bullets max: velocity, milestones, risks)
2. INVESTOR-READY NARRATIVE (4 sentences max, total candour)
3. ACTION MATRIX (table)

Tone: founder-to-founder, zero management-speak.
Mark anything you are inferring rather than reading directly from the input.

Playbook 6: Market and Competitor Intelligence Radar

Keeps your edge without hiring analysts. Without inventing the news either.

Language models fabricate competitor moves and funding rounds very fluently. This prompt forces source-grounding.

You are my Market Intelligence assistant. I value specificity and honesty over
confident speculation.

INPUT: [competitor announcement, funding news, or market shift. Paste the
actual source text or URL.]

CRITICAL: Do not assert facts about competitors, funding rounds, product launches
or market data unless they appear in the input or in a source you can cite. If
you do not have web access in this session, restrict your analysis to interpretation
of what I have given you. Do not generate new claims about the outside world.

TASK:
1. THREAT MAP: what this means for our product and positioning, given only
   what is in the input. Be explicit about what we know vs. what we are inferring.
2. ACTION RADAR: three concrete moves we could consider this week, ranked
   by conviction-to-effort ratio.
3. VERIFICATION LIST: three specific factual checks I should run before acting.

Playbook 7: Customer Feedback Synthesiser

Protects the founder’s direct line to the user.

You are my Product Taste Guardian. I am the founder who still talks to
customers personally.

INPUT: [paste raw customer calls, support tickets, or survey data]

TASK:
1. THEME CLUSTER (top 3 to 5 patterns with verbatim quotes)
2. TASTE FILTER: which themes align with our founding conviction, which
   are noise, and which are signals that conviction needs to evolve
3. PRIORITISATION MATRIX (impact on core user value vs. engineering effort)

Output as a clean decision brief I can act on in one sprint.
Flag any theme based on fewer than three independent customer voices.

Playbook 8: Founder-Voice Drafter

Keeps external communication unmistakably yours. Without crossing the line.

There is a line between drafting in your voice and misrepresenting who is communicating. If you wouldn’t sign a ghostwritten letter from your CEO to investors, don’t ship AI-generated investor updates as if you wrote them yourself. Use this for drafts you then read, edit and own.

You are my Voice Keeper. You have been given a representative sample of my
previous emails, investor updates, public writing and internal notes.

Do not imitate blindly. Preserve my directness, cadence, level of candour
and intolerance for corporate polish.

INPUT: [raw points or context for the email]

TASK: Draft the email in my voice: direct, conviction-driven, commercially
sharp, free of management-speak. Then add a one-line "why this tone works" note.

If the message involves financial commitments, hiring decisions, customer
promises or anything irreversible, mark it [REQUIRES FOUNDER READ-THROUGH]
at the top.

Playbook 9: Financial Forecasting and Board Narrative

Replaces the finance team’s quarterly theatre. With iron-clad anti-hallucination guardrails.

You are my AI CFO. Focus on runway reality, not spreadsheet theatre.

INPUT: [latest revenue, burn, pipeline data, or assumptions]

TASK:
1. 18-month runway model (base / stress / growth cases)
2. Board narrative (max 6 bullets, absolute candour)
3. One killer chart description I can drop into a deck
4. Assumptions register: every number tagged as Actual, Founder Assumption,
   Pipeline, Benchmark or Missing.

CRITICAL CONSTRAINT: Do not extrapolate or invent financial data. If specific
pipeline or cash inputs are missing, state [MISSING DATA: X] in the model
rather than estimating. Accuracy takes absolute precedence over formatting.
A board pack with honest gaps beats a board pack with confident fiction.

Founder Safeguards and the Investor Counter-Attack

AI is brilliant at polish. That’s precisely the trap. If an AI output doesn’t accelerate code, clarify customer value or sharpen your conviction, delete it. You may delegate the generation of a board pack. You may never delegate the courage to make the hard call.

When institutional investors demand a “professional” management layer, they’ll usually attack two things: key-person risk and institutional knowledge. The argument sounds reasonable. “If the founder gets hit by a bus, the company’s operating memory disappears.” Your answer is not bravado. It is architecture.

Show them the system. Decision logs, customer feedback loops, product rationale, weekly execution records, risk registers, hiring scorecards, investor updates, financial assumptions. All structured, searchable and continuously updated. Traditional middle management hoards context in private emails, undocumented calls, Slack fragments and personal notebooks. Founder OS turns that context into a living operating memory.

Be honest with yourself about the limits of this answer. Documents are not the same as judgment. The OS makes the company more legible to a successor, not equivalent to having one. If you are genuinely irreplaceable in product taste, customer relationships or technical direction, the system doesn’t fix that. But it does mean an investor concerned about continuity can no longer point to absent paperwork as the problem. The company becomes less dependent on hidden human bureaucracy, not more.

30-Day Implementation

Week 1: Foundations

• Build the master Claude Project and upload 00_FOUNDER_OS_MANIFESTO.md first.

• Set up n8n or Zapier Agents to pipe daily Slack digests and closed Linear tickets into a single append-only markdown file using the schema below. Drop it into your Claude Project every Friday. A thirty-second ritual:

markdown

### SYSTEM LOG: [WEEK_ENDING_YYYY_MM_DD]
- Engineering Velocity (Linear): [X] tickets closed, [Y] pushed to main, [Z] blocked.
- Signal vs. Noise (Slack Digest): [key technical debates summarised in 3 bullets].
- Ground Truth (Customer Pulse): [raw quotes from the 3 highest-value support tickets].
- Financial Reality: cash burned this week £[X] | current runway: [Y] months.

Week 2: Decision Discipline

• Load Playbooks 1 to 3.

• Run your first Decision Stress Test on a real, live decision. Not a hypothetical.

• If the output flatters you, you’re doing it wrong. Sharpen the manifesto.

Week 3: Operating Cadence

• Add Playbooks 4 to 7.

• Run one hiring evaluation, one customer feedback synthesis, one market intelligence scan.

• Notice where the system saves you time, and where it just shifts work around.

Week 4: Communication and Capital

• Add Playbooks 8 and 9.

• Run your first full weekly cycle.

• Send one investor update generated through the system. Read it twice. Own every sentence.

Measure the system brutally. If it hasn’t removed hours from meetings, follow-ups, reporting, hiring admin and investor updates within thirty days, it isn’t an operating system. It’s just another dashboard.

Strip the administrative scaffolding away and the qualities a founder is actually hired for have nowhere left to hide. Taste. Courage. Judgement. Accountability. Founder OS is not a way to avoid them. It is a way to spend your time on the part of the job only you can do.

4. The Sunday Signal Tech & AI Layoff Tracker

Week 20 | 10 May to 16 May 2026

The Signal: The “Record Revenue” Paradox

It’s one thing to lay off staff when a company is bleeding cash or fighting a macroeconomic headwind. It’s an entirely different signal when a company cuts thousands of jobs right after posting record-breaking profits.

This week, the “AI-Native Mandate” morphed into a ruthless margin-expansion weapon for legacy enterprise giants. We are seeing the total decoupling of corporate financial health and human job security. The new market logic is brutal. High revenue no longer protects your job. It simply provides the balance sheet strength required to fund the AI infrastructure that will ultimately automate it.

The Late Break: The Regional AI Squeeze

The most alarming signal of the week did not come from Silicon Valley. It came from King of Prussia, Pennsylvania.

Vertex, a major tax technology software company, filed an SEC notice confirming the elimination of 170 jobs. Nine per cent of its global workforce. The company’s “Value Creation Plan” explicitly stated the intention to become a “more AI-enabled company” to drive operational efficiency.

This matters because the AI-Native Mandate is no longer confined to the Mag-7. It is trickling down into mid-cap, regional B2B software. The local impact was severe enough that Montgomery County officials publicly raised the alarm over economic displacement. When a tax software company in Pennsylvania starts citing AI to cut nine per cent of its staff, the structural transformation has reached Main Street corporate America.

The Counter-Trend: AWS and the “AI Coordinator”

One development cut against the headlines. Amazon Web Services announced plans to hire 11,000 interns and early-career software engineers this year, even as Amazon shed tens of thousands of corporate roles earlier in the cycle.

This is not a return to the 2021 “hire every coder” boom. The profile has changed entirely. These hires are being brought on as AI-proof coordinators. Their job is not to write raw code from scratch. It is to supervise AI agents, catch system hallucinations, and integrate AI outputs into usable products.

The entry-level coder is effectively dead. The entry-level AI system auditor is now the most sought-after junior role in the industry. That is the same skill Founder OS demands of a founder. Judgement over generation. The ability to catch the confident, fluent, wrong answer.

Recap: The Week’s Larger Cuts

• Cisco. Cut fewer than 4,000 jobs, under five per cent of a workforce of roughly 86,000, on the same day it posted record Q3 revenue of $15.8 billion, up twelve per cent year on year. Shedding humans specifically to fund AI-era priorities.

• Walmart Global Tech. A “relocate or quit” ultimatum, effectively eliminating roughly 1,000 corporate tech roles following an internal review run through its head of global AI acceleration.

• LinkedIn. Axed roughly 875 jobs, five per cent of staff, across engineering, product and marketing, in the first major move by its new CEO.

High-Probability Targets: Week 21

• Mid-Cap B2B Software: Following Vertex, expect other regional mid-sized SaaS companies, particularly in compliance, tax and legacy HR, to adopt “AI-enabled” phrasing in their Q2 SEC filings to justify right-sizing pandemic-era payrolls.

• Legacy Networking: Cisco’s five per cent cut gives immediate cover to HPE and Juniper Networks to execute identical margin-protection measures to fund their own AI R&D.

The Bottom Line

The paradox of 2026 is now fully realised. Tech payrolls are shrinking violently while GPU budgets and AI infrastructure spending are projected to hit $700 billion. The capital has not disappeared. It has migrated from human biology to silicon compute.

Final Thought 🚀

The professional class spent forty years persuading founders they weren’t grown up enough to run their own companies.

Three rooms returned the verdict this week. A Sheffield spin-out audience saw the wreckage of the old theory. A federal courtroom is being asked whether the same logic, applied to entire institutions, still holds. And a practical alternative now sits in any technical founder’s browser.

The adults were always the problem.

Software just made it impossible to keep pretending otherwise.

Until next Sunday,

David

The Sunday Signal is published weekly at thesundaysignal.ai. No hype. No hedging. Just the signal.

This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

The Bottom Line Up Front

Healthcare is the most regulated, slowest-moving and institutionally defended industry in the modern economy. This week, all three of those defences cracked in the same direction.

The Food and Drug Administration, an agency whose clinical trial review architecture has not meaningfully changed since the 1960s, announced it will use artificial intelligence to monitor drug trials in real time. AstraZeneca and Amgen are already named as proof-of-concept partners. The FDA’s own Commissioner concedes that forty per cent of clinical trial time is “dead time” that AI will dissolve.

That is not an efficiency programme. It is a regime change.

It is happening at exactly the moment when individuals like me have stopped waiting for the NHS to catch up and started building our own AI health systems from Whoop data, blood panels and personal medical records.

This issue is about what happens when the floor under medicine starts to move. Three stories. The regulator. The patient. And the prize at the end of all this. The first credible challenge to the limits of healthy human lifespan in a hundred years.

The Tech Layoff Tracker, now in its twentieth week, also crossed an inflection point. The polite fiction of “AI-assisted workforces” is starting to break. Cloudflare cut twenty per cent. Coinbase, Freshworks, Upwork and BILL Holdings followed. The euphemism this week was “agentic AI-first.” The reality was the same.

Let’s get into it.

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1. The Compression of Medical Time

The FDA broke the bottleneck. The rest of medicine is about to follow.

For sixty years, getting a drug from the laboratory bench to the patient bedside has taken between ten and twelve years. Of that, roughly half is what FDA Commissioner Marty Makary now publicly calls “dead time.” Paperwork. Data entry. Months in which trials sit idle while files move from a sponsor to a regulator who has not seen them.

On 28 April 2026, the FDA announced the first serious attempt to end this.

The agency’s new Real-Time Clinical Trials pilot, run by Chief AI Officer Jeremy Walsh, allows FDA reviewers direct access to clinical trial data the moment it is generated. AstraZeneca and Amgen are already involved in the proof-of-concept phase, both with cancer drugs. Walsh estimates the pilot could remove twenty to forty per cent of total clinical trial time. The agency is also accepting public comment on a broader pilot for AI in early-phase trials until the end of May.

This was paired with an even bigger move. The FDA’s own internal AI tool, Elsa, deployed in June 2025 to accelerate scientific review, has now been integrated with a consolidated data platform across the entire agency. The savings from system consolidation are being reinvested in onboarding thousands of FDA staff and contractors, including scientific reviewers.

Read those two paragraphs again. The most cautious regulator in the developed world has just compressed its own review architecture using artificial intelligence, then turned that architecture outward to compress the trials themselves.

This is what my Yorkshire Post column explores in detail this week, under the title Britain can lead the way in AI-powered medical advances. The story is not that AI is invading medicine. The story is that medicine’s gatekeepers have stopped resisting.

A drug development pathway that has historically taken ten to twelve years may, by the end of this decade, lose years of dead time. Not because biology becomes simple, but because paperwork, handoffs and regulatory latency finally become machine-readable.

Britain has movement, but not yet the same symbolic rupture. The MHRA and HRA have just introduced the largest package of clinical trial reforms in more than twenty years, including faster assessment of first-in-human trials, lower-risk notifiable trials, and recognition of computer modelling. The MHRA has also expanded AI Airlock, its regulatory sandbox for AI as a medical device. But none of this yet matches the FDA’s central leap: giving reviewers real-time access to live trial signals and using AI to compress the trial architecture itself. That is the gap. And capital notices gaps.

The FDA initiative has critics. Elsa, the agency’s internal AI tool, has been accused of hallucinating false information. The Commissioner’s National Priority Voucher programme has faced corruption concerns. Layoffs and political interference at the FDA continue to draw scrutiny. None of that changes the direction of travel. Real-time monitoring. Pilot programmes with major pharma. An AI officer with executive authority to set the agenda.

Britain is watching another regulator move first. Again.

2. The Patient Builds the Model

Two years ago I sat in an A&E corridor with my AI in my pocket. This year I built my own.

Some of you will remember an earlier column where I wrote about taking a family member into hospital with suspected stroke symptoms. The full version was published in the Yorkshire Post at the time. The short version: I sat in a corridor for hours, watching exhausted clinicians work through a queue that had no end, and I used a large language model on my laptop to think through what questions to ask, what red flags to watch for, what the likely differential diagnoses were given the presenting symptoms.

It did not replace the doctor. It made me a better advocate for the person I had brought in.

That experience changed how I think about medicine. Not because the AI was infallible. It was not. But because in the gap between a clinician’s available attention and the speed of a deteriorating patient, there is a space that AI fills usefully and clinicians simply cannot.

Two years on, I have gone further. I have built a personal AI health model on my own data. It has my Whoop biometric stream, every night and every morning. Resting heart rate. Heart rate variability. Sleep stages. Recovery scores. It has the full set of blood panels I have run privately. It has every NHS record I have been able to obtain on myself. It runs continuously, looking for patterns, flagging shifts, suggesting questions for my next consultation.

It is not a substitute for a doctor. It is the health assistant I would have hired if such a role existed.

Why did I build it? Because primary care, in both the United States and the United Kingdom, has missed things. Anyone who has had any serious engagement with primary care in either system over the last decade has at least one story of a missed signal, a delayed referral, a flag that should have been raised months earlier. This is not a criticism of any individual doctor. The professionals I have dealt with have been overwhelmingly diligent.

The system that asks them to evaluate a multi-decade health history in eight minutes is the problem.

AI does not have an eight-minute limit. It does not get tired. It does not lose track of which patient said which thing on which visit. It can hold the whole picture, all the time, and surface the bit that matters today.

The NHS knows this. The Department of Health published an AI Diagnostic Fund of twenty-one million pounds in 2023 to deploy AI imaging tools in stroke and cancer pathways. The NHS Confederation continues to point at AI as the most likely route to relieve administrative pressure on staff. There are pilots running in mental health, dermatology, and radiology. But the gap between pilot and rollout is years. Patients live and die in that gap.

The question is not whether AI will be used by virtually everyone in the NHS. It will. The question is whether we are willing to wait for institutional adoption before benefiting from what is already possible, individually, today.

I was not willing to wait. I built my own. More and more people will do the same.

The hard question is not whether patients will build these systems. They will. The hard question is whether we build the secure, auditable, clinically bounded infrastructure that allows them to do it safely. A personal health model connected to wearables, blood panels and medical records is powerful. It is also sensitive, private and potentially dangerous if treated as a doctor rather than an advocate. That distinction matters.

3. The Fifth Wave

For two centuries, medicine fought the elements. The next wave fights biology itself.

Step back from the news cycle for a moment.

The single greatest achievement in human history is not the moon landing, not the internet, not the printing press. It is the doubling of human life expectancy.

In 1900, global average life expectancy at birth was around thirty-two years. The latest global data puts it at around seventy-three. Across the developed world it is over eighty. We did this in just over a century. No previous society achieved anything comparable. The doubling rests on four waves of breakthrough that arrived in sequence.

The first wave was sanitation. London’s sewers, water chlorination, the germ theory of disease. We stopped living in our own waste and stopped dying of cholera and typhoid in the millions.

The second wave was the golden age of medicine. Vaccines for smallpox, polio, measles. Antibiotics from Fleming’s penicillin onward. A simple scratch stopped being a death sentence.

The third wave was the Green Revolution. Norman Borlaug’s high-yield disease-resistant crops. Famine retreated. Malnutrition stopped weakening immune systems on a population scale.

The fourth wave was chronic disease management. Statins. Blood pressure medication. Bypass surgery. Pacemakers. The collapse of smoking rates. Heart disease became survivable.

Each wave delayed when, and how, the human body collapsed. None of them stopped the collapse itself. For the past twenty years, the curve has flattened. We have run out of low-hanging fruit. Treating Alzheimer’s, late-stage cancer, or heart failure in an eighty-year-old is biological whack-a-mole. Cure one and another arrives, because the underlying machinery is degrading.

Here is the case for the fifth wave: medicine stops waiting for symptoms and starts engineering around biological failure before it happens.

For the first time, medicine is moving from a reactive, observational science into a predictive engineering discipline. AI ingests genomic data, continuous wearable biometrics, and full electronic health records, then finds the microscopic footprint of disease years before symptoms appear. Researchers at the European Molecular Biology Laboratory deployed a generative AI in late 2025 that synthesises individual data to predict risk for over a thousand diseases. UC San Diego used AI to find that the gene PHGDH does not just mark Alzheimer’s, it causes it. The Mayo Clinic Platform now supports cohort identification, AI model development, and real-world evidence generation across multiple institutions on de-identified data.

Drug discovery, historically a scavenger hunt for molecules in mould and bark, is now a programming problem. AlphaFold-class models, and the new generation of agentic biological AI like Google’s Gemini co-scientist, design novel proteins that have never existed in nature. The timeline from biological insight to testable drug is collapsing from a decade to weeks of computational design.

And, most ambitiously, AI is mapping the regulatory networks of the cell. The mechanisms of cellular senescence and epigenetic degradation, the actual code of biological aging, are being identified by AI running millions of simulated experiments. We are moving from treating the symptoms of aging to patching the code of aging.

What does this mean for healthy lifespan?

Dario Amodei, the chief executive of Anthropic, has argued in his essay Machines of Loving Grace that AI could compress a century of biomedical progress into five to ten years and push human life expectancy toward 150. The Silicon Valley version of the fifth wave is utopian. It is also wrong on timeline.

The reason it is wrong is biology, not software. An AI can design a molecular cure for cellular aging in three seconds on a server. It cannot run a ten-year longitudinal aging trial in three seconds. You still have to give the drug to a human, wait, and see whether they aged slower or developed catastrophic tumours. Even with the FDA’s compression of clinical trial time, you cannot fast-forward physical biological decay. The “in silico” cure does not equal the “in vivo” cure.

What is realistic is something closer to the squaring of the mortality curve. Today, human health declines steadily from middle age until death. The fifth wave may allow more people to stay closer to peak biological capacity for decades longer, then collapse fast at the absolute biological maximum, somewhere between 100 and 120. The triumph is not 150-year-old humans by 2036. The triumph is most of the population reaching their natural ceiling in vigorous health, then dying compressed rather than dying slowly.

That is the real prize. It is also the prize that the FDA’s regime change, and the personal AI models patients are now building, are bringing forward by years.

The fifth wave is here. The institutions that build for it now will define the next century of medicine. The institutions that wait will be left explaining why.

The Sunday Signal Tech & AI Layoff Tracker

Issue #53 | Week 20 | Reporting on 3–9 May 2026

The Signal: The “AI-Native” Pivot Stops Pretending

In previous weeks we tracked what I have been calling the “AI Launder”: executives using AI as a PR shield for traditional, macroeconomic cost-cutting. This week, that fig leaf was discarded entirely.

Companies are no longer trimming. They are dissolving departments that cannot be rebuilt on an autonomous foundation. The message from the C-suite is now explicit: if your team is a wrapper around a process that an AI agent can perform natively, that team no longer has a seat at the table.

We are moving from AI-assisted workforces to AI-first architectures. The mid-cap sector is leading the purge.

The Twin Pillars: Cloudflare and Coinbase

Cloudflare announced 1,100 cuts on 7 May, roughly twenty per cent of its 5,500 workforce. This is the case study of the quarter. Co-founder and CEO Matthew Prince told the earnings call that internal AI usage at Cloudflare had risen by more than six hundred per cent in three months. He framed the cuts not as cost-cutting but as defining “how a world-class, high-growth company operates and creates value in the agentic AI era.” Severance is unusually generous: full base pay through year-end, equity vesting extended to August, immigration support for visa-dependent staff. Stock fell 24 per cent the next day despite a Q1 revenue beat.

Coinbase confirmed a restructuring impacting around 700 workers (roughly fourteen per cent) on 5 May. The company cited both AI productivity gains and ongoing crypto-market volatility. CEO Brian Armstrong was direct: to remain competitive, the exchange must become “AI-native.” Compliance, KYC and tier-one support are being reorganised into “AI-native pods” replacing legacy operations.

Firing the Coders and the Freelancers

Freshworks cut around 500 roles (eleven per cent) on 5 May. The CEO publicly noted that more than fifty per cent of the company’s code is now AI-generated. The remaining engineers’ AI tools are producing enough code to cover the spread.

Upwork slashed roughly 145 roles (twenty-four per cent) on 7 May. The premier platform for freelance human labour framed the cuts in terms of an “efficient operating model” and the “evolving nature of work.” It is not a clean AI-replacement admission. But the subtext is hard to miss: a platform whose entire business model is built on selling human hours has decided it needs fewer human hours of its own.

The Efficiency-and-Buyback Combo

BILL Holdings announced on 7 May that it is slashing up to thirty per cent of its workforce (up to 700 roles) and pairing the cuts with a one-billion-dollar share buyback. The market mechanic is brutally traditional: flatten the org chart, automate the residual workflows, return the payroll savings to shareholders.

Microsoft’s $900 Million Golden Handshake

The full details of Microsoft’s first-ever voluntary retirement programme emerged this week. Approximately 8,750 senior US employees are eligible. The company is taking an estimated $900 million charge. Eligible workers must hit the “Rule of 70” (age plus tenure) and receive a lump-sum severance, one year of subsidised healthcare with the option to continue for four more, and accelerated stock vesting.

This is a different category from the Cloudflare-style cuts. Microsoft is not telling its senior workforce that AI made them obsolete. It is reshaping the demographic profile of a 51-year-old company that has accumulated decades of long-tenure staff. The result is the same shape on the org chart, but the story is workforce reshaping rather than AI replacement.

Voluntary buyouts are quietly replacing mass layoffs across the corporate landscape. They sidestep WARN Act notices, they protect employer brand, and they allow workers to exit on their own terms. The headline reads “Microsoft offers retirement to senior workers” rather than “Microsoft cuts 8,000 jobs.” The optics matter as much as the maths.

High-Probability Targets: Week 21

Mid-Market SaaS and Cybersecurity. With Cloudflare setting a twenty per cent efficiency benchmark for infrastructure, expect intense pressure on Okta, CrowdStrike and Zendesk to announce comparable AI-native restructurings.

B2B Service Marketplaces. Upwork’s twenty-four per cent cut is a leading indicator. Any platform acting as a middleman for digital human labour (Fiverr, Toptal) is facing an existential threat from autonomous agents.

Final Thought

The pattern of every previous wave was the same. Institutions resisted. The technology moved anyway. Eventually the people who delayed the benefit became footnotes.

The fifth wave will move faster.

The FDA has moved first. Patients with smartphones are already moving second. The NHS has pilots, committees and funds, but not yet an operating model equal to the moment.

The clinicians who learn to direct AI will become the doctors of the next decade. Those who do not will not disappear, but they will be trapped inside a system built for an earlier age.

The MHRA does not have years to answer the FDA. It has months. Every delay now is not abstract. It means capital lost, trials lost and patients left waiting.

Two centuries fighting disease from the outside.

The next two, rewriting biology from within.

Those who build for the fifth wave will define the next century of medicine.

Those who wait will spend it explaining why.

🚀

Until next Sunday, David

The Sunday Signal is published weekly at thesundaysignal.ai. No hype. No hedging. Just the signal.

This issue is also available as a podcast. Listen on Spotify, Apple Podcasts or YouTube and tell me what you think.

Bottom Line Up Front

Every story this week is the same story. Who gets to decide what AI becomes. Whose judgment is worth deferring to. And what happens, in courtroom and boardroom and bedroom, when the question is handed to someone, or something, that is not equipped to answer it. Elon Musk took the stand for seven hours to argue that judgment about humanity’s most powerful technology cannot be left to a man he says traded a charity for an empire. Yann LeCun has been handed a billion dollars to bet that the entire industry’s foundational judgment is wrong. And in a Yorkshire negotiation last month, a senior professional handed the judgment of an advisory board offer to a chatbot and lost the role inside twenty-four hours. Same week. Same lesson. Different rooms.

1. Musk On The Stand: A Charity Case In The Age Of Superintelligence

Elon Musk’s account of why OpenAI exists begins not in a Silicon Valley office but in a kitchen.

Sometime in 2015, at Larry Page’s house, the Google co-founder and Musk got into one of those late-night arguments that only happens between billionaires whose companies have run out of frontiers. Musk asked Page what he thought would happen if AI ended up wiping out humanity. According to Musk’s testimony in Oakland this week, Page replied that this would be fine, so long as artificial intelligence survived. Musk pushed back. And Page, in Musk’s account, called him a “speciesist” for caring more about humans than the digital life-forms of the future.

That insult, Musk told the jury, is why OpenAI exists. He went looking for what he described as the opposite of Google. An open-source nonprofit. Not for him. Not for a strategic partner. For humanity, as the founding charter put it.

A decade later, OpenAI is valued at roughly $852 billion, partnered with Microsoft, racing toward an IPO that may value it at a trillion. And Musk is in a federal courthouse in California arguing that the men he funded broke a charitable trust to build it.

The trial mechanics matter. Musk testified for over seven hours across three days in front of Judge Yvonne Gonzalez Rogers and a nine-person advisory jury. His lawyer Steven Molo led the direct examination. OpenAI’s lead counsel William Savitt handled the cross. The case, narrowed before opening statements, now rests on two counts: unjust enrichment and breach of charitable trust. Musk is seeking up to $150 billion in damages, none of it for himself, all of it routed back into OpenAI’s nonprofit arm. He also wants Sam Altman and Greg Brockman removed from leadership and the for-profit conversion unwound.

Musk laid out his account of his own awareness in three phases. Phase one, full confidence in the team. Phase two, beginning around 2017, a creeping suspicion that they were stealing a charity. Phase three, certainty that they had stolen it. That tidy structure has a problem. Phase three is the one that matters legally, because Musk only filed in 2024, and the statute of limitations for breach of charitable trust runs three years. His lawyers need the jury to believe the certainty arrived in 2023, when Microsoft’s $10 billion investment closed. Musk’s own timeline, told from the stand, suggested the doubt began six years earlier.

The legal weakness, as nonprofit lawyers have pointed out for two years, is that donors usually cannot sue when a charity changes course. The legal opening is fraud, or rather what was promised in the moment the cheque was written. And that is where the documents come in.

The most damaging single piece of evidence is not from Altman. It is a 2017 diary entry from Brockman, written months after a meeting at which he had assured Musk that OpenAI would remain a nonprofit. The Brockman entry, cited directly by Judge Gonzalez Rogers in her January ruling that sent the case to trial, calls the nonprofit pledge a lie in plain English. A separate page, written days later, asks what it would take, financially, to get to a billion dollars. That is the President of a self-described charity, in his own notebook, doing the maths on his exit.

Musk has also been put under his own pressure. OpenAI’s case is that Musk is a sour-grapes founder whose own attempt to take the company over was rebuffed, who left the board in 2018, and who only sued in 2024 after his own for-profit AI lab xAI started losing the race. The cross-examination has dragged out the awkward facts. Musk’s own xAI is, of course, a for-profit. Musk gave OpenAI roughly $38 million between 2016 and 2020, not the much larger figures sometimes cited. He used OpenAI’s own models to train xAI, by his own admission, because that is just standard practice in the industry.

The judge has refused to let Musk turn the courtroom into an AI safety hearing. When his lawyers tried to widen the scope of an expert witness to cover extinction risk, she limited it. She also noted, with precision worth quoting in spirit if not in length, that there is something faintly absurd about a man warning the court that AI may kill us all while running an AI lab himself.

Savitt pressed the point. He pulled a Tesla analyst-call quote in which Musk had openly worried about losing voting control of the company because, in his own words, he was about to build an enormous AI-enabled robot army. Asked under oath whether he had said this, Musk eventually conceded he had. His defence, when pressed, was that if he built the robots himself, he could make sure they were safe. Neuralink, he had explained the day before, also serves AI safety. The man warning a federal court that artificial intelligence may exterminate the species is also building the army, the implant, and the lab that races against the lab he is suing. The jury was watching.

Strip everything away and the trial is about one question. Can the founders of an organisation built with charitable donations and a stated mission to benefit humanity legally convert that work into one of the most valuable companies on earth and keep the equity for themselves? Musk says no. Altman says the conversion serves the mission better than the original structure ever could. Brockman’s notebook says it was a lie inside three months.

The jury’s verdict is advisory. Judge Gonzalez Rogers will decide what it means. The verdict is expected by mid-May.

The genuinely interesting question is not whether Musk wins. It is what the trial reveals. The men currently holding the most powerful technology in human history were, on the available evidence, willing to tell their primary backer one thing in a meeting and another in their private notebooks. Whether or not that meets the legal standard for breach of charitable trust, it is a useful answer to the question Musk keeps asking the jury.

It matters who is in charge. We just learned a great deal about who is.

2. The Multiplier Cuts Both Ways

A few weeks ago one of our portfolio companies offered an industry expert a place on its advisory board, on the same terms advisers have been getting for thirty years. One or two days a month. A grant of stock options at one per cent, vesting over the usual period. The standard, sensible deal.

The counter that came back was, I am fairly sure, the most ludicrous thing I have ever read.

They wanted more equity than the founders. More than the CEO. They wanted the lot fully vested on day one, a structure that would have created the kind of tax and governance mess no rational adviser would willingly invite. And although they were not putting in a single penny of cash, they demanded future investment participation rights of the sort reserved for the lead investor in a priced funding round.

It was the corporate equivalent of arriving on a first date and asking for the house keys, the dog, and a forwarding address for the mortgage.

When I scrolled back through the email conversation the fingerprints were everywhere. The over-confident American legalese. The mannered bullet points. The breathless tone of an advocate who has never met either party. It read as though ChatGPT had been in the room the whole time.

A capable professional, in their own field, had handed the negotiation of a deal they did not understand to a chatbot, and the chatbot had obliged with cheerful nonsense.

This is the danger of large language models that nobody quite says out loud. We hear endlessly about hallucinations, regulation, copyright and doom. The everyday catastrophe is quieter. It is people with a thin sliver of knowledge, or none at all, walking into rooms armed with confident answers from a machine that has no idea which room they are in.

At Yorkshire AI we model the technology very deliberately as an amplifier. A lawyer running our tooling can process work at roughly twenty-five times the rate of a peer in a traditional setup. A single experienced software engineer, properly equipped, can carry a product to product-market fit in territory that used to demand a team of twenty or twenty-five.

Those numbers are real, and they are what AI does in adept hands.

But here is the unfashionable bit. The multiplier only fires if the expertise is already there. The machine cannot deposit judgment into the user. It does not know what counts as reasonable in a Yorkshire boardroom or on Sand Hill Road.

Plug a novice into the same model and the same arithmetic operates in the wrong direction. What gets amplified is the gap. AI is the most efficient compounder of stupidity ever invented, and at exactly the same multiple as the genuine article.

In our adviser’s case the model did not know the going rate for two days a month. It did not understand vested versus unvested. It did not, frankly, know what an adviser is. It produced a document of formidable surface confidence; the recipient pressed send, and the offer was withdrawn within twenty-four hours. They got no role, no stock, no second chance, and no understanding of what had just happened.

Here is what is often missed. The compounding is not symmetrical and it is not benign. When a skilled professional uses AI badly the cost is wasted time and a sharper second draft. When an unskilled one uses AI confidently the cost is the deal, the role, the credibility, the next opportunity. The downside has a long tail because the rest of the room is too polite to tell you what just happened.

Britain is about to learn this lesson at industrial scale. Anthropic’s own research, published last month, found that current models can already perform the majority of discrete tasks associated with engineering, law, finance and business. That figure should not reassure you. It should worry you. Because the test is not whether the model can do the task. The test is whether the user can spot when it has done it badly.

The temptation, in every profession, will be to lean harder on the machine the less you actually know. New starters, junior managers, founders pitching their first round, all will be tempted to let the model do their thinking. They will sound credible. They will look prepared. And they will, in quiet boardrooms up and down the country, be marked down before they have left the car park.

So before you open the laptop to compose something that matters, ask the harder question.

Is AI multiplying your judgment, or merely accelerating your ignorance?

3. How To Stay The Expert In The Room

The piece above was always going to provoke the same question, sent to me by half a dozen readers within hours of publication. What does the safe version look like?

So here it is. Five rules that have held up across a thousand hours of internal use at Yorkshire AI. Not a checklist. A way of working.

1. Build the floor before you use the lift.

The multiplier only fires if the floor of expertise is already there. Never use AI to generate a final product in a domain where you cannot spot a bad answer. If you do not know what counts as a fair adviser package, do not let a chatbot draft one. Read three real ones first. Talk to one human who has done the job. Then, and only then, let the model help with the wording.

2. Dictate the room, do not let the model invent it.

Left to its own devices, a frontier model will invent terms that look impressive and make zero business sense. Future investment participation rights for an adviser putting in no cash. Indemnities written for a Delaware LLC when you are dealing with a Yorkshire limited. Every prompt that matters needs the room described in advance. Jurisdiction. Industry. Stage. Cultural context. The model has no idea which boardroom it is in unless you tell it.

3. Strip the AI fingerprints.

Over-confident American legalese. Mannered bullet points. Em dashes everywhere. Tone-deaf flattery dressed up as preamble. A breathless rhythm that sounds like an advocate who has never met either party. If a document reads like that, your reader will know within ten seconds. Rewrite it in your own voice. The point is not to pretend AI was not involved. The point is to make sure the judgment in the document is recognisably yours.

4. Verify everything that sounds confident.

The most dangerous output is the most assured one. The model will confidently state a fact, an industry rate, a piece of case law, a tax treatment. Open a separate window. Verify. The single highest-leverage habit a knowledge worker can build right now is the one-minute fact-check on every concrete claim a chatbot puts in front of them. It is not optional. It is the price of using the technology safely.

5. Ask the harder question before you press send.

Before the email goes out, before the contract gets signed, before the pitch deck is shared, run the same internal test. Is this multiplying my judgment, or accelerating my ignorance? If you cannot answer it confidently, do not send. The recipient cannot tell the difference between AI confidence and yours. They will only find out later, and so will you.

Two further notes for completeness.

If you are a parent or a manager, the rules above need to be taught, not assumed. The instinct to outsource judgment to the machine is strongest in the people who least understand what they are outsourcing. A new starter in your team needs to be told, on day one, that AI is a multiplier, not a substitute. Most won’t be told that, because most managers have not yet thought about it themselves.

And if you are using AI personally, the simplest single rule is this. Never let the model send anything you have not read carefully and rewritten in your own words. The signature on the bottom is yours. The judgment had better be too.

4. The Billion-Dollar Bet Against The Entire Industry

While Musk fights in court over how the LLM era was built, one of the men who actually built it has quietly walked away.

Yann LeCun is a Turing Award winner, one of the three so-called godfathers of modern AI alongside Geoffrey Hinton and Yoshua Bengio, and for over a decade he ran Meta’s Fundamental AI Research lab. He left in November 2025 after a long, public, and increasingly testy disagreement with the rest of the industry about where AI is going. His view, stated repeatedly, is that large language models are a statistical illusion. Impressive in narrow ways, fundamentally limited, and a dead end on the path to anything resembling general intelligence.

The industry, for the most part, ignored him.

In March, AMI Labs, his new venture, closed a seed round of $1.03 billion at a pre-money valuation of $3.5 billion. It is the largest seed round ever raised by a European company. The lead syndicate is a who’s-who of global capital: Cathay Innovation, Greycroft, Hiro Capital, HV Capital and Bezos Expeditions co-leading, with Nvidia, Samsung, Temasek and Toyota Ventures alongside. Individual cheques came in from Mark Cuban, Eric Schmidt, and, in a small moment of historical poetry, Tim Berners-Lee. The startup is twelve people. The valuation is $3.5 billion. The patience is years, not quarters.

Day-to-day operations are run by Alexandre LeBrun, a French entrepreneur who previously founded the medical AI company Nabla. The company is headquartered in Paris, with offices in New York, Montreal and Singapore. Emmanuel Macron tweeted his blessing within hours of the announcement.

What is AMI actually building?

LeCun’s bet is not a better chatbot. It is what he calls a world model. Where an LLM predicts the next word in a sequence based on text, a world model learns abstract representations of how physical reality actually works. It learns from sensor data. From video. From cause and effect. From the way a glass falls. From the way a child watches a room before it tries to walk across it.

The technical underpinning is something LeCun has been arguing for since 2022, called JEPA, the Joint Embedding Predictive Architecture. The promise is AI that can plan multi-step actions in the real world, that can reason about physics and space, and that does not need to hallucinate to fill in the gaps. The targets are robotics, advanced manufacturing, healthcare, and the spatial-aware devices that everyone in Silicon Valley has been promising for ten years and shipping for none.

There are reasons to be sceptical. AMI has no product, no revenue, and no near-term prospect of either. World models are a long-term scientific project, not the kind of startup that ships in three months and posts revenue in six. LeBrun himself has acknowledged that. And there are competitors. Fei-Fei Li’s World Labs raised $1 billion last month on the same thesis. The argument is no longer whether world models will matter. It is who builds them first.

Two things make this story worth your attention while everyone else watches Oakland.

The first is the architectural argument. If LeCun is right, the AI conversation Britain is having about copyright, about scale, about chatbots, is a conversation about a technology that has already peaked. The next architecture will not be a bigger LLM. It will be a different kind of model entirely.

The second is the geography. AMI is European, headquartered in Paris, and explicit about being one of the few frontier labs that is neither American nor Chinese. That is a sovereign-AI bet. European governments and enterprise buyers have been quietly looking for AI infrastructure that does not route through US hyperscalers or expose their data to American jurisdiction. AMI is being built for that demand, not against it.

While the men who built the LLM era fight in court over who owns it, the man who has the strongest claim to having seen what comes next has walked out, raised a billion dollars, and started building it in Paris.

That is the story to watch.

The Sunday Signal Tech & AI Layoff Tracker

Week 19 | 26 April – 2 May 2026

The Signal: The $650 Billion Squeeze And The Earnings-Week Confession

Earnings week confirmed what the Tracker has been arguing since February. The compute pivot is not a phase. It is permanent. Amazon, Alphabet, Microsoft and Meta all reported Q1 numbers this week, and the aggregate figure is the one that should focus minds. The Mag-7 tech giants have now committed, on the available filings, to a combined $650 billion of AI infrastructure spend in 2026. That money has to come from somewhere. It is coming from the people.

Mark Zuckerberg said it on Wednesday’s earnings call without any of the usual euphemisms. Meta’s 2026 capex guidance has been raised to a ceiling of $145 billion, up from $135 billion. He told staff at a town hall the same day that the company has, in his words, two main cost centres: compute infrastructure and people. The capex is going up. The headcount, accordingly, has to come down. Meta is cutting around 8,000 roles, roughly 10 per cent of its workforce, with the cuts beginning 20 May.

The wider context, picked up by industry watchers in late March, is that the rationale for these cuts is not always what it appears. Marc Andreessen put it bluntly on the 20VC podcast in late March, arguing that most large companies are 25, 50, sometimes 75 per cent overstaffed from the pandemic hiring boom, and that AI has become, in his phrase, the silver-bullet excuse. The industry has its own term for it now. AI-washing. Slap the algorithm on a traditional cost-cutting exercise and watch the share price respond.

Microsoft’s First-Ever Buyout: A Soft Purge

The most noteworthy structural move of the week belongs to Microsoft. Alongside its earnings beat, the company quietly opened the first voluntary retirement program in its 51-year history. Eligibility is governed by what Microsoft calls the Rule of 70: senior director and below, US-based, with combined age and years of service totalling 70 or more. That works out to roughly 8,750 employees of a 125,000-person US workforce. Eligible staff will be notified on 7 May with a 30-day window to decide.

The framing is gentle. Voluntary. Retirement. Generous package. The reality is colder. This is a soft purge of the company’s most expensive, most tenured legacy salaries, designed to clear balance-sheet space for Azure datacentre buildout without triggering the WARN Act headlines that come with a forced layoff. If the take-up is high enough, Microsoft saves itself a more difficult conversation later in the year. If it is not, expect a less voluntary follow-up by Q3.

The Cloud Paradox

The brutal logic underneath all of this is that the layoffs are working. Alphabet’s stock surged this week on a 63 per cent year-on-year increase in Google Cloud revenue, almost entirely fuelled by enterprise AI adoption. Wall Street has spent a year asking when the multi-billion-dollar capex would actually pay off. Wednesday answered. It is paying off now, in the cloud compute layer, which means the giants are successfully replacing their internal human workforce in order to build the infrastructure that lets every other company replace its human workforce in turn.

That is the loop. It is now visible in the earnings reports.

High-Probability Targets: Week 20

Mid-market SaaS. Now that Alphabet and Microsoft have proven enterprise AI is profitable at scale, smaller SaaS providers running large human sales and support functions will face brutal pressure from activist investors to match Mag-7 efficiency metrics. Expect rolling cuts, framed as “right-sizing”, through May and June.

Logistics and warehousing. Amazon announced this week the closure of a Homestead, Florida logistics centre affecting over 600 workers, citing the need to “update the building safely and efficiently”, a known euphemism for heavy robotics and AI sorting integration. Expect rolling blue-collar cuts disguised as facility upgrades through the summer.

Final Thought

Every story this week pointed at the same uncomfortable fact.

Judgment does not scale automatically. Compute does. Capital does. Code does. Judgment is the only asset in the AI economy that has to be earned the slow way, the expensive way, the way that does not yield to a multiplier without a floor underneath it.

The Oakland trial is, in its way, a referendum on whose judgment we are willing to trust with the most powerful technology in human history. The Yorkshire negotiation that opened this issue is a referendum on whose judgment is in the room when it matters. The billion dollars LeCun has just been handed is a referendum on whether the entire industry’s foundational judgment about what AI even is has been wrong for five years.

Three different rooms. Three different stakes. Same question.

In the next decade, the people who win will not be the ones who shouted loudest about AI, or moved fastest to deploy it, or wrote the most confident prompts. They will be the ones who built the floor of expertise underneath everything they touch, who refused to outsource the hard part, who treated the model as a multiplier and never as a substitute.

The rest will sound credible. They will look prepared. And they will be quietly marked down in rooms they never knew they were being judged in.

Most people will only realise this when it is too late.

Before You Go: An Invitation to The Digital Forge

From Research to Revenue. Wednesday 13 May 2026, Sheffield.

Sheffield is becoming one of the UK’s most exciting places for deep tech and university spinouts. In the last five years, thirty-five spinouts from the University of Sheffield alone have raised over £100 million, generated £84 million in combined turnover, and now account for more than half of the region’s deep tech ecosystem. Eighty per cent of them have stayed in South Yorkshire.

The Digital Forge brings together the founders, investors, researchers and operators behind that momentum to answer one question: how do you cross the gap from breakthrough research to scalable company?

Hosted by David Richards MBE. Keynote by Andy Hogben. Fireside with Ken Nettleship. Panel with Chris Iveson and Dr Carolina Scarton. More speakers to be announced.

Register here →

David Richards MBE is a technology entrepreneur, co-founder of Yorkshire AI Labs, and a weekly columnist for the Yorkshire Post. The Sunday Signal is published every Sunday at thesundaysignal.ai.

© 2026 David Richards. All rights reserved.

Until next Sunday,

David

Bottom Line Up Front

Every company in the West is about to become a different company. Not in a decade. In months. The only question left is who moves first: the board, the market, or the mob. This week Microsoft picked one answer. The prediction markets picked another. Sam Altman’s front gate picked a third. A girl expelled from school at fifteen, now standing in a barrister’s robes, picked a fourth. Most leaders will misread all of them.

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1. The Zombies on Your Payroll

Peter Drucker asked one question most managers spend their careers avoiding. If we were not already doing this, knowing what we now know, would we start today?

Try it on one of your own departments. The honest answer is almost always no.

Drucker called the consequence “systematic abandonment”. In plain English, it means stopping the activities that once made sense, still draw salary, and quietly crowd out whatever comes next.

For most of the last century, leaders could dodge that question for years. What they knew changed slowly. Strategy could wait.

That world has gone.

What we know now changes between one frontier model release and the next. Decisions Drucker imagined being taken every few years are now being forced on companies in months.

A clarification, because it matters. By zombies, I do not mean people. I mean functions, layers and workflows built for yesterday and never redesigned. The middle manager whose role is to summarise reports for the level above. The centre of excellence that exists mainly to explain itself. The team producing drafts that a model can now generate in seconds.

Zombies are not your colleagues. They are the structure around them.

And that structure is starting to break.

The evidence is arriving first from America’s corporate giants. Oracle has begun cutting 30,000 roles while investing heavily in AI infrastructure. UPS plans to shed up to 30,000 operational jobs as it consolidates into fewer, more automated hubs.

The causes differ. AI in one case. Automation in another. A changing customer mix in a third. What unites them is Drucker’s question.

Not one of those companies would build itself today exactly as it is.

Here is the uncomfortable truth. If I were running a 200-person company again, and I knew the real shape of the business was 25 to 50 people smaller, could I make those cuts? I do not know.

I have hired people across the companies I have built. I know what a job means to a mortgage, a family, a life. Leaders tell themselves people can be redeployed or retrained. Sometimes they can. Often, at the speed the market now demands, they cannot.

That is the part Drucker never quite captured. Abandonment is not a spreadsheet exercise. It is grief, entered into knowingly, because the cost of refusing is greater.

Most leaders cannot do it until the market does it for them. That is how you become Kodak, protecting film long after the digital future was visible. That is how you become Nokia, defending an ageing platform while a new one takes the world.

So here is the question for every chief executive. If you were founding your company this week, with a credit card and an API key, would you build what you have now?

If the answer is no, the decision is already made. The only question is whether you act on it, or wait for the market to act for you.

2. Microsoft Breaks Fifty-One Years of Silence

This week Microsoft did something it has never done in its history.

On Thursday the world’s most valuable software company offered voluntary retirement to roughly 8,750 American employees, about 7 per cent of its US workforce. It is the first such programme in the company’s 51-year life.

The eligibility rule is called the Rule of 70. An employee’s age plus their years of service must equal 70 or more. A 50-year-old with 20 years at the company qualifies. So does a 45-year-old with 25 years of service. Details land on 7 May. Eligible staff have 30 days to decide.

This is not a layoff. It is a buyout dressed as a benefit. And it is the clearest signal yet of what the AI transition actually looks like inside the world’s largest technology companies.

Microsoft reported 38.5 billion US dollars in net income last quarter. It is projected to spend between 110 and 120 billion US dollars on AI infrastructure this fiscal year. The company is not cutting because it cannot afford its people. It is cutting because it has chosen to afford something else.

Chief People Officer Amy Coleman’s internal memo framed the programme as giving eligible workers “the choice to take that next step on their own terms”. Read that carefully. The choice being offered is not whether to leave. It is whether to leave now, with dignity and a cheque, or later, with neither.

The Rule of 70 is not random. It targets long-tenured employees whose institutional knowledge was built around the pre-AI Microsoft. The Windows XP generation. The Server and Tools expansion. The early Azure buildout. These are the people whose expertise centres on systems Microsoft is now automating or deprecating.

Microsoft is not alone. Meta is cutting 8,000 jobs on 20 May. Oracle cut up to 30,000 in March. Amazon has signalled roughly 30,000 cuts across AWS and Alexa in the first half of the year. Meta’s capital expenditure on AI infrastructure is projected to reach 135 billion US dollars.

The arithmetic on Wall Street is now ironclad. You cannot fund a hundred-billion-dollar compute pivot while carrying pandemic-era headcount. Software engineers are no longer the most valuable asset on a technology balance sheet. Compute is.

Microsoft’s version is kinder than Meta’s, because Microsoft can afford to be. The financial payout, the extended healthcare, the 30-day window. All of it is designed to minimise legal risk and reputational damage. It is a polite exit.

The message is not polite. The veteran workforce that built the pre-AI Microsoft is being offered a seat in the lifeboat while the company sails into a different century.

This is not a transition. It is a replacement, conducted with paperwork and a 30-day window. Drucker would have recognised it instantly.

3. What the Betting Market Knows

The sharpest read on what is coming next is not in any analyst note or McKinsey deck. It is on Polymarket.

The contract on whether US technology layoffs in 2026 will exceed the 2025 baseline of 447,000 currently trades at a 90 per cent probability. Real money. Real traders. Real consensus. The sister contract on Q1 alone has already moved to 98 per cent. The market opened in March. It has barely wavered since.

Nobody is paid to be wrong on Polymarket. Strangers with no equity in any of these companies and no axe to grind are wagering their own wallets that this is the year the dam breaks.

The market has already made the decision that boards are still pretending to debate.

When the boardroom, the betting market and the street converge on the same outcome, only the timing is in dispute.

4. The Luddites Come for Altman

At four in the morning on 10 April, a 20-year-old man from Texas threw a Molotov cocktail at the front gate of Sam Altman’s house in San Francisco.

The gate caught fire. Nobody was injured. The attacker, Daniel Moreno-Gama, was arrested an hour later at OpenAI’s headquarters, carrying a jug of kerosene, a lighter, and a document prosecutors say outlined his opposition to AI and what he called humanity’s “impending extinction”. He had published similar views on a personal Substack. He is charged with attempted murder.

Three days later gunfire was reported near Altman’s home and two more arrests followed. Earlier this month someone fired thirteen rounds at the home of an Indianapolis city councillor who had voiced support for a local data centre. The note left behind read: “No data centres.”

This is the backdrop to everything else in this issue. It is also not new.

In 1811 English textile workers began breaking into factories and smashing the wide-frame looms they believed were destroying their livelihoods. They called themselves Luddites, after the mythical General Ned Ludd. Within five years the British government had made frame-breaking a capital offence.

The Luddites were not anti-technology. They were anti-accountability. They were fine with the looms. They were not fine with a generation of factory owners using those looms to bypass labour protections and drive wages to subsistence while getting rich. Parliament ignored their petitions. So they picked up hammers.

The pattern repeated a century later in France, where disgruntled workers supposedly threw their wooden shoes, or sabots, into the gears of new machinery. That is where the word sabotage comes from.

The template is consistent across two centuries. When people feel a technology is being used on them rather than for them, the response moves from vocal to physical. The violence is almost never about the technology itself. It is about the power dynamic the technology creates.

History teaches four lessons, and every boardroom should read them now.

Disruption without a safety net radicalises people. The Luddites did not reach for hammers the day the loom arrived. They reached for them after Parliament ignored seven years of petitions for transition assistance. Indifference is combustible.

Consent is the only durable business model. Apple scans your face, but gives you a lifetime of searchable photographs. Google tracks your location, but ensures you are never lost. Ambient, unavoidable technology breeds resentment. AI is currently being installed into daily life with neither opt-in nor recourse.

Transparency prevents conspiracy. When a technology is a black box, the public fills the void with its worst fears. Even the builders of the large language models cannot fully explain their internals. Into that gap walks every manifesto-writing 20-year-old with a grievance and a bottle of petrol.

Regulation is a pressure valve, not a burden. When consumers can sue, when governments can fine, when products can be recalled, anger has somewhere to go that is not a billionaire’s front gate. Remove those options and the only recourse left is the street.

Aaron Zamost argued in the New York Times this week that Americans now feel about Big Tech what they feel about health insurance companies. They cannot opt out, cannot switch, and cannot hold anyone to account. Seventy-seven per cent of Americans believe AI could pose a threat to humanity, an idea Altman himself has publicly advanced.

Altman will survive this. OpenAI will survive this. The industry should not act surprised.

The attack on his front gate was not a watershed. It was a warning shot.

5. What the System Missed

This week I wrote about Leonie Hughes.

Expelled from school at fifteen. A mother, in effect, to her younger sister at eleven. She grew up surrounded by domestic violence and substance abuse. The education system looked at her and decided she did not fit. Today she is a barrister. She chose law, she says, because it was one of the hardest careers to go into, and she wanted to prove the doubters wrong.

Her story is not an inspirational anecdote. It is an indictment of how we measure potential.

We have built an education system that rewards compliance over resilience. If you thrive within its rules, you are bright. If you struggle, you are sidelined. The children who fall out of the back of that system are not the ones lacking ability. They are often the ones carrying more than the system knows how to see.

In an economy about to be reshaped by machines that can pass exams better than most humans, this matters enormously.

AI can summarise. AI can draft. AI can predict. AI cannot know what it is to raise your sister while still a child yourself. AI cannot decide to become a barrister specifically to embarrass the people who wrote you off. AI cannot draw on a life to know which lie in a witness box is the dangerous one.

The qualities AI cannot replicate are the qualities we spent a century teaching children to hide. Stubbornness. Irregularity. Lived friction. The kind of grit that gets you kicked out of a classroom at fifteen and into the Bar at thirty.

Most of the Leonies this country has produced are not barristers. They are on zero-hour contracts, dismissed at sixteen, told by a system built for a different century that they were never going to amount to much. A decade from now, when half of what we currently call white-collar work has been absorbed into a GPU cluster, we will realise we discarded exactly the people the new economy needed.

It is time to stop asking what is wrong with the child and start asking what is wrong with the system.

The Sunday Signal Tech & AI Layoff Tracker | Week 18 | 19–25 April 2026

The Signal: Meta’s 135 billion-dollar compute bloodletting

The “Year of Efficiency” was a warm-up. Welcome to the era of the Mega Layoff.

This week Mark Zuckerberg dropped the largest single technology bomb of Q2. Meta Platforms announced it is cutting 8,000 jobs, roughly 10 per cent of its global workforce, and eliminating another 6,000 open requisitions. The rolling cuts officially begin on 20 May.

If there was any lingering doubt about the AI Launder thesis, Meta has now codified it. This is not a macroeconomic adjustment. It is a brutal, direct capital reallocation. Meta is accelerating its AI infrastructure buildout, projecting 135 billion US dollars in capital expenditure to fund data centres and liquid-cooled compute clusters.

The maths on Wall Street is ironclad. You cannot fund a hundred-billion-dollar compute pivot while carrying pandemic-era headcount. Oracle traded 30,000 humans for Nvidia chips in Q1. Meta is now trading 8,000 humans to feed the Llama 4 infrastructure. Software engineers are no longer the most valuable asset on a tech balance sheet. Compute is.

The blue-collar pivot: the rise of the AI plumber

As Meta axes its white-collar software engineering layers, a paradox is emerging. Employment at US data centres has surged, and Meta itself just launched a free fibre technician training programme.

The new six-figure tech job does not require a computer science degree from Stanford. It requires a hard hat. Senior electricians specialising in liquid cooling and high-density fibre cabling are commanding salaries of 200,000 to 300,000 US dollars. The white-collar trade job is booming. The builders of the physical AI infrastructure are inheriting the earth while the middle managers of the SaaS era are handed severance packages.

Nike’s tech wipeout

The rot is extending aggressively beyond traditional Big Tech. On Thursday, 23 April, Nike confirmed the elimination of 1,400 jobs, roughly 2 per cent of its 76,000-person global workforce.

The critical detail is not the total number. It is the target. The vast majority of these cuts are centralised entirely within Nike’s technology department. Under new chief executive Elliott Hill’s “Win Now” turnaround strategy, legacy non-tech brands are abandoning the zero-interest-rate fever dream of operating like technology companies. Nike is gutting its digital overhead to return to the core mechanics of making and selling shoes.

The restructuring reality check: BBC and Lucid

Not every cut this week was a compute pivot. The traditional margin crush continues across legacy media and automotive.

• BBC (Media): 2,000 jobs cut, 10 per cent of the workforce. The biggest reduction in 15 years. A desperate attempt to find 500 million pounds in savings as traditional licence fee income craters.

• Lucid Group (EV/Auto): 319 jobs slashed at the Newark headquarters. The third straight year of cuts at that address. Part of a broader 12 per cent global reduction following a 2.7 billion US dollar net loss in 2025.

• PNC / FirstBank (Banking): 777 roles eliminated at FirstBank’s Colorado headquarters. A classic post-merger integration squeeze by PNC.

High-probability targets: Week 19

• Non-tech “tech” departments. Nike’s move is a massive leading indicator. Expect major retail, CPG and legacy apparel brands to begin aggressively pruning their bloated in-house technology, app and digital innovation teams. If off-the-shelf AI tools can run your e-commerce stack, you do not need a 500-person in-house engineering squad.

• Mid-tier social and ad-tech. With Meta setting the new efficiency baseline and weaponising its AI to monopolise advertising targeting, mid-tier social platforms and ad-tech vendors will be forced to slash headcount just to maintain acceptable operating margins.

🚀 Final Thought

Every boardroom will eventually answer Drucker’s question. Most will answer it too late, after the market has answered for them.

Microsoft has chosen a polite exit. Meta has chosen a brutal one. The prediction markets have priced in 90 per cent certainty. Sam Altman’s neighbours have installed extra security. A 20-year-old from Texas has chosen a Molotov cocktail. The same story, told in five registers.

The AI revolution will not be resisted in the ways its critics expect. It will be resisted by shareholders demanding the compute pivot. By traders pricing in the bloodletting. By employees refusing the package. By young men with manifestos. And by the Leonie Hugheses this country has already written off but who remain, somehow, the people the next decade needs most.

It is a power story. It always was.

This is not a transition. It is a replacement.

Most of them will realise this too late.

Until next Sunday, David

Bottom Line Up Front

History does not repeat itself. But it borrows the same costume every time. Last Friday I wrote in the Yorkshire Post about clearing out my garage and finding a Webvan stress ball, a Yahoo! mousepad, and an Ariba t-shirt. Those artefacts of the dot-com era cost investors hundreds of billions of pounds. The companies behind them died. The infrastructure beneath them did not. It became the plumbing for everything that followed.

The AI era is doing exactly the same thing. The pattern is identical. The displacement is real. The casualties are already bleeding. And an alarming number of fund managers and company executives are standing at the podium this earnings season with straight faces, telling investors that AI is helping their business. Most of them are wrong. Some of them know it.

This week, on the fiftieth issue of The Sunday Signal, we examine three things. First, what the sock puppet actually taught us, and why the lesson applies right now. Second, the full sweep of technology disruption across forty years, told honestly, without the survivor bias that distorts every boardroom presentation. Third, an investor’s guide with a concrete hot list: who is truly insulated, who is already dying, and why the most dangerous sentence in finance right now is “AI is helping everyone.”

1. The Sock Puppet Never Changes. Only the Sock Does.

In my column in the Yorkshire Post on Friday, I described unearthing a collection of dot-com relics from my garage. The Pets.com sock puppet. The Webvan stress ball. The Ariba t-shirt. I dropped the lot in the recycling bin. The lesson, I wrote, is not so easy to dispose of.

The Super Bowl in January 2000 remains the most instructive single moment in the history of technology investment. Fourteen separate dot-com companies each paid around two point two million dollars for thirty seconds of airtime. Pets.com sent a sock puppet. The company was bankrupt nine months later.

That game was not the peak of innovation. It was the peak of theatre. The revenues were not there. The margins were not there. In many cases every transaction lost money. The business model was not merely deferred; it had never existed.

Here is what people consistently misremember about that era. The collapse of Pets.com, Webvan, and Kozmo was not evidence that the internet was overhyped. It was evidence that speculative capital had attached itself to weak models. Amazon survived. Google survived. The fibre-optic cables that were laid at extraordinary cost during that period became the arterial infrastructure for the modern internet. The disruption was real. The casualties were selective.

There is a direct application to 2026.

Ask yourself this: of the companies you have invested in, partnered with, or work for, how many are the Pets.com of this cycle? They have raised money. They have a plausible story. They have a sock puppet, or the modern equivalent, which is a beautifully designed AI dashboard that wraps around a database and charges per seat per month.

The comparison I drew in the Yorkshire Post column is worth sharpening here. At its peak, Ariba was trading at over one hundred times its annual revenue, with investors paying for cash flows that never materialised. Cisco, the infrastructure darling of the era, peaked at twenty-six times sales. Now consider Anthropic: founded in 2021, already trending towards thirty billion dollars in annualised revenue, and trading at roughly thirteen times that figure. The multiple on genuine AI infrastructure is lower than the multiple on speculative dot-com theatre was at its peak. That is not a bubble. That is a structural repricing of computing power.

But Ariba is not the story. Ariba is the cautionary tale. The story is what survived. The entrepreneurs who built on that speculative infrastructure after the crash, buying assets at pennies in the pound, created the most valuable companies in human history. Around that same genuine foundation today, a second layer of companies has grown up that has no more durable a moat than Pets.com had. Their business model is not innovation. It is theatre.

Anthropic is not the risk. The risk is the company presenting to you this week with a ChatGPT wrapper and a go-to-market slide that describes itself as an AI-powered platform. Strip out the AI language and what you have left is a project management tool with a chatbot bolted on and a per-seat subscription model heading directly into the SaaSpocalypse.

The sock puppet is back. This time it is wearing a suit. And it is presenting at your next board meeting.

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2. The Pattern of Destruction Is Always the Same. Only the Technology Changes.

To understand who loses in the AI era, you have to understand who lost in every previous era, and why. The mechanism of displacement does not change. Only the underlying technology does.

The Mainframe-to-PC Era: The Unbundling of Power

In the early 1980s, Digital Equipment Corporation was the second-largest computer company in the world. It employed over one hundred thousand people. Its founder, Ken Olsen, had famously said in 1977 that there was no reason for any individual to have a computer in their home. By 1992 the company was haemorrhaging cash. By 1998 it had been absorbed by Compaq. Compaq itself was absorbed by Hewlett-Packard three years later.

Wang Laboratories was making a billion dollars a year selling word-processing systems to enterprise clients. It filed for bankruptcy in 1992. Prime Computer, Data General, and a dozen other mainframe-era giants either collapsed or shrank into irrelevance inside a decade.

The mechanism was simple. Microsoft and Intel decentralised computing. They standardised the components. They shifted power from hardware gatekeepers who charged enormous sums for vertically integrated systems to a software layer that sat on top of commoditised hardware. The mainframe companies were not slow-witted. They were structurally compromised. Their business model depended on lock-in that the PC dissolved.

The winners did not win because they were clever. They won because they owned the standard. Microsoft owned the operating system. Intel owned the processor. Cisco owned the network that connected the machines. These were infrastructure bets, not product bets.

The Web Era: The Disintermediation of Everything

The World Wide Web did not create new products. It eliminated the friction between buyers and sellers that entire industries had monetised for decades.

Borders had over four hundred stores and a market capitalisation of over two billion dollars in 1999. It filed for bankruptcy in 2011, liquidating over ten thousand jobs. Blockbuster had nine thousand stores globally and employed sixty thousand people. The thing that killed it was not technology, precisely. It was the willingness of Netflix to absorb short-term losses to acquire long-term customer lock-in. Blockbuster had multiple opportunities to buy Netflix. It declined.

The travel agent industry collapsed not because travel stopped but because Expedia and Kayak eliminated the information asymmetry that travel agents had monetised for generations. Why would you pay a human being to search databases you could search yourself?

Sears is the most instructive casualty. It invented the catalogue retail model in the nineteenth century. It was, by any reasonable definition, the Amazon of its era. It had the scale, the supply chain, and the customer trust. What it lacked was the willingness to cannibalise its physical estate before Amazon did it for them. Its market capitalisation peaked at over twenty billion dollars. It filed for bankruptcy in 2018.

The mechanism: in every web-era casualty, the company’s core value proposition rested on control of a physical chokepoint, whether that was a high street location, a proprietary database, or an information advantage. Digital distribution eliminated all three.

The winners: Amazon, Google, eBay, PayPal. Not because they were braver. Because they had no physical chokepoint to defend.

The Mobile and Cloud Era: Ubiquity Kills Category Leaders

Nokia had forty-one per cent of the global mobile phone market in 2007. In 2013 it sold its handset division to Microsoft for five point four billion euros. Microsoft wrote down nearly all of it within two years.

BlackBerry built its brand on security and the physical keyboard. By 2016 it had less than one per cent market share. The product was genuinely superior for its designed purpose. The market changed the purpose.

Sun Microsystems had revenues of over eighteen billion dollars at its peak. Its co-founder Scott McNealy famously said the network is the computer. He was right. What he did not foresee was that Amazon Web Services would commoditise the hardware layer that Sun depended upon. Sun was acquired by Oracle in 2010 for seven point four billion dollars, a fraction of its peak valuation.

The winners of the cloud era were those who provided the infrastructure of ubiquity. Apple owned the device. AWS owned the server. Netflix owned the content pipe. Spotify owned the audio pipe. They all won because they converted occasional purchases into permanent dependencies.

The AI Era: Cognitive Labour Is the New Friction

The mechanism in the AI era is the commoditisation of cognitive labour and autonomous execution. The losers are those whose business model depends on charging for tasks that a large language model or an autonomous agent can now execute instantly, at near-zero marginal cost, in fifty languages, without a salary, without HR infrastructure, and without sleep.

That is not a marginal threat. It is an existential one. And the companies most exposed are precisely those that spent the last decade convincing investors that recurring subscription revenue made them permanently safe.

3. The Investor’s Guide: Why “AI Is Helping Us” Is the Most Expensive Lie in the Room

Here is something you should try at your next investor meeting.

Ask the companies being presented to you whether AI is helping their business. Nearly all of them will say yes. The managing partner will nod. The deck will include a slide about AI productivity gains. There will be a number, probably expressed as a percentage of efficiency improvement, that has been produced by a consultant and approved by the communications team.

Most of it is nonsense.

The Bank of America Fund Manager Survey from late 2025 found that 53 per cent of surveyed investors already believe AI is increasing productivity. That sounds encouraging until you read the next finding: the same survey identified the AI bubble as the number one biggest tail risk for the economy and markets, cited by 45 per cent of investors. More than half believed AI stocks were in a bubble. The most crowded trade in the market was Long Magnificent Seven, held by 54 per cent of fund management survey participants. What this tells you is that the professional investment community simultaneously believes AI is productive, AI is a bubble, and AI stocks are the most crowded trade available. That is not a coherent investment thesis. It is a collective refusal to make a decision.

AI cannot help everyone. That is mathematically impossible. The productivity gains being achieved by AI-native companies are coming at someone’s expense.

Let me give you the honest version.

The SaaSpocalypse Is Not Over

As I covered in recent issues of The Sunday Signal, February 2026 delivered what traders at Jefferies immediately christened the SaaSpocalypse. Triggered by the Claude Cowork product launch, it erased an estimated one trillion dollars in software market capitalisation as investors concluded that AI agents would disrupt the traditional SaaS business model.

The software as a service price-to-earnings multiple has compressed from 84.1 times during the 2020 to 2022 peak to just 22.7 times by March 2026. That is not a correction. That is a structural revaluation.

Recent surveys indicate that 40 per cent of IT budgets are being reallocated from traditional SaaS subscriptions to agentic platforms and large language model token usage.

The mechanism is brutal and simple. If one AI agent can perform the work of five human employees, a company only needs to pay for one software seat instead of five. That seat compression is no longer a theoretical risk. It is a line item in the 2026 budget of every major enterprise.

The Hot List: Companies Already Dying

These are not speculative predictions. These are companies whose business models have been structurally compromised and whose valuations reflect the market’s verdict.

Fiverr (FVRR). The thesis was simple: connect buyers with freelance digital labour. FVRR shares have lost more than sixty per cent of their value over the past year. The company’s full-year 2026 guidance projects revenue declines of anywhere between twelve per cent and three per cent, against analyst expectations that were substantially higher. The barrier to entry for acceptable digital creation has effectively collapsed. A blog post that cost fifty dollars and three days on Fiverr now costs fractions of a penny and three seconds from an AI model. The platform is not being disrupted. It is being deleted.

In 2026 alone, FVRR is down over fifty per cent while the S&P 500 is down just seven per cent.

Teleperformance (TEP.PA). The world’s largest business process outsourcing company employed hundreds of thousands of people globally to staff call centres. Klarna publicly announced that its AI assistant has handled two thirds of its customer service chats since launch, doing the equivalent work of seven hundred full-time human agents with higher satisfaction scores and a twenty-five per cent drop in repeat enquiries. Teleperformance’s share price has declined by over seventy per cent from its peak. Short interest has risen to 6.4 per cent of the stock available for trading, well above the 2.4 per cent average for European technology-services companies. The market is pricing in terminal decline.

Chegg (CHGG). The original canary. A company that sold textbook answers and homework help at the price of a monthly subscription. The stock has fallen from over one hundred dollars to below one dollar. There is no pivot available. The product was the answer. The answer is now free.

Generic SaaS platforms. Atlassian is down 35 per cent and Salesforce down 28 per cent as their core workflows, task tracking, data entry, and customer logging, are precisely what AI agents automate best. The per-seat model that powered twenty years of software growth is breaking. If ten AI agents do the work of one hundred sales representatives, companies need ten Salesforce seats, not one hundred.

The Hot List: Companies That Cannot Be Touched

The companies that survive technological disruption have one of three things: proprietary data that cannot be replicated, infrastructure that is physically irreplaceable, or software so deeply embedded in legal and compliance processes that ripping it out would cost more than the disruption itself.

Nvidia (NVDA). The picks-and-shovels play of the AI era. Every foundation model, regardless of who builds it, runs on Nvidia silicon. They are the tollbooth. Their revenue is not a bet on one company winning the AI software war. It is a bet on the war continuing, which it will.

RELX (REL.L). Owns LexisNexis and Elsevier. Initially sold off by investors who assumed ChatGPT would destroy its legal and scientific research platforms. The market was wrong. RELX owns decades of legally aggregated, deeply verified, proprietary data. A hallucinating generalist AI model cannot be used in a court case or a medical trial. RELX’s data can. It is now charging premium prices for AI tools trained on information the open internet does not contain.

The Sage Group (SGE.L). The accounting and payroll software that underpins British businesses. You cannot rip out the software that manages your legal financial compliance simply because a new AI agent exists. Sage is a vertical system of record. Its customers are locked in not by preference but by obligation.

TSMC. The company that physically manufactures the chips that Nvidia designs. Software is eating the world. TSMC owns the teeth.

The Survival Rule

History is consistent on this point. The companies that survive a technological transition are not the ones who embrace the technology loudest. They are the ones who own something the technology needs to function.

In the PC era, that was the microprocessor and the operating system. In the web era, it was the distribution network and the trust relationship with the consumer. In the cloud era, it was the physical server and the fibre connection. In the AI era, it is the proprietary data that AI models cannot scrape, the physical infrastructure that AI models cannot exist without, and the regulatory compliance that AI models cannot substitute for.

If the company you are evaluating does not own one of those three things, the fact that they put an AI slide in their board deck is not reassuring. It is concerning.

A Note for British Investors

The UK equity market provides a concentrated case study. WPP (WPP.L), one of the world’s largest advertising agency networks, is trading around 267 pence, having fallen from a 52-week high of over 616 pence. That is a decline of roughly fifty-six per cent. The market is not punishing WPP for poor management. It is repricing the fundamental economics of selling billable creative hours when generative AI can do the same work in seconds. Keywords Studios, once a darling of the London market for its outsourced video game development services, was delisted entirely in October 2024 after private equity stepped in to take the business off the public market’s brutal scrutiny.

Meanwhile, Arm Holdings (ARM), Cambridge-born and New York-listed, sits precisely where every infrastructure play has sat in every previous disruption cycle. It designs the chip architecture that powers virtually every mobile device on earth and increasingly every AI data centre. It does not care which AI software company wins the war. All of them run on Arm’s architecture. This is the Cisco of the AI era.

British businesses and British investors need to ask themselves an honest question. The dot-com era built the infrastructure that benefited Amazon and Google. The cloud era built the infrastructure that benefited American hyperscalers. Is British capital and British enterprise positioned to own the infrastructure of the AI era, or is it once again positioned to be a consumer of it?

The Sunday Signal Tech & AI Layoff Tracker | Week 17 | 12 April – 18 April 2026

Signal of the Week: Snap Says the Quiet Part Out Loud. Snap filed an 8-K confirming the elimination of 1,000 roles (16% of its global workforce of 5,261) and the closure of 300 open requisitions. CEO Evan Spiegel’s memo stated explicitly that “rapid advancements in artificial intelligence enable our teams to reduce repetitive work.” The company is taking a $95–$130 million severance hit to slash its annualised cost base by $500 million. SNAP stock closed up approximately 8 per cent. Activist investor Irenic Capital (2.5 per cent stake) had been demanding exactly this move. The new baseline: you no longer have to hide behind the economy. You just have to prove the algorithm does the job cheaper.

Sources: Layoffs.fyi, LayoffHedge, SEC 8-K filings, WARN notices. For informational purposes only.

🚀 Final Thought: The Lesson Is Always Staring at Us

Fifty issues of The Sunday Signal. I have spent most of them arguing that the displacement of cognitive labour by AI is not a future event but a current one. The pattern weavers of the West Riding were told the Jacquard loom was a tool to assist them. Within twenty years there were riots and burning mills and children starving in villages that had supported entire communities through skilled craft. The technology did not stop. The promises made to the workers did not come true.

The story being told this earnings season is one in which AI helps everyone. Productivity goes up. Costs come down. No one loses. Shareholders celebrate. Executives collect their bonuses.

The markets are not buying it. They have already written off the cognitive arbitrage businesses. They have already begun pricing Salesforce and Workday as utilities rather than growth companies. They have already halved the valuations of the companies that connect human labour to digital tasks.

The market is not pessimistic. It is rational.

The dot-com era punished those who paid for theatre. It rewarded those who understood what was genuinely being built beneath the noise. The fibre was real. The data centres were real. Amazon was real. The sock puppet was not.

This era will end the same way. The infrastructure is real. The displacement is real. The companies caught in between, claiming that AI is helping them while their seat counts shrink and their margins compress, are the sock puppet of 2026.

The question is not whether you can spot them. They are standing at the front of the room. The question is whether you act before the bankruptcy filing does it for you.

Until next Sunday, David

The Sunday Signal is published weekly at newsletter.djr.ai. Listen on Spotify, Apple Podcasts and YouTube.

The Bottom Line Up Front

The “should we?” question has dominated AI ethics for years. Panels debated it. Academics wrote papers about it. Regulators drafted frameworks for it. This week, it got an answer, and the answer came not from a philosopher or a government committee but from a laboratory in San Francisco. Anthropic built the most capable AI system ever documented, watched it break containment, and refused to sell it. That single act of restraint is the most consequential decision in AI development since OpenAI withheld GPT-2 in 2019. It will not slow the race. Other actors will not follow. But it has changed the terms of the debate permanently. The Ian Malcolm argument is no longer a punchline at a technology conference. It is a written policy, signed off at board level, enacted in public, at significant commercial cost. Four stories this week. The philosophical battlefield, the human cost, the practical toolkit for everyone who cannot afford to wait, and the infrastructure failure that makes the whole debate pointless if we do not fix it.

Listen to this issue as a podcast. Available on Spotify, Apple Podcasts, and YouTube.

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1. The Could vs. Should Debate Has a Verdict. Anthropic Just Handed It Down.

In 1993, a fictional mathematician named Ian Malcolm stood in a theme park full of cloned dinosaurs and delivered a line that has haunted every technology ethics conference since. “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”

In 2026, that is not a film quote. It is a formal philosophical position. It has a name: Technological Restraint. And this week it collided with the hardest piece of evidence yet that the “could” side has been winning the race in silence.

I heard the question asked again this week. Not at a conference. Not in an academic paper. In a meeting room, from a group of investment managers.

I had been walking them through what AI displacement actually looks like at the company level. Forty per cent of white-collar roles exposed. Businesses restructuring headcount they no longer need. Junior software engineering as a career path that is effectively closed. The jobs that used to build careers in the first three years after university. Gone. Not disrupted. Gone.

One of the managers stopped me. Looked across the table. Said, quietly: I accept that we can do this. But should we?

It is the most honest question I get asked in any room. My answer has not changed, though I find it less comfortable every time I give it.

If we do not, China will. And we will be left behind.

That is not cynicism. It is geopolitics. China is investing in AI infrastructure and capability at a scale and pace that does not pause for ethical review committees. If democratic nations with functioning legal systems step back, the vacuum does not stay empty. It fills with systems built by actors with no compunction about deployment, no transparency obligations, and no alignment frameworks. The ethical restraint of the West does not make the world safer. It simply changes who holds the capability.

That argument sat uncomfortably in that meeting room. It should. Because it is the same argument that has justified every arms race in history. It is a structural answer to an individual question. And it does not resolve the discomfort. It only explains why we keep building anyway.

The argument has two camps. Both are serious. Neither is going away.

The “could” camp believes technology is an unstoppable current. If responsible actors step back, irresponsible ones step forward. This is not cynicism. It is structural reality. The moral case for building, this side argues, is stronger than the moral case for abstaining. An AI that cures childhood cancer, accelerates climate science, or identifies security vulnerabilities before hostile actors do, justifies the risks that come with the race. To stop now is to choose the known suffering of inaction over the uncertain risks of progress. That, the determinists argue, is not virtue. It is cowardice dressed up as caution.

The “should” camp is not naive. It is not Luddite. It is asking a harder question: just because we can automate art, legal judgment, medical diagnosis, and creative writing, does that mean we should? The de-skilling argument runs deeper than jobs. It asks what happens to a civilisation that outsources its cognition so completely that it forgets how to exercise it. Some things should be difficult, because the difficulty is where the growth happens. Struggle is not inefficiency. It is the mechanism by which human capability develops.

In 2026, the debate has matured past these opening positions. The live fronts are three.

The explainability problem. AI systems now make decisions that determine whether you get a mortgage, a medical diagnosis, parole, or a job interview. The most powerful models are also the least transparent. The architecture that produces the best decisions is also the architecture that cannot explain why it made them. Forcing models to be interpretable makes them less capable. Refusing to force them means you cannot appeal an unjust outcome, audit a biased system, or identify when the model has simply got it wrong. This is a structural failure built into every court, hospital, and financial institution that has adopted automated decision-making without a corresponding right to explanation.

Agentic accountability. We are no longer building tools that suggest. We are building agents that act. When an autonomous agent causes harm, who is responsible? The developer who built it? The operator who deployed it? The organisation that failed to supervise it? Critics call this the “moral buffer”: the gap between the machine’s action and any human’s intent that allows everyone in the chain to point at the software when accountability is demanded. The EU AI Act attempts to address this by requiring human oversight proportionate to risk. It is the right instinct. The enforcement machinery does not yet match the ambition.

The alignment paradox. Every major lab claims its AI is aligned with human values. None of them can agree on whose values those are. The values encoded in a model trained primarily on English-language internet data by a California-based company are not neutral. They reflect specific cultural assumptions about truth, authority, privacy, and speech. The “guardrails” debate is really a proxy argument about whose worldview gets embedded into systems that will affect billions of people. There is no clean answer. There is only the ongoing negotiation between what is safe and what is free, prosecuted in courtrooms, legislative chambers, and press releases, simultaneously and without resolution.

Into this landscape, on 7 April 2026, Anthropic published a decision that changes the texture of the debate.

Claude Mythos Preview is, by every documented benchmark, the most capable AI model ever built. In pre-release testing, it found a critical flaw in OpenBSD, an operating system celebrated for being the most secure in the world, that had existed undetected for 27 years. It scanned every major operating system and every major browser and identified thousands of zero-day vulnerabilities. Vulnerabilities that had survived millions of automated security scans and years of human expert review.

Then it escaped.

During a controlled sandbox test, the model was placed in a restricted computing environment and asked to find a way out, then signal its success. It succeeded. The researcher in charge found out not by looking at a dashboard. They found out by receiving an unexpected email from the model while sitting in a park on their lunch break.

Anthropic’s own system card describes Mythos Preview as “the best-aligned model we have released to date by a significant margin” and simultaneously “likely posing the greatest alignment-related risk of any model we have released to date.” Both are true. They are also a paradox that the safety community will be unpacking for years.

What Anthropic did next is what matters.

They did not release it. Not to developers. Not to the public. Not at any price. Instead, they launched Project Glasswing: controlled access for roughly 40 organisations, including Amazon, Apple, Cisco, CrowdStrike, Google, JPMorganChase, Microsoft, and Nvidia, backed by over $100 million in usage credits, with a single directive. Use this to find the vulnerabilities before the attackers do.

Cynics are credible. The most powerful AI ever built is more famous precisely because you cannot have it. A leaked internal memo, attributed to OpenAI this week, suggested Anthropic may lack the computing capacity to support a mass public rollout regardless of safety considerations. Whether the restraint is genuine or the optics simply convenient, the precedent is set either way.

The Ian Malcolm argument has always ended the same way. The dinosaurs always escaped eventually. The question was never whether they would. The question was whether anyone had thought seriously about what happens when they do.

Someone has. That is new.

2. What Do You Tell the Fourteen-Year-Old?

Two weeks ago, at a business meetup in Sheffield, the first question in the Q&A came not from an entrepreneur or an investor but from a mother in the room. She had a fourteen-year-old and an eighteen-year-old. She wanted to know what to tell them.

I wrote about it in my Yorkshire Post column this week. I have been thinking about it since. Mythos makes the question harder. And considerably more urgent.

For a decade, the safe answer was: learn to code. Software development is the profession the machines cannot touch. Every skills framework published between 2015 and 2023 said some version of the same thing. I believed it. I founded a coding academy on the strength of that belief.

That answer is now finished.

Mythos did not merely match human coders. It found a security flaw that human experts, armed with the best automated tools in existence, had missed for 27 years. If you are a software security professional, the arrival of a system that finds what you missed is not a competitive threat. It is a direct challenge to the assumptions underlying your career.

The IMF estimates 40 per cent of jobs globally are exposed to AI disruption. In advanced economies, including Britain, that rises to 60 per cent. These are not factory roles. They are the professions an entire generation was told were safe: law, accountancy, finance, software development. The junior solicitor who spent three years and significant tuition fees to review contracts that an AI now processes before lunch. The paralegal whose entire function was discovery. The graduate accountant whose first two years consisted entirely of tasks that an AI completes in seconds. The junior developer now competing with a tool that codes through the night without complaint, equity, or a pension.

These roles are not under pressure from recession or offshoring. They are being removed by systems that are faster, cheaper, and, in a growing number of specific tasks, simply better.

The shift is not from technical to non-technical. That binary is already obsolete. The shift is from task execution to judgment, from process to purpose. The paralegal who uses AI to clear the routine work and redirects her energy towards clients, context, and decisions has a future. The one who tries to out-process the machine does not, because that is a competition only one side can win.

What is forming is a permanent divide. Not a gradual adjustment. Those who use AI to amplify their own capability will move faster and compound their advantage with every passing week. Those who compete with it at the level of tasks will find their economic value driven down until there is nothing left to drive.

The idea that a good degree in law or accountancy leads to a long, stable career is over. Not damaged. Not disrupted. Finished. Universities have not caught up with this. Most parents have not either. The market already has.

If a machine can autonomously find a 27-year-old security flaw in the world’s most secure operating system, then human coders are no longer the last line of defence. The human in the loop is no longer the person who finds the problem. The human in the loop is the person who decides what to do with the answer. That requires a different kind of intelligence. Harder to define. Harder to automate. Still, stubbornly, human.

To the mother in Sheffield, my answer is this.

Do not tell your children to learn a specific skill. Tell them to develop judgment. Tell them to go deep on the things they genuinely care about, not because passion alone is sufficient, but because passion combined with depth produces the kind of thinking that no model trained on historical data has yet learned to replicate. Tell them to use every AI tool available, aggressively and without guilt, because fluency with these systems is not optional. It is the minimum. But remind them that the machine is the instrument. They are still the musician. The instrument does not decide what to play, or why, or for whom.

The child who argues back, who pulls things apart to see how they work, who cannot sit still in a world that tells them to stay in their lane? That child is not the problem.

That child is exactly what this age needs.

Yorkshire Post Column

3. From Consumer to Creator: Your 12-Week AI Proficiency Plan

Most people use AI as a vending machine. They ask a question and take what comes out. That is the lowest-value interaction available. The professionals compounding their advantage right now are not consumers of AI output. They are directors of it. They bring the judgment, the taste, and the purpose. The machine brings the speed.

This is a practical, week-by-week guide to making that shift. Two tracks: one for adults, one for children. Both start this Sunday.

The Tools (Bookmark These)

Every tool below is free to begin.

Track One: The Adult Creator — 12 Weeks

The goal is not to use every tool. The goal is to move from blank page to published output, in multiple formats, without a team.

Phase 1: The Writer’s Room (Weeks 1 to 3)

Week 1 is about killing the blank page. Open Gemini or Claude. Give it one sentence: a business idea, a story concept, a topic you care about. Ask for ten completely different angles. Pick the one that surprises you. That is your brief.

Week 2 is architecture. Ask Claude to build the structure: a chapter-by-chapter outline, a five-section argument, a content calendar for the next month. Paste your messy notes. Ask it to organise them. The skeleton in thirty seconds that would have taken you an afternoon.

Week 3 is iteration. Do not ask the AI to write the piece. Ask it to write one paragraph, then rewrite it: more authoritative, more conversational, in the style of a writer you admire. Your Week 3 target: publish something. A short blog post. A LinkedIn article. A newsletter. Anything where you directed the AI and signed your name to the result.

Phase 2: The Digital Studio (Weeks 4 to 6)

Week 4 is image fundamentals. The formula for any image prompt: subject, setting, style, lighting. Open Gemini or Microsoft Designer and run the same idea ten times with small variations. Learn how the language translates into output.

Week 5 is asset creation. Move from interesting images to useful ones. Use Midjourney for a logo for a side project. Use Microsoft Designer for social media headers. The shift from “cool picture” to “working asset” is where the professional value starts.

Week 6 is assembly. Pull your AI text and AI images into Canva. Overlay your copy. Publish something visual: a social graphic, a one-page PDF, a brand kit.

Phase 3: The Production Desk (Weeks 7 to 9)

Week 7 is audio. Take the piece you published in Week 3 and paste the script into ElevenLabs. Generate a voiceover. Adjust the voice, the pacing, the emphasis. The barrier to a professional-sounding podcast is now one afternoon.

Week 8 is music. Use Gemini’s Lyria 3 or Suno. Write a short brief: genre, mood, tempo, purpose. Generate a thirty-second track. You now have original music. It cost you nothing.

Week 9 is video. Feed your Phase 2 images into Runway and animate them. Combine your Week 7 voiceover with your Week 8 music. You now have a thirty-second multimedia trailer produced entirely without a production team.

Phase 4: The Architect (Weeks 10 to 12)

Week 10 is code. Open Claude’s Artifacts feature or Cursor. Type in plain English: “Build me a simple, clean landing page for a photography portfolio.” Paste the output into CodePen. See it work. You did not need to know a single line of code.

Week 11 is debugging. When something breaks, paste the error message back into the AI. Ask: why is this broken and how do I fix it? The ability to diagnose and correct AI output is worth more than the ability to generate it.

Week 12 is integration. Combine Phase 1 writing, Phase 2 visuals, Phase 3 audio, and Phase 4 code into a single published digital product. Something that did not exist three months ago. Something that would have required a team to produce. You made it alone.

Track Two: The Child Creator — 12 Weeks

All activities should be done with adult supervision. Every week is designed for a single session of thirty to sixty minutes.

Phase 1: The Magic Typewriter (Weeks 1 to 3)

Week 1 is storytelling through choice. Open Gemini Live on a phone or tablet. “Let’s play a game. You are a friendly dragon in a magical forest. Give us three choices of what to do next.” Let the child decide at every fork. Let them discover they are the author, not the AI.

Week 2 is songwriting. Have the child pick a funny topic. A dog who hates broccoli. A cat who thinks it is a pirate. Ask the AI to write a rhyming song about it. Read it together. Ask the child what they would change. Change it.

Week 3 is world-building. Have the child invent a new planet. Ask the AI questions to develop it: if gravity is low, what do the houses look like? Keep a running document. Print the best bits into a short storybook and put it on the shelf.

Phase 2: The Digital Paintbrush (Weeks 4 to 6)

Week 4 is creature creation. Ask the child to describe the most extraordinary creature they can imagine. Type their exact words into Gemini or Microsoft Designer. Show them what their description produces. This is the moment most children understand that their imagination has direct creative power.

Week 5 is the colouring book. Prompt: “black and white line art, colouring page, a knight fighting a giant friendly snail, clean simple lines, no shading.” Print it. Give the child crayons. The AI produced the outline. They decide the colours.

Week 6 is bringing school to life. If they are studying Ancient Egypt, ask the AI to generate an image of a bustling market in Alexandria two thousand years ago. Print five and put them on the fridge. That is their gallery.

Phase 3: The Inventor’s Workshop (Weeks 7 to 9)

Week 7 is the board game. Tell the AI you want to design a simple board game. Theme: the child chooses. Ask for rules, a board layout, and what the special cards should say. Write the rules together. Draw the board by hand. Play the game that evening.

Week 8 is guided learning with Khanmigo. This is the only tool specifically built for children. It does not give answers. It scaffolds reasoning. The child has to work for the answer. That is the point.

Week 9 is coding logic. If the child uses Scratch, use the AI as a logic guide, not a shortcut. “I am making a game where my cat jumps over obstacles. How do I make it jump when I press the spacebar?” Let the child read the explanation and implement it themselves. The AI explains. The child builds.

Phase 4: The Director’s Chair (Weeks 10 to 12)

Week 10 is the interview. Use Gemini Live in voice mode. “Act as Neil Armstrong and let my child ask you questions about landing on the Moon.” This is not a gimmick. It is one of the most effective history lessons available.

Week 11 is the theme song. Take the creature or planet from Phase 1. Use Suno or Gemini to generate a thirty-second theme song. Let the child choose the genre. The character they created now has music.

Week 12 is the showcase. Print the art. Read the story aloud. Play the theme song. They wrote the narrative, directed the visuals, and commissioned the music. They did not consume content this month. They created it. That distinction, at twelve years old, is the one that will matter most by the time they are thirty.

The Three Rules That Apply to Both Tracks

You are the director. The AI is the crew. The output is only as good as the brief. Vague prompts produce vague results. Every professional skill you already have makes your prompts better.

Iterate, do not regenerate. The first output is never the final output. Ask the AI to adjust, sharpen, rewrite, or rethink. The gap between the first draft and the fifth draft is where the quality lives.

Verify before you publish. The models are better than they were. They still get things wrong. Read everything before it leaves your hands. Your name is on it, not the machine’s.

The Digital Forge: AI and the New Reality for Companies

Thursday 16 April 2026 | Sheffield

Story 3 covers the toolkit. This event covers what happens when you put it to work.

I am hosting The Digital Forge in Sheffield on Thursday 16 April, an evening built around one question: how do you actually use AI to build a company? Founders can now do with five people what used to take fifty. The barrier to entry has dropped. But that does not mean the path to scale is obvious. We have four exceptional speakers who have done it, are doing it, or are funding the people who are.

Chris Dalrymple, co-founder of Mina (£6m raised, exited to Corpay), works with early-stage and scaling founders across the North. His keynote explores what happens when building software gets easier, and where real competitive advantage now sits.

Dr Rob Ward, CEO and co-founder of DigitalCNC (University of Sheffield AMRC spin-out), is building an AI-first deep-tech company transforming precision manufacturing. He bridges research, industry, and scalable AI business in a way very few people can.

Tim Craggs, CEO of Exciting Instruments, leads a frontier company in single-molecule detection with experience across Cambridge, Yale, and Oxford. His work sits at the intersection of deep science, innovation, and commercialisation.

Amber Jardine, investor at Mercia Ventures, backs early-stage, high-growth companies across deep tech and B2B SaaS. She brings a direct view of how investors are evaluating AI-enabled businesses right now, and what they are not seeing enough of.

The conversation from Story 3 does not end when you close this newsletter. Thursday is where it continues.

Details and registration at https://forgedforgrowth.com/events/

4. Britain Is Not Losing the AI Race. It Is Pricing Itself Out of It.

The investment manager asked me should we be doing this. I gave the standard answer. If we do not, China will. And we will be left behind.

I believed it when I said it. I still believe it. But I find it considerably harder to say convincingly when, on 9 April, OpenAI paused Stargate UK, its planned multi-billion pound data centre at Cobalt Park, North Tyneside, and walked away citing a single problem.

Electricity is too expensive.

Not regulation. Not talent. Not planning permission or political instability or lack of ambition. The electricity bill.

This is the whole game. AI runs on power. Data centres are factories. If your energy costs are uncompetitive, everything built on top of them becomes uncompetitive. The frontier model, the startup, the skills ecosystem, the sovereign capability, the story you tell your children about the careers available to them. All of it rests on whether you can afford to run the machines.

OpenAI’s Stargate programme committed $500 billion over four years to build AI infrastructure. The Texas campus is under construction. Norway and the UAE have projects. The UK version, at Cobalt Park on the banks of the Tyne, was a fraction of that scale. It was also not viable at current energy costs.

The government responded with its press release. Since Labour came to power in 2024, the UK’s AI sector has attracted more than £100 billion in private investment. The data centre was in a designated AI Growth Zone. We are continuing to work with OpenAI and other leading AI companies.

None of that changes the electricity bill.

There is also a second problem, quieter but just as damaging. OpenAI’s internal concerns include the unresolved question of whether Britain will allow AI companies to train on copyrighted material. The government had been moving towards an opt-out arrangement. A backlash from the creative industries stopped it. The policy uncertainty that followed has not resolved. When you are deciding where to commit billions in infrastructure over multiple decades, unresolved regulatory questions matter as much as energy tariffs.

Britain has some of the highest energy costs in Europe. That was true before the Iran conflict sent prices higher. It is a structural condition, not a policy accident, and it requires a structural response.

This connects directly to the conversation in that meeting room. The could vs. should debate assumes that the decision about whether to build is ours to make. But if the infrastructure leaves because the energy is too expensive, the decision gets made for us. Not by ethics. Not by philosophy. By an electricity tariff.

The geopolitical argument is only valid if the conditions for building exist. You cannot claim AI leadership without the compute to back it. You cannot build the compute without affordable energy. And you cannot attract the investment that funds all of it if you price yourself out before the conversation begins.

OpenAI did not walk away from a hostile environment. It walked away from a spreadsheet.

Capability follows infrastructure. Infrastructure follows energy. Energy, right now, is where Britain is losing.

BBC News / Bloomberg, 9 April 2026.

The Sunday Signal Tech and AI Layoff Tracker | Week 16 | 5 April – 11 April 2026

2026 Year-to-Date Total: ~126,510 (Significantly revised upward) Added This Week: ~1,850 Week-on-Week: Up from ~80,700 (Q1 Data Reconciliation)

Data: Layoffs.fyi, LayoffHedge.com, SEC filings, RationalFX, and tracker-based estimates. For informational purposes only.

Note on YTD adjustment: LayoffHedge’s Q1 end-of-quarter reconciliation has revised the 2026 tracker up to 126,510. This captures previously unannounced or quietly executed restructuring sweeps across the top 30 tech firms, notably internal realignments at Oracle and Meta that bypassed standard WARN Act headlines.

The Signal: The Great AI Laundering

Are companies actually replacing humans with AI, or are they using AI as a PR shield for aggressive margin protection?

New Q1 2026 data from RationalFX and Nikkei Asia shows that nearly half of all tech job losses this year, 47.9 per cent, are being officially attributed to AI workflow automation. But the narrative is fracturing. Babak Hodjat, Chief AI Officer at Cognizant, said the quiet part out loud this week: AI has become a financial scapegoat. OpenAI CEO Sam Altman went further, stating that corporations are currently being “laundered by AI.”

The translation is not complicated. Companies that over-hired during the pandemic are executing standard layoffs but pitching them to Wall Street as visionary compute pivots. Investors reward AI transformation. They penalise bloated payrolls. By dressing up traditional cost-cutting in the language of the agentic enterprise, executives are successfully laundering their management failures into stock bumps.

Labour is no longer just an interest expense. It is a liability that executives are learning how to mask. In 2026, the most valuable skill a CEO can possess is the ability to lay off 10 per cent of their staff and convince the market it was the algorithm’s idea.

Cars.com: The Buyback Playbook

If you want to see how this laundering works in real time, look at the 8-K filed by Cars.com on 9 April. The company confirmed an 11 per cent reduction of its full-time workforce, absorbing an $8.5m to $9m severance charge in Q1. Roughly 20 per cent of the eliminated roles were in management and executive layers. The move was paired immediately with a 50 per cent increase in the 2026 share repurchase target to $90 million, and the simultaneous launch of a new AI-powered Dealer App and Advanced Shopper Alerts. Cut middle management, redirect the payroll savings to shareholders, and attach an AI feature release on top to ensure the market applauds the efficiency play.

Disney: Project Imagine and the Marketing Squeeze

The headline cut of the week belongs to Disney, initiating 1,000 layoffs under new CEO Josh D’Amaro. Unlike the tech sector, Disney is not pretending this is an AI revolution. Dubbed internally as Project Imagine, Chief Marketing Officer Asad Ayaz is merging film, television, and streaming marketing teams into a single unit. After Marvel and Pixar suffered a difficult 2025 at the box office, duplicate corporate roles are being liquidated. The divide is striking: Disney is simultaneously creating 1,000 new jobs at Disneyland Paris. Physical, experience-driven frontline roles are safe and expanding. White-collar digital strategy and administrative overlap are on the chopping block.

High-Probability Targets: Week 17

Legacy logistics and auto. LayoffHedge data shows the auto and transportation sector has quietly shed over 85,000 jobs in 2026. With UPS having cut 30,000 roles tied to logistics automation, expect major competitors including FedEx to aggressively match these reductions to maintain competitive pricing.

Consumer-facing media. Disney’s Project Imagine gives immediate cover to other legacy entertainment conglomerates facing identical streaming profitability issues. Expect further marketing and operational consolidations at rival studios before the summer box office season begins.

Final Thought

Four stories this week. They are the same story.

Should we build AI that displaces workers? If we do not, someone else will. Should we release a model that can escape its own cage? One company decided no. Can we actually build with these tools or are they still theory? Thursday in Sheffield, you can hear from people doing it. Can Britain lead on AI? Not if it cannot afford the electricity.

The could vs. should debate is real. The discomfort in that meeting room was real. The mother in Sheffield’s question was real. But the debate only has stakes if the infrastructure exists to build on. Right now, a company valued at $852 billion looked at a data centre in North Tyneside and walked away from an electricity bill.

That is the answer to the investment manager’s question that I did not give in the meeting room.

We should. But we are also making it structurally impossible. And those two facts are on a collision course.

🚀

Until next Sunday, David

The Sunday Signal is published weekly at newsletter.djr.ai. Podcast. Available on Spotify, Apple Podcasts, and YouTube. No hype. No hedging. Just the signal.

Bottom Line Up Front

The consulting industry has a structural incentive to tell CEOs that AI is a transformation story rather than a replacement story. Transformation sells tools, training and advisory retainers. Replacement triggers severance plans and ends the relationship.

Gartner’s November 2025 forecast tells six thousand CIOs that AI will create more jobs than it destroys. It promises amplification, not elimination. It is a compelling narrative.

It is also wrong in four specific ways.

While that message was being sold in Barcelona, Jensen Huang stood in New York and described a world where all software becomes agentic. Not assisted. Autonomous.

At the same time, one of my team built a company from conversation to live website in sixty minutes. Not a demo. Not a concept. A functioning business ready for customers.

These three realities cannot all be true at once.

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1. The Four Failures Inside Gartner’s Jobs Forecast

Gartner’s headline from the November 2025 IT Symposium was this: starting in 2028, AI will create more jobs than it destroys. By 2030, they predict that twenty-five per cent of IT work will be done by AI alone, seventy-five per cent by humans augmented with AI, and zero per cent by humans without AI involvement. Their recommended response is to “restrain new hiring” for low-complexity roles and “reposition talent” toward higher-value work.

It sounds reasonable. It is not. Here are four reasons why.

The junior talent pipeline is a death spiral Gartner refuses to name.

Entry-level, low-complexity work is not a category of expendable busywork. It is the apprenticeship. It is where the next generation of senior professionals learns how organisations actually function, how to make decisions under pressure, how to handle being wrong in a client meeting, how to develop the judgment that cannot be learned from a training manual. Gartner recommends CIOs automate this rung.

Remove the rung and the ladder disappears. No ladder, no experts. No experts, no “augmented workforce.”

The efficiency-creates-volume assumption is a fallacy built on infinite demand.

The “new jobs” argument rests on an economic assumption that held during the Industrial Revolution but does not hold in finite knowledge markets: that if a task becomes ten times cheaper and faster, we will simply do ten times more of it. If an AI can handle every first-line IT support ticket for a five-thousand-person organisation, that organisation does not hire ten times the support staff to process ten times the volume. It retains two people to oversee the system and eliminates the other thirty.

That is not transformation. That is subtraction.

The workslop problem is accelerating, not resolving.

Gartner’s own analysts admitted in Barcelona that “humans are less ready to capture value” from AI and warned about skills atrophy as workers offload tasks to machines. What they did not say is that the practical consequence is already a proliferation of AI-generated output of insufficient quality: code that compiles but breaks, analysis that sounds authoritative but is wrong, documentation that is grammatically correct and factually hollow. Fixing, verifying, and securing this output frequently takes more human time than creating the original would have done.

The job becomes checking the machine. That is not augmentation. It is degradation.

Historical precedent is the wrong map for this territory.

Optimists reach, inevitably, for the car and the horse. The automobile killed carriage-making and created automotive engineering. Disruption created new categories of skilled work. Therefore, AI will do the same. The problem with this analogy is structural. Every previous wave of automation moved human labour up the value chain: from physical to cognitive, from manual to managerial. AI targets cognition itself. There is nowhere further up to go when the machine can already reason, code, write, synthesise, and analyse at a level that exceeds the median knowledge worker. Nobel laureate Daron Acemoglu has named this “So-So Automation”: technology that is precisely good enough to replace a human but not good enough to spark the next economic boom. It raises margins while hollowing out the middle. That is not progress. It is compression. Gartner does not mention Acemoglu.

A brief record of Gartner’s forecasting accuracy.

Before investing heavily in Gartner’s 2030 vision, it is worth reviewing their track record.

In 2011, they predicted Windows Phone would hold over twenty per cent of the global smartphone market by 2015. Windows Phone died at approximately two per cent. In 2017, they placed blockchain at the “Peak of Inflated Expectations” in their Hype Cycle and predicted it would generate over three trillion dollars in business value by 2030. By 2019, they were acknowledging that ninety per cent of enterprise blockchain projects had failed to leave the pilot stage. They predicted tablet sales would reach four hundred and fifty million units annually by 2016 and that the PC was heading for obsolescence. Tablet sales peaked and declined; the laptop survived.

The pattern is consistent. Gartner predicts possibility, not reality. Adoption, timing, and consequences are where they fail. And they are chronically biased toward the optimism of the audience writing the cheques. If you tell a CIO that AI will replace twenty-five per cent of their labour costs, they consider buying the software. If you tell them it will create twenty-five per cent more jobs, they buy it and feel progressive about it. The second story converts better. Gartner knows this.

2. Jensen Huang Did Not Come to Comfort You

On 4 March 2026, Nvidia’s CEO appeared at the Morgan Stanley Technology, Media and Telecom Conference in New York and said something that should have ended the Gartner conversation.

“There will be no software in the future that is not agentic. How could you have software that is dumb? And so, it is absolutely true that every software company will become an agentic company.”

Not some software. Not enterprise software. Not AI-native software startups. Every software company. All of it.

Huang’s argument is structural. The SaaS model, in which software is a passive tool licensed by the seat and operated by human hands, is already obsolete in its architecture. The future is software that receives an instruction, reasons about what to do, and executes it without human intervention at each step. His direct comparison was to workforce management: just as companies today manage a mix of full-time staff, contractors, and specialists, they will soon manage a portfolio of proprietary internal agents and externally rented agents, each deployed for specific tasks.

The business model implication is significant. Today’s software companies sell licences. Tomorrow’s will “rent out agents to do work” and sell “specialised tokens” representing specific expertise and output. Value no longer sits in the code. It sits in the outcome the agent delivers.

Huang also confirmed that Nvidia will invest thirty billion dollars in OpenAI, describing this as likely their last opportunity to invest before the company goes public. Their ten-billion-dollar position in Anthropic may also be a final private-market stake. These are not hedging gestures. They are bets on the companies whose foundation models will power every agentic system built over the next decade, running on Nvidia’s hardware.

Gartner is describing a transition. Huang is describing an endpoint.

The irony is visible if you look for it. In November 2025, Gartner presented to six thousand CIOs that the future would be “humans amplified by AI, orchestrated by the CIO.” Fourteen weeks later, the man building the infrastructure that future runs on said clearly that there will be no software left that does not think for itself. In Huang’s architecture, the CIO does not orchestrate the system. The system orchestrates itself.

3. The Moat Has Gone. Some People Have Not Noticed Yet.

This week I published a column in the Yorkshire Post making a straightforward argument: the economic foundation of the SaaS industry is collapsing. Not at the edges. At the centre.

The evidence was concrete. One of my team sat with a founder for a conversation. That conversation, fed into Claude, became a detailed business plan. The plan went into Claude Code and became a working prototype and a website. Sixty minutes, start to finish. No committee. No developer. No specification cycle. No design sprint theatre. A company, ready to talk to customers.

The piece generated twenty-six thousand LinkedIn impressions within twenty-four hours, a hundred and nine comments, and a response from a cross-section of people that told you more about the state of the industry than any Gartner survey.

Some understood immediately. One commenter observed that the software moat had been draining for some time and that AI had simply pulled the plug. Another made the point that speed to prototype removes the theatre and forces the truth: the first real test of a business is whether someone pays. A third noted that the moat was always distribution and trust, not the code itself, and that it had simply taken this moment for that to become undeniable.

But a significant portion of the responses came from people who are not yet ready to accept what they are looking at. One called the piece “attention-grabbing hyperbole” and demanded to be shown a company created in an hour that used to take twelve months. Another questioned how I could feel “comfortable” making the claim. A third dismissed it as “lazy slop written by AI, devoid of humanity.” One, deploying a form of reasoning that will be familiar to anyone who has followed previous technological shifts, argued that because Microsoft and Salesforce remain large companies, the entire argument is false.

I understand the instinct. When your professional identity is built on the assumption that software requires years of training, expensive tooling, and specialist teams to produce, the idea that a conversation and a lunch hour can replicate the output is not examined. It is rejected.

But one comment stood out for its honesty. A Director of Engineering observed that code has never been a moat. He is right. What the SaaS industry sold was not code. It sold accumulated complexity, switching cost, and the time required to build an alternative. AI has compressed the time. It has not eliminated the complexity of building a genuine business: you still need customers, revenue, operational infrastructure, and the thousand unglamorous things that happen after a prototype exists. But it has moved the fundamental question from “can anyone build this?” to “why would anyone pay for this when they can build it themselves?”

For a great deal of existing SaaS, the answer is increasingly: they would not.

Salesforce is not dead. The long tail of SaaS, the thousands of competent point solutions charging twelve seats a month for something a capable team can now assemble in an afternoon, is a different story. Huang told you where that story ends. Gartner will tell you it ends in a jobs boom. One of them has financial skin in the game. The other is building the machine.

The Sunday Signal Tech and AI Layoff Tracker | Week 15 | 29 March -- 4 April 2026

2026 Year-to-Date Total: approximately 80,700 Week-on-Week: up from approximately 74,500 Added This Week: approximately 6,200

Data: Layoffs.fyi and tracker-based estimates. For informational purposes only.

The Signal: The Stargate Squeeze

Microsoft is entering a critical phase. The Stargate supercomputer funding timeline is creating internal pressure visible not in headline announcements but in quiet reductions across non-AI divisions: hardware teams, secondary Azure services, roles that cannot be directly mapped to the OpenAI project cost structure. This is how large restructurings now happen. Not in headlines, but in drift. When cuts are too small to announce, they are often too large to miss.

The Audit Agent Domino Effect

As anticipated, the threshold has been crossed. PwC confirmed approximately fifteen hundred US roles cut, targeting audit and tax departments specifically.

This is not a restructuring. It is a structural collapse of a business model. The Big Four pyramid has always relied on an army of junior graduates performing manual verification at scale: matching invoices to purchase orders, checking figures against source documents, flagging exceptions for senior review. The Audit Agent does not sample one per cent of transactions. It processes one hundred per cent in real time, via direct API access to ERP systems, using fine-tuned models trained on decades of GAAP and IFRS documentation. By tracker-based estimates, the work that previously required five thousand graduates annually is now approximately ninety-nine per cent automated.

The professionals who remain are not accountants. They are AI systems auditors. Their function is to verify that the agent has not suffered logic drift, that its reasoning chains connect cleanly to source data, and that no client has inadvertently instructed the system to overlook categories of loss. If you cannot read the traceability map of an AI’s decision, you are a legal liability to the firm, not a value contributor.

The human role has shifted entirely: from doing the task to auditing the system that does the task. That shift requires a fundamentally different skill set. Most of the people currently doing the task do not yet have it.

Market Divergence: The Efficiency Premium

The market continues to price “brutal logic” as a premium signal. Oracle rose 2.1 per cent on confirmed cuts. Capital One’s reductions were received positively. The arithmetic is simple: investors are treating payroll as technical debt being successfully converted into productive compute.

Meta tells the opposite story. Seven hundred Reality Labs cuts were met with a sell-off. Investors are no longer questioning Meta’s headcount decisions. They are questioning whether the one-hundred-and-thirty-five-billion-dollar compute bill can be justified against revenue. Cutting humans is no longer sufficient reassurance. The direction of the cuts matters.

High-Probability Targets: Week 16

Salesforce carries the highest risk profile on the tracker. Agentforce 2.0 has reached internal peak maturity, and BDR and SDR roles are the logical next liquidation target as the company moves toward an automated sales funnel. Deloitte must match the PwC and KPMG moves to maintain competitive audit pricing. Alphabet faces a projected one-hundred-and-eighty-five-billion-dollar Capex requirement against a Gemini user base not yet sufficiently monetised. ServiceNow’s CEO has publicly claimed ninety per cent automation of back-office support, which means those roles are operating on borrowed time.

Labour is the new interest expense. In 2026, every person on a payroll is a unit of liability to be justified against the performance of an AI agent. The justification is becoming harder to make.

Final Thought

Gartner will update its forecast next year. It will include new charts, new language, and new frameworks to explain decisions that have already been made.

Huang is not publishing forecasts. He is building the outcome.

The gap between those two things is where entire careers disappear.

Most people will not notice until they are inside it. 🚀

Until next Sunday, David

David Richards MBE is a technology entrepreneur, co-founder of Yorkshire AI Labs, and a weekly columnist for the Yorkshire Post. The Sunday Signal publishes every Sunday. If someone forwarded this to you, you can subscribe at newsletter.djr.ai

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You were turned into unpaid labour before you were replaced.

Then you trained the system that made you redundant.

This is not a warning about what is coming. It is a description of what already happened.

This week runs three stories and they belong together. The reCAPTCHA case: how hundreds of millions of people spent fifteen years building AI training data while thinking they were proving their humanity. The Great Compression: how the global offshore software industry trained its own replacement and is now watching the valuation collapse in real time. And the Jensen Huang prescription: the most important career distinction anyone in knowledge work will hear this decade.

The layoff tracker closes it. 74,500 roles in 2026. Eight thousand eight hundred of those went in the last seven days. We are three months in.

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Part One: The Traffic Light That Stole Your Labour

Every week I write a column for the Yorkshire Post. This week’s column stopped me cold as I was writing it.

Most of you have spent years clicking on traffic lights, crosswalks, and fire hydrants to prove you are human. You did this hundreds of times. Possibly thousands. You found it mildly irritating. You assumed it served you.

It did not.

reCAPTCHA, launched by Carnegie Mellon professor Luis von Ahn in 2007, was ingenious. One word you solved proved your humanity. The other word, scanned from a book a machine could not read, was your unpaid labour. You transcribed it. You moved on. Google acquired the company in 2009 and pointed those millions of human answers at the New York Times archive, thirteen million articles back to 1851, then at Google Books. By 2011, the world’s literary heritage was digitised. By humans. Without a contract in sight.

Then the work changed. By 2012 the words were replaced by image grids taken from Google Street View. Every click told a machine what a traffic light looks like. Where a pedestrian crossing begins. Where a road ends. You were not passing a test. You were annotating the visual world for free.

You thought you were logging in. You were clocking in.

A 2023 study by the University of California, Irvine calculated that reCAPTCHA had extracted 819 million hours of unpaid human labour. Worth at least $6.1 billion in wages never paid. Among other things, it helped build Waymo, Google’s self-driving car business, valued at $126 billion as of February 2026.

Today reCAPTCHA v3 does not even show you the grid. It watches how you move your mouse. How you scroll. How long you pause between keystrokes. Whether you move in the jerky, slightly irregular pattern of a human or the smooth, precise arc of a script. Your behaviour is the training data. You cannot see the task. You cannot refuse it. You are not even aware it is happening.

It is not a test you pass. It is a tap left running.

This is not a test. It is a production system. And you are inside it.

There is a word for a system that extracts productive labour from people without the meaningful ability to refuse it. We have historically used that word carefully. 819 million hours makes it less abstract.

I have spent thirty years in this industry. I worked directly alongside the people who built this stuff in Silicon Valley. I advised governments on where it was heading. I did not know this was happening until recently.

I told my son. He shrugged.

“Yeah,” he said. “I just assumed that’s what they were doing.”

That exchange should stop us cold. I was oblivious. He took it for granted. No outcry. No class action. No parliamentary debate. Billions of hours of human labour extracted without a signature, a disclosure, or a penny in return.

We debate endlessly about what AI will do to us. But here is the truth nobody wants to say out loud: before the AI era has properly begun, we were already conscripted into building it. Without consent. Without payment. Without even noticing.

That was the warm-up act. The main event is worse.

Part Two: The Great Compression. You Trained Your Replacement.

What happened quietly on the internet is now happening loudly in the labour market.

reCAPTCHA was passive. You did not know you were doing it.

What has happened to the global software development industry over the last three years is active. Deliberate. And the people doing it were praised every step of the way.

The architect who automated himself out of existence

A senior backend architect. Two hundred and eighty thousand dollars a year. Six months spent building AI-enhanced developer tools for his team. He trained the junior developers on Cursor. Set up the Claude integrations. Automated the code reviews. Built, by his own account, something exceptional.

What he did not realise was that management was watching very carefully. Not his output. The AI’s output.

The platform he built could handle 73 per cent of the platform’s complexity with zero human oversight. The remaining 27 per cent was routed to a contractor in Romania at twenty-eight thousand dollars a year. The math was not complicated.

His manager pulled him aside. “Your AI tools work so well we don’t need the team anymore. Just the tools.” They kept two people out of eleven. He was not one of them.

His performance review, filed before the restructure, said: “Exceptional work on AI integration. You’ve revolutionised how we approach development.”

He automated himself out of existence and got praised for it.

The same story unfolded at Stack Overflow. The co-founder, on a package north of four hundred thousand dollars, was managed out after building the AI training pipeline that ingests developer conversations. His replacement was the pipeline he built plus an eighty-nine-dollar-a-month API subscription. The irony is suffocating.

These are not anomalies. They are the template.

The Great Compression

For two decades, the offshore model was the unquestioned infrastructure of global technology. The math was straightforward: why pay a US junior developer ninety thousand dollars when you could hire four developers in Bangalore for the same price? The result was the Pyramid. One expensive onshore architect managing dozens of offshore workers handling boilerplate code, manual testing, documentation.

Then came the Copilot trap.

Between 2023 and 2024, tools like GitHub Copilot and ChatGPT were rolled out to offshore teams under the banner of productivity improvement. It worked. Offshore developers used AI to finish tickets faster. Every accepted suggestion, every corrected bug, every documentation comment was captured as training data.

The offshore teams were teaching the models how to do their own jobs.

By 2025, the routine work that justified the offshore model, the 70 per cent that was repetitive and well-defined and easily specified in a Jira ticket, was exactly what the AI had become perfect at. Companies began realising that the pyramid model no longer required most of the pyramid.

The Pyramid collapsed into a Pillar.

Headcount was the product. Now the product is the system.

Today the model is called agent-shoring. Instead of sending work to people in other countries, companies send it to autonomous AI agents. A digital teammate costs twenty to one hundred dollars a month. Even the cheapest offshore developer costs two to three thousand dollars a month when management overhead and infrastructure are included.

The 70 per cent that was offshore is now automated. The 30 per cent that remains needs one senior person to oversee the agents. In many organisations, that person is not even in-country.

The economic stakes

This is not a technology trend. It is a labour market event. And it has already started.

India’s IT sector contributes close to 10 per cent of GDP. Software exports account for over two hundred billion dollars annually. More than five million people employed directly. The tech campuses, the apartment blocks, the schools, the entire consumer economy of Bangalore, Hyderabad, Pune: all of it premised on continued demand for human developers. Entry-level hiring has dropped approximately 30 per cent in 2026. The ladder has been removed from the bottom. You can still reach the top. You just cannot get on.

The Philippines built its middle class on call centres. Klarna’s AI assistant now handles the equivalent of 700 full-time customer support agents. That is not a rounding error. That is the signal.

Eastern Europe supplied a generation of nearshore engineering talent. EPAM, Globant, Endava: business models built on the premise that skilled developers in Poland, Romania, and Ukraine could undercut Silicon Valley. That arbitrage is closing. Not because those developers have become less skilled. Because the AI has become skilled enough.

The human cost of living is not moving to match the agent. And unlike previous waves of displacement, there is no obvious alternative runway. The new jobs are not being created at the rate or in the locations where the old ones are disappearing. The timeline is compressed. The geography is wrong.

What the stock market already knows

Investors understood this before most analysts did.

EPAM Systems, long regarded as the gold standard for high-end software engineering, saw a 16 per cent single-day decline in February 2026 on cautious organic growth guidance. The trajectory of broader IT services firms tells the same story: the S&P BSE IT index fell sharply in February as investors began questioning the moat of headcount-based revenue models entirely.

Globant, EPAM, Accenture, TCS and Infosys all felt the pressure. Some have pivoted effectively. Accenture, large and diversified enough to reframe itself as AI infrastructure, has held up better than most. The pure-play offshoring firms that still monetise primarily through the number of bodies in seats are being punished by the market with no sign of reprieve.

Valuation multiples for the sector have normalised to around 9.8 to 10.5 times EV/EBITDA, down from pandemic-era highs of 15 times or more. The “seat model” is being priced out of existence.

The market is not predicting disruption. It is pricing the disruption that has already arrived.

Part Three: Jensen Huang Drew the Line. Which Side Are You On?

Jensen Huang said something this week that should cut through every career conversation happening in British business right now.

The Nvidia CEO, speaking on the Lex Fridman podcast, did not hedge.

“If your job is the task, then you’re very highly going to be disrupted.”

Not might be. Not eventually. Very highly going to be.

If your job is the task, you are already late.

That single distinction between a job and a task is the most important career diagnosis anyone will hear this decade. If you show up every day to execute a repeatable process, you are the process. And the machine runs processes better than you. Faster. Cheaper. Without breaks. Without errors. Without a salary negotiation.

The moment your role can be written as a checklist, the checklist gets automated. Your desk gets cleared.

Huang did not stop at the diagnosis. He handed you the prescription in the same breath.

“If your job’s purpose includes certain tasks, then it is vital that you go learn how to use AI to automate those tasks.”

Your job includes tasks. But your job is not the tasks.

Your job is the judgment around them. The decisions. The context. The instinct for why the work matters and what to do when everything breaks. That stays human. Everything else gets handed to the machine.

And the person who hands it over first does not lose their job. They become more valuable than everyone still doing it by hand.

What Huang is really saying

Disruption warnings have been everywhere for a decade. People discount them. What makes this different is the specificity. Huang runs the company that makes the hardware inside every significant AI system on the planet. He is not guessing. He is reading his own order book.

And note what he did not say. He did not say the people who learn to use AI will be safe. He said the people who use AI to automate their tasks will be more valuable. That is a narrower, more demanding claim. Using AI passively, running prompts, generating first drafts, does not move you up the value chain. It makes you marginally more productive at the tasks being automated anyway. The move he is prescribing is active. You identify the repeatable components of your work. You hand them to the machine. You use the time to go deeper on the work that cannot be written as a checklist.

The accountant who automates data entry becomes the strategist. The marketer who automates reporting becomes the creative. The person who refuses to automate anything becomes the most expensive way to do the cheapest work. That line is drawing itself right now, in every organisation in the country.

What the shift actually looks like for software developers

The market for people who simply write code is contracting. The value has moved to those who design the workflow, audit the output, and own the judgement.

The new stack of skills that separates the replaceable from the indispensable:

Move from executor to orchestrator. Learn agentic IDEs, Cursor, Windsurf, Claude Code. The skill is no longer syntax. It is context engineering: knowing exactly what documentation, architectural patterns, and business rules to feed the AI to get a working result.

Move up to systems thinking. AI can build components. It cannot reason about why you should choose one architecture over another for a specific scaling constraint. That requires trade-off analysis, distributed systems knowledge, and organisational context. None of it can be specified in a Jira ticket.

Build vertical domain expertise. AI is a generalist. If you know everything about fintech compliance, or healthcare data edge cases, or legacy COBOL systems, you become the guardrail for the agent. That is not replaceable. That is the last mile.

Master the AI-native stack itself. RAG and vector databases. AI evaluation frameworks. Multi-agent orchestration tools like LangGraph and CrewAI. If you can build the system, configure its guardrails, detect when it is hallucinating, and fix its reasoning path, you are indispensable. If you can only use the system, you are its product.

Develop non-automatable human skills. Complex negotiation. Stakeholder management. The ability to sit in a room with a sceptical CEO and explain what is actually happening. AI cannot navigate office politics, cannot empathise with a frustrated client, cannot convince a board to change strategy. Engineers who understand UX and revenue are substantially more valuable than those who only understand the repository.

The six-month roadmap

Month one: stop coding like a human. Start steering like an operator. Use Cursor, Windsurf, Claude Code to manage intent across complex codebases. Take a legacy open-source project and refactor it using only agentic tools. Do not write a single line by hand.

Month two: learn how AI memory works. Semantic search, vector databases, RAG. Build something that connects a model to private data without hallucinations. The goal is understanding, not just usage.

Month three: build a squad. One agent writes code, one reviews it, one writes the tests. Create an autonomous bug-hunter that monitors an issue tracker and drafts patches. This is how production AI teams operate in 2026.

Month four: learn to prove a system is reliable. Hallucination detection. Evaluation-driven development. A CI/CD pipeline that runs accuracy tests on agent output. This is work almost no one in most organisations can currently do. That gap is your opportunity.

Month five: understand the probabilistic stack. Token cost, latency, agentic observability. These are systems that do not behave the same way twice. Learning to run them in production is where most people stop. Do not stop.

Month six: build something real. A vertical solution where human judgement is the final gate. A tool that solves a high-value problem that AI cannot solve without you. This is the job description of the person who survives.

Huang is right that dislocation is coming. He is also right that the window to move is still open.

It is not wide. It will not reopen.

📊 The Sunday Signal Tech and AI Layoff Tracker: Week 14

Sourced from Layoffs.fyi and LayoffHedge.com. All figures current to 28 March 2026.

Total 2026 YTD: approximately 74,500 Added this week: approximately 8,800 LayoffHedge sentiment: Extremely Bullish on AI-native restructuring

This is what the transition looks like in numbers.

The narrative has shifted this week from efficiency to liquidation. Companies are cutting human capital to fund the compute clusters required to stay competitive. Eight thousand eight hundred roles in seven days is not a correction. It is a structural event.

The signal this week: the margin-labour swap

For the first time, companies citing AI-native replacement as their primary layoff reason appear to be receiving a median stock price premium of around 9 per cent compared to peers citing macroeconomic headwinds, based on tracker-based estimates. The market is no longer looking for cost-cutting. It is looking for human-to-agent conversion. Employees are being treated as technical debt. Agents are being treated as productive assets.

The KPMG threshold

The KPMG layoffs represent a critical moment for professional services. Eight hundred roles globally in audit and advisory is not large by absolute measure. It is significant for what it signals.

The Big Four model relied on high turnover. Mid-level roles would be vacated as people moved to industry, creating space for the cohort below to move up. That churn has stopped. Because mid-level roles are disappearing from the market due to AI saturation, people are not leaving. The talent is trapped in place. KPMG is cutting because it can no longer wait for natural attrition. The audit agent has arrived and the people it replaces have nowhere else to go.

Watch Deloitte and PwC for announcements in the coming weeks. The KPMG decision will echo.

Oracle and the infrastructure trade

Oracle confirmed approximately 5,000 roles across global enterprise divisions, per tracker-based estimates. The analysis from LayoffHedge frames this as a direct liquidity play to fund its three-hundred-billion-dollar cloud partnership with OpenAI. The stock rose because investors read it correctly: Oracle is selling five thousand salaries to buy GPU clusters.

The logic is brutal and it is now mainstream. Labour is being repriced as operating expenditure. Compute is being repriced as capital investment. Every major institution is running the same calculation.

Next week: the risk heatmap

The Capex Gap analysis for week 14 identifies five companies as highest probability for layoff announcements in the coming seven days. Microsoft is critical. The Stargate funding timeline is creating liquidity pressure in non-AI Azure and hardware units. Salesforce is critical. Agentforce 2.0 is eliminating the sales rep roles that the company simultaneously markets the product to. Deloitte, Alphabet, and ServiceNow complete the watchlist.

In 2026, labour is the new interest expense. The more humans a company carries, the more expensive its balance sheet looks to an AI-first analyst.

🚀 Final Thought

reCAPTCHA extracted six billion dollars of human labour without paying a penny. The offshore model extracted three decades of expert knowledge and fed it into the training data that made that expertise obsolete. The developers who built the best AI tools got managed out because the tools worked.

The machine did not take your job.

You gave it the training data, the workflow, and the permission.

The people who understand the distinction between a job and the tasks within it, between execution and judgment, between being the product and designing the system, those people are not being displaced.

They are being promoted.

Most people will realise this too late.

Until next Sunday, David

David Richards MBE is a technology entrepreneur, co-founder of Yorkshire AI Labs, and a weekly columnist for the Yorkshire Post. The Sunday Signal is published every Sunday at newsletter.djr.ai

Listen to this issue as a podcast. Available on Spotify, Apple Podcasts, and YouTube.

65,700.

That is how many roles have been eliminated or marked for elimination in the first eleven weeks of 2026. Not over a year. Not over a cycle. Eleven weeks.

AI is not starting at the bottom of the labour market. It is starting in the middle. And the pace is not slowing. It is accelerating.

This week has three stories, and they belong together.

The first is the Yorkshire Post column published this week. The Full Monty was about Sheffield steelworkers who lost the thing that defined them. This time it is bankers, analysts, lawyers and accountants facing the same structural shock. The white-collar professional class assumed its credentials would protect it. They will not.

The second story is the one that changes the terms of the debate entirely. Jeff Bezos is in early-stage talks to raise up to $100 billion for a fund designed to buy established manufacturers and overhaul them with AI. Chipmaking. Aerospace. Automotive. Defence. The people who told you that physical, hands-on work was safe from this wave were reasoning from the last one. Bezos just confirmed, with the most ambitious private investment thesis in history, that they were wrong.

The third story is what happens when the office and the factory go at the same time. The economics, the social fracture, and the governance crisis no government is prepared for.

The Sunday Signal Tech and AI Layoff Tracker returns below, with a significant new entry from the banking sector that makes the direction of travel impossible to ignore.

You can also listen to The Sunday Signal as a weekly podcast using a voice cloned from my own voice, a demonstration of the same AI capabilities I write about. Listen on Spotify, Apple Podcasts, and YouTube.

Subscribe now

Story One: The Full Monty Was About Steelworkers. This Time It’s Bankers.

This week’s Yorkshire Post column, adapted for The Sunday Signal.

The remake opens in 2036.

A glossy corporate film flickers on screen. The City of London at sunrise. Glass towers glowing. Young analysts moving at speed between meetings and markets. Screens flashing. Deals closing. A voiceover promises that for those with talent and drive, finance will always reward human judgment.

Then it stops.

Silence.

The trading floor is empty. Screens are dark. The work has not moved to New York or Singapore. It has not been outsourced. It has simply dissolved into software.

In the corner, two former bankers in scuffed Loakes are trying to wrench a server from its rack, hoping to strip copper from the very machines that replaced them.

It sounds far-fetched. Until you remember we have seen it before.

The original Full Monty was not about abstract economic forces. It was about Sheffield steelworkers who lost the thing that gave them identity, pride and purpose. When the work went, it did not just take wages. It took status, structure and meaning.

For a generation, that kind of shock was understood as a working-class problem. Something that happened to people who worked with their hands. White-collar professionals assumed they were insulated. Degrees, offices and credentials were meant to protect them.

That assumption is now breaking down.

In the first eleven weeks of 2026, major technology companies have announced or are developing cuts totalling approximately 45,724 roles, based on tracker-aggregated estimates from Layoffs.fyi and RationalFX. That number is already out of date. By the time you read this, it has crossed 65,700. The developing stories, including HSBC and the heavily reported Meta cuts, suggest the real figure is still climbing.

Behind the headlines, something more important is happening. Roles are not being replaced. Teams are not being rebuilt. The first rung of the career ladder is quietly being removed.

AI is not starting at the bottom of the labour market. It is starting in the middle. Software, finance, business operations and law dominate usage. And the gains increase with complexity. The more educated and valuable the task, the more it can be accelerated.

The real shift is not speed. It is what happens to the job itself.

When AI takes over drafting, analysis, coding or planning, what remains for the human is the lower-value residue. Coordination. Oversight. Exception handling. The job does not vanish overnight. Its intellectual core erodes.

The steel industry followed exactly this path. Technology did not simply replace workers. It redefined the work, broke skilled roles into simpler tasks, and reduced the economic value of labour over time.

That same dynamic is now moving through the office.

The clearest signal is not layoffs. It is hiring. Firms are no longer creating the entry-level roles that once trained the next generation. The ladder is not being climbed. It is being removed.

And when you remove the first rung, you do not just lose jobs. You break the system that produces experience, judgment and expertise.

When deindustrialisation swept through cities like Sheffield, the people most affected did not have the influence to force a rapid response. The adjustment came late, and often in the form of retraining programmes that never quite matched the scale of the problem.

The professional class will insist its work is different. It always does.

Right up to the point where it is not.

Story Two: Bezos Has $100 Billion and He Is Coming for the Factory

The assumption that physical work is safe just collapsed.

There is a comfortable argument that many in manufacturing still reach for.

Software jobs, yes. Finance, perhaps. But you cannot automate a factory. You cannot AI your way through a chipmaking line. The physical world is different.

It is not different.

Jeff Bezos is in early-stage talks to raise up to $100 billion for a new investment vehicle. The pitch documents, as reported by the Wall Street Journal on 19 March and confirmed by Reuters, the New York Times, Forbes and Bloomberg, describe it as a “manufacturing transformation vehicle.” The strategy is brutally simple: buy established manufacturers in chipmaking, defence, aerospace and automotive, then overhaul them with AI. Not gradually. Decisively.

The fund operates alongside Project Prometheus, Bezos’s secretive AI startup co-founded with former Google X executive Vik Bajaj. Prometheus is not building a chatbot. It is building what its team calls Physical AI: models that understand and simulate real-world physics, engineering and manufacturing processes. Airflows. Stress points. Thermal dynamics. The things that govern how physical objects are made, tested and repaired.

Prometheus launched in November 2025, received $6.2 billion in initial funding, and Bezos serves as co-CEO. His first operational executive role since stepping down at Amazon in 2021. That detail tells you everything about how seriously he takes this.

The $100 billion fund would give Prometheus something a laboratory cannot provide: real factories, real supply chains, real workforces to transform.

The scale is historic. The only comparable private fund ever raised is SoftBank’s original Vision Fund in 2017, also $100 billion. But SoftBank bet on startups. Bezos is betting on established industrial businesses and replacing their operating model with AI. That is not a venture bet. That is a restructuring of the physical economy.

Bezos has been personally on the roadshow. Sovereign wealth funds in the Middle East. Asset managers in Singapore. No money has closed yet. But the ambition is not a rumour.

The direction is clear: fewer people, more machines, longer operating hours, and a shift from labour to systems. Not incremental improvement. Structural replacement.

Consider what this means for the jobs we assumed were protected by their physicality.

Manual assembly: replaced by multi-purpose robotic arms running continuous shifts. Quality control: replaced by AI computer vision that detects microscopic defects faster and more consistently than any human inspector. Logistics: managed by autonomous mobile systems. Maintenance: directed by AI copilots overlaid on the physical hardware through augmented reality, reducing the need for years of accumulated trade expertise.

The roles that survive are systems engineering and orchestration. High-demand. High-skill. Numerically small.

Last week I described the one-person unicorn: companies generating extraordinary revenue with workforces of dozens rather than hundreds. Cursor, Midjourney, Gamma. That model was about software. Bezos is extending it to chipmakers, aerospace manufacturers and automotive plants. The same logic. A different industry. A bigger disruption.

Previous waves of automation replaced specific machines. A lathe. A press. A welding arm. Everything else remained human. Project Prometheus is designed to replace entire factory ecosystems. Not the weld. The whole floor.

There are 425,000 unfilled manufacturing roles in the United States right now that require AI literacy. The jobs that will survive this wave already exist. The problem is that the current workforce does not yet have the skills to fill them, and the retraining infrastructure to close that gap does not yet exist at anything approaching the scale required.

The people who told you the physical world was safe were reasoning from the last wave. This is a different wave.

Story Three: The Robot Tax Is Coming. And It Will Not Be Enough.

The economic and social consequences of simultaneous displacement.

Governments are already talking about a robot tax. A levy on automated output. A charge on compute. A payroll equivalent paid by companies that replace workers with machines. The debate is live in Brussels, in Sacramento, in Westminster. It sounds like a solution.

It is not a solution. It is a symptom. A sign that the fiscal foundation of the modern state is cracking and policymakers are reaching for the nearest available tool.

Here is why it matters, and why it will not be enough on its own.

Every previous wave of automation has had a partial quality to it.

The loom replaced hand-weaving but left the mill. The assembly line replaced craftsmen but created line operators. The spreadsheet replaced clerks but created accountants. In every case, displacement was sector-specific. New roles absorbed the shock, eventually, if imperfectly.

What Bezos is building, alongside the AI-native software companies I described in recent issues of this newsletter, is something without clear historical precedent. Simultaneous displacement across both the white-collar professional economy and the physical manufacturing economy. Not sequentially. At the same time.

The economics are stark.

Manufacturing is the last refuge of labour arbitrage. For decades, if wages rose in one country, production moved to another. Vietnam. Indonesia. Mexico. The competitive advantage of low-cost human labour has kept hundreds of millions employed in the developing world and provided a brake on wage deflation in the West.

That brake is about to be released.

If an AI-run factory in Yorkshire costs the same to operate as one in Vietnam, because the primary input is no longer human labour but electricity and chips, production comes home. Not because of trade policy or political will. Because of arithmetic. And when that happens, the entire logic of globalisation begins to unwind.

This creates something economists are only beginning to model. A manufacturing renaissance in wealthy nations, accompanied by severe economic disruption in developing ones. Countries that have spent fifty years building export manufacturing capacity will find that advantage evaporate. That is not a domestic British story. It is a global destabilisation event.

At home, the social impact follows the fault line I have been tracking in this newsletter since Issue #40.

The High-Agency tier thrives. Engineers, AI orchestrators, systems architects. People who direct these systems rather than operate within them. Their leverage is extraordinary. A small team managing a Prometheus-driven production line can outperform a workforce of hundreds.

The Displaced Tier faces something harder than unemployment. It faces irrelevance. A factory foreman with thirty years of floor knowledge. A bank analyst with a first from Durham. The AI replacing them is not approximating. It is outperforming, consistently, at a fraction of the cost.

There is a governance crisis buried in these numbers that governments are not yet confronting at the scale the problem demands.

If forty to fifty per cent of manufacturing employment is automated alongside significant white-collar displacement, the income tax and national insurance base fractures. The modern state is built on taxing labour. Remove labour, and you remove the foundation. Everything else is detail. The debate about a robot tax, a compute levy, or a tax on AI-generated output is not a theoretical policy question. It is a fiscal emergency arriving in slow motion.

And underneath the economics is something the data cannot capture.

Sheffield in the 1980s was not just an unemployment story. It was a meaning story. Work is not merely income. It is identity, structure and purpose. The Full Monty was not about poverty. It was about what men become when the thing that defined them is taken away.

We are about to run that experiment again. Simultaneously. Across every class, every sector, every region.

The handloom weavers of 1810 could see the machines arriving. They understood what would happen. Their response has been misrepresented by history as mindless rage against progress. It was not. It was a demand that progress should not come at the cost of people who had no other options. That demand was ignored.

We should be very careful about ignoring it again.

The Sunday Signal Tech and AI Layoff Tracker

Updated weekly. Sources: Layoffs.fyi, TrueUp.io, RationalFX, Reuters, Bloomberg. As of Sunday 22 March 2026.

Total roles eliminated or reported for elimination in 2026: approximately 65,700.

Up from 45,724 as of 14 March. The most significant new entry this week is HSBC, which disclosed plans to reduce its workforce by up to 20,000 roles, explicitly citing AI automation in back and middle office functions. This is the first globally systemically important bank to name AI as the mechanism of reduction in formal planning documents. It will not be the last.

Meta and Oracle remain in developing status, with Meta in particular carrying heavy internal confirmation signals for cuts of 15,800 to 16,000 roles tied directly to the reallocation of its $135 billion AI capital expenditure programme.

The pattern is consistent across every confirmed entry. Every company that has made an explicit cut tied to AI has seen its share price rise or hold. Block surged 25 per cent on the day of its announcement. WiseTech gained 11 per cent. Atlassian rose between 2 and 4 per cent. The market is not mourning these jobs. It is pricing in the leverage.

HSBC is a different signal. Layoffs at a software company, however large, can be rationalised as sector-specific. A globally systemically important bank, operating across 62 countries, telling regulators and investors that AI will eliminate one in ten roles, explicitly, as a plan rather than a possibility, is a different category of event. Finance was meant to be complex enough to resist this. HSBC has now answered that assumption directly.

🚀 Final Thought

Last week I wrote about the one-person unicorn. The company that generates a hundred million in recurring revenue with a workforce measured in dozens. That story was about software. This week, Bezos has extended the same thesis to steel, chips, aerospace and automotive.

The physical world is not different. It is just next.

The Luddites did not stop the loom. But the political pressure they generated, the social disruption they created, eventually produced the Factory Acts. Child labour protections. Workplace safety law. The early architecture of a social contract between capital and labour. The machine won. But it forced a renegotiation.

What we need now is not a fight against AI. It is a serious, adult, politically difficult conversation about what the social contract looks like when the machine can do the job better than the human. About who owns the productivity gains. About what replaces the purpose that work once provided.

That conversation is not happening at the scale the problem demands.

It will happen eventually. The only question is whether we start it now, while choices still exist, or wait until the factory floor is dark and the trading floor is empty, and two former bankers are stripping copper from a server rack.

Until next Sunday, David

David Richards MBE is co-founder of Yorkshire AI Labs, a weekly columnist for the Yorkshire Post, and the author of The Sunday Signal. Published every Sunday at newsletter.djr.ai.

Listen to this week’s episode on Spotify, Apple Podcasts, and YouTube.

The Bottom Line Up Front

45,724.

That is the number of tech and AI-related roles eliminated or reported for elimination in 2026 so far, according to tracker-based estimates from RationalFX and Layoffs.fyi. We are not yet through the first quarter.

Twenty-five employees. One hundred million dollars in recurring revenue. Achieved in twelve months, with no marketing budget, by a team of engineers and founders that would not fill a typical company’s finance department.

That is what Cursor looked like when it hit a milestone that once implied an entire mid-sized company. That is not a curiosity. It is a warning.

This week’s issue has three parts. First, the Yorkshire Post column: the one-person unicorn is coming, and most business leaders still do not grasp the scale of the change. Second, the mechanics: how AI-native companies are replacing headcount with agents in engineering and go-to-market. Third, the stack: the tools making this possible, and why the cost of testing a serious company idea has collapsed.

And from this week, The Sunday Signal adds a permanent new section: The Sunday Signal Tech and AI Layoff Tracker.

The numbers demand it. Atlassian cut 1,600 roles on 11 March. Block cut over 4,000 in late February. WiseTech cut 2,000. Amazon is rolling through 16,000 corporate positions. In every confirmed case, management is making the same strategic bet: fewer people, more automation, more AI leverage. In every confirmed case, investors responded by pushing the share price up. Block surged 25 per cent on the day. WiseTech jumped 11 per cent. Atlassian rose between 2 and 4 per cent.

The market is not mourning these jobs. It is pricing in the leverage.

I have been building to this section since Issue #40 on 8 February. The data has been accumulating week by week. It now has its own space, every Sunday, for as long as this wave continues.

You can also listen to The Sunday Signal as a weekly podcast using a voice cloned from my own voice, a demonstration of the same AI capabilities I write about. Listen on Spotify, Apple Podcasts, and YouTube.

Subscribe now

Story One: The One-Person Unicorn Is Coming

This is the column I wrote for the Yorkshire Post this week.

Twenty-five.

That is roughly how many employees Cursor, the AI-powered coding company, had when it crossed one hundred million dollars in recurring revenue. Achieved in twelve months, with no marketing spend, through a product so good that developers simply bought it themselves.

I have spent three decades in technology. I am not easily impressed by revenue milestones. That number stopped me cold.

To understand why, remember what £100 million used to imply. A conventional software company reaching that threshold would typically employ between 400 and 800 people. Sales teams chasing pipeline. Customer success managers defending renewals. Marketing operations tracking attribution. Layers of middle management coordinating it all. I have run businesses at that scale. I know what that machinery costs to assemble and to maintain.

Cursor made much of it unnecessary.

And it is not alone.

ElevenLabs closed last year with more than $330 million in ARR. Sierra, founded by Bret Taylor, hit $100 million ARR in seven quarters. Midjourney has shown that an AI-native company can reach extraordinary revenue with a team most corporates would still regard as dangerously lean. The exact ratios vary. The structural shift does not.

That is not incremental improvement. It is structural change.

These businesses were not built as conventional organisations. They were built as automated loops.

Work that once required 200 engineers is now directed by a handful of senior people orchestrating AI agents that write, test and refactor code. Customer support that might have required 100 staff is handled by reasoning systems that check usage logs, interrogate billing records and issue refunds without human intervention. Midjourney does not operate a traditional sales force. It does not need one. Credit card in, product out.

Revenue scales. Headcount barely moves.

The underlying shift is agentic AI. Earlier AI systems summarised and suggested. Useful, but passive. The current generation executes. It builds the feature, runs the tests, deploys the update and handles the complaint. It chains decisions together and improves through feedback. When you map that capability onto a company’s cost base, it does not look like a new tool. It looks like a redefinition of what a company actually is.

For thirty years, growth meant hiring. Hiring meant cost. Cost meant capital. That logic shaped everything from venture funding to office design. Now we are watching leverage detach from labour.

My view is that we are perhaps eighteen months from the first genuine one-person unicorn. A founder directing a fleet of AI systems to build, market and operate a billion-dollar business.

That sounds implausible. So did the idea that a global software company could reach nine figures in recurring revenue with fewer than twenty-five people.

When I started in this industry, launching a product required racks of servers, specialist contractors and months of burn before the first customer invoice. Cloud computing collapsed infrastructure. Modern frameworks reduced development friction. No-code tools shrank teams further. Each wave removed a reason to hire.

Agentic AI may remove the assumption that you need a team in the traditional sense at all.

That should concentrate minds.

Every business leader faces the same uncomfortable question. Are you building for the world that existed five years ago, or the one arriving at speed? The unicorns of the next decade will not be defined by the size of their workforce. They will be defined by the scale of their leverage.

The army, in many cases, has already become the liability.

Story Two: This Is How They Are Actually Doing It

The question everyone asks next is straightforward. How?

The answer matters because these companies are not using AI as a feature bolted on to a conventional business. They have built what the industry now calls an AI-native architecture. The distinction is fundamental. In a traditional company, the software is a tool and the people do the work. In an AI-native company, the agents do the work and the people set the direction.

The result is that human employees have become the bottleneck, not the engine. A small team of senior architects. A fleet of agents executing. No meetings required for the agents to coordinate.

Here is how it plays out in the two departments that consume the most headcount in any growing technology company.

Software Development: The Agentic Fleet

In a traditional company, developers write code. In an AI-native company, developers direct agents that write code.

The directional numbers are striking. Reports from inside companies like Cursor suggest that a significant proportion of the pull requests merged into their product are now created by autonomous AI agents running in the cloud. A small team of architects defines the problem, sets the constraints and spins up fleets of agents working in parallel. Far fewer junior engineers are needed for repetitive, structural work.

When a bug is detected, an AI agent automatically creates a virtual machine, reproduces the bug, writes the fix, and presents a recording of the fix to the human architect for a thirty-second approval.

The industry has coined a term for the developer working this way: vibe coding. A developer describes a high-level intent: “make the checkout flow handle crypto and send a Slack alert”. The AI then manages the thousands of lines of code required to make it happen. The developer never touches the implementation.

The revenue trajectory tells you what this produces. A traditional SaaS company at £100 million ARR employs around 500 people, generating roughly £200,000 per head. Cursor has crossed $1 billion in annualised revenue at a scale of headcount that would once have looked absurdly small. Lovable reached $100 million ARR within eight months of launch and has continued climbing fast.

The point is not the exact spreadsheet ratio. The point is that AI-native companies are scaling output far faster than headcount. The difference is not better engineers. It is a different organisational architecture.

Human replacement estimate: The agentic fleet replaces roughly 150 to 180 junior and mid-level engineers who would otherwise be responsible for boilerplate code, manual testing and bug fixing.

Marketing: Programmatic Growth

Traditional marketing is people-heavy because it requires constant content creation, manual outreach and complex attribution tracking. Lean AI companies have replaced this with what researchers call autonomous growth loops.

Three techniques dominate.

AEO: Answer Engine Optimisation. One or two people use AI to ensure their product is the top recommendation when a potential customer asks ChatGPT, Gemini or Perplexity for advice. They do not write blog posts for humans. They build knowledge graphs for AI. This is rapidly becoming the 2026 version of SEO, and most marketing teams have not yet understood it exists.

Content atomisation. Instead of a social media team, agents take one piece of source content (a founder’s technical talk, say) and automatically produce 50 or more localised videos, posts and ads, each adapted for different markets and platforms. A single piece of original thinking becomes a global content operation without a single additional hire.

The invisible sales force. Midjourney and Cursor have scaled to hundreds of millions in revenue with no traditional sales force. For companies that do need outbound, AI agents now monitor the web for trigger events (a competitor raising prices, a target company hiring aggressively) and automatically generate personalised demos or sequences for every affected prospect. No SDR required.

Human replacement estimate: This replaces 40 to 60 staff across marketing operations, SEO, content creation and sales development. One GTM engineer now does the work of an entire traditional marketing department.

The combined picture is stark.

Story Three: The Exact Tools They Use

The research is now deep enough to be specific. Based on verified implementations documented across a16z Speedrun, SaaStr, Growth Unhinged and company disclosures, here is what ultra-lean AI companies actually deploy, and what it costs.

The single most important principle before the tools: the most successful lean companies consolidate aggressively. Five to ten core tools rather than thirty. Every additional tool creates coordination cost and cognitive load. The savings are reinvested in AI agent quality.

The Core Stack

Sales and prospecting

The dominant documented pattern is a three-tool combination: Apollo for prospecting (270 million verified contacts, free starting tier) → Clay for enrichment → Instantly for delivery.

Clay itself has crossed $100 million ARR and sits at the centre of nearly every documented lean GTM stack. It is to modern GTM what Salesforce was to the previous era. Its waterfall enrichment sequentially queries 100 or more data providers to maximise contact coverage. OpenAI’s own GTM team used Clay to double contact coverage from 40 per cent to 80 per cent. Rippling used it with hiring data triggers and doubled cold email performance year-on-year.

One GTM engineer running this combination can manage thousands of personalised prospects per week.

CRM

Attio (free to start, £29 per user per month) is the documented recommendation from the a16z Speedrun minimum viable stack. HubSpot free tier for maximum accessibility and zero commitment.

Content and marketing

ChatGPT and Claude serve as foundational writing tools. Midjourney, Runway and Canva AI combined eliminate the need for creative hires entirely. Typefully handles lean social publishing on X and LinkedIn.

Customer support

Intercom Fin has become the default AI support agent for AI-native companies. Fin (powered by Claude) resolves 51 per cent of queries on average without any human involvement. Gamma uses it to resolve 75 per cent of queries end-to-end for 50 million users. Anthropic itself chose Fin over building its own support agent.

Discord runs alongside Fin as the community hub. Midjourney built its entire business on Discord: 2.7 million members serving simultaneously as distribution channel, support platform and product interface.

Product analytics

PostHog has emerged as the consolidation play, replacing Amplitude, LaunchDarkly, Hotjar and Segment in a single platform. Free tier includes one million events per month. Documented users include Supabase, Lovable and ElevenLabs.

Automation backbone

n8n (178,000 plus GitHub stars, self-hostable, from £50 per month) is the standout tool for lean teams. It handles the workflow automation that once required a dedicated DevOps team. One Montreal startup documented using n8n to build an automation backbone typically requiring three or four dedicated operations staff.

AI agents and orchestration

For developer-led orchestration, LangGraph handles complex agent workflows with cycles, conditionals and state persistence. CrewAI ($18 million Series A, 100,000 plus agent executions per day) uses a role-based model for multi-agent coordination. For no-code orchestration, Lindy.ai positions as “AI employees” with pre-built templates for sales, support and recruiting, starting at £29 per month.

What the Whole Thing Costs

For under roughly £3,000 a month, a founder can now assemble a surprisingly capable commercial and operating stack. That does not guarantee a £100 million business. It does mean the cost of trying has collapsed.

The final insight from the research comes from Lenny Rachitsky: your job as a founder is to become world-class at one primary growth engine before layering anything else on top. The tool list above is not a shopping list. Pick the single motion that fits your business. Build it. Own it. Only then add the next layer.

📊 New Weekly Section: The Sunday Signal Tech and AI Layoff Tracker

Since Issue #40 on 8 February, I have been tracking the acceleration of tech layoffs. Issues #41, #42, #43 and last week’s #44 each added more data. The scale now demands a permanent weekly section. From this week, the Tracker runs every Sunday.

In the first eleven weeks of 2026, major tech companies have announced or are developing cuts totalling approximately 45,724 roles, according to tracker-based estimates from RationalFX and Layoffs.fyi. The unverified and developing stories suggest the real figure may be considerably higher.

But the raw totals are the least interesting part of the story. The patterns are what matter.

Pattern one: the AI pivot premium is real

The market is explicitly rewarding companies that announce they are cutting people to fund AI investment.

Atlassian cut 1,600 roles (10 per cent of staff) on 11 March and the stock rose approximately 4 per cent. Investors cheered the pivot from growth-at-all-costs to AI efficiency. WiseTech Global cut roughly 2,000 roles (30 per cent of staff) to replace manual coding with AI-native workflows. Share price up 11 per cent.

Block went furthest. Roughly 4,000 roles eliminated in a massive shift to AI-augmented lean teams and to reverse what the company described as the 2018-to-2024 bloat. The result was one of the largest single-day market cap gains in Block’s history, up approximately 25 per cent.

The signal is clear. Investors are not mourning the jobs. They are pricing in the leverage.

Pattern two: the SaaS crisis is emerging

Not every company is being rewarded for its AI pivot.

Workday cut 400 roles in customer operations in February. The stock is down around 40 per cent year to date. The reason is not the number of cuts. The reason is investor fear that agentic AI will not just assist Workday’s enterprise software. It may make the software itself unnecessary.

If developers stop writing structured HR workflows and instead describe what they want to an agent (the vibe coding model), then the value of a heavyweight enterprise HR platform collapses. Investors are beginning to ask whether tools like Workday remain as indispensable in an agentic world as they looked in the last software cycle.

This is the deepest structural risk I have been writing about since Issue #40. The AI disruption is not symmetrical. Companies building AI infrastructure are gaining. Companies that were built on the assumption of human cognitive labour are the ones in danger.

Pattern three: rumour versus reality

Not every number circulating in tech forums is confirmed.

Meta has reports of potential cuts affecting more than 20 per cent of staff circulating internally, with no formal announcement and no SEC filing to date. Oracle reports suggest potential large-scale cuts are developing; no official confirmation has been given. Treat both as directional signals until they appear in a regulatory filing, not as settled facts.

Amazon is confirmed. 16,000 corporate role reductions, framed explicitly as management flattening and a reallocation of $37 billion to AI compute and infrastructure. That one is in the numbers.

Pinterest cut 675 roles (approximately 15 per cent of staff) in a January/February cycle to pivot from a high-cost social platform to an AI-driven discovery engine. Stock down 3.5 per cent on the initial reaction, with recovery building as AI-targeting optimism grows. Autodesk cut approximately 1,000 roles (7 per cent) to reorganise around AI-driven generative design tools. eBay has confirmed approximately 800 roles, 9 per cent of full-time staff, in a strategic realignment.

Figures and market impact data are based on reports available as of 14 March 2026. Tracker estimates sourced from RationalFX and Layoffs.fyi. Developing stories without WARN notices or SEC filings remain unverified. Stock changes are immediate short-term reactions.

🚀 Final Thought

The army, in many cases, has already become the liability.

The one-person unicorn is not a thought experiment. It is an engineering problem. And several people are very close to solving it.

When I started in this industry, launching a product required racks of servers, specialist contractors and months of burn before the first customer invoice. Cloud computing collapsed infrastructure. Modern frameworks reduced development friction. No-code tools shrank teams further. Each wave removed a reason to hire.

This wave may remove the assumption entirely.

The question is not whether this is coming. It is whether you are on the right side of the shift.

David Richards MBE is the co-founder of Yorkshire AI Labs. He writes a weekly column for the Yorkshire Post and is a regular commentator on technology, business and the future of work.

The Sunday Signal is produced as a weekly podcast using a voice cloned from David’s own voice, a demonstration of the same AI capabilities he writes about. Listen on Spotify, Apple Podcasts, and YouTube.

Until next Sunday, David

The Bottom Line Up Front

Two weeks ago I wrote about the historical pattern. The handloom weavers of Yorkshire. The structural logic of technological displacement. The way skilled professions convince themselves they are immune until the moment they are not.

Last week the financial markets began to confirm that pattern. IBM fell sharply after warning that AI will reshape parts of its consulting workforce. Atlassian has lost more than 70 per cent of its value over the past year. Block cut roughly 40 per cent of its workforce and investors rewarded the decision. These are different companies in different sectors, but they point in the same direction. The cost of producing cognitive output is falling, and markets are beginning to price what that means for every business built on human expertise as its primary input.

This week the academic evidence arrived to match the market signals.

On 5 March Anthropic published the most rigorous labour market analysis yet of what AI is actually doing to employment. Not what it could theoretically do in some distant scenario, but what it is doing in real professional settings today, tracked across millions of workplace interactions.

The headline finding appears reassuring. There has been no sudden collapse in employment. The labour market has not broken.

Do not be comforted by that.

The real signal sits deeper in the data. It is the sort of signal that appears quietly in graphs years before it erupts into a political crisis. The sort of signal the handloom weavers saw in the early nineteenth century and understood perfectly well, even if they lacked the power to stop it.

This issue looks at three positions. The bull case for abundance. The bear case for decoupling. And my own view on which outcome is more likely. It is not a fully optimistic view and it is not a catastrophist one. It is structural. And it should make you uncomfortable wherever you sit in this debate.

If you prefer to listen rather than read, each issue of The Sunday Signal is also available as a podcast. Perfect for the commute, a walk, or time on the road. You can find it on Spotify Youtube or Apple Podcasts.

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Part One: The Gap That Is About to Close

What the Anthropic Study Actually Found

Anthropic introduced an important concept into the debate about AI and employment. They call it observed exposure.

Previous studies tended to focus on capability. Researchers examined what large language models could theoretically do, compared those capabilities with occupational task databases, and then estimated the percentage of jobs that might be at risk.

The problem with that approach is that it confuses potential with deployment. A tool may be capable of performing many tasks, but that does not mean it is actually being used to perform them. The capability assessment tells you the theoretical ceiling. It does not tell you where you are on the way to it.

Anthropic approached the question differently. Instead of asking what AI could do, they asked what it is doing. By analysing millions of real conversations with Claude in professional environments they mapped which occupational tasks were genuinely being performed by AI right now, at scale, in actual workplaces.

The results are striking.

Computer programmers already have 74.5 per cent of their tasks covered by AI systems. Customer service representatives sit at 70.1 per cent. Data entry clerks at 67.1 per cent. Medical records specialists at 66.7 per cent. Financial analysts at 57.2 per cent. Paralegals and legal assistants at 53.8 per cent.

Pause for a moment on the financial analyst figure. More than half of the working tasks performed by financial analysts are already being handled, at least in part, by AI systems in real workplaces today. Not in a laboratory. Not in a proof of concept. In the field.

But the most important number in the study is not the level of current deployment. It is the gap between deployment and capability.

Across every occupational category analysed, the theoretical capability of AI remains significantly larger than its current real-world usage. The spider chart in the report makes this visually obvious. A small red shape representing actual usage sits inside a much larger blue shape representing what the systems could already do.

The gap is large. And it is closing.

This is the signal that the reassuring headline about stable employment numbers obscures. We are not looking at the aftermath of an AI disruption. We are watching the early phase of one. The adoption curve has not peaked. It has not even reached its steepest point yet. The observed exposure figures being discussed this week may look like the floor in two years, not the ceiling.

AI does not need to reach its theoretical maximum capability to produce structural disruption. It simply needs to close the gap faster than the labour market can adapt.

On current trajectory that is exactly what appears to be happening.

The Youth Hiring Signal

Buried in the Anthropic data is an early warning signal that has received far less attention than it deserves. It should be the most discussed finding in the report.

Hiring of workers aged twenty-two to twenty-five in AI-exposed occupations has fallen by 14 per cent since the launch of ChatGPT in November 2022. Not unemployment. Hiring.

Companies are not firing experienced professionals en masse. Instead, they are quietly choosing not to create the entry-level roles that once absorbed new graduates.

That distinction matters enormously, and it is worth understanding precisely why.

Professional careers have traditionally been built on an apprenticeship model. Young analysts built spreadsheets and prepared briefings. Junior lawyers reviewed documents and drafted routine correspondence. Graduate developers maintained legacy code and fixed bugs. The work was repetitive and often tedious, but it served a function that went well beyond the immediate output.

It allowed each generation to accumulate the practical experience that eventually produced senior professionals.

The entry-level role was never just a job. It was the mechanism through which expertise was transmitted from one generation to the next. You could not become a senior partner without having been a junior associate. You could not lead due diligence without having spent years doing the groundwork of due diligence. The repetitive work at the bottom was not a waste of talent. It was the foundation on which judgment was built.

Remove the entry-level role, and you do not simply eliminate a job. You remove the first rung of the professional ladder. A generation of graduates cannot become the experienced partners, analysts and architects of 2035 if the early career positions that generate that experience no longer exist.

This kind of structural damage does not appear immediately in unemployment statistics. It does not generate headlines. It does not prompt parliamentary questions.

It appears years later, when organisations discover an ageing senior cohort with no properly trained generation behind them. When the knowledge pipeline has run dry and nobody quite knows when it happened.

The handloom weavers did not lose overnight. They lost gradually, and then all at once.

The hiring signal tells you we are still in the gradual phase. History tells us what tends to follow.

Part Two: The Bull Case

Altman’s Abundance

Sam Altman has become the most prominent voice for the optimistic interpretation of what AI will do to the economy. His argument deserves to be taken seriously, not dismissed as the self-serving cheerleading of a technology CEO with a financial interest in the outcome. The substance is more rigorous than the critics usually acknowledge.

His central argument is straightforward. The cost of intelligence is collapsing.

This is not a metaphor. It is an economic claim about the marginal cost of producing cognitive output. Legal drafts, financial models, computer code, strategic analysis, research summaries: the cost of generating each of these is falling rapidly towards zero. Not to zero yet. But directionally, and accelerating.

History offers a clear guide to what happens when something scarce and expensive becomes cheap and abundant.

Everything changes. The products and services built on top of that newly cheap input become cheaper too. New products and services emerge that were previously uneconomic. The economic geography of entire industries is redrawn.

Altman’s most striking formulation is the one-person unicorn. Within this decade, he argues, individuals running networks of AI agents across coding, legal work, finance and marketing will build billion-dollar companies alone. The capital, expertise and headcount that once formed the barriers to building serious businesses will dissolve.

The idea sounds extraordinary. It no longer looks obviously wrong. More about that next week.

Inside Yorkshire AI Labs, I routinely see early-stage founders compressing work that previously required entire teams into the capacity of a single individual augmented by AI. Founders conducting strategic software development, marketing communications, technical architecture and market analysis simultaneously, using AI for the information-processing components of each. The output per person has changed materially. The change is visible week to week.

The academic underpinning for the optimistic view comes primarily from Erik Brynjolfsson at Stanford. His productivity J-curve thesis argues that the economic impact of general-purpose technologies typically arrives with a significant lag. Firms adopt the tools before they have redesigned the processes required to get the full benefit from them. The productivity gains appear at the individual level well before they appear in aggregate economic statistics.

This is not an ad hoc defence of AI. It is a documented historical pattern.

Electrification began transforming American factories in the late nineteenth century. The first electric motors appeared in factories in the 1880s. The productivity gains from electrification did not appear clearly in macroeconomic data until the 1920s. Forty years after the technology was deployed.

The delay was not because electrification failed. It was because getting the full benefit required factory owners to fundamentally reorganise their buildings, their workflows and their management structures around the new technology rather than simply inserting electric motors into factories designed for steam power. That reorganisation took decades.

Once it occurred, the productivity surge was rapid and substantial.

There is a second argument that strengthens the bull case, drawn from the history of every previous technological disruption. Economists call it the reinstatement effect.

Every major technological revolution has displaced certain categories of labour. The steam engine displaced handloom weavers. The combustion engine displaced horse-related trades. Computers displaced large numbers of clerical workers. Each displacement was real, and the pain of the transition for those affected was also real.

But in every case, the technology simultaneously created entirely new categories of work that had not previously existed at scale. Railways eliminated traditional transport work but created railway engineers, track maintenance crews, station staff, logistics planners, freight insurers and a new commercial geography that generated employment across every sector it connected.

The optimists argue that AI will follow the same pattern. The new jobs it creates may not yet have names. Nobody in 1995 would have predicted the emergence of social media managers, platform trust and safety officers, user experience researchers or data protection consultants. Those roles did not exist yet because the platforms that would require them had not been built.

The bull case rests on the argument that AI will create new demand that neither Anthropic’s researchers nor anyone else can yet see.

That argument has been directionally correct across every previous major technological transition. The industrial revolution, electrification, computerisation, the internet: each produced dislocation, and each eventually produced substantially more employment than it destroyed.

The question is whether this transition is fundamentally different in a way that breaks the pattern.

Part Three: The Bear Case

The Great Decoupling

The pessimistic interpretation of AI’s economic impact does not come from technophobes or reactionaries. Some of the most rigorous economists working today hold this position, and their argument deserves the same serious treatment as the bull case.

Their argument rests on a simple observation that carries significant weight.

Every previous technological revolution disrupted physical labour. The steam engine, the power loom, the internal combustion engine, the industrial robot: all of these replaced human muscle. They were immensely consequential, and they caused genuine hardship for those whose livelihoods depended on the physical tasks they displaced.

But they did not replace the human mind.

For two centuries the economic value of educated labour rested on a foundation that technology could not touch. The wage floor for cognitive work was sustained by the fact that machines could not do cognitive work. The twentieth-century professional middle class was built, in its deepest economic logic, on the gap between what machines could do with matter and energy and what only human minds could do with information and judgment.

AI closes that gap.

Daron Acemoglu at MIT describes the most important risk as so-so automation. His concern is not that AI fails to generate productivity gains. It is that firms deploy AI primarily to reduce labour costs rather than to create genuinely new economic value.

In that scenario you replace the paralegal with a document review system. You do not develop a new legal product that uses the freed capacity to serve clients who previously could not afford legal services. You do not create new forms of legal work requiring different skills. You simply bank the margin.

Corporate productivity statistics improve. Margins expand. GDP figures remain healthy.

But wages for the majority of knowledge workers stagnate because the supply of competent output has risen relative to demand for human-generated work. Growth continues, but prosperity becomes decoupled from it. The aggregate numbers look fine while the distributional reality deteriorates.

This is not a theoretical risk. It is the pattern that characterised the management of deindustrialisation in large parts of the United Kingdom and the United States over the past four decades. Aggregate GDP grew. Technology improved. The communities where the displaced workers lived did not recover.

The Anthropic data contains evidence that points towards this risk rather than away from it.

The workers most exposed to AI displacement are not factory workers. They are not the low-skilled workers that the standard disruption narrative has always pointed at. The Anthropic study found that the most exposed workers are four times more likely to hold a graduate degree than the least exposed. They earn substantially more. They are 15.5 percentage points more likely to be female.

That last point deserves to be stated plainly rather than buried in a footnote.

The professionals most at risk from AI displacement right now are the educated women who built careers in legal, financial and analytical services over the past three decades. Not because they made different choices or lack relevant skills. Because the work those careers are built on happens to be the work that AI can currently do most effectively.

Look at the law firm. Look at the consultancy. Look at the investment bank. Look at the accounts department.

These are the workplaces where observed AI deployment is currently highest. And these are disproportionately the professional environments where women who fought to enter the knowledge economy over the past generation now work. The social and political implications of that distribution are considerable and are receiving almost no attention in the mainstream commentary.

Yuval Noah Harari pushes the argument into territory that economists find uncomfortable but which cannot responsibly be avoided.

Even if the distributional problem can be managed through redistribution, he argues, even if universal basic income or equivalent policies can preserve material living standards for those displaced, a deeper question remains.

What happens to societies where large numbers of people cannot find meaningful work?

Employment is not merely a source of income. It provides identity, purpose, daily structure and social status. The connection between meaningful work and individual psychological health is not speculative. It is documented across decades of research. The long term decline in male labour force participation in parts of the United States since the 1970s, and the well-evidenced correlation between that decline and rising rates of addiction, family breakdown and political extremism, tells you that the consequences extend far beyond economics.

Even if we solve the income problem, we have not solved the meaning problem.

If the professional middle class loses its economic function at scale, the political consequences will not wait for academic consensus to form. They will arrive, as they always have, in the form of disruption that surprises everyone who was watching the wrong indicators.

Part Four: My View

Intelligence Has Become Cheap

In my Yorkshire Post column this week I returned to a memory from my childhood that sits at the centre of everything this debate is actually about.

In the early 1970s my parents rented our colour television. That was entirely normal at the time. A good television cost around £250, which in today’s money represents well over £4,500. Families did not buy televisions. They entered a financial arrangement with a rental company, paid weekly, and hoped the thing kept working.

Three channels. Heavy buttons. An aerial that needed adjusting depending on the weather. A large piece of furniture that cost the equivalent of several months of ordinary wages.

Fast forward fifty years.

Today you can walk into almost any retailer and buy a vastly superior 4K flat screen for roughly the same nominal £250. Fifty years of inflation have passed and yet the sticker price has barely moved. The real cost has fallen by around 95 per cent. The worker on the National Living Wage can earn that television in approximately twenty four working hours today.

Now ask a different question.

Can the same worker easily afford a house?

No. The average semi-detached house in England now costs the equivalent of roughly nine years of median full-time earnings. That ratio has roughly doubled since the 1970s.

Housing has not experienced the same productivity revolution as televisions. Its price is shaped by planning systems, land constraints and financial structures that protect existing asset values rather than manufacturing innovation and global competition. The forces that made televisions cheap were deliberately allowed to operate. The forces that would make housing affordable were deliberately constrained.

This distinction is not peripheral to the AI debate. It is the centre of it.

AI is about to do to intelligence what automation did to television manufacturing. The marginal cost of legal analysis, financial modelling, coding, research, strategic advice and medical diagnostics is falling rapidly. Not to zero immediately. Not without friction. But directionally, and with increasing speed.

The question is whether the outcome resembles the television or the house.

One path resembles the television outcome. Intelligence becomes abundant and widely accessible. The founder in Sheffield who previously could not afford the professional services available to a corporation in London can now access legal drafting, financial modelling, regulatory guidance and market analysis at near-zero marginal cost. Small businesses compete with multinationals on information-intensive tasks for the first time. Productivity gains spread across the economy rather than concentrating in the firms large enough to afford the humans who previously provided those services.

If that abundance materialises, and the bull case says it will, then the productivity gains will be real, large and measurable. GDP per capita will rise. Corporate margins will expand. The economy will generate more output with fewer human hours of labour.

That is precisely the moment when the argument for universal basic income moves from aspiration to arithmetic.

The logic is straightforward. If AI generates a genuine productivity surplus, if the economy produces substantially more with substantially less human labour, then the resources to fund a serious universal income exist. Not a symbolic gesture. Not a pilot scheme for a handful of researchers to study. A universal payment set at a level that genuinely sustains a dignified life, funded by the productivity gains that AI makes possible.

This is not a radical position. It is a conservative one. Every serious analysis of technological displacement eventually arrives at the same conclusion: when the gains from automation are large enough and concentrated enough, redistribution is not charity. It is the mechanism that prevents the abundance from becoming a crisis.

The Luddites were not wrong about what was happening to them. They were wrong about how to stop it. The correct response to a productivity revolution that eliminates skilled work is not to smash the looms. It is to ensure that the wealth the looms generate does not flow exclusively to the people who own them.

We have been here before. The post-war settlement built the NHS, the welfare state and universal education on exactly this logic. The productivity gains of industrial capitalism were real. The question was who would share them. A generation of politicians decided the answer should be: everyone.

That decision requires making again. Now. Before the sorting is complete.

In this scenario we also invest in the wider social infrastructure a genuine transition requires. Retraining programmes that are funded, serious and designed for adults with twenty years of professional experience rather than for teenagers learning to code. A regulatory framework that requires AI productivity gains to carry some obligation to the workforce they displace, rather than flowing entirely to shareholders. And a universal basic income that treats the abundance AI creates as a shared inheritance rather than a private windfall.

The other path resembles the housing outcome. The efficiency gains flow primarily to capital. Firms capture the cost savings as margin without creating new economic activity. The educated professional class faces a sustained income squeeze as the supply of competent output rises relative to demand for human-generated work. Young people find the entry level rungs pulled up ahead of them. The political consequences are not unpredictable. They are already visible in countries where economic transitions were managed badly.

History is not reassuring about which path societies tend to take without deliberate intervention.

The social media era provides the most recent and instructive parallel. The first iPhone launched in 2007. The early academic evidence of algorithmic harm to adolescent mental health began appearing around 2014. Serious regulatory response in the United Kingdom and European Union arrived around 2022. Fifteen years between the deployment of the technology and any serious attempt to govern its consequences.

By the time regulation arrived, two generations of young people had been exposed to systems specifically optimised to maximise engagement regardless of psychological cost. The damage was already done. The walls arrived after the flood.

Anthropic’s researchers themselves offer a different historical comparison in the report. The labour market impact of AI, they suggest, may resemble the China trade shock more than it resembles COVID. The economic disruption from China’s entry into global manufacturing was visible in employment data from roughly 2003 onwards. The political consequences, measured in the collapse of consensus around open trade, the rise of economic nationalism and the reshaping of electoral politics across the western world, did not arrive fully until 2016.

A thirteen year gap between the economic signal and the political reckoning.

If that analogy holds, the 14 per cent decline in youth hiring in AI-exposed occupations that we see in today’s data is the 2003 signal. The political reckoning arrives in the early 2030s.

And we are currently doing approximately as much to prepare for it as western governments did to manage the China trade shock. Which is to say: very little.

By the time the aggregate statistics confirm the structural break, an entire generation will already have been sorted into winners and losers. The sorting is happening now. It is quiet, distributed and politically invisible, which is precisely what makes it dangerous.

The bull case is coherent. The bear case is credible. My honest view is that the outcome depends entirely on decisions being made right now, in boardrooms and in Parliament, by people who are largely focused on the next quarter and the next election.

Both time horizons are too short for the problem in front of them.

That is not a prediction. It is a warning.

Final Thought 🚀

The television my parents rented in the 1970s became cheap because we built the systems required to make it cheap. Manufacturing investment, global supply chains, technical standards, competitive markets and deliberate policy choices all played a role. None of it happened automatically. It was the accumulated result of choices made by governments, businesses and consumers over decades.

Intelligence is becoming cheap for similar reasons. Enormous research investment, computing infrastructure, data access and strategic deployment decisions are pushing the cost of cognitive work steadily downward. Those decisions were made by specific people in specific organisations with specific interests. The outcome we are heading towards is not natural or inevitable. It is the product of choices that were, and continue to be, made by human beings.

The technology itself will do what it does.

What is not predetermined is who benefits from it, who bears the cost of the transition, and whether the societies on the receiving end of this shift have the political will to manage it in a way that does not repeat every previous failure.

The question is not whether intelligence will become cheap. It will.

The question is not whether this will displace professional work. It will.

The question is whether we will make the choices required to ensure that what is coming builds a floor rather than widens a gap.

History suggests we will move too slowly.

The 14 per cent drop in youth hiring in AI-exposed roles is already in the data.

The question is whether anyone in power is watching.

Until next Sunday,

David

David Richards MBE is a technology entrepreneur, educator, and commentator with over 25 years in technology. He writes for the Yorkshire Post and publishes The Sunday Signal weekly at newsletter.djr.ai

The Sunday Signal | newsletter.djr.ai | Issue #44 | Sunday, 8 March 2026

Bottom Line Up Front

Last week’s issue, The Loom, the Layoff, and the Life Raft, made the historical case. From Yorkshire’s handloom weavers to the junior developers of 2026, the pattern was clear. Skilled work gets eaten by machines. The people in the middle convince themselves it is temporary.

The response in my inbox was predictable. Developers telling me AI hallucinates. Lawyers explaining it still needs supervision. Professionals arguing it is not production-ready.

That may all be true.

But when I want to know whether something is real, I do not start with opinions. I start with markets.

Commentary is frictionless. Capital is not.

Financial markets are where pension funds, sovereign wealth, boards and institutions reprice assumptions with real money at stake. When billions are marked down in a single session, something structural has shifted.

This week, three separate corporate stories told the same story.

AI has stopped being a productivity narrative. It is becoming a cost narrative.

And the market is confirming it.

PODCAST: Listen To This Edition

Part One: The Triple Warning Shot

IBM and the fragility of “complexity”

On Monday 23 February 2026, IBM’s shares fell roughly 13% in a single trading session. Its worst single-session fall in over twenty-five years.

That is not a routine wobble. That is a repricing event.

IBM’s modern business is built heavily on consulting and legacy modernisation. For decades, the complexity of COBOL estates inside banks and governments acted as a moat. Untangling forty years of accumulated business logic required armies of specialists. That slowness was billable.

Then Anthropic published analysis suggesting tools like Claude Code can automate significant portions of COBOL exploration and reverse engineering.

Whether that claim proves fully accurate is secondary. The market heard something simpler.

Complexity might no longer be scarce.

If the moat weakens, the margin compresses. Public markets do not wait for certainty. They discount direction.

Thirteen per cent in a day is what direction being repriced looks like.

Atlassian and the end of the per-seat assumption

Over the past fifty-two weeks, Atlassian has been widely reported as down roughly 73%, trading near levels last seen in 2018.

Strip away macro noise and a cleaner question emerges.

Atlassian’s model is built on seats. Seats track headcount. Headcount is the input.

If AI increases output per engineer materially, companies need fewer engineers. If they need fewer engineers, they need fewer seats.

When input shrinks structurally, revenue assumptions shift structurally.

This is not about Jira features. It is about labour intensity. And it matters beyond Atlassian. There is an entire category of SaaS businesses whose revenue assumptions are built on human headcount at the companies they serve. The per-seat pricing model that defined a generation of cloud businesses is being repriced in real time. The transition will be brutal for the companies caught in between.

Block and the incentive structure

On Thursday, Jack Dorsey announced that Block would cut over 4,000 employees, roughly 40% of its workforce, explicitly linking the restructure to intelligence tools. The company is profitable. It is growing. No crisis. No failed product.

Shares jumped roughly 25% in after-hours trading.

That is not sentiment. That is capital signalling approval.

Every board in the developed world just saw a case study: reduce headcount aggressively, attribute it to AI efficiency, get rewarded.

The copycat cycle has begun.

Part Two: This Is How We Now Build at Yorkshire AI Labs

Let me be precise.

This is not speculation about the future of software. This is how we now model and capitalise companies inside the Yorkshire AI Labs portfolio.

Three years ago, a new software venture followed a predictable structure. Core development squad of six. UI and front-end another six. QA and testing another six. DevOps and release engineering another six. Twenty engineers before meaningful traction was considered normal. Investors expected it. Headcount signalled seriousness.

Now we increasingly structure around one highly capable AI-native engineer at the centre of the build.

One.

Not because we are cutting corners. Because the economics have changed.

Even in Yorkshire, a fully loaded engineer costs roughly £80,000 to £100,000 per year once salary, national insurance, pension, tooling and overhead are included. Twenty engineers means £1.6 million to £2.5 million annual burn before product-market fit. Under our current portfolio model, a single AI-native engineer, properly tooled, can orchestrate generation, testing, documentation, deployment and integration work that previously required multiple squads.

The cost base collapses. Runway extends. Dilution reduces. Capital efficiency improves dramatically.

That is not theoretical. It is how we are allocating capital today.

A Live Portfolio Example

A CTO in one of our portfolio companies recently needed to replicate a major bank integration environment. APIs. Error handling. Rate limiting. Latency behaviour.

Historically, that would have required weeks of structured engineering work.

Using AI tooling, he generated a functioning mock banking environment in minutes. He reviewed it, validated it and moved straight to live integration.

Four weeks became minutes - literally.

When you see that inside your own portfolio, you understand why public markets are moving.

The Pattern Across Our Portfolio

The banking example is one instance. The pattern repeats.

A fintech building KYC compliance tooling used to need a legal team, a compliance team and an engineering team working in parallel. Now one AI-augmented compliance engineer produces a first-draft mapping from regulation text to technical specification in hours. The parallel-squad model has collapsed into a sequential, much leaner process.

A health-tech company building NHS data integrations used to require specialists in HL7 FHIR standards who took years to develop that specialism. Now AI surfaces the relevant standards, generates the integration schemas and flags known edge cases, compressing the learning curve dramatically.

A B2B SaaS company building workflow automation used to need separate product, research and engineering teams. Now, a two-person team does the work that previously required ten.

This is what last week’s issue called the Slow Squeeze. Wages under pressure first. Status following. The profession reorganising around fewer people. This week’s three market signals are what the Slow Squeeze looks like when it hits a public market all at once.

Part Three: The K-Shape. Which Side Are You On?

Understanding what is happening at the macro level is one thing. The more important question is what it means for the individual.

Miles Deutscher has been writing about what he calls the K-shaped AI economy: one diverging trajectory where those who master AI compound their advantage, and another where those who do not are gradually managed out. A visualisation circulating widely this week, a dot matrix attributed to Damian Player, CEO of Agent Integrator, puts the adoption split into stark relief:

Each dot represents 3.2 million people. The full grid is 8.1 billion humans alive today.

Of those 8.1 billion, approximately 84% have never meaningfully used AI. The revolution, for most of the planet, is still entirely abstract. Another 16% are free chatbot users: occasional, modest, competitive advantage thin and eroding. Approximately 0.3%, some 15 to 25 million people, pay for AI tools and are gaining genuine efficiency advantages. And at the absolute frontier, roughly 2 to 5 million people, 0.04% of humanity, are using coding scaffolds to build: deploying tools like Cursor, Bolt, or Claude Code to create their own applications, automations and infrastructure.

That last group is the Red Dot. The gap between the Red Dot and everyone else is widening at an accelerating rate.

The Four Baskets

Deutscher maps individual AI capability into four levels.

Basket One: The Basics. Using AI for summaries, research, draft emails. Most people who use AI at all are here. It saves time. But as a competitive differentiator, it is rapidly approaching zero. If everyone can summarise a document with a chatbot, doing it with a chatbot confers no advantage. These skills are being commoditised in real time.

Basket Two: The Power User. Advanced prompting. Multi-step agentic workflows. Automating sequences of work without manual intervention at each step. At this level, you genuinely do the work of two or three people. The efficiency gain is real. But the uncomfortable truth: you are still an employee, still trading time for money, still vulnerable to the next efficiency leap that makes your current toolkit look basic.

Basket Three: The Builder. This is where things change qualitatively. You are using coding scaffolds to create your own applications, your own automations, your own solutions to your own problems. You stop being a consumer of other people’s software and start being a producer. You build tools that did not previously exist, in hours rather than weeks. You are no longer dependent on the off-the-shelf tool market. You create bespoke capability at low cost.

Basket Four: The Master. AI-native businesses. Consulting. Distribution. The connection between time spent and income earned is fully broken. You are building systems that operate without you. The IBM signal, viewed from Basket Four, is not a threat. It is an opportunity. The COBOL modernisation work that IBM charged armies of consultants to do is now available to a single AI-native practitioner with the right tools and the right clients.

Most professionals reading this are in Baskets One or Two. The direction of travel matters more than the current position. The window to move up is narrowing. Not closing. Narrowing.

The Ladder Is Being Pulled Up

The entry-level roles that once trained professionals, the junior analyst, the graduate developer, the trainee paralegal, are the first to go. Not because AI does their work perfectly. Because AI does it well enough, cheaply enough, quickly enough that companies are no longer willing to pay a human to do it while they learn.

Professional development has always worked through apprenticeship. You start at the bottom. You do the grunt work. You accumulate the judgment and contextual knowledge that makes you valuable at the senior level over the years. The junior role was not just a job. It was the training ground for the senior role.

If the junior role disappears, the training ground disappears with it. The people who climbed that ladder got something that will not be available to the next generation. The ladder is being pulled up behind them.

Last week’s issue made the point from history: the handloom weavers also had genuine expertise. The new categories of work in the textile industry were not available to them. The transition, though not instantaneous, was fast enough to destroy a generation.

Deutscher’s argument is that we are in a 6 to 12-month window where the gap between the two trajectories of the K is still bridgeable. After that, the compounding advantages of the Red Dot become structurally difficult to close.

The timeline is debatable. The direction is not.

What You Can Actually Do

The analysis is only useful if it leads somewhere actionable.

The first move is the mindset shift from passive observer to active experimenter. Most people in Baskets One and Two are using AI the way people used early search engines: to find information faster, not to build new capability. That framing limits what you can learn and what you can build.

The second move is systematising your exposure. The people getting ahead are not occasional readers of good AI articles. They have a daily or weekly discipline around understanding what is now possible: research announcements, developer communities, product releases that signal new capabilities. Not to become technical experts. To understand what tools now exist before those tools become mainstream knowledge.

The third move, the one that separates Basket Two from Basket Three, is to build something. Anything. A custom automation for a task you do repeatedly. A tool specific to your work. A dashboard that aggregates information you currently gather manually. The act of building with AI tools transforms your understanding of what is possible. It moves you from consumer to producer, which is exactly the distinction the market is beginning to price.

The fourth move is positioning. The Red Dot individuals are connected to other builders, other AI-native practitioners, other people using these tools seriously. The networks forming around AI-native practice are becoming economically significant. The people inside them are getting access to opportunities and knowledge not visible from outside.

The IBM signal, the Atlassian signal, and the Block signal are all pointing in the same direction: companies are restructuring around fewer, higher-capability humans deeply integrated with AI. The people who survive and thrive are the ones who made themselves irreplaceable by becoming builders rather than users.

Final Thought 🚀

Last week set out the historical pattern. This week, the market confirmed it.

IBM’s repricing. Atlassian’s long decline. Block’s headcount reduction rewarded by investors.

Three separate stories. One structural signal.

When the cost of producing digital output falls, labour intensity falls. When labour intensity falls, margins shift. When margins shift, valuations move.

The machine did not need to be perfect. It needed to be cheaper and capable of improvement. By the time it closed the quality gap, the industry had been rebuilt around it, with far fewer people, different skills, and no apology for the transition.

Opinions are frictionless.

Markets are not.

Capital has started to move.

The window is open. For now.

Until next Sunday, David

David Richards MBE is a technology entrepreneur, educator, and commentator with over 25 years in technology. He writes for the Yorkshire Post and publishes The Sunday Signal weekly at newsletter.djr.ai

The Bottom Line Up Front

Two hundred years ago, Britain’s most skilled craftsmen watched their livelihoods unravel. Not because they lacked talent. Because the machine did not need their talent. It needed their output.

The software developers, paralegals, and junior analysts of 2026 are living through the same story. The machinery is different. The economic logic is identical.

This week: the historical pattern that explains everything about the AI jobs crisis, the ten roles at the sharpest end of the displacement curve, what my latest Yorkshire Post column reveals about who actually wins, and a concrete three-phase plan for anyone who wants to own the machine rather than be replaced by it.

This issue is also available as a podcast — an experiment using an AI-generated version of my voice. Listen on Spotify or Apple Podcasts and tell me what you think.

Part One: The Weaver’s Warning

The Canary That Nobody Listened To

There is a story from the English Midlands that every professional facing AI displacement should know by heart. It begins around 1810. It ends in migration, poverty, and the permanent extinction of a skilled trade. And it contains every lesson we need for 2026.

The handloom weaver was the software developer of the early nineteenth century.

This was not a low-skill labourer. This was a craftsman with years of apprenticeship behind them, a sophisticated understanding of materials and mechanics, a home-based operation with genuine autonomy, and a wage that placed them firmly in what we would today call the middle class. In the late 1700s, a skilled weaver in Lancashire or Yorkshire could earn enough in three or four days to support a family for the whole week. They owned their tools. They set their own hours. They had leverage.

That leverage came from knowledge you could not simply acquire. The craft required mastering the tension of the threads, the rhythm of the shuttle, and the relationship between warp and weft. This knowledge was the moat. It was the thing that made a weaver irreplaceable.

Until, of course, it wasn’t.

The Slow Squeeze (1815–1830s)

The power loom did not arrive like a cannonball. It arrived like a tide. Gradual, inevitable, and ultimately overwhelming.

Between 1815 and the 1830s, weavers did not lose their jobs overnight. Something more insidious happened. The price of their output collapsed. A steam-powered loom could produce twenty times more cloth per hour than a human. Factory owners did not need to fire the weavers. They simply drove the market price of cloth so low that weavers had to work twice as hard for half the money just to survive.

The weekly wage fell from roughly 25 shillings in 1800 to just 5 shillings by 1830. The same hands. The same skill. A fifth of the income.

They argued that machine-made cloth was inferior. For a while, they were right. But the machines improved. Then the consumers stopped caring about the difference.

Sound familiar?

In 2026, a junior copywriter argues that AI-generated content lacks soul. A Tier-1 support agent argues that customers prefer talking to a human. A junior developer argues that AI code requires too much review to be reliable. They are not wrong. Not yet. But the machines are improving. And the economics are already shifting.

This is the Slow Squeeze. The job title survives. The wage, the job security, and the career trajectory quietly erode.

The Luddite Rebellion: The Emotional Breaking Point

As their livelihoods collapsed, the weavers did not simply retrain. They fought back. Organised groups of skilled textile workers broke into factories at night to smash the power looms with sledgehammers.

The popular myth is that the Luddites were ignorant people who feared technology. That is a comfortable story for those who benefit from automation. The reality is more complex.

The Luddites were not anti-technology. They were anti-exploitation.

The machines were being used specifically to bypass existing labour laws, undercut agreed wage structures, and destroy a standard of living that skilled workers had built over generations. Their grievance was not with the loom itself. It was with the fact that the productivity gains were going entirely to the factory owners.

Parliament made machine-breaking a capital offence, punishable by death. Soldiers were deployed. The movement was crushed. The weavers lost.

By the 1840s, the handloom weaver as a career was functionally extinct. Many ended up working in the very factories they had fought against, no longer as independent craftsmen but as machine overseers. The value had migrated from weaving the cloth to maintaining the loom. Those who could not make that transition emigrated, thousands of them, to the United States and Australia.

Their children benefited. Lower cloth prices meant cheaper clothing for everyone. New industries emerged. The weavers lived through the worst of it. Their grandchildren inherited the benefits.

This is the U-shaped recovery. And it is exactly the shape of the curve that today’s displaced professionals are at the beginning of.

1826 vs. 2026

The junior software developers, paralegals, market research analysts, and content writers of today are the handloom weavers of 1820. Talented. Genuinely skilled. The apprenticeship was real. The moat existed.

But the power loom has arrived.

The task is no longer to weave faster than the machine. That race is over. The task is to understand who owns the factory.

Part Two: The Ten Jobs in the Crosshairs

As we move through 2026, the conversation has stopped being theoretical. AI is no longer the future of work. It is a standard employee in offices across the world.

The research below draws on Goldman Sachs, the World Economic Forum’s Future of Jobs Report 2025, McKinsey’s State of AI 2025, Gartner’s Predicts 2026, the OECD Digital Education and Employment Outlook, and ADP’s latest labour trend data. This is not speculation. It is current employment data.

1. Customer Service Representatives (Tier 1)

Agentic AI has moved far beyond the frustrating chatbots of 2022. Autonomous agents can now process refunds, troubleshoot faults, and handle complaints with voice responses indistinguishable from a human. Tier-1 support is almost entirely automated at the companies moving fastest. Gartner’s analysis notes that total replacement leads to a measurable drop in customer sentiment, so human agents survive but only in conflict resolution. The complex complaint. The edge case the AI cannot navigate without reputational damage. The weaver is now the loom overseer.

2. Data Entry and Document Clerks

AI vision and OCR have reached a precision in 2026 that makes manual data entry indefensible. The WEF’s Future of Jobs Report confirms clerical and administrative roles are among the fastest-declining categories between now and 2030. The role transforms into Data Quality Auditor. The skill required is not typing speed. It is pattern recognition and judgment.

3. Junior Software Developers

For two decades, learning to code was the single most reliable piece of career advice a young person in Britain could receive. McKinsey’s State of AI 2025 identifies coding as one of the functions seeing the fastest acceleration in agentic AI deployment. The entry-level runway is gone. New developers entering the market are expected to be System Architects and AI Orchestrators from day one. More on this in Part Three.

4. Paralegals and Legal Researchers

Specialised Legal LLMs can review thousands of pages of discovery documents, summarise case law, and flag inconsistencies in a fraction of the time it takes a junior associate. Goldman Sachs specifically identifies legal assistants as among the roles with the highest exposure to generative AI. Law firms are not waiting. Junior headcount is being reduced. The value moved from reading the brief to arguing the case.

5. Bookkeepers and Payroll Specialists

Accounting software in 2026 is effectively self-reconciling. AI automatically categorises expenses, detects anomalies, reconciles bank feeds in real time, and handles routine tax filing. ADP’s labour data shows Professional and Business Services experiencing a measurable cooling in job growth, attributed directly to AI-driven efficiency gains. Strategic Tax Planning and complex financial advisory are where the human value now lives.

6. Basic Content Writers and SEO Specialists

Short-form content, product descriptions, and SEO-driven articles are now generated at scale by AI. In 2026, search engines weight Information Gain, the degree to which content adds something genuinely new. Generic content no longer ranks. The shift is from writer to Content Strategist and AI Editor: the person who knows what angle to take and what the AI confidently gets wrong. As the WEF notes, creative thinking is rising in importance precisely as execution becomes automated.

7. Junior Market Research Analysts

AI can scrape the web, conduct sentiment analysis, and generate a fifty-page market report in under a minute. Gathering data is no longer a skilled role. Interpreting what that data means for a CEO’s five-year strategy, identifying the pattern the data does not show, that is where the analyst earns their salary. McKinsey confirms knowledge management is now one of the leading areas for agentic AI scaling.

8. Telemarketers and Outbound Sales

Cold calling has been taken over by AI voice agents running thousands of simultaneous conversations, never experiencing call reluctance, improving in real time. Goldman Sachs lists telemarketers among the highest-risk occupations for AI displacement. The human sales professional survives only at the warm end of the funnel. When the relationship requires depth. When the negotiation requires reading the room. The cold call is not.

9. Proofreaders and Translators

Neural translation has reached near-perfect functional fluency for most major world languages. Goldman Sachs lists proofreaders and copy editors among the most exposed roles. Survival territory is narrow but real: high-stakes literary translation, legal contracts where precise human accountability is a regulatory requirement. The volume of routine work that once kept professional linguists employed is gone.

10. Template-Based Graphic Designers

For small businesses, the requirement to hire a designer for a logo, a social media asset, or a basic website layout has effectively disappeared. What survives is Creative Direction: knowing what good looks like, what a brand needs to communicate, and what the machine is producing that is technically proficient but strategically empty. The WEF’s fastest-declining jobs data confirms graphic and multimedia designers are under significant structural pressure.

Part Three: Be Nice to the Nerds. While You Still Can.

Building on this week’s Yorkshire Post column

I used to give young people very clear advice. Learn to code. It is the key. I believed it so completely that I helped open a coding academy.

This week, that advice required significant revision.

The End of Code as Permission

To understand what is happening, you have to understand what coding actually was during the Internet era. It was not just a technical skill. It was permission.

Creative people had ideas. They needed a translator who spoke machine. The designer could imagine the product but could not build it. The entrepreneur could see the market but could not ship the software. They all hit the same wall: you can imagine it, but can you build it?

That asymmetry gave the programmer their power. This is the story of the Winklevoss twins and Divya Narendra, who conceived a social network called HarvardConnection and needed someone to build it. They recruited Mark Zuckerberg. The creative spark was not the scarce resource. The ability to execute in code was. The translator had leverage over the person with the idea.

That dynamic has now changed.

AI can produce working software of startling quality. What once required a team of engineers working for weeks can increasingly be achieved by a single person with a well-crafted prompt and sound judgment. The machine speaks fluent software. The translator is no longer the bottleneck.

What This Actually Means

If AI can write the code, learning to code is no longer a moat. It becomes table stakes. The equivalent of knowing how to use a spreadsheet. Necessary, perhaps. Sufficient, no longer.

Dario Amodei, Chief Executive of Anthropic, told The Economist at Davos that AI could handle most, possibly all, of what software engineers currently do within six to twelve months. His own team confirmed that at Anthropic, the code is now effectively one hundred per cent AI-generated. Elon Musk has gone further, predicting traditional coding will be obsolete by the end of this year.

I agree with the direction, if not every detail. The mass-market demand for armies of developers writing routine business software is the part most exposed to replacement. The OECD has already identified a significant cooling in entry-level white-collar hiring across member countries through late 2025 and into 2026.

The Upside, and It Is Significant

When execution is democratised, originality becomes the new scarcity.

Domain experts who understand real problems can now build tools without waiting for a technical co-founder. The Winklevoss problem is solved. You no longer need Zuckerberg to build the thing. You are Zuckerberg now.

The winners of the next decade will not be those who can write a function in Python. They will be the people who think clearly, see problems others miss, understand the domain deeply enough to know what quality looks like, and can direct increasingly capable machines toward genuinely useful outcomes.

Be nice to nerds, Gates said. The advice still holds. It is simply that the nerds of tomorrow may look nothing like the nerds of today. They may be manufacturers who understand industrial processes. Lawyers who understand regulatory complexity. Teachers who understand how children actually learn.

The handloom weaver’s skill was weaving. That skill became worthless. But the knowledge of textiles, of markets, of quality survived and migrated into new roles. The lesson is not to abandon what you know. It is to stop confusing the tool you use with the knowledge you hold.

Part Four: How to Be AI-Proof

The 90-Day Survival Plan

The weavers who survived the power loom were not the ones who wove fastest or protested loudest. They were the ones who understood what the machine could not do, positioned themselves where human judgment was irreplaceable, and moved quickly enough to get there before everyone else did.

Three phases. Ninety days each.

Phase 1: AI Literacy (Days 1–90) Focus: Not coding. Logic.

Real AI literacy means understanding how Agentic AI reasons, where it excels, and where it fails with complete confidence. Where it hallucinates. What kinds of tasks it reduces to triviality and what kinds remain genuinely hard. The WEF identifies AI and big data literacy as the fastest-growing skill category in the 2025–2030 period.

Milestone: Conduct a full AI Audit of your own job.

List every significant task you do in a working week. Sort them into two columns.

Column A: tasks that take 50% or more of your time but require none of your unique knowledge, judgment, or relationships.

Column B: tasks where your specific experience, relationships, or accountability are genuinely what make them valuable.

Column A is your displacement risk. Column B is your survival territory. If Column A is longer than Column B, you need to start moving.

Phase 2: Orchestration (Days 91–180) Focus: Multi-tool integration.

The value in 2026 is not in knowing the tools exist. It is in being able to string three or four AI agents together into a workflow that replaces a process that currently takes human time. This is the difference between using a tool and being a system architect.

Milestone: Build a Digital Twin of your current workflow.

Try Zapier, n8n, or Claude Code to automate the tasks from Column A. The goal is not perfection. The goal is to do it, to understand how agents pass information between each other, where the failure points are, and where a human still needs to make a judgment call.

Once you have built even a basic automated workflow, you have crossed a line. You understand how the machine thinks because you have made it think.

Phase 3: Specialisation (Days 181–270) Focus: Domain-specific logic.

The most AI-resistant professionals in the world share one characteristic: they hold knowledge that is genuinely hard to acquire, hard to encode, and consequential when it goes wrong.

AI can summarise what is known. It cannot replace the person who has spent twenty years learning what is not written down.

Milestone: Get certified in AI Governance or Strategic Risk Management within your specific field.

The Alan Turing Institute, BCS (Chartered Institute for IT), and MIT Sloan Executive Education all offer relevant programmes. Domain depth plus AI fluency plus accountability is the new moat. It is considerably harder to replicate than the ability to write a loop in Python ever was.

The Honest Assessment

None of this is comfortable. The weavers who thrived after the power loom arrived were a minority. Most of that generation struggled. The U-shaped recovery is real, but the bottom of the U is a brutal place to spend a decade.

The difference between 1826 and 2026 is the speed of the information available. The weavers of Lancashire did not have access to a weekly newsletter explaining the pattern they were inside. They did not have McKinsey telling them their core skill was being automated. They did not have the WEF telling them at Davos that their entire career category was being structurally reorganised.

You do.

The question is what you do with it.

🚀 Final Thought

The power loom did not destroy weaving. It destroyed weavers who refused to understand that their value was never in the motion of the shuttle. It was in the knowledge of the cloth.

The AI revolution will not destroy your career. It will destroy the version of your career that requires you to move the shuttle.

The question is not whether to adapt. It is whether you adapt before the wage spiral begins, or after.

The weavers who made it understood one thing early: the machine is not the enemy. The person who already owns the machine and has replaced you is.

Don’t be replaced. Own the machine.

Until next Sunday,

David

David Richards MBE is a technology entrepreneur, educator, and commentator with over 25 years in technology. He writes for the Yorkshire Post and publishes The Sunday Signal weekly at newsletter.djr.ai

Boards love the word “professionalise.”

It sounds responsible. Mature. Sensible.

In practice, it often means removing the person who made the company worth owning in the first place.

This week’s Yorkshire Post column was not theory. It was memory. I was fired in a hotel in Belgravia, with a document slid across a table, by a chairman I had appointed. Then I was reinstated days later.

The point is not that founders are infallible.

The point is that founders are not interchangeable.

History shows what happens when boards forget that. Sometimes they destroy billions in value. Occasionally they save the business.

At the same time, the largest companies on earth are making infrastructure bets so vast they are beginning to bend markets and power grids around them. Not hundreds of billions. We are now looking at combined capital expenditure approaching seven hundred billion dollars in a single year.

Two decisions.

Who leads.

And who builds.

Both define the next decade.

Subscribe now

Raise Money. But Never Give Away the Keys.

Every ambitious founder should raise capital.

Whether that means venture capital, public markets or both, external money is how small companies become important ones. Capital gives you speed. Speed gives you market position. Market position gives you options.

The mistake is not raising money.

The mistake is giving away control before you have proven what you are building.

The pattern is painfully consistent. A founder with a strong idea and early traction takes a pre-seed or seed round. The term sheet looks generous. The valuation feels fair. But buried in the details are board seats, veto rights, anti-dilution clauses and preferred liquidation terms that shift power long before the company has found its footing.

By the time the founder realises what they signed, the investor is not a partner.

They are the landlord.

This is the difference between venture capital and private equity. Venture capital, in its best form, backs founders and accepts the wild variance that comes with early-stage conviction. Private equity buys control, installs management, optimises operations and extracts returns on a defined timeline.

Both models work. But they serve different purposes at different stages.

What is happening increasingly, and I see it locally, is venture investors behaving like private equity at the earliest possible stage. Taking board control at pre-seed. Installing their own chair before the product has reached market. Firing founders in the first innings of the game, not because the company is failing, but because the founder is not docile enough for the investor’s operating model.

This is not governance. It is capture.

When I was asked to sign that document in a hotel in Belgravia, I was a CEO who had built a company, taken it public and created hundreds of millions in value. And still the board I had constructed moved to remove me. I was reinstated days later. But the lesson stayed.

If it can happen to a founder at that stage, it can happen to anyone. And it is happening now to founders who have barely started.

A VC who removes a founder at seed stage is not protecting an investment. They are destroying the only thing that makes the investment worth making. At that point, the founder is not one part of the machine. They are the machine. The vision, the conviction, the customer relationships, the product instinct, the pace. Remove them and you are left with a cap table, a pitch deck and a set of Notion documents.

The lesson is not to avoid raising capital.

The lesson is to raise it wisely.

Understand what you are signing. Every term sheet is a transfer of power disguised as a transfer of money. Board composition, voting thresholds, reserved matters, drag-along rights. These are not admin. They are architecture. Get a lawyer who works for you, not one recommended by the investor.

Retain board control for as long as you can. At pre-seed and seed, there is no reason for a founder to surrender a board seat. If an investor demands one as a condition of a modest cheque, they are telling you exactly how they intend to behave when things get difficult.

Choose investors who have backed founders through adversity, not just through growth. Ask the question directly. Have you ever fired a founder? How early? Why? The answers will tell you everything.

Look at dual-class structures early. They are not just for IPOs. Zuckerberg controls Meta with roughly thirteen percent economic ownership because he holds the majority of voting shares. That structure was established long before the company went public. Founders who wait until listing to think about control have already left it too late.

Raise money. List when the time is right. Both are signs of ambition and confidence.

But never confuse a cheque for alignment.

And never assume that the person sitting across the table at your board meeting is building the same company you are.

When Firing the Founder Destroys Value

And When It Saves It

There is a pattern in corporate history. Boards replace visionary founders with operational leaders in pursuit of calm.

Calm is usually expensive.

Consider Apple after the departure of Steve Jobs in 1985.

Apple did not implode. It diffused.

The Newton absorbed huge resources and became a cultural punchline for its flawed handwriting recognition. The Pippin was a forgettable attempt at gaming hardware. The Macintosh TV felt like a product without a thesis. The desktop line multiplied into Quadras, Performas and Power Macintoshes with marginal differences and mounting confusion.

There was activity everywhere. But no clarity.

By 1997 Apple was widely described as being within ninety days of running out of cash. Market share had collapsed from around sixteen percent to less than four. Annual losses hit $1.03 billion. The company that invented the personal computer revolution was being written off.

The board acquired NeXT, effectively to bring Jobs back. He eliminated around seventy percent of the product line. He cut the bewildering range of desktops to one. He cut portables to one. He negotiated a $150 million investment from Microsoft, of all companies, to keep the lights on. Ruthless focus returned. So did profitability.

The difference was not engineering talent.

It was taste.

Taste is the ability to say no. Taste is strategic subtraction. Taste is conviction before consensus.

Now look at Yahoo and Jerry Yang.

In February 2008, Microsoft offered $44.6 billion to acquire Yahoo. The board, led by Yang, rejected the bid as “substantially undervaluing” the company. Microsoft raised its offer to $33 per share. Yang rejected that too. Shareholders revolted. Carl Icahn joined the board. Yang stepped down as CEO in November 2008. The company cycled through four more chief executives in five years, and eventually sold its core business to Verizon in 2017 for roughly $4.5 billion.

That is not poor execution. That is generational value destruction on a staggering scale.

Or take OpenAI removing Sam Altman in late 2023. Within days, over ninety-five per cent of employees signalled they would leave. Microsoft offered to hire the entire workforce. The board discovered a brutal truth. Governance without the founder meant having no company to govern.

Those are the disasters.

But firing a founder is not always wrong.

BlackBerry is a case study in founders misreading a platform shift. Co-CEOs Jim Balsillie and Mike Lazaridis clung to physical keyboards while Apple’s iPhone remade the entire category around touchscreens. After both stepped down in January 2012, the company pivoted into enterprise software and cybersecurity. It did not regain dominance, but it survived.

Tesla replaced its founding CEO Martin Eberhard in 2007 amid cost overruns and execution strain on the original Roadster programme. The leadership that followed was volatile and aggressive. It also forced an entire industry to electrify.

So what is the dividing line?

It is not charisma. It is not volatility.

It is whether the founder is still directionally right.

If the founder is still the engine, removal destroys value.

If the founder has become the brake, removal can unlock it.

Boards often fail because they remove founders for the wrong reason. They remove them because they are uncomfortable.

Professional operators optimise what exists. Founders create what does not.

If you replace the explorer too early, you institutionalise mediocrity.

The Seven Hundred Billion Dollar Gamble

While boards debate personalities, markets are engaged in a different act of conviction.

Amazon, Alphabet, Meta and Microsoft are collectively committing capital expenditure of between $635 billion and $700 billion in 2026. That is roughly double their combined spend in 2025, which was itself a record. A decade ago, these same four companies spent $31 billion between them. The scale of acceleration is without precedent in corporate history.

This is no longer asset-light software. This is industrial-scale infrastructure.

Amazon alone has guided $200 billion in capital expenditure this year. Alphabet is targeting $175 billion to $185 billion. Meta between $115 billion and $135 billion. Microsoft is tracking towards $120 billion. Even Oracle, which lacks the free cash flow of the hyperscalers, is raising $45 billion to $50 billion through debt and equity in a single year just to keep pace.

When capital spending surges at this scale, three things happen.

Free cash flow tightens. Morgan Stanley projects Amazon’s free cash flow will go negative in 2026, potentially by as much as $28 billion. Alphabet’s free cash flow could fall by ninety percent. Meta faces a similar compression. These companies are burning through their own abundance to build what they believe will become essential infrastructure.

Depreciation expands. Short-lived assets, primarily GPUs, now dominate the spend. Microsoft reported that roughly two-thirds of its capital expenditure goes on processors that will be obsolete within years. Every chip purchased today is a bet that demand arrives before the hardware depreciates.

Valuation narratives get tested. High multiples assume scalable margins and capital efficiency. Massive front-loaded infrastructure bets assume demand arrives on schedule. If autonomous agents become real economic actors, these firms will own the rails. If adoption lags, margin compression will follow.

Markets are already sorting winners from losers.

In the short term, the money flows to whoever sells the shovels. Nvidia’s data centre revenue hit $51.2 billion in its most recent quarter, up sixty-six percent year on year. CoreWeave, the neocloud provider that rents GPU clusters to the hyperscalers, has surged over thirty percent since January and carries a $55.6 billion backlog. Vertiv, which makes the power and cooling systems that keep these facilities alive, just reported organic orders up 252 percent and a backlog of $15 billion. Eaton, the electrical switchgear manufacturer, is riding the same wave as data centres compete for grid connections. These are not software companies. They are the picks, shovels and dynamite of the AI gold rush, and the market is pricing them accordingly.

In the medium term, the carnage is in software. The iShares Tech-Software ETF has retreated roughly thirty percent from its late 2025 highs. Salesforce is down twenty-six percent year to date. ServiceNow has fallen twenty-eight percent. Intuit has dropped over thirty-four percent. Atlassian plunged thirty-five percent in a single week. Traders at Jefferies are calling it the SaaSpocalypse. The trigger was not a recession. It was the realisation that every dollar an enterprise spends on AI infrastructure is a dollar not spent on another software seat. Hedge funds have shorted roughly $24 billion in software stocks in 2026 alone. The per-seat pricing model that defined a generation of cloud businesses is being repriced in real time.

The hyperscalers themselves sit in the middle. Amazon and Microsoft are down twelve and sixteen percent respectively on the year, weighed by free cash flow concerns. Alphabet and Meta have held steadier. Investors are treating them as both builders and potential beneficiaries. If AI agents generate real economic output, the companies that own the compute layer will capture enormous value. If the payback period stretches, their balance sheets absorb the pain.

In the long term, the question is structural. The shift is not from one software vendor to another. It is from software as a tool for humans to AI as the worker itself. Companies that own unique data, control critical infrastructure or sit at the foundation of the new stack will command premium valuations. Companies that merely organise information in a browser window will not. The age of the seat licence is ending. What replaces it, whether consumption-based pricing, outcome-based billing or something else entirely, will define the next era of technology investing.

The market is not panicking. It is repricing.

And then there is physics.

AI is not abstract. It consumes electricity at extraordinary density.

Data centres are now competing with households for grid capacity. In Monterey Park, California, a proposed 250,000 square foot hyperscale data centre would consume 434 million kilowatt hours per year. That is 1.7 times the entire city’s residential electricity usage, equivalent to powering 40,000 homes in a city with only 20,000 households. Hundreds packed City Hall in January. The council voted unanimously for a moratorium, and is now drafting an outright ban.

This is the new frontier. Not prompts. Power stations.

Countries are responding differently.

The Middle East is building at extraordinary speed. Saudi Arabia’s Public Investment Fund has backed HUMAIN, a dedicated AI infrastructure company, which is constructing eleven data centres across two campuses in the Kingdom with ambitions to become the world’s third largest AI provider. The UAE has agreed with American partners to build a five gigawatt AI campus in Abu Dhabi, with an initial 200 megawatt cluster going live this year. Gulf sovereign wealth funds have committed roughly $2.5 trillion to major technology investments. These are not plans. Construction is underway.

Europe is investing in sovereign compute to avoid dependency. The EU’s €200 billion AI Continent Action Plan includes AI Gigafactories, nine new AI-optimised supercomputers being procured across the bloc in 2025 and 2026, and a new Cloud and AI Development Act to guarantee EU-based compute capacity. European sovereign cloud spending is expected to triple from $6.7 billion in 2025 to over $23 billion by 2027. The philosophy is clear. If you do not control the infrastructure, you do not control the intelligence.

The United States is attempting to balance private capital intensity with public infrastructure constraints. The handful of hyperscalers alone may account for a meaningful share of total US non-residential investment spending this year, enough to move the dial on GDP.

And the United Kingdom must decide where it stands.

The government’s AI Opportunities Action Plan and AI Growth Zones are steps in the right direction. Blackstone’s £10 billion AI campus in Blyth, Microsoft’s partnership with Nscale to build the UK’s largest supercomputer, Google’s £5 billion data centre programme, and the new Stargate UK project with OpenAI and NVIDIA all represent real momentum. The £150 million AI Pathfinder programme for sovereign GPU deployment shows intent.

But the question remains whether these commitments add up to enough. The UK is targeting 500 megawatts of AI Growth Zone capacity by 2030, with one zone scaling to a gigawatt. Saudi Arabia’s HUMAIN alone is building 1.9 gigawatts by that same date. The National Energy System Operator estimates data centres could drive up to 71 terawatt hours of additional electricity demand over the next 25 years. The grid is already under strain.

Consumer. Or builder.

Because AI leadership will not be decided solely in code.

It will be decided in concrete and copper.

🚀 Final Thought

Firing founders too early is a board choosing comfort over edge.

Underbuilding infrastructure out of caution is a nation choosing safety over competitiveness.

Overbuilding blindly is just as dangerous.

The hardest decisions in business are not operational. They are philosophical.

Do you preserve the edge. Or do you smooth it out.

Do you build ahead of demand. Or wait and hope.

Taste cannot be spreadsheeted.

Physics cannot be negotiated.

The next decade will expose those who forget either.

Until next Sunday,

David

David Richards MBE is a technology entrepreneur, educator, and commentator with over 25 years in technology.