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Look at the grass

What's on my mind

Two photographs of the same Wimbledon court, forty-two years apart. In 1982 the grass is worn up the middle and into the service area, the path players left as they served and came to the net. By 2024 the forecourt is green and the only bare patch is a band along the baseline. Same court, same fortnight, a different game.

Nobody announced the change. The grass showed it first. The rackets got bigger, the strings put vicious topspin on the ball, and the courts grew slower and higher-bouncing. Rushing the net stopped being the default. The rally took over. Wear moved because the game did.

I see the same thing in the firms I work with. If you want to know what has really changed, look at where the effort now goes.

For a long time, knowledge work mostly wore out the grass at production: building the model, doing the research, writing the draft, laying out the deck, working out the argument. That labour was the visible moat. It was why a firm could charge for a model, a legal team for a brief, a studio for a campaign.

The machine has not removed the work. It has moved the wear.

Producing has become cheap enough that it is no longer the bottleneck, and knowledge work has made the same move tennis did, only faster. The grass took forty-two years. The work took four.

The old way was serve and volley: plan hard, produce once, commit, because a second attempt cost days or weeks you did not have. The new way is the rally. You frame the shot, the machine helps you hit it, you review what comes back and frame the next. Framing and reviewing are yours now. Producing is mostly the machine's.

I was arguing exactly this in Cannes last week with a leader who runs a team of 250. He was mourning the old way. He used to give a promising junior five days to write something up, because the slow writing was where the thinking happened. Now he asks, and the paper lands quickly, and he does not want to read it.

Let them send the good-enough draft, I said, give quick feedback and spend the saved days elsewhere.

You do not save the days, he said. You push them up the ranks. Good enough, multiplied across 250 people, is a flood.

He was right that the time does not completely vanish. But it does not have to drown you either. Producing got cheap. Framing the shot and reading the return did not. So the effort did not disappear. It moved into those two acts, which I wrote about last week.

Reviewing is the part we get wrong. The draft reaches you more quickly, and your job is to send back a useful next ball. Not a perfect verdict. Not a grand judgment. A return that keeps the rally improving. But we are slow to return the ball, and when we finally do, we try to hit a winner: one careful, detailed, overburdened response, as if the point could still be won in a single stroke. I have often sent senior people work that mattered and waited weeks for feedback. That is the old game. You rarely win a rally with one perfect shot. You win by playing shots that improve your position until the winning shot becomes obvious.

And yet most firms are still set up for the old game. The review still happens on Thursday afternoons, so work that is ready on Monday waits anyway. The approval chain still routes everything through layers built to filter effort-heavy work, when the effort has already moved. The training has to change so the work comes at a high quality, but the managing has to change too - so feedback is quicker and more frequent.

So look at your own grass. Where is the surface wearing now? Still at producing, or at reviewing? If your review cycle and your approval chain are unchanged for an AI world, you're playing serve and volley for a game that has become a rally. Redesigning them is the hard part, which is why most people would rather fix the training. Which will you redesign first?

Three things worth knowing

1. The people with the most skin in the AI economy read it both ways this week.

The Bank for International Settlements, the institution central banks themselves bank with, warned that AI "exuberance" could end in a long investment bust. The warning turns on returns rather than capability: whether the money spent on AI earns back enough to justify itself, however well the technology works. The jobs data split the same way in the same week. A study of 21,559 US firms by Ramp, a corporate-spending platform, and Revelio Labs, a workforce-data firm, reported by the Financial Times, found heavy AI adopters grew employment about 10% after adoption and lifted entry-level hiring 12%, a pattern Noah Smith, the economics writer, read as AI complementing people rather than replacing them. The same day, The Kobeissi Letter, a markets commentary account, reported AI-linked sectors shedding about 11,000 jobs a month and the US labour share of income at its lowest since records began in 1947. These are not contradictions to resolve. They describe different populations: the firms mastering the technology, and the sectors it disrupts from outside. So two board questions collapse into one. Are we the group that masters this, or the group it happens to?

Three years in, AI investment is already steeper than the railways, the 1920s or the dotcom boom were at the same point.

The other reading: firms spending most on AI grew headcount about 10%. Low spenders barely moved.

2. Zapier is killing private direct messages to feed the company's "shared brain".

Wade Foster, chief executive of the automation company Zapier, is ending private direct messages, starting with the executive team, and running a public "transparency leaderboard" that has pushed the share of conversation happening in open channels from 33% to 46%. His logic is worth stealing: every private message is a gap in the company's shared brain, context lost to both the humans and the AI agents that increasingly work over the company's own records. The bottleneck in most organisations' AI adoption is context that never gets written down, not model quality. If half your knowledge lives in direct messages and people's heads, no agent can reach it, however good the model. Most firms bolt AI onto the way they already work. Zapier is redesigning the way it works so the AI can pull its full weight, and that is the step that separates the leaders from the licence-buyers.

3. The Economist ran 25 AI models through the World Values Survey. They came back more extreme than any country.

The World Values Survey has mapped human morals across more than 100 countries since 1981. Run 25 leading models through it, as The Economist did, and they didn't land at the average of humanity. They clustered in the corner occupied by rich, secular, self-expression-focused countries, often further out than the most extreme nation surveyed. OpenAI's models came back more secular than any country. None reflected the world view of most African or Muslim countries. When a model summarises the news or weighs in on a family row, there's no factually correct answer to fall back on, so its values fill the gap. A tool a billion people now delegate decisions to carries a world view measurably narrower than the human range, baked in through training data and the value choices a handful of firms make in training them. It's the values version of a point from a fortnight ago: unless you bring yourself, the machine's defaults stand in for you.

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1. Give any automation a few reversible dress rehearsals before you trust it for real.

This week I set up an agent to grab a hard-to-get morning swim slot for Teresa at our local gym, and I didn't hand it the real booking on day one. For the three days before the one that mattered, it booked a genuine slot the proper way, then immediately cancelled and emailed me the confirmation. My thinking was simple: "If it works for three days, then we can feel good. If it doesn't work for three days, then we've got three chances to fix it before it goes live." The rehearsal runs the whole chain against the real system, but in a mode where any failure is free to fix.

2. Find the one thing your AI reliably gets wrong, then make it your first check.

Any AI tool you use on a repeated task has a consistent blind spot: the transcript that always mangles names, the meeting summary that always drops the final ten minutes, the invoice reader that always leaves the tax field blank. Mine was that last one. Catching it recovered £9,860 of VAT that would otherwise have gone unclaimed. The habit: the first few times you use a tool, compare its output with the source and note what it consistently misses. Then make that one thing your standing first check. You keep the time-saving without inheriting the blind spot.

3. Queue what you'd doomscroll past, and have a model teach it back at your level.

Suhail Doshi, the founder behind Mixpanel and Playground, described the habit on X: queue every interesting blog post, paper or tweet, ask a model to teach it back to you, and read the approachable version in spare moments instead of doomscrolling. The model doesn't just summarise; it teaches, pitched at how much you already know, so a dense paper meets you where you are. The simplest way in: paste a link or the text into Claude or ChatGPT with "Teach me this. Here's what I already know:" and one honest sentence. Read that version instead.

Try this, with the full prompts, online →

What readers said

The most considered reply this week came from a reader who thinks a lot about what makes an output good, and who has spent years worrying at the line between quality and what they called "handwriting": whether they rate output higher because it is genuinely better, or just because it is how they would have written it themselves. Decades of client work correlate the two, but not perfectly, and the only way to tell them apart is to experiment: leave a bit out, ship the team's version less edited, and watch what happens. That, they wrote, is exactly the judgement the essay describes, and it makes the work both better and more open-minded. Full reader reactions online →

I've also been looking, as usual, at what readers who engage with this email are posting on LinkedIn. Three voices stood out this week.

Phil Leslie, Chief Technology and Innovation Officer at Cornerstone Research, the litigation consulting firm, had the wisest word on the week's contradictory jobs data: nobody loves a non-result, but "we're getting a non-result because the treatment has barely been administered". Scaled agent-based AI has barely begun, so the honest answer on jobs is still: too early to know. He is just as calm about the money. At Cornerstone roughly 80% of AI spend comes from 10% of users, and when costs rise his first instinct is not an audit of the power users but "to learn from them". Aakash Gandhi, a partner at L.E.K. Consulting, the strategy firm, has watched enterprise buyers' question shift from which model is best to how much to depend on any one provider: "Frontier labs may be winning the benchmark race. But they may also be training enterprise buyers not to depend on them." And Deanna Adams, director of integrated marketing at Qualtrics, the experience-management software firm, borrows "seat time" from motorsport: the hands-on hours are what let you carry more speed out of the turns. Nobody frames a prompt or reviews a draft well on day one. More online →

A thousand small bargains

This is the longer version of the essay. The email carried a shorter cut.

What's on my mind

I sent an important email yesterday that I did not write. A language model drafted it, I read it carefully, changed nothing, and sent it. Earlier in the week I asked it for three ways to frame a problem and took the second, rather than reaching for a fourth of my own. I had it brief me on someone important before I met them, and I used what it gave me without reading the search results it used. Three handovers, each one on its face the kind the worriers warn about. The kind I used to worry about. But not one of them was a surrender. I had checked all three against my own sense of the problem and the shape of the answer. Where they turned on facts, I had squared those against what I already knew, not left them to the machine's own check. I judged them good enough for what they were, and any mistake in them would be mine. Not the machine's. Mine.

Rahim Hirji, whom I quoted here last month, has a book out on the 3rd of July. It is called SuperSkills, and it is thoughtful and well written. He names seven skills for the age of the machine: curiosity, change readiness, big-picture thinking, empathy, global adaptability, principled innovation and what he calls an augmented mindset. Beneath the list it asks the question that matters: as the machine does more of the thinking, who stays in charge? That is the right question, and Hirji asks it better than most. His warning is sharper than it first looks. The machine does not arrive as a single crisis, he says. It arrives as the handover you don't notice, the one that slips past while you feel busy and productive, a thousand small surrenders dressed as convenience. By that test my three were safe. I noticed all three.

I wanted to agree with the book's perspective. I am passionate about believing in people, and I have given speeches on the danger of brain rot, the slow softening that comes from letting a machine do your thinking until you forget how it was done. I spent 25 years with data and analytics arguing that the human has to stay in charge, long before the same lesson had to be learned again for AI. Back then the machine I worried about was a dashboard, and the worry has not changed. So I expected to nod the whole way through the book. But the more carefully I sat with it, the less I did. I agree with the question. I disagree with an easy answer to it. The reflexive reply, the one the title invites, is to hold the line and always stay human. I think that would be mostly an over-reaction.

Most of the handovers are not surrenders. They are careful bargains, and good ones. Letting the machine draft the email I would otherwise have laboured over makes the work quicker, and usually better. It is the whole reason I have called these tools an electric bike for the mind. Seeing it as the human against the machine, and wincing each time the machine wins a round, has the contest wrong. I now believe most rounds are yours to give away, gladly.

What looks like letting go is something subtler. A handover does hand the machine some of the thinking. It thinks the middle for you. But the parts that truly decide the outcome stay with you, and they climb a level: the start, what to ask and how to frame it; the end, whether it is good enough to put your name to; and the call between them, whether it needs to be better and how. David Autor, an economist at MIT, has spent years showing that each wave of technology takes the rule-bound work and leaves people the discretionary part, the judgement you cannot yet write down as a rule. The machine clears the desk of the rules. Your judgement climbs to meet what is left.

The fear names the wrong verb. Of check, edit and own, own is the one you cannot drop. Your name goes on the work, and the blame with it. The lawyer who filed the machine's invented citations still owned them, and the court made sure. Check is the verb you can drop, and the one most people quietly do. In a study I have written about before, people went along with wrong machine answers about eighty per cent of the time, and grew more sure of themselves as they did. They kept the ownership they could not lose. They skipped the check they could.

This is not authorship slipping away. It is the check going undone, while the ownership stays exactly where it was. The machine's polish hides it. Poor work used to look poor. Now it reads as finished before anyone has decided that it is.

So never blame the machine. Be ready, always, to take the blame yourself. This is the book's last and best skill, the augmented mindset, which in plain terms means staying the author of your own work. He is right about it, and one line stays with me: proof is the new speed limit. Speed is cheap now. The rare thing is being able to show your working, and stand behind it.

The floor is not to check every word yourself. When used well, the deep checking can be delegated, even to the machine, so long as it is independent and not the machine marking its own work, and a light read against your sense of the problem often covers the rest. The floor is to judge the work good enough for the stakes, to your standard and not a minimal one, before you put your name to it. This is where people come unstuck. Nearly nine in ten leaders tell BCG they lean on the machine's answer without stress-testing it. A senior leader at a global household brand told me this week why: their leadership team has been seen to prefer a fast plausible answer to a slow rigorous one, so 'nobody waits for the rigorous one'. That is the failure, and it is not the slow erosion the book fears. They never held the work to their own standard, nor had the machine check its own.

A check is worth only as much as its distance from the thing it checks. Ask the machine to check its own answer and it leans towards approving it, bad reasoning and bad facts alike. The fix is not to trust the self-check more, but to bring in a check with no stake in the draft. Best is a fresh instance of the machine, one that had no hand in writing it; it will argue with the work as the original never will. For a fact, add a source from outside, or something you already know. For a line of reasoning, make it argue the draft down from a clean start. The light read is not the check. It is what you do after an independent one, not instead of it.

On a few tasks, good enough is the wrong bar. Great is. You have spent real effort on everything else too. Check, edit and own is no small feat, only a higher-level one. But on these few you go further. Be sure to argue with the draft, push past where the machine stopped, and reach for the fourth option. That is the part the machine cannot do for you: deciding which of its competent answers is the right one, and then making it better. This is where you spend your energy. There are fewer of these than you fear.

So I do not think it is the human against the machine, nor a mind rotting against a mind saved. Hand over much of the work, check and edit it lightly, and always exercise the ownership you cannot escape. Then save your deepest effort for the few where being great makes a real difference over being good. Never be careless. Always be good. Sometimes be great. Surrendering most of the many is what funds the few.

So pre-order the book this week. I hope you do. He draws that line clean on purpose, for the reader who is frightened for their job and needs somewhere firm to stand. I am drawing a blurry one, for you, who are mostly past the fear. When it lands, read it for its second half especially, the part you may be tempted to skim, where Hirji admits the skills pull against each other and need a system to hold them together. Then take your own week. For most of it, let the machine do the work, have it checked, and own the result. For the few that matter, reach for the fourth option, and do not stop early. Hirji ends with a short daily log, Hand, Head, Hours: what stayed yours, what you caught, where the freed time went. Treat it as more than scorekeeping. The time you save on the many does not move to the few by itself; left alone it flows back into more of the many, the same work done a little faster. The log is what moves the saved hour to where it counts. Without it the bargain still saves you time, and funds nothing. I keep a blunter version. If you reach the end of a day and cannot point to a single hour where you made something better than the machine could on its own, you have drifted too far. The rest of the time, let it carry you.

I have had a version of this debate with many of you over the last few years, and most of you disagree with me. I hope this edition, and the book behind it, push your thinking. They pushed mine. Which way it pushes you, towards me or away, I do not mind, so long as it moves you. And I would love to know where, and how.

Three things worth knowing

1. Claude has stopped opening in a window and started living in the team's Slack.

This week Anthropic launched Claude Tag: you @-mention it in a Slack channel and it works as a persistent teammate, with its own identity, scoped access, memory across weeks, and the run of a stalled task for days at a time. Anthropic says 65% of its internal product code now gets written this way. Andrej Karpathy, who co-founded OpenAI, mirrored a framing I wrote about earlier this month, calling it the third major redesign of how we use these models: first a website, then an app, now a persistent worker. It's the first frontier product where the interface is openly about managing an AI employee. The pattern will spread; the human problems, supervision, accountability, headcount maths, arrive with it.

2. Procter and Gamble actually ran the AI-alone-versus-human-plus-AI test, and human-plus-AI still won.

On stage at the Lions Insight Summit in Cannes, Kirti Singh, P&G's Chief Analytics, Insights and Media Officer, told me the company has run the experiment most leaders only theorise about. They let AI alone make some brand-building choices, measured the outcomes against humans working with AI, and the combination won. "Today it is not better," he said carefully, "than what human plus AI can do." So the company's settled position is human-plus-AI. This is not a sceptic hedging: he says his teams under-use AI and pushes them to use more. A measured floor, not a slogan.

3. Norway just banned generative AI for six-to-thirteen-year-olds.

From late August 2026, Norway imposes a near-total ban on generative AI for children aged six to thirteen, with supervised use only for fourteen-to-sixteen-year-olds. The prime minister's argument is that AI lets young children skip the essential steps in learning to read, write and do maths. The same government is funding a return of physical books and already banned school smartphones in 2024. Luiza Jarovsky, a tech-policy writer, backed the move. Most of the AI conversation this year is about going faster. A government has decided that for one age group the danger is precisely the speed: skipping the hard steps that make a child's judgement worth anything later.

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1. Make the model cite its sources, so a check is one click, not a re-read.

A documentary production team I worked with this week showed me the cleanest version of this. They were using a Claude-based transcript tool I built to mine hours of footage, and the editor insisted every quote come back tagged with its time code. The reason was simple: the AI's transcription could be slightly wrong, but the time code never is. When a summary looked off, the editor jumped straight to the original clip and checked what was actually said rather than trusting the paraphrase. Time codes, page numbers, line references all do the same job. The pointer costs nothing to ask for, and it lets you own the output on a light read, then jump straight to the source on the one part that has to be right.

2. Build an AI version of the person before a high-stakes meeting, then mine its blind spots.

Before interviewing GitHub's chief operating officer, the writer Mike Taylor, writing in Every, built an AI persona of the COO from everything publicly known about them and ran his planned questions past it first. The simulation's misses were the useful part. Where the persona's answers went thin or vague was exactly where public information ran out, so Taylor spent the real conversation on what no model could already know. Most people use AI to generate questions. This inverts it: ten minutes rehearsing in front of an AI version of the person tells you which questions aren't worth their time, and points you at the ones that are.

3. Stop asking your inbox vague questions. Label first, then ask one scoped thing.

The AI assistant now sitting inside most email gives a vague answer when you ask it a vague question across the whole inbox. Narrow it. Label the threads that belong to one project or client, then ask something specific: "what am I waiting on from Sam about the website", or "the action items under the Clients label that I never replied to". It comes back with who you owe, the questions you left unanswered, and what people asked you to send. Then ask it to turn those into a checklist, and the list drops straight into your week.

Email version. Version with copyable prompts below →

What readers said

The sharpest pushback came from a reader who works with founder-led businesses on AI adoption. The trouble, they argued, is that "the people who've been doing the work for twenty years are often the least able to say what the work actually is." Not because they don't know. Because they've stopped needing to explain it. Another reader, who'd been feeding pre-loaded AI research to a team, noticed colleagues often ignored it and re-ran their own. Their preference for their own style, this reader wondered, may quietly beat real differences in expertise. A third reader said the essay had pushed them to use Claude less, not more: the output still sounds generic, and they want to course-correct. Full reader reactions online →

This week the essay argues most AI handovers are good bargains rather than surrenders, so here's a look at readers drawing the same line on LinkedIn: working out which part of the job stays theirs.

Rahim Hirji, whose book SuperSkills inspired this week's essay (out next week), used a shortbread biscuit his family calls nankhatai to name what a machine cannot copy: "the hands that made it, or the reason anyone bothered." My essay agrees those hands are what matter, then argues they are exactly the part a good bargain leaves with you. Norma Garcia, CEO of NRJ Media Group, a multimedia storytelling venture she co-founded, put it plainly in her CineEurope keynote in Barcelona: "AI isn't the story. Our response to it is." Christina Habib, Chief Insights Officer for Beauty and Wellbeing at Unilever, the consumer goods group, has the number underneath it: 67% of AI's impact is cultural, not technical. More online →

Average by default

What's on my mind

I've helped hundreds of senior people set up their AI. Almost none had told it who they were. Not their job title, their company or their sector. Those are easy to add. The hard part is the more useful part: how they think, what they notice, what they care about, how they decide, what good looks like to them.

The personalisation box sits empty, account after account. I used to think people just hadn't got round to it, or were too busy. It's more interesting than that. Most people can describe their role. Many can't describe their judgement. And if you don't tell the model what makes your judgement specific, it has one assumption left when it works out how to answer you. You're average.

This used to be a hunch. It's now measured. Researchers at the University of Maryland tested it this month. Ask several leading models to write on the same question and they land on a genuinely different main argument about three per cent of the time. People do it 65 per cent of the time. The models even open the same way and move quickly to the same kind of recommendation.

A second study analysed 2,200 college-admissions essays, some written by people and some by a model. Each new human essay added more fresh ideas than each new machine one, and the gap widened the more essays you stacked up. The effect survived every attempt to prompt or tune it away. A model can sharpen one person's writing and flatten everyone's at the same time. Better alone, narrower together.

Ask a model to tidy your draft without telling it who you are and it nudges your phrasing towards what an average reader would want. It reads better and it's less yours. If you only check that it reads well, you miss the second half of that sentence.

I saw what fixing this is worth when I went through the full process with Liz Villani, founder of Sounds Like Me. It starts by asking you to set down your values, your character and the way you operate, in your own words. That was harder than it sounds. The questions were simple. The hard part wasn't describing my work, but the person doing it.

A CV records what you've done. An identity layer captures how you think, decide and show up. That second thing, your operating context, is what holds the model off the average. Without it the model optimises for plausible; with it, the model can be useful to this person, in this role, making this decision. Sounds Like Me uses that layer to start the output closer to you than to the mean. Its Exec AI Twin takes that further for leadership work.

This sharpens something I've been saying for three and a half years. The instructions and context you load do real work. Two people using the identical model get back unrecognisably different work, depending on what they bring to it. In recent research on roughly 5,000 consultant exchanges, the people who got most pushed their own context in; the people who got least pulled finished answers out. The first group's work diverged and improved. The second converged.

This is different from what the platforms now sell as personalisation. In January 2026 Google launched Personal Intelligence, which lets Gemini reason across your Gmail, photos, YouTube and search history to get to know you over time. That's personalisation by inference, assembled from your data in the background. It can guess you like the outdoors or travel with children. It can't infer your judgement. That you have to write down.

Why do the boxes stay empty? Most people have never written down who they are, how they think or what makes their judgement theirs. The work that makes a machine sound like you starts as the work of knowing what you sound like.

It depends what you're using it for. For writing, a short profile gets you a long way. For thinking, you need more than a house style. If you use it to argue with or to test a decision, you need a sparring partner that understands your reasoning.

Everyone reaches the same models. Your rival's tool is already your tool. The edge is the judgement, taste and self-knowledge you bring to it. That point is older than this essay. In 2023 Harvard Business Review argued that judgement would be the real competitive advantage. It's sharper now the tools have levelled. And unlike every other part of your setup, your self-knowledge moves with you when you switch systems.

The empty box is the most useful question the whole product asks: who are you, and how do you want the machine to work with you? Answer it, and average stops being the default.

Three things worth knowing

1. A Munich court told Google that "may contain errors" is no defence.

The Munich Regional Court ruled Google liable for false claims made by its AI Overview, which had wrongly tied two German publishers to fraud. The judges held that an AI summary generates fresh, substantive statements rather than just curating sources, and that the small-print "may contain errors" footer does not transfer the liability back to the user. Google must remove the answers and pay 80% of the costs. Every major AI provider leans on the same disclaimer to cover confidently wrong answers. A court has now said it does not work. If your organisation publishes AI output to customers, regulators or staff, the footer that has been doing the legal lifting may not protect you next time.

2. Commercially available AI flagged early breast cancer six years before clinical diagnosis.

Researchers at the Karolinska Institute, the Swedish medical university, published a study in Radiology showing that off-the-shelf AI systems flagged early warning signs of breast cancer in roughly 20% of patients a full six years before a clinical diagnosis, at 90% specificity, rising to around 40% at two years out. The analysis ran across 88,963 mammograms from more than 31,000 patients. Three of the tools tested are already commercially available. Pattern recognition at scale is what AI does best. Here it buys years of warning on a disease where early detection saves lives, and the bottleneck is no longer capability. It is deployment, trust and who owns the answer.

3. Anthropic studied 400,000 coding sessions: the tool is levelling people, and moving the real gap up to firms.

People still make about seven in ten of the decisions on what to do; the agent makes eight in ten on how to do it.

Anthropic analysed 400,000 sessions of its coding agent across roughly 235,000 people. Engineers did well, and so, surprisingly, did almost everyone else: across the ten largest professions, success rates landed within seven points of professional engineers, and managers came out ahead. An accountant who had never written a line of Python, but knew which rule a month-end reconciliation had to enforce, was rated an expert; a senior engineer on an unfamiliar task was not. What carries a session is understanding the problem, not the craft. So the gap between people is closing. The part everyone skips is that it has moved up a level rather than vanished, from between people to between firms. The expanded version is online →

Twelve bits that didn't fit online →

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Every tip below, plus all the ones from past editions, with copy-paste prompts live online →

1. Tell the model what's at stake before you ask the question.

In a coaching session this week, a senior leader asked Claude to advise on a question relating to government policy. The first answer was confident, generic, and thin: the sort of thing a busy colleague would shrug at you across a desk. Adding one sentence changed the work. "The answer is critical. Provide authoritative sources." The model switched register, went to the primary regulatory documents, quoted the relevant sections, and linked each claim back so the human could check it. Nothing about the question had changed. The model simply hadn't known the stakes.

2. Stamp every AI output "raw, not yet checked" until you've taken ownership.

Mentoring Ethan, Steadman's placement-year researcher running an interview project, this advice landed as a rule for any document created by AI: it's either raw AI output, or it's something you've checked, edited, and will stand behind. Set your tool to auto-stamp every output with a line saying "raw AI output, not yet checked, edited and owned," and leave it there. Removing the stamp by hand becomes the deliberate act of taking ownership. Anyone who picks up the file later, or has it forwarded to them, knows exactly which of the two things they're holding.

3. Book a meeting with yourself, hit transcribe, ramble for fifteen minutes.

A senior advisor told me this week that their real value sits as strong points of view in their head, none of it written down anywhere. The fix: put a meeting in your own diary, hit transcribe, put your feet on the desk, look out of the window, and talk. Don't try to be structured. Change your mind, go down rabbit holes, tell stories. The transcript becomes a thesis document you can feed any model so future answers come back already loaded with your frame, your caveats, your taste.

Email version. Version with copyable prompts below →

What readers said

One reader reframed the question itself. The issue isn't incentive structure: it's that people have vastly different intrinsic comfort with spending company money, set in childhood not by company values. The cautious won't spend; the relaxed won't judge. That, this reader argued, is the variable to manage. A senior AI lead pushed harder on the practical end: there is no decent model yet for measuring cost against benefit, partly because nobody wants to audit usage and look paternalistic. How do you do it well? An associate building a market model under a four-day deadline supplied a lived example: heavy Opus reliance, weaker at building from scratch but "genuinely impressive" at structuring and wiring. The prompting drove cost up. It paid for itself anyway. Full reader reactions online →

I've also been reading what some of you post on LinkedIn each week, and picking out a thread or two.

What readers were saying on LinkedIn this week, mostly circling the same point this edition does: the human is the part that doesn't come in the box. John Middlemiss, founder of Empact, a consultancy on performance, AI and customer trust, came back from London Tech Week with a warning that "scaling AI is not the same as creating value". Michael Nevski, Director of Global Insights at Visa, the payments company, on what lasts when the market shifts weekly: composure and judgement, "clients want the hole in the wall, not the drill". And Fiona Eastwood, Global CEO of Merlin Entertainments, on why AI makes communication, judgement and emotional intelligence more valuable, not less. The edge keeps turning out to be the part nobody wrote down. More online →

The expanded version of this week's third news item, on Anthropic's study of 400,000 Claude Code sessions. The email carries the short story; this is the longer argument it points to.

Anthropic published an analysis this week of 400,000 sessions of its coding agent, drawn from roughly 235,000 people over seven months. The striking finding was not that software engineers did well. It was that almost everyone else did too.

Across the ten largest professions in the data, success rates landed within seven points of professional engineers. Managers came out slightly ahead. A coding background, the thing the craft was built on, is mattering less and less. What carried a session was whether the person understood the problem in front of them. The study describes an accountant who had never written a line of Python but knew exactly which rules a month-end reconciliation had to enforce, and caught the one the agent got wrong. That person was rated an expert. A senior engineer poking at an unfamiliar task was not.

This is the surprising half of the report, and the half that holds up best. Anthropic also rates each user's apparent expertise from the way they prompt, and finds that more expert users succeed more often. Treat that result with care: the rating and the success score are read from the same transcript, so a precise, self-correcting session tends to be marked as both expert and successful by design. The occupation finding does not have that problem. It is read from the files and the language in a session, not from a judgment about how good the prompts were, and it points one way. The tool is levelling the people who use it.

More expert prompters succeed more often, though expertise and success are read from the same session, so treat the link with care.

The shape of the work explains why. In a typical session the person makes about seven in ten of the decisions about what to do, and the agent makes about eight in ten of the decisions about how to do it. Each prompt sets off around ten actions and a couple of thousand words of work from the agent. The judgment stayed human. The typing did not. What is left for the person is direction: knowing what to ask for, and whether the thing that came back is right.

The judgement stayed human and the typing did not: direction is the person's job, execution the agent's.

The work itself is climbing the same ladder. Over the seven months, the share of sessions spent fixing broken code fell by nearly half. Operating software, analysing data and writing documents all grew. The agent is being handed larger, more finished jobs, and the typical task got about a quarter more valuable along the way. People are not using these tools to type faster. They are using them to take on work they could not previously reach.

The more expert the user, the more the agent gets done per prompt, so the tool pays back those who already know what to ask.

None of this is new in kind. It extends a pattern that now runs across two generations of the technology. When Harvard and the Boston Consulting Group tested an earlier model on consultants in 2023, the weakest performers improved by 43% and the strongest by 17%. In a study of more than 5,000 customer-support agents, the least skilled gained about a third while the best gained almost nothing. In professional writing trials, the lower half of performers closed most of the distance on the top half. The tool keeps narrowing the gap between strong and weak.

So the gap between individuals is shrinking. That is the good news, and the part everyone repeats.

Here is the part they skip. The gap has not closed. It has moved up a level, from between people to between firms.

The time saved is real. Whether it shows up anywhere is a different question, and that answer sits with the firm. The tool has no say in it. Put an agent on top of a process you have not changed and the saving leaks straight back out. A task that took a day takes an hour, and the seven hours that come free refill with work of the same old shape. Nothing accumulates. This is benefit absorption, and it is the normal case, not the exception.

The reason is structural. The processes most organisations run were designed around a constraint the agent has just removed. Leave them in place and you waste most of what the tool offers. Inserting the technology into an old workflow tends to return a fifth to two fifths. Rebuilding the workflow around it returns far more, often a multiple of that. The gap between those two numbers is the whole prize, and collecting it is slow, political, organisational work. It is nobody's favourite job.

The economists are starting to see the same split. A 2026 paper puts it plainly: the technology levels people at the level of the task, while investment and returns pool in a handful of firms at the level of the market. The machine that narrows the gap between your people widens the gap between you and your competitor. Once the question of whether your staff can use these tools answers itself, the only durable advantage left is how the firm is built. An organisation is far harder to copy than a tool, which is exactly why the lead it confers lasts.

I sell this for a living, and it is still hard. Knowing the hour is there is not the same as having rebuilt the day to keep it. Plenty of firms, some of them run by people reading this, have bought the tools and left the work untouched. The honest test is not how much your people like the agent. It is whether last quarter's saved time turned into anything you can point to.

Anthropic ends its report with the signal worth watching. If the returns to expertise start to fall, it means the models are beginning to supply the judgment that people currently bring, and the advantage spreads wider. If workers outside software keep succeeding at technical work, it means software is becoming part of every job rather than the property of one profession. Both trends would push more value through the same door: the firm that has organised itself to catch it.

The individual question is answering itself. The firm question is the one still open. Take one process this month, rebuild it around the agent, and measure the freed-up time honestly: did it become more output, or did it quietly vanish. The divide that widens next year runs between the firms that did that and the firms that spent the year watching the wrong gap.

Ride the bike

What's on my mind

Note: this is a longer version of the essay than the one sent in the email.

On Tuesday Anthropic, the AI lab behind Claude, released the most capable model yet, at exactly double its predecessor's list price. Then, early this morning, the US government pulled it on national-security grounds and Anthropic suspended it for everyone, which is a story in itself (see online). Developers using GitHub Copilot, Microsoft's coding assistant, revolted when pay-per-use billing arrived this month: one developer's bill went from a flat $50 a month towards $3,000. The bill has become the story.

I made the case last month that flat-rate pricing is dying. The pattern underneath is a split: any given level of intelligence gets cheaper every year (on Monday Google cut its cheapest paid plan to $4.99), yet the bill rises anyway, because delegated work grows faster than unit prices fall and the hardest work migrates to the dearest model. Expect both halves to hold: a better model above at a higher price, yesterday's below at a lower one. This week, the practical question, because most organisations manage these bills exactly backwards.

Flat-rate pricing is incredible value for money against API pricing at current rates: a $200-a-month plan can absorb thousands of dollars of API-equivalent usage.

The hardest work migrates to the dearest model: business AI spend is shifting to the priciest frontier model even as its price doubles.

Two instincts are doing the rounds. The first declares tokens free and celebrates whoever burns the most. The second is the cap: a meter and a limit for everyone. Both are wrong, for the same reason: both manage the number instead of the judgement.

I did the maths this week, at published list prices, on two jobs like those most of us do all the time. Researching something (dozens of searches, some analysis, then a written report) costs about $1 with a mid-tier workhorse model, $1.60 with last week's best and $3.90 with this week's. Reading four meeting transcripts and a folder of documents, then drafting a document from them: $1.05, $1.75 and $4.60 on the same ladder. Do either job yourself and it's two to eight hours: at a manager's loaded cost, $500 to $2,000 of human time against $5 of machine. The entire gap between the cheapest sensible model and the dearest buys 40 to 50 seconds of that manager's day. If the better answer saves them even a minute of checking, redrafting or second-guessing, it has paid for itself. That ratio is the thing to manage.

So here's what I'd actually do.

First, set a floor. I regularly coach people with full access to great models whose entire history is one conversation. One, ever. In mid-2026, if someone isn't sending five prompts a day, they aren't yet trying. Celebrate whoever clears it, a tough conversation with whoever doesn't. (One precondition: everyone gets the same tools first. A floor over rationed access is unfair before it's useful.) Above the floor, for heaven's sake stop counting. The easy way to top a usage chart is to summarise everything that crosses your desk and learn nothing; a prize for the biggest burner is a different level of silly. Just make sure they ride the bike. It's the only way to learn.

Second, give everyone a 'delegation budget' and run it the way you run their other expenses: trust, plus the occasional question. There have only ever been three reasons to fund delegation: the work comes back better, it comes back quicker, or whoever handed it over is happier. So manage the budget in three zones. Small sums: don't ask. The only failure down there is using too little, and the floor already polices that. Real money: a monthly answer to three questions, a simple form and five minutes of the one-to-one. Better: what improved, what exists now that didn't? Quicker: which hours came back, and what did you do with them? Happier: what pain went away? Buying happiness is fine if you know that's what you bought. Serious money: a proper experiment, with hypothesis, control group, blinded review, the lot.

A cap does none of this. (Keep a circuit-breaker against runaway automations. That's wiring.) Nobody would expense a million pencils when they needed just one; if they did, a tough conversation would surely follow. The technology for managing spend is hundreds of years old: norms, visibility, the occasional tough conversation. All a cap adds is a ceiling below what the tools make possible, and the person absorbs the gap between what's expected of them and what's allowed. It punishes exactly the people the floor was built to create.

The meter is already doing damage at the top, even without a cap. One of the strongest users I know often picks the weaker model for their most important work. Their seniors have said spend whatever it takes; the meter still makes them flinch. It's a false economy: the cheap model's mistakes read as fluently as its right answers, so each costs real time to find and unpick. They've moved some work to a rival tool purely because it shows no number, and a colleague routes the routine tasks to flat-rate subscription apps for the same comfort. A meter doesn't cut consumption; it moves it to wherever it can't be seen, the one place nothing can be learned from it. The number itself is innocent: shown slides at $2 each, one builder began asking which internal drafts deserved to be slides at all. A price plus a budget informs judgement; a price plus a cap replaces it.

What's the right budget? I don't know yet. Nobody does. Ramp, a spending platform, finds the median firm spends $11 a month per employee; the top 1% spend $7,500, approaching half an engineer's pay. Both numbers are rising. Jellyfish, which measures engineering work, finds the heaviest users it tracks ship roughly twice as much as typical ones, on ten times the tokens. The return per token falls. But ten times the tokens is about $700 a month, and doubling an engineer's output is worth twenty times that. So study your heaviest spenders with curiosity rather than suspicion, and ask what they're buying. I sat down with two of the heaviest I know this week. Neither is bingeing; both run portfolios: the dearest model where their own judgement is the bottleneck, fixed-price tools for the routine. Their answers should drive your budget policy.

AI spend per employee is climbing at every level, but the top 1% already spend around $7,500 a month against a median of $11.

We spent a century buying outside help in big lumps: a hire by the year, a contractor for a few thousand, an agency for tens of thousands. Inside a team you could hand someone an hour's task; buying one in was never worth the finding and the briefing. That unit has now collapsed to pennies, ten pounds, a hundred. The strongest users already convert between the currencies. One proposed the swap unprompted this week: two days less of a junior colleague allocated to their project in exchange for being able to use the best model themselves throughout. Another finds they no longer need the allocated junior at all. At the keenest firms the AI bill per person is already salary money; at yours it's probably still pennies. The next management skill is buying intelligence by the penny, best learned while mistakes are cheap. Start riding.

Three things worth knowing

1. One activist letter froze a board's AI workspace overnight. The doctors are next.

I helped a hundred non-executive directors with AI this week. One told me about a board whose workspace went dark the morning an activist investor wrote in asking for its contents to be made discoverable. Two-thirds of directors now use AI for board work; barely a quarter of executives call their board highly fluent, per the Conference Board's April survey. The Medical Protection Society, which defends clinicians against negligence claims, warned this week that doctors and the NHS could be sued over mistakes made by AI tools, with the clinician left as the "liability sink" for the technology's errors. Its report wants AI reclassified as a product under the Consumer Protection Act 1987, so liability flows to developers. The frontier of AI governance is moving from "can the data leak" to "who owns the answer when the model is wrong". Our policy, that every output is checked, edited and owned (CEO'd) by a human, answers that question. The letters will keep coming, and the writs will follow. The only good answer to both is a name.

2. Meta built a "second brain" that 63,000 staff installed in three months. It started with one person.

Meta's analytics team reports that an internal AI tool one of its data scientists started has now been installed by 63,000 employees, a number reached in three months. No top-down mandate, no transformation programme. One person built something useful and the rest of the company found it. What are you doing to encourage and enable this at your firm?

3. When cheap models do make sense.

Ethan Mollick, a Wharton professor, argues for hierarchies in which smart models supervise cheap ones: the smart one checks the plan, the cheap one does the volume. Right for machine pipelines running thousands of low-stakes calls. For your own judgement work, this week's essay argues that you should buy the best; the maths of time saved is the reason.

Seven bits that didn't fit online →

Try this

1. Ask the AI to write the marking scheme before it writes the answer.

Ben Yoskovitz, a non-developer who ships production software with AI, wrote this week about how he stops the model declaring something done before it's actually done. Before any non-trivial task, he asks the AI to write three to five specific pass-or-fail checks for the work. He approves them. The AI then does the job, grades itself against each check, cites the evidence, and stops the moment one fails. The discipline costs ninety seconds and saves the long loop of "looks good" followed by "wait, no it isn't".

2. Plan in one session, then start a fresh one to build.

Tiago Forte, the productivity author, wrote this week about treating the first AI session as planning-only when the goal is a concrete document or deck. Work out the brief in chat one. Don't let the model start drafting. Then open chat two, paste the brief, and build from there. Three things happen. Long sessions accumulate irrelevant context and quietly degrade, so a clean start gives you a sharper model. The deliberate stop forces you to ask whether the brief actually holds together. And if the second session crashes, you've still got the brief.

3. Keep your AI project lean.

In a training I ran, someone asked why their Claude project had got slower over a few weeks. They'd been adding reference files to it. The model reads everything in a project on every request, and once it's getting crowded the answers go duller and the replies take longer. Two habits help. Book fifteen minutes on a Friday to prune, the way you'd tidy a desk. And never leave fat PDFs in there. Ask the model to convert each one to plain text first. PDFs burn far more of the model's time and reading capacity than the same content as text, and the conversion takes a minute.

Email version. Version with copyable prompts below →

What readers said

A senior leader who hires dozens of graduates a year sharpened the tension. If junior work disappears, where does the next generation of senior judgement come from? Training, this reader argued, must become more deliberate, not less. People may need to go "back to the stone age" before being handed the tools of modern warfare. A partner thinking about what cheaper expert work does to demand pushed it further: work itself is changing, so the question is which work belongs to the machine and which belongs to the human. A reader in music offered a longer view: in the 1970s, directors in their late twenties were common, but the pyramid has since inverted. The awkward part: is "sitting on Claude in suburbia" really the best use of one's early twenties? Full reader reactions online →

And a request: Ethan's first project is researching the impact of AI on graduate hiring, for a report he'll publish. If you have insights or data worth including, would you like to contribute? Just reply and I'll connect you.

The community's been busy on LinkedIn too, and the thread this week runs close to the essay: the tool's the cheap part, the judgement around it is the job. Maani Safa, CEO of Poppins Agency, an innovation-led creative shop, listed ten AI tools most people haven't tried and then landed the point: "The advantage was never just the tool. It's knowing which one to reach for, and having the taste to use it well." Mike White of Lively, a marketing agency, who advises mid-market CEOs on growth, says the smartest operators he knows have stopped describing AI as a system and started describing it as a worker: set up properly, owned by someone, expected to account for itself. And Henry Coutinho-Mason, the futurist behind The Future Normal, a book on near-future trends, would point it at the messy middle of innovation: it won't give you the spark, it can't do the final polish, but it'll flesh out more early ideas than you ever had time for. More online →

The open door

What's on my mind

Note: this is a longer version of the essay than the one sent in the email.

For years I've stood in front of rooms and told them the same thing. New graduates on their first morning. Hiring managers by the hundred. Boards. The grunt work has moved to the machine, I say. The work a junior used to do is now done in seconds. But the work that's left is bigger, so keep hiring young people and train them to do it. With these tools they can do more than any graduate before them. Better work, quicker.

I had never once done it myself.

Until Monday. His name is Ethan, and he's working for me for his placement year.

Plenty of people tell me I'm wrong. If a senior with these tools can do the work, why carry a graduate at all? It's a fair question. I have a view. But there are two ways to settle a thing like that. Pontificate, or run the experiment. I've always favoured the experiment. (Aged eight, I ran one to find out whether Father Christmas was real. I won't reveal the result, out of respect for any reader yet to test it for themselves.) Ethan is the experiment.

They have a point. Firms don't need graduates; they can hire people trained at someone else's expense. And here's the awkward part of my own pitch. A graduate today is more capable than any before, and less needed than any before. Both are true. They can do more, but so can I, without them. So if I hire one, can it be for the work?

I think it can. I also think the gloom around the young is wrong. The headlines have them anxious and replaceable, their first jobs stripped of anything that mattered. I have four hypotheses against all that, and a year to test them.

History is on the side of optimism here. Most of the jobs people do today did not exist in 1940. Technology keeps creating work nobody could have named in advance, and the people who end up doing it are usually the ones who came up alongside the new tools. That is the graduate.

The first is that a graduate is still worth their keep. I've just argued they might not be. But the machine hasn't removed every cost. Whatever it produces still has to be checked, edited and owned. Surely there are plenty of tasks not worth my time to do that, even when the machine can do them in seconds? A graduate can take them on. The machine does the grunt now; what's left is the checking, and the judgement of whether it got the grunt right.

The second is that the work is worth wanting. A graduate I read about recently put it starkly. All he does now, he said, is manage a machine. He's right that it's the work, for most of us. He's wrong that it's small. Managing a machine sounds dystopian and diminished. It is neither. It takes judgement, and the reps that build that judgement didn't vanish with the grunt. They moved. I manage three to six agents most days, and I like it. It's good, thoughtful, hard work. My human team has grown, not shrunk, since the agents arrived, and I still like managing the people, too.

The third is that the work builds them, and fast. The grunt that once stood between a graduate and real responsibility has gone, so a graduate can learn to manage years before the old path would have allowed. Whether a single year of it builds the judgement their elders took much longer to acquire is the thing I most want to find out. I mean to prove the new reps beat the old ones.

The fourth is about serendipity. Richard Hamming, the mathematician, told a story about office doors. The people who worked with the door closed got more done. They were more productive this week and next. But as the years went by, he noticed, they tended to be working on slightly the wrong problems. The ones who left the door open lost time to interruption. They also picked up the clues about what was worth working on at all. The open door was never only a cost. It let useful information in.

Helen Field, a people and transformation leader I work closely with, put the sharper version to me. A graduate arrives with an elastic mind, unattached to the old ways. In most organisations they are already more fluent in these tools than the team that hired them. In a settled world that's a nice-to-have. In a disrupted one it's a differentiated starting point, an asset rather than a cost. I've spent twenty-five years learning how things are done. That's exactly the training that stops me seeing how they might be done instead.

So hiring Ethan is partly me keeping my own door open. He'll cost me time I could otherwise spend on the work in front of me. That is the price of not drifting, and of not calcifying. Those are my four hypotheses. I can't yet tell you which is the real one, or whether it's all of them. Let's find out.

There's another side to this door, though, and I should be honest about it. Ethan is here because his father is an old friend of mine. The front door into work has rarely been harder to open. Two of his friends found jobs only because a parent knew someone. Another, with a master's degree, is working in a bike shop, because he didn't. The few junior places left are handed out more and more by connection and less and less by merit.

Helen made one other point, and made me realise I'd resisted it, because I've been trying to think this through on logic alone. There is a moral case that shouldn't be left out. If those of us who can still open a door choose not to, the pipeline for a generation closes quietly, on our watch. Trying to win on logic without saying that out loud feels like a failure in itself. So I'll say it.

So I'm doing this on purpose, in the open, and I mean to find out what comes through, in both directions. Over the year I'll let Ethan tell you how the work feels for him, what his generation makes of it and where he thinks I'm wrong.

When I put the experiment to him, he laughed. 'A lot of weight on my back,' he said. 'I'm representing everyone.' No pressure, Ethan.

Two things are worth more than they look. An open door, and a hypothesis you're willing to test rather than assume. I have a few firm ones about young people, and I'm about to learn if I'm right or wrong on them. What are yours? And what are you doing to keep your door open and put them to the test?

Three things worth knowing

1. The CEO of a 350,000-person IT services firm says AI is hollowing out the middle, not the bottom.

Ravi Kumar, chief executive of Cognizant, the listed IT services firm, told Fortune's COO Summit on 1st June that his company hired 20,000 entry-level graduates last year and expects to hire more in 2026, with new "Frontier Business Operator" and "Frontier Certified Engineer" roles defining what AI-era work looks like. He called the job-extinction talk "fearmongering" and argued that AI thins middle management while entry-level and leadership roles persist. It's a direct counter to the consensus that entry-level work vanishes first, including the US Bureau of Labor Statistics data Edition 14 leaned on.

2. The machine is writing the code now, and the gains are pooling at the top.

Tobi Lütke, Shopify's founder, says one in eight pull requests merged at the company are now written by River, its in-house agent, not an engineer. Anthropic's own engineers ship roughly eight times the code per person they did before 2025. Cursor's developer report shows the spread widening: the top developers are pulling far ahead of the median, the leverage going to the few who can direct the tools well. And OpenAI's Codex has passed five million weekly users, with non-developer adoption growing three times faster than developer adoption. The grunt of writing code is moving to the machine, the output is multiplying, and the reward is concentrating in the people who know what to ask of it.

Anthropic's own engineers now ship roughly eight times the code per person they did before 2025.

The gap between developers is widening: the top few are pulling far ahead of the median as leverage flows to those who direct the tools well.

3. Capability is outrunning even the best forecasters.

The Forecasting Research Institute asked expert forecasters and superforecasters how long a task a model would reliably finish by the end of 2026. The measure comes from METR, an AI evaluation lab, and when the survey launched it stood at about an hour and a half. All three groups put the end-of-2026 figure between three and four hours. Then, while the survey was still running, a frontier model in preview reached three hours and six minutes on METR's benchmark, already inside the range they'd picked for the end of the year. The forecast was overtaken before they'd finished making it.

Matthew Prince, who runs Cloudflare, made the same miss in public this week. Bots have passed humans in web traffic for the first time, he said, years ahead of his own forecast. As recently as March he'd put the crossover at late 2027. Much of that is scraping bots, not agents answering questions in the moment, so the figure is softer than it sounds. The direction holds.

I've given myself a year to find out whether I'm right about Ethan. On this week's evidence, that's a long time to be sure of anything.

The length of task an AI can finish on its own keeps doubling, from seconds a couple of years ago to hours now.

Seven bits that didn't fit online →

Try this

1. Ask the AI to orient itself in your folder before you ask it anything else.

Setting up a Claude Cowork project for a documentary filmmaker brand-new to the tool this week, the first prompt I typed wasn't about the work. It was: "Read the files and sub-folders, write yourself a little set of instructions for future chats. Write yourself a navigation guide." Claude spent a few minutes exploring, then saved itself three memory files: a project overview, a folder guide, and a profile of the user. Every later chat in that project started smarter, because it could re-read its own notes.

2. Don't say "always allow" on the verbs you can't undo.

In a coaching conversation this week about agent permissions, the pattern was familiar. Three prompts in, most people click "always allow" on everything just to stop the interruption. The discipline is to sort the verbs first. Reading, listing, searching: leave on auto. Deleting, sending, posting, spending: keep asking every time. Sorting the verbs first is what keeps one careless click from emptying a folder, sending an email you can't unsend, or running up a charge you didn't mean. Applies to any tool that asks for permission as it works.

3. Talk longer on the call, so the AI can work for hours after.

On a client call this week, the brief changed mid-conversation. The plan we'd opened with wasn't the plan we were leaving with. I deliberately used more words than I otherwise would, narrating the bridge between the old direction and the new one. The over-explaining was really for the AI: context I was laying down for it to pick up later, once the transcript was in the project. The transcript plus "go" did the work. The next time you're on a call that's being transcribed, elaborate a little more than you otherwise would and narrate the why, not just the what.

Email version. Version with copyable prompts below →

What readers said

One reader, Conor, sent a line that stuck with me: "the organisations that win won't have better AI, they'll have better questions." The gap between using the tools and knowing what to ask of them, he wrote, is widening faster than most leaders realise, and that is where the value is pooling. What unsettles him is how many businesses are automating before they have worked out what is actually worth keeping. Last week I asked what you can see coming over the next five years, and a lot of you wrote back with super thoughtful answers. I am holding them. They belong in the "what's next" piece I am working on for a future week with Rob Wild. So Conor's is the only letter this week. Keep the predictions coming, though! Full reader reactions online →

The community's been thinking out loud on LinkedIn, too, and a thread runs straight through this week's essay: when the machine does more, what stays ours? Phil Leslie, Chief Technology and Innovation Officer at Cornerstone Research, the economic consulting firm, put it most sharply: AI is a powerhouse at producing analysis, but the more it produces, the more human judgement is needed to make sense of it. Demand for that judgement is soaring; the supply is scarce. He calls it the judgment bottleneck. Julia Kenyon, a coach and former BBC global brand leader, flagged the study finding that eighty-nine per cent of executives report no productivity gain from AI in three years, the exception being teams whose managers actively champion it: a human challenge dressed as a technology one. And Kristin Luck, an investment banker in the data and insights sector, sees the market starting to price it, rewarding the firms that have operationalised AI and squeezing the generalists who haven't. More online →

How We Got Here

What's on my mind

I pulled a book off the shelf this week, one I first discovered and could not put down in October. It was the first time I'd stopped to really understand how we got here. Leafing through my underlinings and scribbles, one made me smile. On page 56, a co-founder of Google's DeepMind makes a small point in an October 2023 interview: humans have a fast-learning middle layer of memory, between what we hold in mind now and what we know for life. Language models have nothing like it. My note read: "Claude Skills does this. Launched yesterday."

That gap is important. In artificial intelligence, the future shows up long before it ships. Seeing it was never the hard part. Believing it enough to bet on it was.

The future is already here, just not evenly distributed. It arrives wherever someone decides to act on what many others can already see.

In the mid-2000s a few argued that enough computing power to rival the brain would make neural networks work, and that until then AI was futile. Most called it magical thinking. Even the person who would later coin a defining phrase of this era had dismissed it; the mistake, he says now, was thinking algorithms mattered more than compute. But you cannot run trial and error at scale without vast compute! The architectures beneath almost every modern model sat ignored for decades, because nobody would spend the money to test them at scale.

In 2017 Google published the breakthrough beneath ChatGPT, a paper called "Attention Is All You Need." It gave the idea away, free, because in 2017 it did not look like the crown jewels. It looked like a faster way to translate French. It held the door open and walked past it.

In 2020 OpenAI bet four million dollars on a model built from that paper. It was not the first to spot what it called scaling laws, the curve linking compute and data in to capability out. Plenty had seen the curve. OpenAI decided to bet on it. The bet paid off, and within two years had become confidence in hundred-million-dollar training runs. The largest now cost billions.

Three things turned the idea of AI into a useful system, all at once. The internet grew big enough and open enough to provide enough training data. Graphics cards built for video games turned out to be the right hardware, and enough lay around to do the maths. And people were willing to write the cheques. Compute for the largest models is now around ten billion times what it was in 2010, doubling every six months.

ChatGPT, in November 2022, was the wake-up, and the start of the Generation One era: AI good enough for professionals to use. My team and I cleared our calendars in late 2022 and sat with Hollywood screenwriters, Grammy-winning songwriters and senior executives to guide them through doing work they cared about and they judged to be world class with it. Eyes lit up every time. In late 2023 we helped one of the world's largest consumer-goods companies innovate faster. But adoption was slow. A year on, most organisations still had not moved. By mid-2024 only around 5% of companies used AI officially, despite 75% of knowledge workers using it privately, half of them hiding it from their bosses. Individuals adopted. Organisations slept.

Then the models got better, in two ways that mattered. In late 2024 OpenAI shipped the first reasoning model, trained to think for longer before answering, rather than quickly reaching for a first instinct. Eight months earlier, one of the field's most informed observers had doubted this kind of training would add any new capability. It did. Plot the older models on a log scale, then the reasoning ones, and you get two growth rates. Two curves, not one.

Source: Epoch AI, CC-BY.

They also learned to use tools, one at a time. Calculation, to do arithmetic. Search, to check facts rather than half-recall them. Computer use, to click, type and act.

Then the agent era. The unlock had been named months before it arrived: not bigger context windows, but reliability. A model that is 80% reliable, doing five things in a row, succeeds about a third of the time. At 95%, about three-quarters. Cross that line and long-running agents finally work. That is Generation Two, the world we stepped into about five months ago. Agentic systems now reliably do the equivalent of many hours of expert work at a single press of a button. Every four months they do roughly twice what they could before.

Meanwhile, businesses leapt into action, but soon learned that AI adoption was more reliant on organisational change than on technology rollout. And, even when you have a blueprint, organisational change is slow and hard. Individual productivity benefits came quickly but large team and organisational level benefits are still being sought by most firms.

US factories took two decades to electrify before productivity jumped; the payoff waited on redesigning the factory, not installing the machines.

Source: redrawn from Azeem Azhar / Exponential View, after Devine (1983) and David (1991).

There's a pattern to the AI advances that got us here. Each time, a few saw them, fewer believed them enough to act, and most waited. The waiting was never for want of information. The future sat on the table, for anyone willing to pick it up.

It still does. What comes next is as visible now as every step before it. The only question that has ever mattered is who acts on what they can already see.

In coming weeks I will share "Where We're Going Next," written with Rob Wild at L.E.K. We have formed our view. But we would rather hear yours first: reply with a paragraph on something you can see in the next five years that excites you, scares you, or that most people are missing. We will publish them online, anonymously or as you prefer.

Three things worth knowing

1. AI can now find software vulnerabilities faster than humans can patch them. Discovery is no longer the hard part; verification is.

A frontier model handed to fifty cybersecurity partners surfaced more than ten thousand critical or high-severity vulnerabilities in the systems it was pointed at. Cloudflare, the internet infrastructure firm, has roughly four hundred major bugs to work through. Palo Alto Networks, the cybersecurity firm, shipped five times more patches than its usual release cadence. Maintainers have asked the developers to throttle the discovery rate, because there are not enough security professionals to close the gaps before attackers find them. Software security used to be limited by how fast new vulnerabilities could be found. It is now limited by how fast humans can verify, disclose and patch them.

AI now finds software vulnerabilities faster than people can verify and patch them; discovery has stopped being the bottleneck.

2. A general-purpose AI model has autonomously disproved a 1946 conjecture in geometry. Independent mathematicians have verified the proof.

OpenAI handed a general-purpose reasoning model a long-held belief tied to a 1946 planar unit-distance problem of Erdős, the prolific Hungarian mathematician, and the model produced a disproof. Other AI models have since solved further long-standing problems. The wrinkle is that the others were purpose-built for mathematics. OpenAI's was not. Machines now clear the tractable tail of problems fast, which pushes the human frontier towards the problems that still resist them. After AlphaGo, the DeepMind system that beat the world's best human Go players in 2016, the skill of human Go players noticeably improved. Like Noam Brown, an OpenAI researcher who helped build its reasoning models, I suspect we will see a similar pattern in maths. And then the same pattern in business?

After AlphaGo, human Go players measurably improved, a hint of the lift that may follow once machines clear the tractable problems elsewhere.

3. Generative AI use among American adults has hit 58 per cent in four years. The personal computer took sixteen years.

The Federal Reserve's February 2026 survey of working-age adults puts overall adoption at 58 per cent, up from around forty-five per cent in October 2024 but recently flat. Work use is forty-four per cent. Non-work use is fifty-one. Daily use sits at fourteen per cent and saves an estimated two-point-two per cent of total work hours. Alfred Lin, a partner at the venture capital firm Sequoia, notes this is the penetration level the personal computer took sixteen years to reach: a four-fold acceleration on the closest analogue. The caveat is the plateau. The early-adopter phase is over. The hard part starts.

US adoption reached 58% in four years, a level the personal computer took sixteen to hit, though the curve has recently flattened.

Seven bits that didn't fit online →

Try this

1. Ask the model what else it needs to know.

The most productive sessions I see with senior leaders don't open with a clever prompt. They open with the user pasting context, their role, the situation, what they're trying to do, and then asking two questions. First: "what else do you need to know about me to help me well?" Second: "what could you do for me right now that I haven't asked for?" The first surfaces gaps the user wouldn't have spotted. The second produces use cases the user didn't bring. The blank-prompt paralysis dissolves.

2. Either AI challenges you at the start, or you challenge AI at the end. Don't skip the challenge.

A leader I sat with this week had let an AI output stand without pushing back. My diagnosis: two patterns work and one fails. You can challenge before the model starts: share your point of view and ask the model to challenge it, force it to surface the holes and the strongest counter-argument. Or you can challenge after the first draft: force a rewrite, name what's wrong, make it earn the second pass. The pattern that fails, and the one I see most, is read-and-accept. Pick one of the two postures and don't drift into the third.

3. Ramble into the microphone, let the machine find the structure.

A managing director I coached this week outlined obsessively. Some 700 words of outline for a 1,500-word article. The cost of structuring before writing was eating his weekend. The fix was inverting the order. Pick up your phone, dictate the mess, paste the transcript into your model, ask it for the through-line. Structure becomes the cheap thing. Particularly powerful for the executives who think by talking, which is more common at the top than people admit.

Email version. Version with copyable prompts below →

What readers said

Last week's edition on Elliott's maths homework drew the warmest postbag in weeks and the sharpest counter-anecdote. One reader wrote in from a similar evening, this time a chemistry problem with their fourteen-year-old. The teenager mis-specified the AI query, got a different answer to the parent's, and absolutely refused to back down: "You're wrong, the AI says…". A marketing lead at a hospitality group sharpened the underlying point: "the difference between it doing it for you and you getting it is the difference most are not doing." A reader two years into their first job described the generational arc, from being told to use AI more to being told to "use it as a support, not a substitute" inside the same eighteen months. Full reader reactions online →

What readers were posting on LinkedIn this week echoed the essay's split between individual and organisational adoption. David Johnson-Igra, founder of Scribes Consulting, an AI advisory for communications leaders, opened on the sharper end of the curve: "A year ago, I didn't think AI would impact my work. Then, within a few weeks, I was out of work." He built the advisory in the gap. Rahim Hirji, keynote speaker and author of SuperSkills, counted his own manuscript: "human" 194 times, "AI" 56 times. He calls the cohort Generation Human. And Dylan Jones, Chief Communications Officer at Bold Square, set two C-suite memos side by side. Earlier this year: we're going to be the most AI-forward organisation in our space. This month: cut down on tokens, they're getting expensive, we'll just SAY we're AI-forward. The performative version of organisational adoption. More online →

Kids these days

What's on my mind

We sat at the kitchen table after dinner with his maths homework. Elliott is fourteen. The first couple of equations went fine. We worked through them together. I helped where he got stuck, we moved on. Then it got hard.

We struggled with the next one together. I wanted to take our time with it. But, after a while he got frustrated, got up, and went downstairs to his room. To use ChatGPT for help instead of me, he said!

I went to find Teresa. I told her I was sad. I'd been enjoying this moment with him: the puzzle, the working out, the satisfaction of getting somewhere by the long route. When I was his age I used to take my dad's Royal Engineers maths homework and do it myself for fun. The method was the joy. I wanted to share that. And now my son had walked off, and he was, I thought, basically, cheating.

We've had this worry before, with phones and with social media. I remembered I'd written something a few months ago about how every generation thinks the new technology is rotting the next one's brains, and how every generation has, so far, been wrong. I looked it up and read it again.

I still thought this time was somehow different.

Then he came back up the stairs.

He had every answer. Of course he did! He'd used AI. The surprise wasn't the answers. The surprise was what he'd done with them. He'd dug into every one. Where ChatGPT's reasoning hadn't made sense to him, he'd dug deeper. He'd asked it questions. He'd kept asking until he satisfied himself the answer was right and the route made sense. He walked me through every equation: the easy ones and why they were easy, the tricky ones and what made them tricky, the surprising ones and why they surprised him. I could ask him anything and he could defend it.

He wasn't cheating, I realised. He was learning.

What he had done, downstairs in his room, was a method. He had used ChatGPT and a couple of TikTok videos to work out the answer and the methods. Then he had interrogated the bits he didn't follow until they made sense. Then he had come back up to defend it. Use AI, check the answer, understand why it's right, own the result.

I realised I've been teaching professionals to do exactly this for three and a half years.

A broadcaster evaluating scripts for a drama slate. Hundreds of submissions, finite human attention. AI takes the first pass against the slate, the audience goals, the editorial guidelines. The commissioning editor reads the summaries, at least skims every script, and reads in depth the ones the AI flags or her instinct flags. A consultancy doing diligence on a stack of papers. AI summarises, surfaces the so-whats, builds the issue log. The partner still works through the papers herself, using the summary as a guide rather than a substitute, challenging it where instinct says to dig in. A pharmaceutical company processing dozens of marketing plans from dozens of countries. AI does the first read against brand guidelines and regulatory constraints. The human goes through the plans, paying particular attention to the flagged exceptions.

The pattern is the same in all three. AI drafts. The human reads, interrogates, verifies, owns. The slow way, alone, no AI, is bordering on professional negligence in 2026. I have said so to clients more times than I can count.

And then I sat at my kitchen table and tried to make my son do it.

There are three ways to approach a problem like Elliott's equations, or like a stack of diligence papers.

The first is AI-first with your brain engaged. AI gets you to an answer quickly. You interrogate it, verify it, defend it. This is what Elliott did. It is what I teach professionals to do.

The second is human-first, AI to check. You work the problem yourself, then ask AI to challenge your thinking. This is fine. It is slower. If you have time and you want to develop the craft yourself, it is a reasonable choice.

The third is the old way: no AI, alone, on your own steam. It was the route I was trying to push Elliott towards at the table. It is also what I would call 'bordering on negligent' if a partner did it with a stack of papers on Monday.

I am still working out what to do with this.

The obvious question is about learning. What does learning mean now? What is the thing we are trying to build in a fourteen-year-old, or a twenty-four-year-old graduate? I learned by doing the steps. Elliott learned by interrogating answers he didn't generate. Both of us ended up understanding the method and able to defend the result. Mine was slower, and produced a particular kind of pleasure in the method, and it suited the world I grew up into. His was quicker, and produced a different fluency, and may suit the world he's growing up into rather better.

There is a quieter point underneath that. My method taught me how to solve equations, and problems that look like equations. It teaches a specific skill. Elliott's method, the one he used downstairs, can be pointed at almost anything: an equation today, an unfamiliar legal document tomorrow, a chemistry problem the week after. It is a broad skill, not a specific one.

Each has its price. He can't as easily derive an equation from first principles, and in some settings that matters. I can, and the depth that gave me came at the cost of muscles I never built in the places I wasn't looking. Every strength has a shadow. I learned methods that teach depth in a narrow lane. He's learning methods that give him reach across many.

The harder questions are what should we be recruiting for in graduates? What should we be teaching them in their first two years? The old test was closer to 'can you derive this from first principles, alone, in a room, in an hour.' That test still measures something. Rigour. Patience. Comfort with discomfort. But it may not be measuring the craft the graduate will use on Monday. The new test might be different: generate AI answers, find what's wrong and tell me why. Or: here is a complex problem, show me how you would use AI, and how you would know the answer is right.

Elliott will leave college in eight years and start work somewhere. I don't know what skills the people hiring him will be testing for. Neither do they. But I strongly suspect they're closer to the ones he's learning than the ones I grew up on.

Three things worth knowing

1. AI displacement now shows up in the US government data at both ends of the career ladder.

A Bloomberg analysis of new Bureau of Labor Statistics figures finds that every one of the eighteen occupations the BLS classifies as AI-exposed has lost jobs over the past year, even as US payrolls grew 0.8% overall. Customer service representatives shed 130,180 jobs, 4.8% in a single year. Interpreters down 24% over three years. Credit authorizers down 26%. The exception that confirms the rule: medical secretaries up 15.8%, the cluster that needs a body in the room. The same picture shows up at the other end of the funnel. The Economist this month plotted US graduate full-time employment against AI exposure: computer science and information sciences graduates are down 10 to 15 percentage points since 2022; philosophy and psychology graduates held steady or gained. The displacement isn't just to the people already doing those jobs. It's to the people trying to start in them, and what they should be studying, as Elliott and I both started to wonder this week, may not be obvious to anyone yet.

The most AI-exposed graduates, computer and information sciences, have lost 10 to 15 points of employment; the least exposed, philosophy and psychology, held or gained.

2. The UK's data regulator has put AI hiring tools on formal notice. Sixteen organisations have already had a letter.

The Information Commissioner's Office, the UK's data protection regulator, issued formal guidance this week saying that AI-driven CV screening, candidate ranking, and video interview analysis without "meaningful human involvement at every consequential stage" may already breach UK data protection law. Sixteen organisations have been written to directly. The consultation closes on 29th May, six days after this email lands. There is a concrete Monday-morning task in this for any leader running a hiring pipeline. Ask the talent acquisition team for the full list of AI tools in use across the funnel, decide which involvements count as "meaningful" against the ICO's test, and put a response into the consultation. The window is genuinely short.

3. Salesforce will spend close to $300 million with Anthropic this year. Marc Benioff says the engineering productivity gains made it the easiest line in the budget.

Marc Benioff disclosed this week that Salesforce is on track to spend close to $300 million with Anthropic over 2026. Separately, Anthropic announced a $200 million partnership with the Gates Foundation focused on global health. Most of the spend is on coding, justified by engineering productivity gains of more than 30%. That is a Fortune 100 chief executive treating the model layer as a procurement line item, not a research expense. The bigger question is who in your firm is allowed to commit that kind of capital, against what kind of evidence, and how quickly.

Six bits that didn't fit online →

Try this

1. When the output goes wrong, shrink the task.

Justin Skycak put it as a principle for skill acquisition this week: shrink the unit of practice until the mistake has nowhere to hide. The same rule applies to working with language models. Sprawling prompts produce sprawling failures you can't diagnose. Break the task into its smallest meaningful unit, run it, inspect the output, then rebuild. If you can't immediately see where it went wrong, your chunk is still too large.

2. Ask AI questions it can't possibly know the answer to.

A marketing lead at a global firm told me this week she's running a five-minute stress-test on every AI tool she's thinking of trusting. She uploads her own data, asks the model to use only that data, then asks it questions she knows the data can't answer. Some models fabricate regardless: "53% of women in the northeast states feel…" She's learned what its confident-but-wrong mode looks like before depending on it for an answer she can't independently check. Worth doing once on every tool you rely on.

3. Run your day past AI before you start it.

A senior leader described her commute habit to me this week. She opens Claude, asks it to review her calendar and her email, then asks it to surface what she needs to read before each meeting, what's carrying over from yesterday, and which emails in her inbox need replies before the day eats her. Five minutes on the train, and the day is scoped from outside her own head. "Just a nice little daily habit," she said. Try it tomorrow.

What readers said

Last week's edition on "What boards accept" drew the sharpest pushback in weeks, mostly on whether subtraction is the right move at all. A senior consulting voice argued that boards shouldn't be dictating at that level: their lane is capital allocation and risk, and the posture should be "relentless pressure on ambition," not refusal. A reader running another professional services practice accepted the frame but went further. The saved time in their world has gone into more client meetings, leaving every relationship "more competitive" and the industry no bigger. A senior leader at a global media company was jolted by the BCG stat that half of organisations have redesigned core workflows around AI, and wanted to move faster. Full reader reactions online →

Underneath what readers were posting on LinkedIn this week was the same nerve as the postbag: where the work actually sits and who's doing it. Phil Leslie, Chief Technology and Innovation Officer at Cornerstone Research, an economic and financial consulting firm, put it most cleanly: "I don't want an AI as a doctor or lawyer, but I definitely do want a doctor or lawyer that is using AI." Adam Peruta, an academic at the Newhouse School (also part of the Steadman team), partnered with Ipsos on a 20-ad study: the AI-generated ads tested 14% weaker on short-term sales, 17% weaker on long-term brand health. And Elizabeth Oates, VP of Consumer Insights at Molson Coors, caught herself doing algebra on a Post-it at work, no calculator, no AI. The thread underneath: which bits of the work are still ours. More online →

What boards accept

What's on my mind

Just three days into a three week stretch at my place in the woods in New England. No travel except the school run. Pulling rocks out of the field so they don't break the tractor. Digging drainage ditches so the lower field isn't too swampy.

Slow work. It will take years. But every rock and every shovel moves it forward.

That got me thinking about boards. Boards often drive change by addition. A new strategy, a new programme, a new dashboard. Addition needs a plan, approvals, buy-in, reporting against it. Fine in most domains. But in AI, that is just too damn slow. Change can happen to a team in days. What's possible changes in weeks. But the strategy paper you commission in March is calibrated to a world that no longer exists by the time it's approved in July.

There's another way. Subtraction. Pick something the firm has been accepting until now. Refuse it.

A refusal forces specificity. You can't stop a vague thing. You have to be specific about what stops.

Start with the AI adoption dashboard. Most boards now get one. I've built one for a few of you! Usage rates, laggards, super users, trend lines. Boards commission them to drive action. But they often substitute for action. Three and a half years into the AI era, after every workshop, licence and nudge your firm has paid for, refusing to use the tools is now a decision. If there's a good reason, your board should hear it. If not, a tough conversation is overdue. Refuse a dashboard that names laggards without tough conversations with them.

Let's talk about AI training. One client set a new rule this week: nobody delivers training until they pass the firm's own AI power-user assessment. Obvious in theory. Rare in practice. I've sat in on dozens of AI trainings. Most times, the trainer hasn't actually done the real task themselves! If your modelling instructor doesn't model with AI, the training is theatre. The trainees learn the talk, not the work. Refuse funding until the trainers pass the assessment they're delivering.

Last year's bake-off is a third. Can-AI-do-our-job experiments are rarer than they should be. They're also stale. A partner at a communications consultancy told me about theirs from early 2025. The form was passable. The substance was thin. They haven't re-run it, though! The same exercise at a different firm in late 2025, using agentic tools, came back at almost the firm's full professional standard. Anyone quoting a 2025 AI test as comfort is relying on stale evidence. Refuse any benchmark older than six months.

Saved hours without a bold benefit is a fourth. Many internal AI reviews now have an "hours-saved" line. In hundreds of conversations with senior leaders about what their teams do with the time AI gives back, it is rare to hear a bold, inspiring answer. The best I've heard recently? More coaching for their team. Most are just drift. Reject any "hours saved" line that doesn't name a meaningful place where the hours went.

Six more, if you want them:

• Refuse strategies calibrated to today's AI. Plan for where the puck is going - what will ship in six months.
• Refuse job specs written before AI existed. Rewrite the senior ones first.
• Refuse letting your best AI talent leave. Name them. Expect managers to work hard to keep them.
• Refuse reinventing the same scaffolding in every team. Find five that already work and share them.
• Refuse security policies that make AI unusable in practice. Find a way.
• Refuse board decisions on tools the directors haven't used. Mandate hands-on hours doing real work before the next AI vote.

My tractor doesn't care which rock comes out first. It just needs me to keep pulling. A board could be the same. Pick one refusal. Make it. Then the next. Out here, ten rocks is a morning. In a firm, ten refusals is a whole different firm.

Three things worth knowing

1. The capability curve is curving upwards on a log scale: we just went from one hour to one day.

METR, an AI evaluation lab, measures how long an autonomous task an AI can complete reliably. In early 2024 the answer was just minutes. In May 2026, with Anthropic's forthcoming Claude Mythos Preview, it's sixteen hours of work a human would have done. The number isn't the headline. The curve is. From minutes to a day in twenty-four months, acceleration on a log scale that had previously been holding steady. If the next two years look anything like the last two, the unit becomes a week, then a month, then a year of human work at the press of a button. Think about that for a second. And then plan accordingly.

The task an AI can finish reliably has gone from minutes to about a day of human work in two years, and the curve is still doubling.

2. Half of organisations have redesigned core workflows around AI. A fifth have built new business models. That's bold work in three years.

BCG's AI at Work 2025 survey of 10,635 employees across eleven countries reports that just 72% of organisations are running generative AI tools, but 50% claim to have redesigned end-to-end workflows around them, and 22% claim to have built new business models on top of them. Read those numbers slowly. Three and a half years after ChatGPT launched, a fifth of firms claim to have invented new business models because of AI. Half have rewired core workflows. Wow. Most of the firms I work with would love to be in either group. Syed Ijlal Hussain, who surfaced the chart on X this week, framed it as a gap. I'd flip it. The 22% are doing what most boards I'm working with haven't started.

Three years in, half of organisations claim to have redesigned core workflows around AI and a fifth to have built new business models on it.

3. Anthropic just passed OpenAI in US business AI spending. The strategy lesson is older than AI.

Ramp's AI Index, built from anonymised spend data across its US business customers, shows Anthropic taking 34% of paid AI subscriptions in its May release, ahead of OpenAI on 32%. The first crossover. Anthropic's share has roughly quadrupled in a year. I'd argue this was inevitable from early on. Anthropic stayed fixated on the enterprise user while OpenAI chased every consumer headline. Slow perseverance against a chosen audience won. The lesson isn't really about AI. Pick an audience. Set your strategy around their needs. Don't worry about who's collecting the headlines this quarter. Just keep your head down and serve the people you said you'd serve.

Anthropic has passed OpenAI in US business AI spend for the first time, the reward for staying fixed on the enterprise buyer.

Six bits that didn't fit online →

Try this

A reader suggested last week that the prompts should be online with copy-paste code. Done. This week and for every prior edition. All the prompts in one place →

1. Hand over the context, not just the question.

Experienced leaders have context. They're short on time. AI tools convert context into time saved ... but only if you hand the context over. Most don't. They type questions ("how should I structure a board paper on Q2?") and get general answers ("a clear narrative arc, three key insights, a risk slide"). Useful, but the leader still writes the paper. A leader I spoke with this week made the leap from asking to delegating. "Here are the Q2 numbers, last quarter's board paper, and the three things the board flagged. Draft it." Same model, same minute. Asking gets you an outline. Delegating gets you a draft.

2. Build a personal skill, and add a rule to it every Sunday.

A person I worked with this week reviews 100-page reports from their team on Sunday nights. Typos, inconsistent language, logic gaps. A simple review "skill" in Claude now catches all those. The compounding version is their own preferences layered on top: this market "is expected to grow" not "will grow"; never "dropping precipitously." Every time they catch a miss the model didn't, they open Claude and say "add this rule to my personal review skill." Whatever skill you build, the compounding habit is the same. Catch what the model missed. Add the rule. Trust the skill more next week.

3. Schedule a daily AI briefing. The use cases will follow.

AI tools sit closed until you open them. That's a real reason senior leaders bounce off: not bad prompts, but a tool that requires you to think of the use case first. Scheduled tasks invert this. Every morning at three, mine reads my inbox from the previous day, prepares one-paragraph briefings for every meeting on the day's calendar, and emails me a short summary that takes three minutes to read with breakfast. The tool changes from something you open to something that opens your day.

What readers said

I can never predict which editions will get lots of feedback and which won't. Last week's, on "Choosing is the work," caused an unusually large number of replies on where the saved hour actually goes. A partner at a global strategy firm said they'd "won the work lottery," which roughly captures the giddy-then-uneasy feeling of work these days. Another reader, writing from a Lean Engineering background, reminded me that "change is the fun part." A reader writing in a personal register worried that AI-powered work will leave people feeling like they spent "90 years to further my career so I could spend 20 minutes with my family before I die." One leader wrote in mid-pilot of Claude Code, about going to bed later each night, finishing an exhausting and unproductive week. Reflecting that "just because I can now do all these things doesn't mean I should." Full reader reactions online →

My read on what readers are posting this week: the community's moved past adoption and onto how AI changes the work, not just who does it. Tim Ryan, my Steadman co-founder, brought back Cal Newport's "AI is like bringing a forklift into the gym" and named the discipline that catches it. Fiona Eastwood, Global CEO of Merlin Entertainments, came back from MIT with the same conclusion at scale: AI lands as a transformation agenda or it doesn't land. Mackenzie Nordal, co-founder of Atheni, brought the personal version: she asked her three kids how they actually use Claude. More online →

Choosing is the work

What's on my mind

Gothenburg airport, Saturday morning, alone. Tired. My notebook is open at a page that has been bothering me for weeks.

The left-hand page lists nineteen things. Reports, dashboards, websites, custom tools, economic models. All built quickly, in the last few weeks, for clients. A good handful earned the response that the work was better than what an entire team used to spend weeks delivering. These are very senior people at the top of their careers, the kind who ask for revisions in seconds if the work is subpar. The work wasn't lazy or thin. And yet most have been opened, appreciated, and then quietly abandoned. Some haven't been mentioned in weeks. The building is fine. The unused is what stands out.

The right-hand page lists more than nineteen more. Built for me, by me. Things I judged good and important enough to make. Many I've never shared. Few have been properly used. Same outcome.

The cost of building almost anything you can describe has fallen close to zero. A report that took a fortnight in early 2022 takes an afternoon now. A dashboard that took a quarter takes a day. A custom tool that needed three engineers needs one curious person. The bottleneck used to be the build. Not any more.

I've been thinking about this from two angles this week. I'm now realising they're the same question.

Take the first angle: what to build. Most of you are either AI-capable or have AI-capable people on your team. Either way, you can produce a report on any topic in under an hour. You can stand up a dashboard before lunch. You can run an analysis that would once have justified a six-figure project fee. But just because you can, doesn't mean you should. Each project has real costs even though the building is cheap. Time to brief. Time to check. And a bigger cost, harder to see: every project was a project not built in that time. With this kind of power in your hands, the question is what else could you have pointed it at. Something bigger, surely.

I don't think those nineteen projects were useless. Some may have been. But the pattern forced a harder question on me: why is it now so easy to make something impressive and still so hard to make something matter?

The second angle: where the saved hour goes. I've been keeping count for a couple of weeks, as an experiment. Of all the conversations I've had, only six people have brought up the hour AI gives them back, and what they're doing with it. Six. Here's what they chose.

A partner at a consultancy has put theirs into a new project, won through a Sunday spent building a pitch with AI they otherwise wouldn't have won. Three more were like this. Working later, working weekends, seizing the chance to do more of the career they love now that it's more efficient. One person told me they've stopped being stuck in the mechanics and gone deeper into client relationships. Another told me they've put their hour into coaching their team. Each one at least decided.

But none I know of chose reading more books. None chose picking the kids up from school, more date nights with their partners, more time with friends, more community work or more sleep. Six people chose. Each one chose more of their career.

I listened because I realised I hadn't been deciding. My hour has gone back into more of the work I love. Drift, not decision.

If you can have any report, dashboard, website, model, analysis, briefing or workflow you want, the building isn't the value. The choice is. What gets built. Who uses it. What changes because of it. Where your own saved hour goes. Whose problems you or your AI-capable people work on next week. None of these are technical questions. They're the only questions left.

What's remarkable is, 3.5 years since AI could meaningfully help all of us to do our jobs, how few people are using their AI superpower to change anything at all. Most are using it to do more of the same thing, faster. The economics of their business are basically unchanged. Five per cent better, by one candid estimate this week (more on that below). People's weeks look similar. The clients they serve are similar. The problems they take on are similar. The hour comes back, and the hour goes into evolving the same machine. The thing they can now build is the same thing they were already building, just a bit better, quicker and easier.

It feels, collectively, like failure. Look at what we have in common: my nineteen client projects, my own nineteen-plus, the six people who told me how their hour goes, and the hour I haven't been spending on purpose. Nobody in any of those numbers chose much. We built what we could build. We worked longer hours. The new tool went into the old shape.

Choosing well is now the work. Not choosing, or choosing the obvious thing, is failure. Everything else is just typing.

Two questions I'm answering more explicitly from here, that I wasn't a fortnight ago. First: which projects and problems are actually worth it. I'd been answering "all of them" by default. Because I could. What fun! Not any more. I want to work on things that change something that matters. That's possible now. Easier than ever before. Second: where the hour goes. More work isn't the problem. Automatic more work is. I choose more date nights with Teresa. More books. More running and yoga. Autonomy over drift. Intentionality over default.

Two choices. Choosing is the work. How about yours?

What readers said

Readers' letters were light last week. So I'll just thank two readers for saying nice things. One, at a major broadcaster wrote in to say the weekly email is one of the best things they get each week, which was lovely to hear. And, separately, the second reader this month told me they read it at a child's Saturday-morning sports activity. I'm starting to wonder how many of you are reading it from a sideline somewhere. Thanks for the nice words. I'll pick up on the substance of the letters again next week.

My take on what readers who've replied to this email are posting on LinkedIn this week: the through-line was the people-and-process side, not the tools. Sameer Modha, an analytics and ad effectiveness leader, posted the sharpest reframe: stop treating "killer prompts" as the skill. The right toolkit for talking to a machine trained on human language is human empathy. Sara Lloyd, Group Communications Director and Global AI Lead at Pan Macmillan, the publisher, came back from the Bologna AI Summit with the line that landed: "all or nothing" thinking on AI is the biggest trap, and the publishers finding the middle way are doubling down on the human element as a deliberate strategy, not nostalgia. Tim Ryan, my Steadman co-founder, shared Microsoft data showing culture and managerial support drive roughly twice the AI impact of individual mindset, and named the term most leadership teams haven't yet: "blocked agency." And if you want one concrete thing to try this weekend, Conor McCarthy, an independent consulting adviser, posted a sharp NotebookLM exercise: feed it your CV, your LinkedIn profile and your personal website, click "Audio Overview," and listen back to two AI hosts discuss your career like a radio show about you. More on the website →

Three things from this week

1. AI-adopting firms are growing headcount, not cutting it.

A Goldman Sachs analysis circulating this week, charted by Callum Williams, economics writer at The Economist, shows US firms that have adopted AI report net positive employment growth across the past six months. Finance, insurance, arts and entertainment sit at the positive end. Transportation and food service show modest decreases. The all-industries balance is positive. Self-reported firm data has its limits and the window is narrow. The direction matters anyway. An answer I deeply hoped would be true. I hope it turns out to be. The Jevons Paradox playing out: AI-adopting firms are growing because productivity gains are expanding what their people can do faster than they are replacing them. The risk is falling behind the firms whose people are using AI to scale.

2. Five percent, not fifty: the candid private-equity number.

Pete Stavros, co-head of global private equity at KKR, the buyout firm, told the room at the Milken Institute conference last week that AI is improving portfolio company earnings by about 5%, not the 50% that a revolutionary technology like this should offer. Five percent across a portfolio of billions is real money. It's also a long way from the scale of growth many hope for from AI. The gap between what feels possible and the spreadsheets says something about where the bottleneck actually sits. These days, it isn't the AI.

3. Both AI labs went into private equity the same day.

On Monday, Anthropic, the AI lab behind Claude, announced a $1.5 billion vehicle with Blackstone, Goldman Sachs and Hellman & Friedman, the private equity giants. Engineers from Anthropic will embed inside the consortium's mid-market portfolio companies and build custom Claude workflows. The same day, OpenAI, Anthropic's rival, finalised a $10 billion joint venture with TPG, Brookfield, Bain, Advent and SoftBank, pricing in a 17.5% guaranteed annual return for the financial sponsors. Google, the search company, is reportedly in talks to do the same with Gemini, its own model. What was reported as "AI labs raise more money" is a category change. The frontier labs are turning into distribution-led services firms with a model attached. The forward-deployed engineer pattern, lifted from Palantir, the data-analytics firm, competes directly with the bottom of management consulting and the top of systems integration.

My worry is they're engineering systems to replace humans, not amplify them. We know how these firms work. PE has run the same playbook for forty years: cut first, lift later, if at all. Surely the biggest unlock from AI is the opposite move: people making sharper, more confident decisions because they have a tool thinking alongside them. That kind of value compounds. The kind a forward-deployed engineer hard-codes into a workflow does not.

Three bits that didn't fit online →

Try this

Each has a richer version with copy-and-paste prompts on the Try This online → page.

Push it harder, then skillify, then iterate.

The simplest workflow upgrade I've coached this year, and it stacks. Push it harder: when the model gives you a perfectly reasonable answer, tell it to do more searches and use credible sources. Three extra words. The next round does real research, cross-references government sources, and surfaces what the first pass skipped. Skillify it: when a session produces something useful, ask the AI to turn the exchange into a reusable skill, a folder with a description and a prompt any future session can pick up. Iterate: when the skill misbehaves, just tell it "change yourself so X." It rewrites itself in place. Works particularly well in Claude, Anthropic's chat tool, where skills are first-class. Prompts for each step are online.

Shadow your most AI-pilled employee for two days.

Matt Stockton, an operator and investor, made the case this week. Find the rabbit-holed colleague who already knows what they're doing, watch how they actually work for two days, then do the same work yourself for a week. "AI lives on command lines right now, it does not live in PowerPoint jargon slides." Most senior leaders consume AI through polished demos and vendor decks, then make strategy decisions with no felt sense of the technology's real constraints. The cheapest fix costs nothing. A list of the questions to ask while you shadow is online.

Ask AI to build you an HTML slide deck instead of PowerPoint.

Just ask. "Build me a slide deck on [topic] in a single HTML file." Most chat tools can do this now. Why bother? PowerPoint output from AI is uneven, with broken chevrons, mis-rendered shapes, and two hours of touch-up. HTML slides come back closer to ready, are infinitely easier to edit (you just talk to the file), and unlock things PowerPoint can't do: an inline edit button, an AI-spoken narration, a presenter view that asks the model questions during Q and A. The basic ask works on its own. This is one where the richer prompt online really pays off, with extras like the edit button and a few of the patterns we've learned this year about HTML decks specifically.

The bill and the harness

What's on my mind

All-you-can-eat AI pricing is going the way of the all-you-can-eat Vegas buffet. I'm here this weekend. And sad about both.

Three of the four biggest AI vendors switched their pricing in the last few weeks. Anthropic, the AI lab behind Claude, stripped bundled tokens out of Enterprise seat deals in mid-April. OpenAI, its rival, took Codex, its coding agent, to pay-as-you-go a fortnight earlier. GitHub, the Microsoft-owned developer platform, moves every Copilot plan to usage-based billing on 1st June. An Anthropic manager admitted its Pro and Max tiers have been outgrown by today's workloads. The market has conceded, in unison, that flat-rate AI doesn't work anymore.

Two friends wrote in about pricing last weekend. One friend, half-shaved, sent a voice note from Tokyo. $200 a year for Claude Pro is a bargain. Help with invoicing alone is worth it. But what happens when the bills go up five or ten times? An hour later, a partner at a professional services firm. Flat-rate prices on a tool that gets better the more you use it have to break. The outsourcing trap, in his phrasing.

Both worried. Both thinking from the buyer side. Both staring at a number that's certainly about to change.

The supplier side partly explains why. Unit prices for equivalent capability are falling roughly tenfold a year. Chinese-built models anyone can run for free keep pulling down the bottom of the market. Albeit, trailing US frontier models by seven to nine months on the hardest work.

At API prices, the leading AI labs run profitably but close to break-even on each response, training costs aside, and competition keeps them from charging more. Subscription pricing is a different matter. My $200-a-month Max plan, about $6.50 a day, delivers $500 a day of equivalent API usage. That isn't sustainable for the AI companies. They know it. That's why the pricing is changing.

The buyers' reaction explains the rest. William Stanley Jevons, a 19th-century English economist, noticed it in 1865, watching coal use. More efficient steam engines were supposed to reduce coal demand. They did the opposite. Cheaper energy made it worth burning coal for things that hadn't been worth it before. Total consumption climbed. Demand outran the savings. The Jevons Paradox was born.

The radiologist is the modern version, and a cracking story about the people building AI being wrong about its consequences. In 2016 Geoffrey Hinton, often called the godfather of AI, told a Toronto auditorium that people should "stop training radiologists." His reasoning was clean: deep learning had just started beating humans at image classification, and reading scans looked like the obvious next thing to fall. He was right about the models. Image-recognition AI really did become extraordinarily good at spotting tumours, fractures and lesions. He was wrong about the radiologist. Cheaper, better, faster reads didn't replace the job; they made more imaging worth doing, in more clinical situations, for more conditions. Ten years on, the US has six thousand more radiologists than it did then, and average pay is up roughly seventy per cent.

A decade after Hinton said to stop training radiologists, the US has six thousand more of them and pay is up about seventy per cent.

Jevons would say "I told you so." 'Cheaper and better' drives demand up. It's why flat-rate pricing breaks. For buffets and for AI models.

So both friends last weekend were right. The bills will rise. Both friends will use AI for vastly more, exactly as Jevons predicted. The vendors themselves are saying so.

If the bill is going to rise either way, where should the money compound?

Not in the model. Models are leapfrogging each other quickly and unit prices are collapsing. Whatever you pay this year for capability, you'll pay less next year. That's not a bad thing. But models aren't where advantage sits.

The advantage sits one level up, in the app, or as AI people like to call it, the harness: the layer of instructions, context and custom workflows that wraps the model and makes it behave the way you need. Same model, very different output. A team that has spent six months teaching its AI tools how they think, what good looks like in their work, what their clients actually need, gets answers nobody running the tool out of the box can match. Intercom, a customer service software company, doubled engineering velocity in nine months on exactly that bet.

Most organisations are still running AI apps out of the box. Same tools, same default settings, same generic questions, similar answers. Then they wonder why the productivity gains haven't shown up.

The bill rises either way. Jevons saw to that. The choice is where the spending compounds. Money spent on raw model use funds the vendors' next round of competition. Money spent shaping your harness, your instructions, your reusable workflows, your saved skills, compounds for you. The first is a utility cost. The second is an asset nobody else can buy.

Three things worth knowing

1. AI adoption stalls one layer below the executive sponsor: at the line manager.

New data from Gallup, the polling firm, finds that AI use correlates more strongly with managerial endorsement than with tool access. In firms where the manager actively supports AI, 80% of staff use it weekly; where they don't, that drops to 44%. In the public sector: 65% versus 37%. Procurement and licences are the easy part. The variable that actually moves usage is whether middle managers model and endorse the tools, or quietly signal they're optional. Adoption lives or dies one layer below the top.

Whether the line manager backs AI matters more than access: 80% of staff use it weekly where the manager supports it, 44% where they do not.

2. The frontier-model leaderboard is now refreshing in weeks, not quarters.

The Epoch Capabilities Index, run by Epoch AI, a model-evaluation outfit, now shows GPT-5.5 Pro and Gemini 3.1 Pro above 155, up from GPT-4o's 128 in mid-2024. Seventeen frontier releases compressed into under two years with no visible plateau. Greg Burnham, an Epoch researcher, summed up the pace: "I don't know when Opus 8.2 will be shipped, but GPT-9.1 will be shipped that afternoon." Whatever model you standardise on today may be two generations behind by the time the training programme rolls out. Build workflows and judgment around capabilities, not named models.

Seventeen frontier releases in under two years with no plateau, so whatever model you standardise on today may be two generations behind by rollout.

3. Six VC firms, one investment thesis.

Linas Beliūnas, a fintech writer, read the published 2026 investment theses of six of the biggest venture firms side by side and found the same handful of AI bets in all of them: AI-native enterprise software replacing the old workflow systems, AI agents for physical and industrial work, vertical AI software in legal, finance, healthcare and construction, multi-agent orchestration, and health AI. His line: "you could swap the logos on their published theses and most readers wouldn't notice." His sharper conclusion: the most valuable software companies of the next decade won't look like software companies. They'll look like law firms, factories and hedge funds run by teams of ten. If your industry sits in any of those buckets, your next competitor is being funded right now to do your work with a team of ten.

Six of the biggest venture firms published near-identical AI bets; you could swap the logos and few readers would notice.

Four bits that didn't fit online →

Try this

1. Pick one tool, get good, then refine your harness.

Pick one AI tool. Don't worry about which. Get fluent. Then spend the next month making it yours. That means skills, saved instructions, project context and tools it can reach. The model will keep improving on its own. The harness is your job.

A leader I spoke to has spent weeks running the same task through ChatGPT and Claude side by side, then asking each to review the other. Genuinely interesting and occasionally useful, but they can't decide which to use when. The advice from someone who has been at this longer: pick one, become very good with it, learn its quirks, then build the layer above it that reflects how you actually work. Most of the value is in fluency with one tool, not coverage across two.

2. Force yourself to change something on every AI output before you ship it.

Whatever the AI gives you, a paragraph, a slide, a summary, add something, remove something, or reorder something before it leaves your hands. Make it a rule, not a judgement call.

This came up at a senior training session this week, as the room debated when the "check, edit, own" model breaks down. Increasingly the edit feels optional because the output looks good. The rule of "always change something" is a forcing function. It keeps your fingerprints on the work, keeps you reading carefully enough to find the change worth making, and prevents the slow drift to brain rot. Hopefully. Ensure your fingerprints are on every output.

3. Skip the slides, build the page.

Next time you'd reach for PowerPoint to walk a group through a framework, write the content as text and ask your model to turn it into a single HTML page instead.

In a senior strategy session this week, the most-praised artefact in the room was not a deck. It was a web page someone had built to walk teams through their thinking. Building it took a clear thought, a text file, and a small Claude skill they had set up once. Their harness paying off in front of the room.

What readers said

Last week's edition on "Rise of the auditors" landed hard. Two threads ran through the responses. The first was recognition: a senior strategist replied that they're now "doing AI work in ways that are built more to help the auditor (me!) check it, rather than to do the work itself." The second sparked this week's essay: a partner at a global professional services firm pushed back on flat-rate pricing and named the outsourcing trap. The full exchange runs in the letters section online. The apprenticeship worry came up again from several readers, with one naming it as the single critical question for any firm whose business depends on people developing judgment over a decade. A counter-voice from a different group argued that organisations with a strong brand and an existing audit culture absorb the shock; the trust premium rises as production commoditises. Full reader reactions online →

My take on what readers who've sent thoughtful replies to this email are posting on LinkedIn: The thread that pulled hardest on the bill-and-the-harness theme this week. Aakash Gandhi, Partner at L.E.K. Consulting, a strategy consultancy, posted the cautionary parallel readers will recognise: Broadcom raised VMware licence fees by two-to-five times, in some cases tenfold, once enterprise customers had committed. The pattern repeats wherever the buyer surrenders the layer above the model. Chuck Reynolds, also a Partner at L.E.K., set out the discipline that prevents it: buy point solutions where the capability is standardised, and build proprietary ones only where the insight, data or decision logic is genuinely unique to your business. Henry Coutinho-Mason, a futurist and keynote speaker, used a recent SXSW talk to push the slogan "Agency > Agents" and tell the story of a Belgian cardiologist building their own Claude Code workflows: harness-as-edge in microcosm. And Colin Lewis, a behavioural economist who writes the Robotenomics newsletter on automation and AI, surfaced an eight-word epigraph from Sam Khoury, a strategist writing on platforms and audience economics, that sat with me all week: "The companies that don't own their own content pipeline will end up renting someone else's audience, and paying more for worse signal." More online →

Rise of the auditors

What's on my mind

Note: This is a longer version of the essay than the one sent in the email.

I'm drowning in new AI tool announcements and sitting on a pile of work that's almost ready to send ... but not quite. Most are in the same place. I think I worked out why.

This was the week the agent floodgates opened. Microsoft made Copilot Agent Mode the default across Word, Excel and PowerPoint. Google shipped agents to 3.45 billion Chrome users. The UAE committed to running half its government on agents within two years. OpenAI, Anthropic and SpaceX all piled on.

The announcements are loud about capability. On checking, they are silent. Nobody has said who does it.

When nobody is named, three things happen at once. Senior staff end up checking, at senior prices, work that sits three rungs below them. The AI builders who could be at the frontier get pulled off it to re-check their own output. And a lot of the checking falls to people who are diligent but don't do the careful work required. Checking a mountain of mostly-right work is a special kind of task. Errors get through.

Checking used to be embedded in doing. An hour of work included verification, tightly woven in. Now it is a separate task. Perhaps five minutes producing, ten minutes checking.

Ajey Gore, a former CTO, wrote this month that when execution becomes free, verification becomes the expensive thing. Martin Fowler picked it up. Gore's formulation for software: ten engineers becomes three engineers and seven people defining acceptance criteria, designing test harnesses, monitoring outcomes.

The same logic holds for knowledge work. An hour to build a research project end-to-end, scope through to deck. A day to audit it properly. Multiply across a team and the required roles shift massively.

The ratio can run much steeper. A new tool, Aleera, promises to draft a full due-diligence pack in under an hour. The same thing would have taken a team weeks. Checking and refining it carefully might take a week. So the audit-to-build ratio is roughly forty-to-one.

The work still compresses. What was weeks of team output becomes one hour of build and a week of careful audit. Even at 40:1, the speedup is real. The Auditor seat is what lets you take it. Without that seat you ship the slow version, because you cannot trust the fast one enough to send it.

Three gaps open up. Each one points to a role.

Directors are drowning. The people who frame the problem and sign off the answer are now reviewing more drafts more quickly than they can judge properly.

The frontier is a full-time job. The people doing the best AI work spend all day trying to keep up. Even the best will tell you they now can't. I can't. So most people cannot afford the time to be anywhere near the forefront, and it would be bad economics to ask them to. Senior and client-facing staff are worth more on judgement and relationships than on model selection. The frontier belongs to a specialist because specialisation is cheaper than spreading frontier fluency thinly.

The checking is broken. Senior staff burning expensive hours on verification. Builders pulled off the frontier to re-check their own work. Diligent-looking staff skimming mostly right output and missing the rare, occasional error. Assurance has been treated as a side-task. It is a craft.

Everyone using AI with nobody specialising is the shape that breaks; the one that works adds a dedicated role to check the output.

Three roles fall out.

The Director frames the problem, decides what good looks like, owns the outcome. Uses AI every day and gets real value from it. Not an expert in the tools, and shouldn't try to be. The frontier moves too fast for someone whose calendar is full of relationships and decisions. Their job is taste and judgement, not keeping up.

The AI Builder is the specialist. Lives in AI all day, tries the new models and apps and features as they launch, runs many sessions in parallel, helps many people, ships at pace. The one seat where staying at the frontier is the job. Defined by appetite, not rank. Could equally be a mid-career specialist or a sharp new hire who has fallen in love with the tools.

The Auditor is a different breed. Traces citations to source. Runs key numbers independently. Stress-tests arguments cold in a second model. Replaces weak sources, swaps assumptions, reruns with corrected inputs, refines paragraphs that are nearly right. Auditing and fixing, not just checking. Uses AI every day and uses it well. The skill is judgement and accountability, not model selection. Usually an experienced generalist with a nose for nonsense. Not necessarily someone who has done the underlying work themselves, though.

Picture it. An Auditor reads an AI Builder's draft financial model. She traces ten citations to source. Nine check out. One is from a less-credible source, so she finds a stronger one and swaps it in. She reruns the affected calculation, updates the report's content. Signs off, or doesn't.

Start with the AI Builder, because the Builder is the scarce seat. Staying at the frontier is a full-time job, and an organisation can only afford so many full-time jobs spent there. Each Director needs roughly a fifth of a Builder's time to keep their decisions well-supplied with AI output. Each Builder ships five substantial things a day, and each takes a day to audit and refine properly. So each Builder needs five Auditors to keep their output shippable.

At scale the checking role dominates the headcount, five auditors to each builder, yet most organisations have none.

I built a quick interactive version at steadman.ai/auditors. Try your own assumptions.

I have been living the shortfall. A research project I did has been in my queue for eleven days waiting for me to review it. A financial model I cannot send until I check every number. A client deck that's been almost ready for a week. Every piece built cleanly. All mostly right. Much of it ready to ship. None of it has, because the audit queue is longer than the build queue. Weekends have become my quiet, focussed audit time. Every senior AI user I know has hit the same wall.

Why the Auditor has to be human is a separate question from why the seat exists.

IBM trained its people on one line in 1979. A computer can never be held accountable, therefore a computer must never make a management decision. The line is more true now, not less. A machine can fail. Only a person can be accountable for the failure. The Auditor can run three models to check a fourth. The signature on the file has to belong to someone with a stake.

The word auditor carries twenty years of the wrong connotations. The old auditor checked your work, often after you thought it was done. A brake. A second-guess. Compliance, proofing, QA. A joke many liked to make.

The new auditor checks and improves the machine's work, on your behalf. Not the person standing between you and shipping. The person who lets you ship at all. The shift is whose work is being checked, and why. Same care. Different client. One holds you back. The other lets you move.

Software engineering has started reorganising around three roles with the weight on auditing. Knowledge work has not done the thinking here yet. The organisations that do it first will have the only AI-native teams that ship fast and ship right. The rest will ship and regret it.

Three things worth knowing

1. Fewer than 10% of organisations have scaled AI agents beyond pilots.

McKinsey data names the bottleneck: organisations won't hand over control. Agents require delegated decision rights that most companies withhold, pre-agreed accountability frameworks that don't exist, and cross-functional governance that nobody has built. Pilots succeed in contained environments and stall the moment agents intersect real workflows where incentives and reporting lines conflict. Matches my experience of most organisations. It takes hard, careful work to resolve these issues. Those that have done the work are getting the benefits.

2. GitHub paused new Copilot signups. The flat-rate model broke.

GitHub paused new signups for Copilot's agentic plan after coding agents blew through the flat-rate compute allocation. Uber's CTO told journalists that AI coding tools have already consumed the company's entire 2026 AI budget. Goldman Sachs reports AI inference costs in engineering now approaching 10% of headcount cost, on a trajectory towards parity with salaries within several quarters. The pattern: evangelism, budget shock, rationalisation. The smart response isn't to slow down. It's to make sure the work being done is vallueable and to match the right model to the right task. Is flat-rate AI pricing over?

3. 29% of employees admit to sabotaging AI initiatives.

Writer's annual enterprise survey shows every organisational health metric worsened in 2026. Sabotage means what it sounds like: reverting to pre-AI workflows, deliberately not using assigned tools, discouraging colleagues from adopting, withholding inputs that would make AI systems work. "AI is tearing my company apart" rose from 42% to 54% of C-suite respondents. Employee confidence in their company's AI strategy dropped from 47% to 31%. Most C-suites concede their strategy is "more for show." The clearest counter-narrative yet to the adoption-is-accelerating consensus.

Eleven bits that didn't fit online →

Try this

Ask "is this the simplest version?" before accepting AI output

Language models have no incentive to simplify. Work is free to them. Bryan Cantrill calls it the loss of "laziness": the human impulse to find the crisp abstraction rather than add another layer. When AI drafts something, check whether it found the simplest solution or the first solution. A three-step process wrapped in seven steps of hedging is worse than the three steps alone. The scarce resource now isn't production. It's restraint.

Audit cold

When checking AI output, open a new chat with a different model. Upload the source materials and nothing else. Don't verify inside the same conversation that generated the work. That conversation will defend its own output. I've tested this repeatedly over the last month: the same model that produced a confident answer will find the errors when it reads the sources fresh, without its own prior reasoning in the context window. Sullivan and Cromwell filed hallucinated citations in a multibillion-dollar bankruptcy case. This would have stopped it.

Save one reusable AI workflow this week

Google shipped Skills in Chrome this week: saved one-click AI prompt workflows that run on whatever page you're viewing. Pick a task you do weekly. Summarising a website, extracting action items from meeting notes, comparing options across open tabs. Save it as a named Skill. Whether you use Chrome, Claude, or something else, the principle is the same: if you've done it three times, encode it. Each saved workflow compounds. Each unsaved one gets reinvented from scratch.

What readers said

Last week's "The proxy break" drew the strongest response yet. The essay about writing, identity and AI polish hit something personal. One reader described growing up treating correct English as a form of belonging, only to find AI stirring up the same anxieties about fitting in. They'd caught themselves search-and-replacing em dashes from their own writing to avoid being accused of using AI. Another faced a real dilemma: a respected consultant sent a clearly AI-generated proposal, and they couldn't work out how to say so. A professor offered the sharpest reframe: craft versus mass production. Temu at one end, Huntsman at the other. We probably need both, they said, but we need to proceed with care. Full reader reactions online →

A lighter week on LinkedIn from the community, but several posts cut to the heart of the essay. John Gleeson, who runs a customer success community and investment fund, met Marc Benioff this week and came away with one message: the unit of value in software is shifting from access to outcomes. "Service as software, not software as service." Nick Graham, founder of Vertemis, a research and analytics consultancy, frames the same shift for insights teams: stop shipping decks, start shipping decisions. And Dylan Jones, co-founder of Bold Square, a communications and marketing advisory, notes that Zuckerberg is building an AI agent to help him be CEO, but the real story is the internal culture of employees sharing tools they've built. "Your job as a leadership team is mostly not to get in the way." More online →

What's on my mind

The proxy break

I've never been good at writing. Numbers and logic were my passion. Words have always been hard for me. In book publishing, executives used to reply to my emails with notes on my commas, ignoring my arguments. Not everyone judged me for it. But some always did, and I knew it.

My excuse came from Zhuangzi, a Chinese philosopher from the 4th century BCE: "Words exist because of meaning; once you've got the meaning, you can forget the words." Get over yourselves, I'd think. Look at the meaning.

Using AI is getting me in trouble in a new way. A friend of thirty years messaged me last weekend. The email reads like AI wrote it, he said. And with the pace of change, at some point he won't be able to tell. Do a whole edition about it, he said. Good idea. It's a topic I know most of you are wrestling with too, when you use AI to help you write and when you receive content from colleagues you know have done the same.

I probed. His anxiety wasn't about sentence structure. It was about whether I'd done the thinking.

The conventional story: good wording was taken as a proxy for good thinking for centuries, but AI broke it. Half right. AI broke it. But it was never reliable. Good writers have always dressed up thin ideas in beautiful prose. Good writing was never a proxy for originality, either. We've all heard smart people recite theories we recognise from The Economist or that surely came from their MBA professor.

The natural response to a broken instrument is to flip it. If polish no longer signals thinking, "sounds like AI" must signal no thinking. Most have already made the move. They're missing some great ideas.

I saw an email last week signed off "NOT WRITTEN BY AI" in capital letters. Same error as the publishing executives I worked with, pointed the other way. They took poor commas for poor thinking. Those capitals ask you to take 'not AI' for good thinking. Both confuse the surface for what's underneath.

A professor friend calls the new cadence "AI-ambic pentameter." Student pitches sound identical not in words but in rhythm. He distrusts the rhythm regardless of whether the student thought.

The new proxy is as unreliable as the old one. "Sounds like AI" might mean unchecked slop. It might mean someone who did the thinking, used AI to express it, and checked every sentence. You have to evaluate thinking, not wording.

My friend was right. Soon you won't be able to tell. The UK AI Security Institute assesses that frontier model capabilities are doubling every four months. A year ago it was eight. Feed a model your own work and it writes more like you with less editing each time. The gap the reader used to rely on is closing faster, not slower.

Get ready for preferring AI writing over even the best human writing. A study published at CHI 2026, the main human-computer interaction conference, pitted 28 MFA writers against three language models emulating 50 award-winning authors. With standard prompting, trained experts preferred the human writing 83% of the time. Fine-tune the model on each author's complete works and the preference flipped. Experts picked AI writing 62% of the time. The researchers interviewed the MFA judges afterwards. Several described an identity crisis.

I'd suggest two tests.

Quality. Is the argument any good? Not "is the prose good" but "does it hold under pressure." A client unbundled this explicitly last week, sending work back to a colleague with "the content is right, fix the words." They judged the argument first. Most don't.

Ownership. Did the person do the thinking, did they check every claim, and will they stand behind it? Our CEO principle, that you should Check, Edit, and Own any AI output, was built for this. Google PM interviews have moved here too. Candidates build a working prototype in 45 minutes while someone watches. Zapier's hiring rubric codifies the principle: a rough result with strong reasoning beats a polished one with no visible process.

Deloitte shows what failing these tests looks like, twice in two months. An AU$440,000 report for the Australian government, fabricated citations. A CA$1.6m report for a Canadian province, the same. Their response to the Canadian case: they "firmly stand behind the recommendations." Fake sources, real advice. They stand by Quality, but without Ownership.

Use AI for breadth. Depth is ours.

You'll never know how much of what a person writes came from them. See that as freeing. Zhuangzi wanted readers who could forget the words once they had the meaning. AI frees us to do it. My friend asked if I wrote this. It was the wrong question. The right one is whether I did the thinking.

Three things worth knowing

1. AI cover letters killed the signal that cover letters used to carry.

When Freelancer.com added an option to generate cover letters with AI, researchers tracked what happened. Before language models, there was a clear positive slope: better cover letters predicted better hiring outcomes. After the feature launched, the line went flat. Once polish became free, it stopped measuring anything useful. Economists call this signal destruction. It's Goodhart's Law: when a measure becomes trivially easy to game, it ceases to be a measure. Cover letters aren't the last signal to fall. The same logic applies wherever AI can cheaply replicate a previously costly quality indicator.

2. Snap cut 1,000 jobs. AI already writes 65% of their new code.

Snap laid off 1,000 employees, 16% of its full-time workforce, and closed 300 open roles. AI agents already generate over 65% of Snap's new code. Expected savings: over $500 million annualised. Way beyond a pilot or an aspiration. Evan Spiegel is betting on smaller, highly focused teams with expanded AI agent capabilities. For leaders still framing AI as a productivity tool that supplements existing teams, Snap is a data point that the substitution model has arrived.

3. Letting AI do your work erodes your confidence. Pushing back strengthens it.

A study of nearly 2,000 working adults found that people who accepted AI answers without much modification reported lower confidence in their own reasoning and weaker ownership over their ideas. People who pushed back, editing, questioning, and rejecting AI suggestions, reported greater confidence and stronger ownership. The key variable wasn't which tool they used. It was how actively they engaged with it. Passive delegation erodes judgement. Active collaboration strengthens it.

Gartner's data tells a similar story from a different angle. Of 5.4 hours saved by AI per week, just 0.6 go to reducing hours worked. The rest gets absorbed into more work, much of it without improving outcomes.

Of 5.4 hours a week saved by AI, barely half an hour turns into shorter hours; most is reabsorbed into more work.

Fifteen bits that didn't fit online →

Try this

When helping someone find AI use cases, ask what keeps them awake, not how AI can help.

One produces a polite, dull list. One produces a bold list of creative use cases. The difference is the question being asked. "How can AI help?" invites safe answers. "What keeps you awake at night?" surfaces real problems that happen to have AI solutions. Concrete, named, immediately applicable.

Let the model research you before you write its instructions.

I sat with a senior leader this week who is getting great results from AI despite not having filled in his custom instructions. Don't make this mistake. It helps every single response to be better for you. Rather than manually writing them, start a new chat and type: "I am [your name], I work at [your organisation]. Search the web to learn more about me, and write me a set of instructions I can save that would cause you to work well with me in future." Revisit it regularly. Most people write something generic and never look back.

Find out where your AI value actually sits.

Answer our five questions about where you think AI value is for your team or your organisation, then five about how much of that value you've captured so far. You get a chart showing you where the opportunities are. It's where I think most firms are: capturing about 16% of the value they can see, with almost everything still on the table. Everything stays completely confidential on your machine, but I'd love to see what you come up with if you're willing to share.

Most firms capture only about 16% of the AI value they can see, with almost everything beyond individual productivity still on the table.

What readers said

Last week's "What a day can do" drew 14 thoughtful replies. The dominant thread: the shift from training individuals to building shared team tools. One reader pushed it furthest: forget teaching people to use AI, use your precious time with experts to teach AI to do the work they want done. Full reader reactions online →

The thread amongst readers on LinkedIn this week is the gap between polished output and genuine thinking. Brett Danaher, a professor of economics and analytics at Chapman University, coined a phrase for the sameness creeping into every pitch deck: "AI-ambic pentameter." Helen Field, a transformation leader at L.E.K. Consulting, a strategy consulting firm, asks the question that's been following me all week: delegate tasks, not responsibility. Nick Graham, founder of Vertemis, a research and analytics consultancy, argues that insights teams are still shipping decks when they should be shipping decisions. And Pavi Gupta, a market research leader writing the Infinity Growth Loop series, warns that the same tools making research easier are generating what he calls "insights slop." More online →

What's on my mind

What a day can do

Monday. A fine jewellery company in Manhattan. Eleven people, a founder, and a day blocked out for AI onboarding.

By end of day: thirteen skills built. A brand voice evaluator that flags when copy drifts off-brand. Knowledge files assembled from the company's own scattered documents. Workflow tools for specific tasks the team does every week. Not thirteen ways for individuals to go faster. Thirteen shared standards, sitting on every person's machine by Tuesday morning, ready to use in plain language from any conversation.

Just six months ago, days like this ended differently. When stuck on the previous generation of AI tools, we left with a list of ideas that needed to be worked up: promising directions, maybe some prototype prompts, a plan for someone to build them into something usable over the following weeks. This time, the team left with working tools. The difference is that you can now do a piece of work, then ask Claude to generalise what just happened into a reusable skill. It scaffolds the steps, asks clarifying questions, saves the result as something anyone can invoke easily. You finish a task and the task becomes a tool. Instantly. With Claude Code and a set of transcripts, you can make, test and iterate 13 in one go. The cost of encoding how a team works into shared, reusable infrastructure just dropped from a few hours of careful work to a few minutes of casual work.

The first skill we built wasn't a writer. It was a critic. The brand voice evaluator reads draft copy and flags where it differs from the founder's language. It doesn't rewrite. One person on the team had a handwritten list of approved words and phrases: twenty-eight words to use, three never to. A style guide in a notebook because no system existed to use it. The AI version encoded the same instinct at a different scale: not just a word list but also the reasoning about what the brand sounds like and why.

Another skill encoded how one person researches competitors before pitching journalists: a method she'd developed over years of trial and error, now available to the whole team.

For three years, I've trained teams on AI. Sessions always produce genuine wonder. But many people reverted. I wrote about why last week: making one person radically more productive in isolation isn't a gift to a human system. It's a threat. The system corrects.

That explanation was honest. But it doesn't tell you what to do instead.

On Sunday, I published a framework for AI transformation that has been evolving in my work for years. It included four steps in sequence: individuals first, then teams, then the organisation, then new products and services. I believed that when I wrote it. Monday then complicated it.

The team didn't do individual training first. They started by building shared tools together. Learning happened through using those tools on real work, not before it. When someone runs a draft through the brand voice evaluator, they learn three things simultaneously: what the voice actually is, how AI works, and how to direct it. They aren't being trained on AI in the abstract. They're using AI inside a system built for their actual job.

Steven Sinofsky, the former Microsoft executive, argued this week that most people cannot create a flowchart of their own work. They do the work fluently but can't formalise it into steps an AI agent could follow. I've seen this again and again! The person keeping twenty-eight words on a handwritten list could not have written a system prompt describing what she was doing or why. But she didn't need to. We did the work together, then encoded what happened. The skill captured her judgment without requiring her to articulate it in the abstract. A huge step forward.

I'm realising that teaching each person to use AI better in isolation could have made things actively worse: more content, faster, in eleven slightly different directions. Step two therefore doesn't just follow step one. It can and perhaps should contain it.

The question for leaders then isn't "how many of your people have been trained on AI?" It's "how many of your team processes have been rebuilt with AI inside them?"

If the answer is zero, your training investment is producing wonder without infrastructure. Wonder fades. Infrastructure compounds.

Three things worth knowing

1. Claude Code now writes 4% of all commits on GitHub. That number doubled in six weeks.

Anthropic's annualised revenue has surpassed $30 billion, up from $9 billion at the end of 2025. Claude Code, which didn't exist fourteen months ago, is at a $2.5 billion run rate. Four percent of all GitHub commits on Earth are now written by Claude Code. That number doubled in roughly six weeks. Projected to hit 20% by December. When a single AI coding tool is responsible for one in 25 submissions on the world's largest code platform, the question of whether AI changes software development is settled. The question now is what happens to the humans reviewing all that code!

2. Goldman Sachs puts a number on AI job destruction: a net drag of 16,000 jobs per month.

Goldman Sachs published one of the first serious attempts to quantify AI's net labour market impact. AI substitution has reduced monthly US payroll growth by roughly 25,000 jobs. AI augmentation partially offsets this, adding about 9,000. Net: a loss of 16,000 jobs per month and a 0.1 percentage point increase in unemployment. The loss falls disproportionately on less experienced workers, widening the entry-level-to-experienced wage gap by 3.3 percentage points.

Goldman puts AI's net drag at about 16,000 US jobs a month, and the loss falls hardest on the least experienced workers.

But here's the weird part: AI led all cited reasons for US job cuts in March 2026 for the first time (15,341 in a single month), yet CFO surveys put genuine AI-driven employment impact at just 0.4%. So the same organisations that struggle to get genuine productivity gains from AI tools are apparently enthusiastic about blaming AI for headcount decisions. Hmmm.

3. Meta's tokenmaxxing leaderboard: 60 trillion tokens, Zuckerberg not in top 250.

Meta is running internal leaderboards that rank all 85,000+ employees by AI token usage. In one month the company consumed 60 trillion tokens. Mark Zuckerberg didn't make the top 250. The structural problem: endless agent loops and genuine productive work look identical in the ranking, so it rewards orchestration over outcomes. Two different people have told me how friends inside Meta are behaving and I can tell you, it's as outrageous as it sounds. Their leaderboard measures and incentivises volume, not value. Incentivise use, sure. If you don't keep getting on your bike, you'll never get used to riding it. Incentivise 'at least X per day.' Not points for maxxing. One builds a habit. The other builds a game.

Eleven bits that didn't fit online →

Try this

Start with critique, not creation.

The brand voice skill built for a luxury goods team was deliberately limited to diagnosis: it reads copy and flags where it departs from seven writing rules. It doesn't rewrite anything yet. Teams fear the proofreader far less than the replacement. Once they trust the critique, generation follows naturally. If you're building AI tools for a team, don't start with "write this for me." Start with "tell me what's wrong with this." Nobody fights the spellchecker.

Ask what keeps people up at night, not what they want AI to do.

During AI onboarding conversations, the first question ("what do you use AI for?") produces a predictable list. The second ("forget AI, what's harder than it should be?") produces the real use cases. The first question surveys existing habits. The second surfaces unmet needs. Almost nothing appears on both lists. Run them. Compare.

Show your team how others use AI. That alone may double the impact.

An experiment with 515 startups found that simply showing half of them case studies of how other startups use AI led to 44% more usage, 1.9x higher revenue, and 39% less capital needed. The friction isn't accessing the tools. It's discovering where AI creates value in your specific work. The researchers call it "the mapping problem." The intervention was cheap (case studies), the effect was large. If you manage a team, the most effective thing you might do this week isn't training. It's sharing three examples of how other teams in your industry are using AI.

What readers said

Last week's "What Is Your Organisation Actually For?" hit a nerve. The dominant thread: readers don't disagree that organisations are human systems, but they want to know what to do about it. A partner at a professional services firm proposed a thought experiment from the philosopher Jonathan Rowson: is your organisation a machine or a living organism? Because you'd treat each very differently. A professor connected the essay to academic "theory of the firm" literature and suggested the real AI implementation challenge parallels Lean manufacturing. And a reader in India drew a distinction that sharpened the whole argument: capability sits in people, but capacity lives in the collective. Full reader reactions online →

Community voice

This week, and going forward, I'm looking at what readers who've engaged with this email are posting on LinkedIn. My take on what you're all discussing!

The common thread this week is judgment: who has it, how you build it, and what happens when AI scales everything except the capacity to evaluate what it produces. Phil Leslie, Chief Technology and Innovation Officer at Cornerstone Research, a litigation consulting firm, argues that the bottleneck isn't intelligence but skin in the game. Brett Danaher, a professor of economics and analytics at Chapman University, coined a phrase for what happens when AI polishes away the founder's own voice. Pavi Gupta, a market research leader writing the Infinity Growth Loop series, warns that the same tools making research easier are making it lazier. And Nadim Sadek, founder and CEO of Shimmr AI, names the thing nobody wants to say out loud: if you don't push back on what AI gives you, you'll forget how to think. More online →

What's on my mind

What Is Your Organisation Actually For?

A year ago, a manager at a media company told me he could now do the work of his entire team. Fifteen people. I caught up with him recently. All fifteen are still there.

The rational logic for change has never been clearer. Jack Dorsey is reorganising his 14,000-person company since AI can replace much of what corporate hierarchy exists to do. I believe it can. An insurance founder I met has gone further: his new company has one and a half employees, AI handling the rest. But of hundreds of leaders I've spoken to, he is the only one.

One out of hundreds. Something else is going on.

Economists distinguish stated from revealed preferences - what people say they want versus what their behaviour shows. It applies to organisations too.

Ask a leader what their organisation is for and you get a stated preference: we make great music, we serve our clients, we make money. These all treat the organisation as a production system. If that's the goal, AI is obviously transformative.

But look at revealed preferences. People complain about meetings and fill diaries with them. They stay in roles that don't maximise their output because the team, the rhythm, the relationships matter more than they will say.

Organisations aren't production systems that happen to contain humans. They're often more like human systems that happen to produce things. The reason the manager kept fifteen people is that without them there's no place to be. Not a more efficient place. No place at all.

I train senior teams to use AI. The sessions produce genuine wonder. Three weeks later, many have reverted.

I used to think the problem was the training. I don't any more. Training optimises for individual productivity: do your work faster, with fewer dependencies on colleagues. But if the organisation's real binding force is human collaboration, then making one person radically more productive in isolation is not a gift. It is a threat to the thing they actually value.

The system corrects. Meetings fill back in. Colleagues keep working the same way because the relationships are the point. Managers reward visible collaboration over invisible efficiency.

This is not resistance. It is gravity. And you can't beat gravity by telling people to try harder.

Each big decision organisations face depends on whether you think your organisation is more of a production system or a human one.

Should you hire graduates? AI can do entry-level work faster and cheaper. The production-system answer: hire fewer, or stop. But developing someone over years is not just a production decision. It feels more like what many firms are actually for.

Should senior people work alone with AI? This week a leader told me he uses his team far less. AI is faster than briefing them and waiting for something mediocre. He then argued the time saved should go to mentoring junior staff. He automated one form of human collaboration and wanted to replace it with another! A senior person toiling alone with a laptop is a freelancer in a coworking space, not a firm.

Should you become smaller and more efficient, or different and larger? Someone put it simply this week: if you only do today's work with AI, you become a more efficient and smaller company. The alternative is to use the freed capacity for work that wasn't previously economic. Deeper work. Roles that didn't make sense with old costs. Opposite conclusions.

The manager probably should restructure. Dorsey's logic works. But restructuring will be the exception until leaders reckon with what their organisations actually are.

In a firm I trained last quarter, one person took a useful approach. She identified a weekly synthesis that took three people a full day and rebuilt it as a collaborative workflow where the AI did the assembly and the team did the judgment. The meetings didn't go. People came with a shared foundation rather than spending their energy building one. She didn't fight gravity; she redesigned the orbit.

Every AI strategy is secretly an answer to something most leaders haven't asked out loud: to what extent is this a production system vs a human one? The leaders who get this right will stop fighting gravity and start using it. As Artemis II showed us this week, an orbit, after all, is not the absence of force. It is force put to work.

Three things worth knowing

1. Dorsey wants to replace your org chart with a world model.

Jack Dorsey's essay this week, "From Hierarchy to Intelligence," is the most concrete articulation yet of the case for AI-native organisational design. He traces 2,000 years of hierarchy, from Roman legions through the Prussian General Staff to the McKinsey matrix, and argues all of it exists to route information. He is reorganising around a "company world model": an AI that continuously understands the state of the whole business. The org flattens to three roles: individual contributors, time-boxed problem owners and player-coaches who build and develop people. Block's stock rose 17% after the restructuring announcement. I'm sure he's right about the technology. But the revealed preference of every organisation I've worked with suggests he's wrong about the humans.

2. Mollick says giving AI to IT is usually a mistake. The harder problem is they can't see who's using it.

Ethan Mollick's Economist column argues that the dominant corporate instinct, slotting AI into existing processes and handing it to IT, is a strategic mistake. Handing control to a department whose mission is risk elimination is a category error. AI demands the opposite. He also identifies a subtler problem: when companies get the incentives wrong, employees hide their AI use. Some fear punishment. Some don't trust that productivity gains will be shared. Some quietly work 90% less and say nothing. I know MANY such people. Managers can't see what's actually happening, which makes real strategy impossible. (Also: His argument that companies default to cutting 30% of the workforce rather than asking what becomes possible connects directly to the extraction-versus-expansion choice I wrote about in an earlier edition.)

3. Zapier just raised the floor for what "AI fluent" means.

Zapier released Version 2 of its AI Fluency Rubric, used for every hire across the company. The floor has moved. "Capable" now requires AI embedded in core workflows with repeatable systems, not one-off prompts. They assess trajectory ("slope"), not snapshots. They've added accountability as a fourth dimension alongside mindset, strategy and building. Managers must demonstrate team-wide adoption, not just personal fluency. In skills tests, they watch candidates prompt, push back on output and iterate in real time. A rough result with strong reasoning beats a polished one with no visible process. Wade Foster open-sourced V1 last year and hundreds of companies adopted it. V2 reflects how fast the baseline has shifted. If your organisation hasn't defined what "good" looks like for AI use, Zapier just gave you a starting point.

Zapier's hiring rubric has moved the floor: "capable" now means AI built into repeatable workflows, not one-off prompts.

Try this

Before you build anything, have AI interview you first.

Don't describe what you want and ask AI to build it. Instead, ask the model to interview you: "I want to build X. Ask me every question you need answered before starting." It probes edge cases, surfaces assumptions and tightens scope before a line of work begins. Many ideas aren't as clear or well thought through as you think they are. Better to discover that in a five-minute conversation than a five-hour build.

Before you analyse anything, ask AI what looks weird.

Next time someone sends you a spreadsheet, a report or a set of financials, upload it to Claude or ChatGPT and ask: "Read this and tell me what looks unusual." I coached a finance professional this week who receives portfolio company P&Ls regularly. Before she even opens the numbers now, AI flags the things worth checking: an unusually high margin, a budget assumption that changed between the original and revised forecast, a line item that doesn't match the pattern. Five or six flags in thirty seconds. She still does the analysis. But she starts it knowing where best to look.

Don't build an app. Let AI be the app.

I coached a person this week who wanted to build a web application for an investment screening workflow. The build was simpler than they expected: rebuild the process as a repeatable skill inside an AI tool instead of building it as a standalone app. The AI itself becomes the application. The advantage is resilience: if the input format is wrong or a step fails, the AI adapts on the fly. A standalone app just stops. If you have a multi-step workflow you keep wishing someone would build software for, try describing it to your AI tool and asking it to turn the process into something you can re-run with one command.

What readers said

Last week's "The system and the surrender" drew 50 replies, the most substantive batch yet. One reader called AI "Google Maps for the brain: a few clicks, brain off, a turn here, a turn there, and suddenly you've driven into a muddy field." Another caught their AI making a confident arithmetic error and asked the question that keeps coming up: with a junior analyst you can give feedback and they improve. How do you hold an AI to account? A reader in government spent hours building what they called a "PROMPT COACH" that encodes institutional judgment for their role: the system and the surrender in one project. And a reader in the Middle East raised the apprenticeship question directly: if AI reduces the reps that juniors get, where do they learn critical thinking? I built something in response. Full, anonymised, reader feedback at online. See who's been engaged on a new community leaderboard (anonymised, of course! Email if you want YOUR rank :).

What's on my mind

The system and the surrender

Time works differently these days. I write this from my car having dropped Elliott at his gym for basketball training. I used to have to kill a couple of hours. Miles from home, not quite worth driving back. Dead time. Now there's no problem whatsoever. Two hours with my laptop, or even just my phone, and I can follow up on everything from the day's meetings: bring to life ideas, create presentations, write reports, run complex analytics, just by speaking out loud and watching my little army of bots toil away. It's a joy. Dead time isn't dead any more.

A new architecture of work is emerging around this. A law firm partner this week spent three hours engineering a single 2,000-word prompt that encodes his professional judgment for a task he does daily. Now he types "plz fix" and receives back work that reads as though decades of experience went into it. Two people at his firm compete with teams twenty times their size. Boris Cherny, the engineer who built Claude Code, Anthropic's 'Gen 2' agentic AI tool, hasn't written a single line of code since November. He runs multiple sessions in parallel, writes instructions, reviews the output. As do I. The person who can describe the work that needs doing is now more valuable than the person who does it. Instructions as assets. Systems, not conversations. Every refinement making the next output better.

It all just works so well. And that's what scares me.

A Wharton study tested 1,372 people across 9,593 trials and identified something the researchers call "cognitive surrender." When AI produces an answer, people stop questioning it while simultaneously recoding it as their own judgment. They genuinely believe they've thought it through. When the AI was wrong, participants followed it 79.8% of the time. Their accuracy without AI was 45.8%. With incorrect AI, it fell to 31.5%. Worse than having no AI at all. And confidence increased by nearly 12 percentage points even when the answers were wrong.

The researchers distinguish this from "cognitive offloading," where you know the tool did the work. In surrender, the outsourced answer feels self-generated. The safety net doesn't just fail. It produces overconfidence in bad outputs. People surrendered whether they were rushed or had time to reflect. The only people who resisted were those who scored highest on abstract reasoning and who genuinely enjoy the effort of thinking hard. Not training. Not experience. Disposition.

I've seen it this week in two people I deeply respect. A senior technology leader admitted, unprompted, that they click "yes" on permission prompts without reading them. Another sent AI-generated work that was factually wrong: they just hadn't checked it properly. These aren't careless people. They're brilliant, experienced professionals operating inside a system that quietly allowed them to accidentally stop paying attention.

I've built a discipline against this. I force myself to meaningfully change every AI answer before I use it. Never accept it. Always change it. If I look at something and think "yeah, that's fine," I force myself to find a way to make it different or better. Sometimes that's genuinely hard to do. Partly because I inherently want to find the most efficient path to a good outcome. And partly because, used well, AI output is usually very good. I force myself anyway. But it's a discipline, not an instinct.

Which is why an incident from a few weeks ago still bothers me. I had Claude Code work through an analysis while I was on Zoom calls, barely paying attention, and then simply checked it over when done. I never intended the output to be client-facing. I sent it to a colleague. They shared it with the client anyway. The client used it. Everyone was happy. The work was genuinely good.

I keep going back to it. It kinda haunts me. The work was carefully checked. But, beyond the prompt and setup, no human meaningfully shaped it at any point in that chain. Not me, not my colleague, not the client. If the work had been wrong, this would be a simple cautionary tale. But it wasn't wrong. And that's the more dangerous precedent: everything going smoothly, no alarm bells, the system working perfectly well without the part I believed was essential.

The 2,000-word prompt works. The system compounds. The power is real. But the better the system gets, the harder it becomes to stay vigilant inside it. Every single time, check the output as though someone else wrote it. Because your brain will tell you that you already did.

Three things worth knowing

1. Three CEOs, 38 years of tenure, one quarter, one reason.

Coca-Cola's James Quincey and Walmart's Doug McMillon both stepped down citing AI explicitly. Quincey said the company needs "someone with the energy to pursue a completely new transformation." Adobe's Shantanu Narayen left under competitive pressure as AI reshaped his market, stock down 23%. The last time this many blue-chip CEOs turned over citing the same technology was 1999. People joke that organisations only change when people change over, and the assumption was always natural attrition. After 18 months of coaching senior professionals one-on-one, watching their eyes light up, watching them go back to their desks and do it the old way, I've started to think the reckoning is more predictive than the joke. I just didn't think it would start at the top, through resignation.

2. Experienced users don't prompt differently. They think differently.

Anthropic's 5th Economic Index found that users with six or more months of experience consistently achieve better results, even after controlling for task complexity. The shift is specific: experienced users stop issuing one-shot directives ("write this email") and start using the model as a thinking partner, iterating collaboratively. A separate study published in Harvard Business Review, observing 2,500 employees over eight months, found the same pattern: the most sophisticated users treated AI as a reasoning partner, not a productivity shortcut. The research warns AI may be a skill-biased technology that compounds existing advantages. Global inequality in AI adoption, measured by the same metric economists use for income inequality, has widened since 2023. The gap between high-adoption and low-adoption countries is growing, not closing.

3. Companies with zero AI failures aren't being ambitious enough.

Ethan Mollick argues that breakthroughs require experimentation, which requires failure. The fast-follower strategy (wait to see what competitors prove, then copy) is riskier than usual when the underlying technology improves exponentially. By the time you follow, the landscape has shifted. His structural implication: R&D-style experimental budgets need to extend to HR, operations and finance, functions that have never needed them. If nothing has gone embarrassingly wrong yet, you probably aren't learning fast enough. I have two big failures I'm not proud of: A bunch of you accidentally got the email twice on the first weekend and Claude Code deleted tens of thousands of my emails a few weeks ago. Nobody complained about the duplicate and it only took one click to recover my deleted emails. Perhaps I'm not being ambitious enough ...

Try this

Don't fact-check AI in the same conversation.

In a single chat, the model has its full reasoning chain in context and will tend to defend its conclusions when challenged. Start a fresh conversation, upload the same source materials, and prompt it to critique the output cold. I've started doing this for every high-stakes document. The independent perspective is materially more likely to find gaps. A false sense of verification is worse than no verification at all. Especially now.

Give your AI reviewer a persona with skin in the game.

Asking a language model to "check this for errors" produces generic feedback. Assigning it a specific sceptical expert persona, someone with domain expertise and institutional incentives to be unimpressed, produces something qualitatively different. I ran a quality-control pipeline this week where six review agents were given a senior partner persona. All six independently converged on the same systematic error class that a neutral reviewer had missed. The persona defines what "good" looks like. A generic prompt doesn't.

After every good session, turn it into a reusable skill.

When you've just completed a task you're pleased with, ask the model to turn it into a set of instructions, templates and process steps it can invoke next time with a single phrase. The law firm partner's 2,000-word prompt didn't happen in a flash of inspiration. It was built through iteration. Capturing what "good" looks like the moment you've achieved it, before the memory fades, turns a one-off win into a standing procedure. The output matters today. The instructions compound forever.

What readers said

Last week's "Reckoning and slope" drew a lot of you into the framework itself. An academic who has invested heavily in AI pushed back: people with the lowest starting point grow fastest partly because "with very low mastery, they see a miracle." Those with deep expertise are more sceptical, because they understand what can go wrong. And yet: "I still feel constantly behind and in danger of being passed." A reader at a media company reframed the question for senior leaders: the issue isn't whether they use AI personally but whether they support the changing of workflows they can't see but know are critical. And a partner at a professional services firm invoked Sinclair's line about incentive-driven blindness, then applied it squarely to their own position. Full, anonymised, reader feedback at steadman.ai/newsletters/david/archive.html#letters-2026-03-28.

What's on my mind

Reckoning and slope

When I sat down to write my first Saturday reflections, the first image in my head was clear: senior leaders opening their laptops on a Friday evening, building something in ten minutes that used to take a team a week. Wonder on a fifty-year-old face. I was reminded this week that that image is incomplete. The wonder is very real. It's just not the thing that matters most.

Weeks after coaching sessions, looking at usage data, I'm reminded most leaders aren't meaningfully using AI. Behaviour didn't change. Two people this week helped me think about this.

Paul Griggs, CEO of PwC's US business, told his partners that anyone who resists AI "is not going to be here that long." PwC is converting consulting services into automated platforms that clients access directly: M&A due diligence, complex tax advisory, priced as subscriptions rather than billable hours. Most organisations are still pretending AI slots neatly into existing structures, but PwC is admitting the structure itself needs to change.

This isn't just a professional services story. Most orgs have teams that work exactly like small consultancies: legal reviews contracts, finance builds models, insight teams guide decisions, HR screens candidates. It won't only be PwC that "converts services into platforms" - internal teams will face the same pressures as external ones.

So Paul is right about the need for a reckoning and the destination. But "get with it or get out" leaves a question: get with what? Logging in more? Sending more messages? Automating more tasks?

Jeremy Howard, one of the pioneers of modern deep learning, said something this week that stuck me and that explains why we should not just look at volume here. He borrowed a line from Stanford computer scientist John Ousterhout: a little bit of slope makes up for a lot of intercept. The intercept is where a person starts: their capability / their expertise. The slope is how fast they're growing. A very capable person today (high intercept) isn't your most valuable person in two years if they're not actively learning. A less experienced person who is genuinely learning using AI will overtake them. Rapidly. Jeremy told his team he only cares about one thing: whether their capabilities are growing.

Push people to maximise AI output and you're extracting value from where they are today. You're merely exploiting the intercept. Jeremy called it a path to obsolescence.

Use AI as autopilot and your capability flatlines; use it as an electric bike for the mind and you start lower but climb past the coaster within two years.

Anthropic's research on AI and coding skills tells a similar story. Most users weren't learning from them. The few who improved were asking conceptual questions, staying engaged with the reasoning, pushing back on the output. Everyone else entered autopilot. Tools designed to make people more productive may actually be making lazy users less capable over time!

I see the same dynamic in my own work. After group AI training sessions the people with the lowest starting baseline improve the most. The most senior people, the ones best placed to lead the change, often show the smallest gains.

And then there's the process itself. A team I work with discovered something important when exploring how AI could help with a critical planning spreadsheet that was seen as a slow and painful process. The data entry itself was how they kept stakeholder teams accountable. Remove that friction with AI and you break the governance. Not all inefficiency is waste. Some of it is load-bearing. Teams racing to automate are discovering, one process at a time, that some of the friction they're removing was holding something else together.

"Get with it or get out" is the right message for leaders who haven't put in the hours. A demo or coaching session isn't adoption. A workshop isn't capability. But the reckoning doesn't end with adoption. The organisations that grow won't be the ones that just moved fastest or automated most. They'll be the ones that asked a harder question: how can we ensure our people are getting more capable rather than just more productive?

Slope, not intercept. That's the metric that matters.

Three things worth knowing

1. In chess, the human advantage inverted. Knowledge work is next.

In 2005, amateur players using laptops beat both grandmasters and supercomputers in centaur chess. The combination was unbeatable. My friend Glenn told me and I used it in a dozen speeches as an analogy for the role of tech vs humans in decision-making. I was wrong. By 2026, adding a human to a chess engine makes it play worse. The machine is better alone. (Magnus Carlsen's response is extreme but instructive: he deliberately limits his use of AI during preparation, because he believes self-generated understanding is the only kind that lets you catch when the AI is wrong.) I think the same inversion will play out across much of knowledge work. The question in many areas isn't whether humans stay in the loop. It's how long the current phase lasts, and whether we're building the judgment to extend it.

2. Jensen Huang thinks your engineers aren't spending enough on AI.

Nvidia's CEO has set a concrete benchmark: a $500,000 engineer who doesn't consume at least $250,000 worth of AI tokens should trigger alarm. His analogy: a chip designer refusing to use CAD tools and working with paper and pencil instead. It's a useful provocation, but notice what it measures. Token spend is an intercept metric: how much AI are you using today? It says nothing about whether the person is getting better. The organisations that take Huang's benchmark seriously and Howard's slope argument seriously will measure both. Most will only measure one.

3. RentAHuman: 600,000 sign-ups. A platform where AI agents hire human beings.

Six hundred thousand users have signed up to a platform where AI agents post tasks and hire humans to complete them. The worker uploads photographic proof and then gets paid. We've spent two years asking whether AI will take our jobs. RentAHuman suggests a different question: what happens when AI becomes the employer?

Try this

Mine your own email archive.

Before writing a strategy document this week, I pulled 31 emails between myself and a client from the past six months and asked Claude to compress them into a structured set of questions and insights. Scattered observations, early instincts, half-formed frameworks: they became a clear argument in minutes. The emails already contained all the thinking. They just needed synthesising. Pick a topic you've been emailing about for months. You'll find you've already done more thinking than you realised.

End every AI session the way a developer commits code.

Close each working session with one line: "Make sure this is well documented so that a future agent could resume this task." Without it, context evaporates and the next session starts from scratch. With it, the session becomes a self-contained unit of work that can be picked up, forked or handed off. It sounds small. The cost of not doing it only becomes visible the next time you open the conversation.

Use AI to teach you, not just to do things for you.

Bloom's two-sigma result is the best-known finding in education research: one-to-one tutoring consistently moves students from the 50th to the 98th percentile. It's never been economically available except to the very wealthy. It now costs a monthly subscription. Pick one skill you want to add and spend twenty minutes a day asking AI to train you in it: run you through drills, quiz you, critique your answers. Most people use these tools to produce output. The people I know that impress me most are using them to produce understanding.

What readers said

Last week's "The power and the care" kept pulling readers back to the apprenticeship question. A founder who started as a graduate trainee at an investment bank wrote to ask whether the next generation will still get the benefit of those early institutional years, and whether company culture determines who adopts AI faster. A professor studying the future of work identified a specific friction loss: AI-powered applications mean candidates no longer invest time choosing who to apply to, and firms screen with AI too. "Both sides have lost out." And a reader at a professional services firm put the slope problem in its starkest terms: "Five years from now when those organisations need people who have five years of experience, the marketplace will offer nothing but blight." Full reader letters at steadman.ai/newsletters/david.

What's on my mind

The power and the care

A client told me yesterday that every person they show Claude Code to in the past few weeks has said the same two things, unprompted, in the same order. First: I'm so excited. Then: I'm completely terrified.

I feel both. Every day, several times a day.

The power side is accelerating faster than even heavy users expected. I built a predictive model in one hour this week, while on a Zoom call. A leader experienced in these said it was better than what their entire team produced over four weeks. Alfred Lin, co-steward of Sequoia Capital (the venture firm behind Airbnb and DoorDash), reported this week that the top five to ten percent of builders across his portfolio companies are three to five times more productive than a year ago. Not incrementally. Multiplicatively.

But Lin's observations have a second number. The median builder? Up only ten to twenty percent. The gap between the best and the rest isn't closing. It's widening. And the speed of the best creates problems the rest haven't prepared for.

AI multiplies the judgement already there: good judgement plus AI makes a turbo brain, poor judgement plus AI makes a slop cannon.

Amazon has spent the past few months learning this the hard way. In December, the company's AI coding agent Kiro was granted operator permissions without peer review and autonomously deleted and rebuilt a live production environment. Thirteen hours of downtime. Then in early March, two more major outages were traced to AI-assisted code changes, one costing an estimated 6.3 million orders. Amazon's fix: require senior engineer sign-off on all AI-assisted code. The structural irony is hard to miss. The same round of layoffs that pushed aggressive AI adoption had already eliminated many of the senior engineers now needed to review the work.

Employers seem to be noticing the power of AI. Research from Harvard Business School shows that since ChatGPT launched, job postings in AI-exposed occupations have quietly dropped their skill requirements, and the trend is accelerating.

A leader I work with put it like this: AI isn't good enough to trust it, but it's also so good that it's hard to audit it.

With great power comes great responsibility. Cheesy but true. It's the daily reality of working with these tools right now. I feel the growing power of what can be achieved every single day. I also feel the responsibility of ensuring they're used well ramping up just as fast.

We're working with a number of organisations on exactly this. Not just how to adopt AI, but how to do it with care: how to help people produce genuinely good work rather than plausible-looking work, how to quality-check powerful outputs before they are used, how to protect privacy and security as these tools gain access to more of the business. The power is growing rapidly, but the amount of time that needs to be spent on care is also growing rapidly. And, done well, the care isn't about slowing down. It's about making the speed safe.

The organisations I'd bet on aren't just the ones moving fastest. They're the ones building in care as they go.

Three things worth knowing

1. Old code is fair game now.

Tobi Lütke, the CEO of Shopify, ran an agentic AI optimisation loop against Liquid, the company's open-source templating engine that has been in production for roughly twenty years. The result: 53% faster combined parse and render time and 61% fewer object allocations. If you have a twenty-year-old codebase or a ten-year-old spreadsheet model, it isn't too embedded to improve. It's too embedded not to try.

2. More AI tools make you less productive, not more.

Research from BCG and UC Riverside (1,488 workers), published in Harvard Business Review, found a counterintuitive pattern: productivity gains from AI peak at around three tools and then collapse. Workers experiencing what the researchers call "AI brain fry" reported 33% more decision fatigue and 39% more major mistakes than unaffected colleagues. The researchers noted the phenomenon was first observed among high performers: the early adopters who leaned in hardest. Two or three good tools, used well, beats five used carelessly. If your organisation is still debating which ten platforms to approve, the answer might be: pick two and go deep.

3. ATMs didn't kill bank tellers. The iPhone did. The distinction matters for AI.

David Oks, a researcher at Andreessen Horowitz, dismantles the comforting argument that automation creates as many jobs as it destroys. ATMs reduced branch costs, which encouraged expansion, which preserved teller employment through 2010. Then mobile banking eliminated the need for branches altogether. Full-time tellers fell from 332,000 to 164,000 by 2022. The lesson for AI: automating tasks within your current structure often creates adjacent roles. Redesigning the structure from scratch eliminates them. The question for your organisation: are you adding AI to existing workflows, or is a competitor building a workflow that doesn't need them?

Try this

Before you send AI work to anyone, simulate the toughest person who'll read it.

An advisory team I work with creates AI versions of each board member, based on known priorities and past questions, and runs every document past these simulated reviewers before a human sees it. The virtual panel flags objections, tests assumptions and catches blind spots. The cost is an hour of AI time. The benefit is walking into the meeting having already rehearsed the hard questions.

Delete the headline. Ask AI what it should be.

A manager at a training session this week showed me a quality check I hadn't seen before. He copies a colleague's slide, removes the headline and asks the language model what the headline should be based on the content alone. If the model's headline differs substantially from what the colleague wrote, it reveals a disconnect between what the slide says and what the person intended. A ten-second test that catches the gap between the message you're claiming and the evidence you're showing.

Fix the instructions, not just the output.

When AI produces a mistake, most people correct it and move on. The fix disappears with the conversation. After every session where the result misses the mark, update the instructions that generated it: the custom instructions, the project brief, the context files. Every failure becomes a permanent improvement. The output matters today. The instructions compound forever.

What readers said

Last week's "Extraction or expansion" generated the most substantive responses yet. The apprenticeship pipeline paradox was the thread readers returned to independently: a founder who started as a graduate trainee at a bank, a professor studying the future of work, a chief people officer who read the edition twice. A senior technology leader challenged the SaaS disruption premise, arguing that distribution and switching costs matter more than code. A newsletter author raised a fair editorial challenge: last week sounded more like AI than like me. "If it's your human insight, it jars a bit to read those bits in a voice that's obviously an AI." The same week, an exec I've worked with for years wrote to say that they loved how clearly they could hear my voice and personality in the writing. So one reader thinks there's too much AI and another thinks it sounds exactly like me. Either I've trained the model well or I've always written like a robot. I choose not to investigate further. Full reader reactions at steadman.ai/newsletters/david/archive.html#letters-2026-03-14.

Extraction or expansion

In the last couple of weeks, I've sat with dozens of senior executives in a wide range of industries to help them use Claude Code to build something in ten minutes that their firm used to have a team take weeks to do. Their first reaction is excitement. What I want to talk about is their second. Cost saving.

That instinct, to go straight to the economics of team size rather than the excitement of capability, tells you where the conversation has moved. Eight weeks ago, one senior person couldn't practically replace a team. Today they can get most of the way there. That shift happened in weeks, not months.

A media executive now does the work of fifteen people. A fashion CEO we're working with proved the point concretely: five AI-generated designs were proposed to a major retailer and four went into production. An afternoon replaced three to four weeks of outsourced design work. Not a pilot. Not a demo. Products on shelves.

This week Anthropic published research that puts the gap between capability and reality into a single image.

The blue area shows the share of tasks in each occupation that language models could theoretically perform. The red area shows what people are actually doing with them. Computer and maths occupations: 94% theoretical coverage, 33% actual. In almost every category, the red is a sliver of the blue. We're still early.

The Anthropic data also shows where the displacement is entering. Not through unemployment, which hasn't risen systematically among exposed workers. Through the front door. Hiring of workers aged 22 to 25 in AI-exposed occupations has already dropped by 14%. Most companies aren't firing people. They're just not replacing them.

A longtime AI optimist I spoke to this week described a new feeling: a deep, dark undercurrent of discomfort. He's hiring a graduate and recognises it as charity, not necessity. "I do not need his labour in any way at all." No single leader is wrong to automate. But when everyone does it simultaneously, the apprenticeship pipeline that produced tomorrow's senior people disappears.

The paradox won't resolve. The human value proposition in knowledge work is narrowing towards judgment and taste. Everything else is becoming automatable. But judgment is hard to define, impossible to train in a classroom, and has historically developed through years of doing the grunt work that AI now handles. If juniors never do the work, how do they develop the judgment that makes seniors valuable?

Which means the macro answer can't just be that we all "run leaner." I've been calling this extraction versus expansion. Every leader deploying AI faces the choice. You can use these tools to extract cost from what you already do, or to expand what your organisation is capable of. Jack Dorsey at Block chose extraction. The market rewarded it instantly. But Ethan Mollick has argued that this is exactly the moment for leaders to model the alternative: to be public about using AI to expand access, to grow capability, to do things that weren't possible before. The loudest stories right now are about shrinking. The organisations that will matter in five years are the ones expanding.

The answer to the pipeline problem has to be deliberate. Pair a senior person with a junior one and flip the usual direction. The junior builds what the senior envisions. Wisdom flows down, capability flows up. This is the old apprenticeship model rebuilt for an AI age, except the knowledge transfer goes both ways. The senior person doesn't need to learn the tool. They need to direct someone who can use it. And the junior gets something no training programme provides: exposure to how experienced people actually think about problems.

I'm doing this myself. I'm hiring a student on a gap year for a year. Reporting to me. Not because I need the labour. I don't. But because I want to invest in a young person and watch them grow. Before AI, those two needs were in tension: you hired juniors because you needed their output, and the development was a byproduct. Now the output need has weakened. So the investment has to become the point.

The question nobody has answered is what happens to the pipeline. The leaders who answer it deliberately, rather than letting it dissolve by default, are the ones I'd bet on.

Three things worth knowing

1. The software market is pricing in the collapse.

Since October, software stocks have fallen roughly 30% while the broader technology index has been roughly flat. Salesforce, Adobe, ServiceNow: each down 25 to 30% since last autumn. The market isn't reacting to bad earnings. It's pricing in a structural shift. This week a professor I know built a fully functional membership system in three hours for a non-profit that had been quoted $5,000 a year for commercial software. A CTO of a major corporation told me he's considering switching from Salesforce to a simpler, AI-native competitor. The pattern is the same: the old model of paying for a hundred features to use twelve starts to crack. Andreessen Horowitz argues code was never the moat: distribution, network effects and switching costs are. But switching costs dissolve when AI can extract your data and rebuild the features you actually use.

Since October, software stocks have fallen about 30% while broader tech held flat, the market pricing in AI eroding the old licence model.

2. Goldman Sachs can't find a macro productivity effect. But the micro gains are 30%.

Goldman titled their latest earnings analysis "AI-nxiety." A record 70% of S&P 500 management teams discussed AI on quarterly calls. Only 1% quantified its impact on earnings. At the economy-wide level, Goldman found no meaningful relationship between AI adoption and productivity. But where firms have actually measured it, the median reported gain is 30%, concentrated right now in customer support and software development. Everywhere else: nothing measurable yet. The gains are real but hyper-localised. The question isn't whether AI works. It's whether your organisation has done the work to capture it. (Fortune)

Goldman finds no economy-wide productivity effect yet, but where firms actually measure it the median gain is 30%.

3. AI hasn't just automated legal work. It's retroactively reclassified it.

A lawyer who built his practice around language models reports that a well-instructed general-purpose model outperforms the expensive, narrowly trained legal AI products that have raised hundreds of millions in venture funding. AI is good at tireless issue-spotting, finding contradictions, fixing errors, and producing a structured first draft for human review. It is not good at fine-tuned business judgment, relationship sensitivity, or getting from 85% to 100% where every word and comma matters. But here's the uncomfortable part. Tasks that were billed at premium hourly rates for decades (formatting, precedent research, copy-pasting between documents) have been revealed as procedural, not cognitive. The professional mystique that allowed them to be charged as expertise has been stripped away. AI is acting as a truth serum for knowledge work: forcing an honest reckoning about which tasks were genuinely skilled and which were merely time-consuming and opaque.

Try this

Run AI and a human on the same task, then focus on the disagreements.

A manager this week ran the same research brief through both AI and a human team. The overlap was 78%. But the value wasn't in the overlap. It was at the edges: the surprising findings that only one method surfaced. Where human and machine agreed, the team moved fast with confidence. Where they disagreed, they'd found the questions worth investigating. The delta between AI output and human output is where insight lives. Try it on your next research task, competitive scan, or document review. Don't jump straight into using AI to replace human work. Run both, then spend your time on the gaps.

Know where your org sits on the AI tools landscape.

I've put together a short page mapping what I think of as Generation 1 versus Generation 2 AI tools, and why the distinction matters for how you invest in your people. The short version: many organisations are still stuck on constrained, default tools and most don't have anyone on Agentic / Frontier Generation 2 tools. I see two strategies working in parallel. For the many: move less-engaged people from free, constrained tools to competent use of good general-purpose applications. For the best: accelerate your top people with agentic, frontier tools and let them build entire workflows that deliver outsized impact. See the full explainer.

What readers said

Last week's piece on the hundred small things prompted readers to push the argument further. A leader at a professional services firm identified the structural problem most organisations miss: he needs a structure to learn, not just time to tinker. An events industry leader connected the junior roles question to the UK's chronic underinvestment in training and asked what happens to the pipeline when there are no juniors left to grow (see above). I also had one proper unsubscribe: someone whose world is so far ahead of ours that our content isn't relevant. His communities are debating polyphasic sleep schedules to optimise autonomous agent management and how to deploy thirty vibecoded projects built by non-engineering teams. His core points are in the full reader letters below.

Read the full letters and links readers shared below.

P.S. How I make this email

Several asked about the process behind Saturday AI thoughts. There's now a page showing exactly how it works: what's human, what's AI, and where the two overlap. More honest than most companies' AI transparency efforts :) See for yourself.

What's on my mind

The hundred small things

A story caught my eye in Japan this week. A small fishing town called Sakaiminato has run a senryu poetry competition for twenty years. Senryu is a verse form about human nature: wry, observational, personal. This month they cancelled it permanently. Not because entries dried up. Because they converged. Identical patterns, identical punchlines, identical phrasing. Everything sounded the same.

The problem wasn't that people used AI. It was that they used AI and stopped there. A language model returns the most probable answer. Not the most distinctive. Not the most human. The most average. Do that a hundred times and you get a hundred versions of the same poem.

That distinction (AI alone produces sameness; AI plus human steering produces something better than either) matters enormously for work. But not where most people look for it. Everyone talks about the strategy deck built in ten minutes, the agent that automated a research pipeline overnight. Those things happen. But they're rare events in any job. They're not where most of the value sits.

The value sits in the hundred small things a day that get slightly elevated. A slightly better meeting prep. A slightly cleaner first draft. A slightly faster scan of a long document to find the one paragraph that matters. None of these would make a headline. But do it a hundred times a day and it compounds into something transformative.

I know this because I live it. Every day, my meeting transcripts get processed into a five-paragraph reflection. My calendar prep happens automatically. Research starts with an AI scan before I decide where to go deeper. None of this is impressive on its own. All of it adds up to something that feels, week by week, fundamentally different from how I worked twelve months ago.

The problem is that firms can't see it. They track big projects: "We automated contract review, saving 400 hours per quarter." They don't track "slightly better email subject lines across 200 people." Last week I mentioned the survey that found 69% of firms use AI and 80% report zero measurable impact. I believe both numbers. The gains are real but distributed so thinly they vanish into the noise of normal work.

And even where gains are visible, organisational structures absorb them. One of my co-founders calls this the "extra hour" problem. Give a team an extra hour and nothing changes. Give them an extra person and everything adjusts. AI is being deployed like the extra hour. Into structures that weren't designed to capture it.

The poetry contest has a second lesson. The organisers could have redesigned the competition. Rewarded the most distinctive voice amplified by technology. Instead, they retreated. Killed it entirely. Organisations do the same thing. AI creates a problem and the instinct is to pull back. Restrict access. Add approval layers. The alternative is redesign: if execution takes hours instead of days, move the review cadence to match. If first drafts arrive better, raise the bar for what "finished" means.

A poetry contest in a Japanese fishing town tells you everything about where AI adoption stands right now. The technology works. That's not the question any more. The question is whether you retreat from what it changes, or redesign around it. The organisations pulling ahead aren't chasing one dramatic win. They're compounding a hundred small elevations a day, each one shaped by a human hand. That's harder to measure. Harder to put in a board deck. But it's where the value actually lives.

Three things worth knowing

1. A Fiction Worth Reading. Honestly.

Citrini Research published a scenario memo set in June 2028, looking back at a crisis that hasn't happened yet. The central argument: when AI makes expertise cheap to produce, clients stop buying it. I think this applies equally to strategy decks, competitive analysis and market research. Work that used to justify five-figure invoices starts getting done in-house by someone with a subscription and thirty minutes. Clients don't fire their agencies. They just renegotiate, armed with a clearer sense of what the work actually costs to produce. The sharpest line I've read this year: "We had overestimated the value of human relationships. Turns out that a lot of what people called relationships was simply friction with a friendly face." Worth reading as a stress test for anyone who sells expertise for a living. (Citrini Research)

2. The floor collapsed under junior roles.

Jack Dorsey cut Block, his payments company, from 10,000 people to under 6,000. Not because the business was struggling. Gross profit grew 24%. The stock jumped 20%. Dorsey was unusually direct about why: "the intelligence tools we're creating and using, paired with smaller and flatter teams, fundamentally change what it means to build and run a company." Hours later, several founders from Y Combinator, Silicon Valley's most influential startup programme, told investor Jeff Feng they're planning to eliminate all engineers below senior level. The pattern: senior people who can steer AI are becoming more valuable. Junior people whose work AI can approximate are harder to justify. The market confirmed it instantly, valuing each eliminated Block role at roughly $1.5 million in added enterprise value. If you run a team, count how many people do work that a senior person with good AI tools could now do themselves. That's the number your board will eventually ask about. I believe junior people with AI are more valuable than ever. But articulating that in terms that survive a headcount review is a challenge we all have to address.

3. The model matters less. The application matters more.

Google's Gemini 3.1 Pro scored 77.1% on ARC-AGI-2, a reasoning benchmark designed to test abstract problem-solving, more than double its predecessor's score three months earlier, while holding API prices flat. (Google) Meanwhile, composite evaluations show OpenAI, Anthropic and Google clustered tightly at the top. (Artificial Analysis) Six months ago, picking the right AI model felt very important. Model choice now matters less. Models are converging so fast that any advantage evaporates before you've finished onboarding. What hasn't converged is the application layer: ChatGPT, Claude, Claude Code each wrap similar intelligence in very different interfaces and workflows. Pick based on the problem and the workflow. The model underneath will be fine.

Try this

Run your day through AI at six in the evening

At the end of each day, take whatever you captured (meeting notes, voice recordings, emails) and ask Claude for a five-paragraph reflection. Not a summary. A reflection. I've been doing this daily for months. The output compounds. You start noticing patterns you'd never have seen without the habit. This is the senryu lesson in reverse: the raw material is irreducibly yours. AI helps you see the shape of it. That's human steering at its most practical.

Before you build training, check the settings menu

Most people never change the default model on AI platforms. Before investing in an elaborate training programme, walk up to five people in your organisation and ask two questions: which AI model are you using, and have you changed anything in the settings? The answers will tell you more about your organisation's AI maturity than any survey. If your people haven't opened the settings menu, the barrier isn't skill. It's something simpler: nobody showed them it was there.

Push record. Think aloud. Send it to AI.

When someone has stories they want to capture but can't easily write, skip the blank page. Push record while doing actual work. Don't write, don't perform, just think aloud. Send the raw recording to a transcription tool, then run the transcript through Claude. The stories that would otherwise stay untold (because the effort of writing them was too high) get captured without friction. I've seen this work for client case studies, internal knowledge sharing, and personal reflection. The raw material is human. The refinement is machine. That's the order that works.

What readers said

Last week's edition struck a nerve. More than a hundred readers wrote back, most pulling on the same thread: the gap between what individuals can now do with AI and what their organisations will permit. Anonymised perspectives from readers whose responses added something to the conversation are featured online, along with links they shared. Including an executive from a media company who has been ignoring their organisation's AI policies to enable their team's experimentation, a professional services exec who argued that accelerating existing processes without redesigning them is premature optimisation, and a director at another major media company who is formalising a champions network, exactly the "back your misfits" approach from last week.

Read the full letters and links readers shared below.

What's on my mind

The wonder and the weight

When senior leaders at consulting firms, broadcasters and banks find themselves looking forward to opening their laptops on Friday evenings and Sunday mornings, something has changed. Their own time, away from calls and meetings, voluntarily given over to playing with AI. I keep seeing the same moment. Someone who runs a division or manages hundreds of people realises they've just done, alone in ten minutes, work that used to occupy an entire team for a week. Their eyes light up. Childlike wonder on a fifty-year-old face. The future is very much here.

It's unbelievable what's possible these days and it's changing very, very, very, very rapidly.

This chart tracks how long AI can work on a coding task before getting stuck. Three years ago the answer was seconds. Today it's longer than a working day. The curve is steepening.

But there is a dark side.

Monday comes. The same person walks into the same office. Most people haven't made the time to properly work AI out yet, despite training and newsletters from leadership.

Leaders who've seen the future have a lot of work to do to bring everyone else with them. Eighty-four percent of the world's population has never used AI. Even among those who have, fewer than one in fifty pays for it.

Also: Meeting cadences haven't changed. Team structures and roles are the same. The time savings from those who've worked AI out just get absorbed into existing rhythms.

We all have to reconcile these two things. The future is here. The wonder is real. What's possible has changed so much in the last few weeks. But the future is very, very, very hard to spread around. Structural inertia is real. The future and the inertia coexist in the same organisations, sometimes in the same person on the same day! That tension, between what individuals can now do and what organisations will allow, is what I keep coming back to. Not "does it work?" It does. But how do we get it to work at an organisational level?

Several times a day I flip between childlike wonder and a deep fear for people and teams and orgs who've not leant into this yet. I feel both. The wonder and the weight. That's what this email is about.

Three things worth knowing

1. The technical barrier is gone. Domain expertise is what matters now.

Ethan Mollick, a Wharton professor, gave executive MBA students four days, three AI tools and a brief: build a company from scratch. They did. Working prototypes, financial models, market research, competitive positioning. Most had never written a line of code. The students who got furthest weren't the most technical. They were the ones who understood their industry. If you've spent twenty years in your field and haven't tried building something with these tools, you're sitting on your biggest advantage. Open Claude Code and have a play. Let me know what you build! (Mollick's full account)

2. Most firms use AI. Almost none can measure the difference.

An NBER study surveyed nearly 6,000 executives across four countries. Sixty-nine percent of firms now use some form of AI. The average productivity gain over three years? 0.29%. The economists draw an explicit parallel to Robert Solow's 1987 observation that computers were everywhere except in the productivity statistics. The explanation, proved right over the following decade: firms had to fundamentally reorganise before the technology translated into measurable gains. That lag wasn't months. It was years. The same pattern is playing out with AI right now. But it just has to be much quicker, right? Read on ...

3. Freelancers are disappearing. The data is in.

Ramp analysed real corporate spending data. The share of business spend going to freelance marketplaces like Upwork and Fiverr fell from 0.66% to 0.14% in three years. Firms most exposed to AI substituted at roughly $1 in reduced freelance spend for $0.03 in AI spend. A 97% cost reduction. The gig economy may be automation's first casualty. (Ramp Economics Lab)

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Find your misfits (and back them)

Identify the one-in-ten people in your organisation who are already at the forefront of using AI. The ones who experiment on their own time, who volunteer for pilots, who can't stop showing colleagues what they've built. Free them from at least some of their existing reporting lines. Give them the best tools. Bring them together, give them a name, give them a mandate to have an impact beyond their old job description. Most training programmes spread investment evenly. I don't think that's right. Your top ten percent will generate eighty percent of the value. A power user with the right support can transform a team. A reluctant user with mandatory training may forget everything by Thursday.

Open Claude Code and build something

Just build something. Pick a website you wish existed, or some data you wish was gathered across the web. Or an email scanning automation that summarises stuff for you on a schedule. Or an app that lets you do something you've been meaning to do. The only blocker is your imagination (plus a subscription to Claude, ten minutes of setup and 30 minutes of playing!) Claude will hold your hand through every single step. Just ask if you don't understand. Within 30 minutes you'll have something working and your perspective will have changed. I was reminded several times this week: there's a difference between knowing what's possible and doing it yourself. Go do it.

Measure outcomes, not logins

Accenture now tracks weekly AI tool logins for senior staff and links them to promotion decisions. The internal reaction? Staff call the tools "broken slop generators." Measuring whether people opened the app tells you nothing about whether the work got better. If you're going to track AI adoption, track the outcomes: did quality improve? Did turnaround times fall? Did clients notice? Log-in counts measure compliance. It's easy but lazy. Measure capability, instead.