--- title: Essays - Saturday AI Thoughts source: https://steadman.ai/newsletters/david/essays.html published: 2026-08-29 summary: All 28 weekly essays from Saturday AI Thoughts. The centrepiece of each edition. --- # Essays The weekly essay from [Saturday AI Thoughts](https://steadman.ai/newsletters/david/). 28 essays from 22nd February 2026 to 29th August 2026. --- ## Edition #28: Fewer, bigger, better *29th August 2026* David's Saturday Newsletter, 29th August 2026 ## Fewer, bigger, better *Note: this is a longer version of the essay than the one sent in the email.* ### What's on my mind People most exposed to these tools are working [about three hours a week longer](https://cepr.org/voxeu/columns/ais-power-grows-so-does-our-workday) since ChatGPT launched than the least exposed. Not what I expected. Not fewer hours. More. And the leisure they give up? Television and video games hold steady. What went is the going out. Ouch. I can understand people DOING more. If you looked at my screens you'd usually see six agent windows on long jobs, six more I nudge between meetings, and twenty scheduled tasks that run before I'm awake. By ten I've produced what used to take a day. It's the most intense my working life has been. Also the most fun. But I go out more than I used to, not less. A walk in the afternoon. A long lunch. More treats: four days in Italy with old colleagues, a Serpentine dinner with Teresa where {PAIRED} brought a fantastic DJ and a top chef together, Ibiza twice in one season with people I wanted quality time with. I'd like to think it's because I have more discipline than others. But the study above says otherwise. The longer-hours effect vanishes among the self-employed: the employment relationship causes the effect, not the job. **I keep my saved hour because nobody is taking it.** A structural advantage most people don't have. Saving hours and choosing what to do with ones you keep are separate decisions, and the second often gets made for you. In [matched surveys and payroll records](https://www.nber.org/system/files/working_papers/w33777/revisions/w33777.rev0.pdf) for 25,000 Danish workers across 7,000 workplaces, AI saved people about three per cent of their working time. Eighty per cent went back into other work. Less than a tenth became time off. [Gartner found the same in April](#email-2026-04-18): of 5.4 hours saved a week, 0.6 went to working less. Ask people or read the payroll. Either way the workers keep about a tenth. The longer day then arrives on top through increased expectations: the tools make time, and work takes it twice. Whether the day gets more intense or longer, the evenings that survive have to carry more, and the market has already repriced them accordingly. Ibiza's clubs took [a record 160 million euros](https://www.billboard.com/lists/ims-ibiza-2026-ims-business-report-key-takeaways-streaming/) in ticket sales last year, across fewer nights. The report [calls it the Ibiza Paradox](https://www.beatportal.com/articles/1386171-ims-business-report-2026-global-dance-music-industry-now-worth-15.1-billion). [The UK told the same story](https://www.ukmusic.org/news/oasis-beyonce-and-dua-lipa-help-attract-massive-26-8-increase-in-overseas-visitors-as-music-tourist-spending-hits-new-high-of-11-2-billion-at-uk/): music tourism spending up 11.3 per cent on attendance up 4.8, in a year when 43 British festivals didn't happen and one Oasis reunion did. That isn't a leisure boom. It's concentration. Fewer nights, bigger ones, chosen harder. Fewer, bigger, better. The trend is older than AI. I've written the UK's electronic music report for the Night Time Industries Association for years: the pandemic accelerated it, cost drives it, AI sharpens it. Ari Emanuel has spent billions on where this goes, buying live-event businesses because he thinks AI will cut the working week to four days, maybe three, and [people who don't want to sit at home will come to his events](https://www.fortune.com/2025/10/16/sports-entertainment-billionaire-ari-emanuel-three-day-work-week-thanks-to-ai-like-bill-gates-work-life-balance/). Others call it [the anti-AI bet](https://huddleup.substack.com/p/the-antiai-bet-why-live-sports-will). His conclusion may be right. His premise is the opposite of the evidence. The week isn't shrinking. For most people the free time is. Which is a better reason to pay dearly for what's left, I guess. Which brings me to you. Most of you aren't just subject to this finding. You're the mechanism in it. I keep my hour because nobody is above me to take it. Your team isn't in that position. The gain goes to whoever has the standing to keep it, and inside your organisation that's often you. I won't tell you to hand it back. I will just suggest that many of you are taking it by default rather than by decision. I doubt anyone voted for it. I bet it's in no plan and no budget. And you're only counting half of it. The productivity gain is counted. What comes alongside it isn't: the same research finds that higher exposure tracks lower job satisfaction and worse work-life balance. That's a retention cost, and I've yet to see an AI programme counting it. There's an organisational answer. [I argued in Edition 7](#email-2026-04-04) that what holds a firm together is togetherness rather than efficiency, and the longer day is eating exactly that. The reflex when togetherness frays is to add gatherings. But how about going the other way. Fewer, made to matter. Real investment in quality time together. The team dinner is infrastructure now. Budget it like infrastructure. There's a version of this for people, and a reader put it to me this week, writing about [last week's item](#email-2026-08-22) on Google buying a dead airline's entire working memory. They pointed out where that ends up. Process somebody's transcripts and their email properly and you can have the useful version of them. But it wouldn't be good company. They're spot on. My guess is that a model built on my own archive would do the useful part about eighty per cent as well as I do. For nothing. And every firm could have one. What that leaves out is the experience of working with me, for better or worse. The machine can have my competence. It can't be my company. So the repricing that found the evenings will, I hope, find people too. When the useful version of you costs almost nothing, the part that has to be in the room is the part that gets paid. Fewer, bigger, better again. Two questions for Monday. What did you take from your team this quarter without deciding to? And what are you doing with the evening you kept? --- ## Edition #27: One inch *22nd August 2026* David's Saturday Newsletter, 22nd August 2026 ## One inch ### What's on my mind Email version. [A longer version is online ->](#email-2026-08-22) I was in a pose I was fairly sure I was nailing. A bit hot. But quietly pleased with myself. Then the instructor came over and suggested I move my left hip about an inch. An inch. Ouch! A whole new zone opened up, muscles I had not worked enough, the good kind of shaking. I had been happily doing the pose, and also, without knowing it, in the way that let me avoid the hard part and the real benefits. I'm back at hot yoga after a long time away. I stopped when the studios closed in the pandemic, and I have been giving that as the excuse ever since. (Yeah, quite a thing to still be saying in 2026!) Going back has produced two kinds of surprise. That was the first. The second was hearing something I have been told so many times I had stopped hearing it, and realising I had been doing one thing wrong for eight years. It is the same sequence every class, whether it is your first time or your ten-thousandth. There is no advanced room to graduate into. The teachers still take classes. The most experienced person on the floor is still being adjusted. Now the work version. Almost every week somebody asks me the same question, but about AI. Am I doing this well enough? This week: a chief executive who reaches for it every morning and is well ahead of her whole leadership team. A senior leader who booked an hour on a feeling rather than a problem: I fear I am not using this in an advanced enough way. Someone who assumed what she does daily was too small to mention. Someone who admitted, quietly, that he was barely using it. They are at wildly different points. They ask the same question. There is no advanced room with AI, either. You have the same product as the most capable person you know. Only the depth differs. The honest answer to "am I ahead or behind" is that nobody knows, including those who are ahead. Plus, you can't easily find your own inch. And everyone I've met has not heard a simple instruction that's been repeated to them multiple times. Every coaching session I run goes one of two ways. Sometimes someone finally hears something they have been told before. Sometimes it takes a nudge so small I feel faintly silly saying it out loud. Both are the same event: something opens that was closed, and the person is immediately doing better work, quicker, and, the part I care most about, happier. This week it was both at once, four times over. Someone I sat with had been trying to get a model to edit an image. It kept changing things he had not asked it to change. He gave up, and went back to Photoshop, which he had used for years. The fix was one sentence: start a new chat. A long conversation gets sent back to the model in full every time you add to it, so your actual request is competing with everything you said an hour ago. Imagine if, each time I spoke, I first recapped our entire conversation back to you. And then again. And then again. He'd heard me say this before. In training I ran. It landed this week because he was stuck in the middle of something he cared about and could not explain what was going wrong. Notice which option was the comfortable one. Carrying on in the same chat costs nothing. Starting again means saying what you want properly, from scratch. The inch made the pose harder on purpose, which makes comfort a poor signal: if using these tools feels easy, that is usually evidence nobody has adjusted you lately. The last bit is for anyone running a business. In a yoga studio the adjustment is free: expected, silent, no verdict. At work we have made it expensive. Being corrected requires someone sticking their nose out to do so. And implies you were doing it wrong. Plus, the more senior you are the less you can afford that in front of people. You can fix that this month, and not with another all-hands demonstration. One hour, one person, somebody sitting beside you while you do your actual work. Someone who won't tell your colleagues that you've been doing something wrong this whole time. Grab me if you don't have someone. And stop asking whether you are ahead. Ask whether last week was better, whether it was quicker, and how you felt on Friday afternoon. And ask whether you heard something for the first time this week. Those you can answer. Sometimes it is the big thing you have been told a hundred times. Sometimes it is an inch. Either way, somebody else has to be in the room. --- --- ## Edition #26: Go talk to them *15th August 2026* David's Saturday Newsletter, 15th August 2026 ## Go talk to them ### What's on my mind *Note: this is a longer version of the essay than the one sent in the email.* ### What's on my mind A common thing senior leaders say to me about AI has nothing to do with the technology. It's about what their teams send them. More than one CEO has told me, in almost the same words, that one thing that stopped them pushing harder was the quality of what arrives from colleagues using AI. Complaints come in different flavours. The clearest is about emails that say "I don't know, but here's what I got from ChatGPT", with a wall of text under it. They asked for thinking and got a paste. Simon Willison, the developer behind one of the best AI blogs going, passed on a phrase for this last week: [the meat proxy](https://simonwillison.net/2026/Aug/3/dont-be-a-meat-proxy/), meaning someone who moves the machine's words along and adds a pulse. Most of it is duller than that. Too long. Padded. Sentences of the same length, one after another, in that sing-song rhythm where nothing is ever said plainly. It rises too fast, reaching for significance a few paragraphs before it has earned it. You smell it before you can point at it, which is part of why people say nothing. The worst kind took effort. Somebody has plainly spent real time on it, and their thinking still isn't in it anywhere. It answers a question nobody asked, or hands back the things you already told them, or arrives shaped like a list of actions while you were still working out the problem. That is the hardest one to raise, because the effort is visible and saying so sounds ungrateful. So what do they do? Many just grumble, then do the work themselves. "That's just AI slop" has quietly become a way of not having a conversation. It sounds like a verdict on the tech. Really, it's frequently a verdict on a person that we've decided not to say to their face. Managers have ducked conversations like this for as long as there have been managers. Go talk to them. Not about AI. Criticise the content: "too high-level," "no insight" or "where's the so-what." Every one of those will change what they send to you next time. I watched somebody do this properly a few weeks ago. A long presentation was emailed round, plenty of people copied in, and the most senior person replied to everyone with a line close to: "this reads a bit like AI, could you have another pass?" It read as an ordinary request, because it was one. Lessons were learned and business carried on. You can just say it. Most think they can't, for some reason. Our rule, [the CEO principle](#email-2026-04-18), is that every AI output gets Checked, Edited and Owned by a person before it goes anywhere. I've always taught it as quality control. It turns out that is just part of what it does. **Another part is that it gives you something to point at.** Without it, "did you check this?" is an accusation about someone's character. With it, you can point at something you both signed up to, whether or not anybody enforces it. Which makes it easy to say: "this reads like model output, so can I make sure you've checked, edited and owned it? I'll read it differently either way." I asked someone senior exactly that recently. They said yes. So I stopped being careful about whether they'd used AI and argued with the work instead, including two claims that were not true, and were now theirs. They could have said no. The errors were the machine's, and saying so would have been true. I suspect they were embarrassed. But they said yes, and took them. That exchange only worked because there were two of us and I could ask. Past the point where you can ask, we seem now to have decided to answer the question with machinery instead. Since the second of August, [Article 50](https://artificialintelligenceact.eu/article/50/) of the EU AI Act has obliged the labs to mark what their models produce, so new Claude models now [weave an invisible watermark](https://interestingengineering.com/ai-robotics/anthropic-claude-text-invisible-watermarks) into the text, which survives copy and paste. [Gemini has done it](https://deepmind.google/blog/watermarking-ai-generated-text-and-video-with-synthid/) since 2024. The rule carves out assistive editing, a grammar fix that leaves your meaning alone; the labs marked those anyway, which is why the first complaints came from people who had asked only for checks on their commas and got their own writing back branded as the machine's. All of it is about whether a machine was involved. But who cares? It says nothing about how much a person put in or whether it was checked and they stand behind the result - which are the only things I want to know! This email would [certainly fail that test](https://steadman.ai/newsletters/david/how-its-made.html), and I'm fine with that. I've never been good at writing. It isn't a skill I ever worked hard to develop. I'd rather be judged on the thinking. The ideas here are mine, I sign off every word after carefully iterating and editing it many times, and I stand behind it. What I won't do is spend my evenings making it read as though no machine was involved, because that measures completely the wrong thing. There is a better idea, published the very same day. Iarfhlaith Kelly, a technology chief who has been writing on the web since 2001, has published an open format called [Authorship Notes](https://iarfhlaith.com/authorship-notes): list the real activities behind a piece of writing (the idea, the research, the drafting, the editing, the verification), mark each one human, AI or both, and name the person who stands behind it. Declaration rather than detection, on his reasoning that the creator knows more than the classifier. A score tells you very little. Thistells you something useful, and gives you the language to have the conversation about it. Even the regulation sees the difference. [Its own guidance](https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act) says a spell-check or a grammar fix is not human review or editorial control, while an editor who reads, revises and takes responsibility for the piece is. Most organisations are working on one half of that: accuracy, sources, the checkable things. Necessary, and the Big Four accounting firms have all now published work marred by invented AI material: [Deloitte](https://fortune.com/2025/10/07/deloitte-ai-australia-government-report-hallucinations-technology-290000-refund/), [EY](https://www.ft.com/content/a61cbcae-95e4-4449-86e1-ef40fb306f4e), [KPMG](https://www.ft.com/content/b3828e92-4961-4b39-84f0-c42f33be3c3f) and [PwC](https://www.ft.com/content/7e149ac8-2ce2-4266-8940-192f9821b33c). Nobody has earned the right to relax. But a team told only to check its facts may still produce documents that are accurate, checked and worthless. No policy tells someone their strategy note said nothing. Only a person can do that. Email took us 20 or 30 years to work out. Don't blind-copy. Don't reply to all. Norms didn't come from policies, though. They came out of thousands of small, awkward, useful conversations between two people about individual messages. We're at the beginning of that for AI. Have one of them this week. --- ## Edition #25: My newest colleague *8th August 2026* ## My newest colleague ### What's on my mind At half past six on New Year's morning, a colleague messaged me: the overnight job that sorts my photos had jammed halfway through a batch. By the time I'd sat down at my desk in Connecticut it had worked out why, fixed it, and written a rule in its notes so the same jam couldn't happen twice. The colleague was an AI. That was the morning, more or less, that I stopped visiting one and started working with one. I think of AI as arriving in [three generations](https://steadman.ai/newsletters/david/three-generations.html). The first is the chatbot: brilliant, patient and amnesiac. You visit it; it forgets you when you leave. Almost every corporate AI policy is written about this one. The second is the agent: you hand it a task and it uses a computer the way you would, clicking, typing, checking its work. It arrived last year; the keenest are already fluent in it, mostly at home, on their own accounts. The third generation isn't a tool you use. It's more like a colleague you manage. Mine has been on the team since the new year. It keeps a diary of everything we do, a thousand entries. It holds a handbook on how I work: how I write, what "done" means, which mistakes I never want repeated. When it gets something wrong we write the correction down, and it stays corrected. I wish I could say the same for myself. All week, scheduled jobs process what I send it for this email; the writing we do together. Once I've edited and signed it off, on Saturday morning it makes the [website](https://steadman.ai/newsletters/david/archive.html), and [audio](https://steadman.ai/newsletters/david/podcast.html) versions, publishes them then presses send while I'm still in bed. It drafts replies to certain emails but may never send one: drafting is delegated, editing and sending are mine. If you've emailed me and waited weeks, there's your proof: the draft, and possibly the work itself, was likely ready in minutes; the human checking, editing and owning it was not. In its own name it sends freely: clearly labelled updates to my team and some clients every morning. It archives things; it may never delete them. When a job needs more hands, it briefs junior copies of itself, writing my rules into every brief. Management, I've discovered, goes all the way down. I reach it the way I'd reach anyone: email, messages, a web page, an app. It can't join a video call yet, though I'm tempted. Others on the team can email it too, but it acts on nothing without my go-ahead. The rule is mine; I'm thinking about loosening it. None of this needed new technology. It all runs on tools anyone can subscribe to, for $200 a month; the model underneath is the same one the chatbot uses. What it needed was management: a written account of how I work, boundaries set in advance, trust extended step by step. Building my third-generation colleague was the least technical work I did all year. I still sit in rooms of people delighted to be stepping from the first generation to the second. I love helping them. What I don't say is that the second generation waiting for them has been [quietly hobbled](https://steadman.ai/newsletters/david/four-freedoms.html) before they touch it. It often may not send, even a morning briefing to its own boss. It usually may not remember. Sometimes it may not reach the files where the real work lives; the fix sits with a policy or an administrator nobody can reach. Between sessions I check on my colleague from my phone; back at my desk, I review the projects it worked on, alone, while I was in meetings. Which is why, I think, I haven't yet met an organisation with a fully functioning second generation, never mind a third. A third-generation colleague raises deep questions. Who answers for what it does at four in the morning? For mine, one sentence: everything it does is mine, I check what matters, and I own the result. An organisation has to answer in policy, and AI policy has barely started. I don't want to oversell my colleague. Tireless and forgetting nothing, it has told me a job was finished when it wasn't, and I found out the way any manager does, by checking. The care doesn't go away. It moves up a level, from doing the work to running the team that does. The first generation arrived as a website you could visit; the second as an app you could install. Nobody has properly packaged the third yet. Partly because what makes it a colleague is the handbook, and only you can write yours. You can start, though. Give an AI a memory, a handbook and standing responsibilities, and manage it like the newest member of your team. The keenest in your building, I'd bet, already has one. --- ## Edition #24: Smooth enough *1st August 2026* ## Smooth enough *Note: this is a longer version of the essay than the one sent in the email.* ### What's on my mind We've been handed something close to cheap superhuman intelligence on demand. It is clearly making even complex work better, quicker and easier. The strange part is how often organisations then carry on much as before. A leader I know started using AI seriously with their team in January. They embraced it rapidly and comprehensively. Machines now help with most of the team's work, and the team delivers far more as a result. This month, they were told that their team was no longer holding the business back. That is progress. But the commercial results have not yet followed. They may. Or the constraint may simply have moved. The harder questions now concern strategy: whether the company is making the right products and offering the right services to its clients. I have seen versions of this in analytics, consumer insight, marketing and plenty of other functions. AI transforms the work. The organisation does not automatically transform with it. I think there is a deeper explanation in the nature of intelligence itself. François Chollet, the researcher who created the [ARC-AGI reasoning benchmark](https://arcprize.org/arc-agi), [offers a useful image](https://x.com/fchollet/status/2038069289643806957). We tend to picture intelligence as a tower: more intelligence adds another floor and gives us a grander view. He suggests something closer to a rough surface being ground smooth. A tower can keep rising. A surface eventually becomes smooth enough. The early passes remove great ridges; later ones remove smaller imperfections. Beyond that, the surface can still improve, but whatever crosses it can barely tell the difference. I know this from my field in Connecticut. I made a trail through the trees. I mow it with a tractor, so I have moved the rocks large enough to threaten the mower and left the smaller ones. The trail is not smooth. It is simply smooth enough for what needs to cross it. Another afternoon's work would now produce almost no return. I could tarmac it tomorrow, but the tractor would still travel at roughly tractor speed. The surface is no longer what makes it slow. A Grand Prix car is different: [tiny bumps matter](https://www.roadsbridges.com/asphalt/article/10584087/racetrack-paving-drive-by-me) at speed. The same improvement to the surface can therefore be essential or beside the point. The vehicle sets the standard. In a business, the decision is the vehicle. The analysis, code, research and administration around it form the surface. Much of what we call knowledge work exists to make decisions easier, quicker or better. Many business decisions are tractors. They need to arrive within the required window, over ground reliable enough not to swallow a wheel. They move slowly for reasons elsewhere: authority, attention, ownership or risk appetite. Give a tractor pristine tarmac and it still arrives at tractor speed. An organisation can cash an efficiency gain directly. Beyond that, it captures wider value only through the decisions and actions that intelligence changes. If the work around a consequential decision is too slow, expensive or poor, smoothing can change everything. Once the work is sufficient, the marginal effect falls sharply. I saw the whole curve at EMI Music. When I joined, an audience segmentation cost something like £100,000 at the going rate, and the company had no appetite for one. We created a way to do it for about half that. The first reduction crossed a threshold: the work happened. We then engineered the process, automated what we could and roughly halved the price again. That made it possible to run the work monthly across 30 countries. Decisions about which audiences to pursue could move from instinct towards evidence. In the years since EMI, working in other businesses and for other clients, we have halved the cost of this kind of work another couple of times. This time, little changed. The previous version was already cheap and quick enough for every team with the time and ability to use it. There were no more capable users to reach, and existing users had no capacity to absorb more studies or make more decisions. The capacity to produce analysis kept scaling. Human capacity to use it did not. Research was no longer the constraint. Decision-making was. That is the shape of much AI adoption now. Work that took a week takes a morning, and the morning version is often better. That saves time, reduces cost, avoids hiring and frees capable people. All are worth having. But the task-level gain becomes the wider result leaders expect only if it changes a consequential decision or action. More code does not create demand for a product nobody wants. More analysis does not resolve internal politics or an unwillingness to choose. So name the result you want, then ask what would genuinely change if the supporting work became twice as good or took half the time. If the answer is "not much", you are buying a smoother journey to the same place. That may still be worth having. But it should be valued for what it is. For the leader I began with, the road may now be smooth enough. The harder task is for their firm to decide which products it should make and which services its clients actually need. The gift of abundant intelligence is real, as is its impact. But so is its limit. We may have mistaken an abundance of intelligence for an abundance of decisions. The machines can smooth the road. The organisation still has to decide where to go. --- ## Edition #23: Burned and earned *25th July 2026* ## Burned and earned *Note: this is a longer version of the essay than the one sent in the email.* ### What's on my mind A reader wrote in with a fair question. What is the environmental cost of all this? The energy, the water, the lot. My first instinct was to reassure. The numbers are small. A single text prompt to a language model uses [about a quarter of a watt-hour](https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference), the energy in less than nine seconds of television. The same prompt runs to 0.03 grams of CO2 equivalent and 0.26 millilitres of water, about five drops. Tiny. I nearly wrote that, pressed send, and moved on. Then I realised it was only half the story. The environmental cost of using AI isn't the cost of the model. It's the cost of the model minus the cost of whatever would otherwise have done the work. The quarter of a watt-hour is what the machine burned. It is one half of a subtraction, and on its own it tells you nothing. The other half is what the machine saved, and that is the half that counts. Start with the cost, though, because there's a clean way to size it. I think of AI in [three generations](https://steadman.ai/newsletters/david/three-generations.html), and the honest unit for each is a typical run. Generation one is chat: a prompt. You ask, it answers. Generation two is agents: a task, where the model plans, searches, writes, checks, calls tools and sometimes wanders into the ditch. Generation three is all-day AI: not a prompt or a task but a whole day of them. Something like [Claude Tag](https://www.anthropic.com/news/introducing-claude-tag), released in late June, that watches a team's channels, does a little prep in the morning, reaches out when it spots something and takes on the odd job you hand it. I wanted to know what each of those actually costs, so rather than guess, I built a small model that counts the tokens a realistic run gets through and turns them into energy, carbon, water, money and human time. The full workings are on a [companion page](https://steadman.ai/newsletters/david/ai-environmental-cost.html). Here is what it found, for a typical run of each. Type What it does Cost Environmental cost Human-hours it draws **One: chat, a prompt** A question, or "synthesise this document" under a penny 0.24 Wh . 0.03 g CO2e . 0.26 ml water (five drops) about 12 seconds **Two: agents, a task** Research a topic, build a model, make a deck about a pound ~65 Wh . ~8 g CO2e . ~70 ml water (a few sips) about 50 minutes **Three: all-day, a day** Triage the inbox, watch the channels, run a few tasks a few pounds ~0.25 kWh . ~30 g CO2e . ~270 ml water (a mug) about three hours Three things fall straight out of it. The columns move together. Cost, energy and carbon all scale with the tokens a run gets through, so by energy a task is a couple of hundred prompts and a day about a thousand. That isn't a coincidence, it's the same meter read three ways. It also means the [often-quoted](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary) line that reasoning and agent work uses hundreds or thousands of times the energy of a text prompt isn't a scare figure. It's just arithmetic. The per-run numbers stay small. A typical whole day of the heaviest generation draws about a quarter of a kilowatt-hour, roughly what you spend boiling the kettle a couple of times, and drinks about a mugful of water. The frightening global total is real, but it's built from runs this size, repeated a very great many times. Hold that thought. And the last column is the one worth carrying. It's the machine's energy turned into the currency we all read without thinking: how long someone would have to sit at a laptop, drawing about 75 watts, to use the same power. Read it carefully, because it is a low bar, not a verdict. It's only the point where the machine's energy equals the human's. It is not what the machine replaced. That gap does the work. What makes AI cheap isn't the power it draws, because a laptop is already frugal. It's the time it saves. A task costs about 50 minutes of human energy but does an afternoon's work, sometimes a day's. There's now a way to put a number on that: [METR](https://metr.org/time-horizons/), an evaluation lab, measures how long a job an agent can finish with even odds of success, using the time an expert would need as the yardstick. That length has doubled roughly every seven months for six years, and by mid-2026 the strongest public models were clearing jobs, mostly software work, that would take an expert around two full working days. The energy is the low bar. The work is the high one. The gap between them runs to ten times over, often much more. You clear the low bar without trying. You'd have to work at losing. So the rule is short. If a run replaced real work, you came out ahead, usually by a wide margin. And if it replaced any real-world activity beyond desk work, the savings are bigger still. A courier van kept off the road for a cross-city run saves a few kilograms of CO2. A 50-mile round trip to a meeting in a [petrol car](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024) is about 13 kilograms. A remote analysis that saves a transatlantic return flight is [about a tonne](https://theicct.org/wp-content/uploads/2021/06/ICCT_transatlantic-airline-ranking-2014_0.pdf) per passenger. The AI's environmental cost is a rounding error compared to travel. There's one way to turn a saving into an environmental cost even on real work: over-power the job. Set a task-sized agent on five minutes of work, and you pay 50 minutes of human-equivalent energy for a few minutes of value. Leave a day-long system running to watch for a need that never comes, and you pay three hours. That's waste in a clever hat. Match the run to the job and the sum falls the right way. A person draws much the same power whatever they do, while the machine's cost per unit of work keeps collapsing. The bigger and more real the job, the more decisively the machine wins. If it replaced nothing, you spent the energy and got no saving back. That's a real environmental cost, and it is likely where most of the AI's rising footprint sits. It is not a scandal, though, it is a decision, and you already make decisions like this every day. You hire contractors, brief agencies and hand people tasks all the time, and every one of those carries an environmental cost of its own: a commute, a laptop, a heated office. You weigh it against what the work is worth. Commissioning an AI task that replaces nothing is the same decision, priced in the same currency, the human-hours in that last column. A pointless task is like paying someone for 50 minutes to make something no one needed, which happens everywhere, every week. A useful one that no one would otherwise have done can be well worth its cost, exactly as a good contractor is. The only new thing is that the machine's version is cheap enough to wave through without weighing it. So weigh it. I'm not innocent. I've told you before that I build reports, models and small tools for myself and abandon most of them. Each was a task or two, so call it an hour or two of human-equivalent labour, spent on nothing. Cheap enough that I never counted. Some of it taught me things and was worth every watt. Some of it was just spent. The point isn't that I shouldn't have run them. It's that I never once asked whether they were worth it. I was asked about this over lunch this week, after a talk to a technology team. How does all this square with a changing climate? The honest answer: if a question is worth asking me at all, the cost of the machine helping me answer it is a fraction of the cost of me answering it alone, in money and in energy. Not a multiple. A fraction. The can of drink in my hand was probably more environmentally destructive than all the AI I would run that day, and the meat in the sandwich I just finished certainly was. Perhaps I should have had the vegetarian option. The waste that matters is the report that sits on a desk and achieves nothing. Most do. Not really an AI problem. It's a problem of what we choose to spend our days on. Now scale it up, because this is where that frightening global total comes from. Much of the machine's rising footprint isn't replacing work a person used to do. It's work no person was ever going to do. When a task costs a pound or so you commission the thing you'd never have paid a human for: the analysis nobody would have booked, the draft nobody would have briefed. Nothing there is subtracted, because nothing would have happened. The human-hours still size what you got. They just don't cancel anything out. That footprint is new. Perplexity's chief executive has [described](https://www.youtube.com/watch?v=OxFyVcO1Yow) power users who run agent loops all day, one of them spending more than $10,000 a month. That is machine work no one would ever have paid human rates to produce. This is the Jevons paradox: cheaper never means less. Data-centre electricity is set to [roughly double](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary), from 485 terawatt-hours in 2025 to 950 by 2030, with the AI slice tripling, even as the energy per task keeps falling fast. For scale, that starting point is [1.5% of the world's electricity](https://ourworldindata.org/how-much-energy-do-data-centers-and-artificial-intelligence-use), with AI about 0.5% of the world total. Google [cut the energy of its median prompt 33-fold](https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference) in a year. Each unit gets cheaper, we run far more units, and the total climbs anyway. So the worth-it question stops being my private edge case and becomes the main event: not whether the machine beat the human it replaced, but whether all this new work was worth its new cost at all. It is how I have long thought about climate change. Be alarmed by the big, long-term picture and work to fix it. Then fly to New York when the trip is worth it, knowing the flight is part of the problem. Both are true at once, not one or the other. AI is the same: use it, and also worry about where the total is going. So the watt-hours-per-query figure, the one everyone argues over, was only ever half of a subtraction. Sometimes there's a human on the other side, and the machine wins by a distance. Sometimes there's nobody there, and the whole cost is a bet that the work was worth doing. Either way, the query was never the thing to count. What it earned always was. *The full workings behind every number here, the model, the ranges and the sources, are on the companion page: [The Environmental Cost of AI](https://steadman.ai/newsletters/david/ai-environmental-cost.html), to follow or argue with.* --- ## Edition #22: Futures and fears *18th July 2026* ## Futures and fears ### What's on my mind A few weeks ago, when I asked what you could already see coming, a chief executive at a media company sent me the bleakest reply in my postbag. Job disruption far bigger than today's numbers admit. Graduates walking into one of the worst job markets in decades. Politicians who appear unwilling or unable to comprehend the scale of it. Then, at the bottom of the same email: at a personal level they find the technology enormously exciting. "I can't reconcile the two sides of this email and I've given up trying." Last week I asked for your big fears, and the same shape kept arriving. Some of the most fluent AI users I know described some of the darkest scenarios. An investor who works with these tools daily described a brief window of arbitrage: half the working population still hasn't grasped what's available, so the people who have can look like superheroes. The same investor fears what follows. On Wednesday, I joined a webinar panel with L.E.K. Consulting, the strategy firm. (I started my career there a quarter of a century ago, which Rob enjoyed pointing out.) The discussion put a third set of futures beside yours. Rob Wild, the partner who leads L.E.K.'s digital and AI work, showed how visible the next era already is: machines that remember, perceive and act with growing independence, and the early signals arriving years ahead, as they always do. Scott Breitenother, founder and chief executive of Kilo, which makes an open-source coding assistant, described a fork in the road: one future where a few giants dominate access to AI, another with many models used on your own terms. He thinks the next couple of years will be telling. My own answer was uncomfortable to me. I don't think we'll see a shiny future. I started my AI journey believing this democratising technology would deliver exactly that: people and firms without privilege, capital or scale suddenly able to take on the big guys. It hasn't happened. The gains are flowing to those doing the work of reorganising around the technology. Most firms have shown they aren't willing yet, and some aren't able: real transformation takes money, attention and time that plenty of organisations won't spare. Both routes concentrate the gains. The benefits compound in the few that do the work. I tried to sort your fears into two piles. One for the machine: it makes things up, or disappears mid-task when the service goes down. One for us: judgement quietly eroding, and the middle of organisations hollowing out along with the apprenticeships it used to run. The sort kept failing. The fears that wouldn't sort were about power: who owns what you depend on, who sets the terms, who gains and who pays. The letters kept returning to dependence, and to being left outside when the window closes. So I've stopped trying to reconcile the two sides, and I'd suggest the chief executive was right to give up too. Fear of this technology and excitement about it aren't competing conclusions; they're both smart readings of the same facts, often held by the same people. We know this technology is revolutionary, and we know some people are willing and able to do the work of embracing it, because we can watch them pulling ahead. Unless you want that gap to widen, you'd better ask yourself how you remove the roadblocks to transformation in your organisation. Your letters help, and the record keeps getting richer. I could fit only part of it here. [The full record of readers' futures and fears, plus the panel's answers, is online ->](https://steadman.ai/newsletters/david/futures-and-fears-full-record.html) Read it, then reply with the future, or the fear, that's still missing. --- ## Edition #21: Sarah stays *11th July 2026* ## Sarah stays ### What's on my mind London was too hot for sleep last weekend. Somewhere in the small hours, probably out the back of a bad dream, a story arrived nearly whole, so I got up and wrote it down. In the morning I posted it on LinkedIn. It goes like this. Sarah resigns on a Tuesday. Her firm keeps her anyway. Not with a counter-offer; she declined that. Before her notice period ends, her manager sits down with the firm's AI and three years of Sarah: her emails, her meeting transcripts, her Slack threads, her prompt history. He asks one question. What did Sarah bring? The answer takes about an hour and is better than her handover document. Of course it is. Her handover is what she thought mattered. The archive holds what actually did. So they keep it. Sarah reappears in Slack, answering in her own cadence. Her email address quietly goes live again. A voice tool puts her back in the Monday meeting, for whenever someone asks what Sarah would say. Six months on, two new joiners assume she works remotely. Then the finance director does the maths: if the firm can keep 80 per cent of a person for the price of tokens, what exactly was the salary buying? Hiring flips from permanent seats to short licensing deals, and the sharpest people arrive on day one carrying their own archives and negotiating royalties. The [full story is on LinkedIn](https://www.linkedin.com/posts/beglen_sarah-resigned-on-a-tuesday-her-firm-kept-activity-7479405959484989440-w7Yk), along with the comments. Every step of it is possible with tools that exist inside firms today. Nothing needs a research lab. It needs an archive your firm already holds, and intent. The comments chilled me more than writing it did. "Wasn't this a Black Mirror episode?" asked one reader. Another told creatives to stop giving corporate psychopaths ideas. Two commenters with legal and governance backgrounds began sketching, entirely seriously, the employment clauses and compensation models that would have to exist. And one reader had already lived a small version of it: one of two final candidates for a job, asked to prepare her best strategic thinking to break the tie. She did her best work. She didn't get the job. She suspects her ideas did. Writing it changed something for me. I'm a glass-half-full person. I've spent most of these Saturdays on what this technology makes possible, because that's what I mostly see: people working better, quicker and happier. I still believe all of that. But I've been under-weighting the other half of the story. The serious risks aren't distant, and they don't wait for new capabilities; they're available now to anyone with bad intent and a subscription. So I want to spend more time on the downsides in the coming weeks. I think we all should. The more clearly we imagine them in advance, the better our chance of actively heading them off: building the contracts, the norms and the habits before the fact, rather than sleepwalking into situations where these things can be done to us. I'd like your help with that. What's your big fear? The one that would keep you up on a hot night, whether it's about your job, your firm, your kids or your data. Reply and tell me. I'll bring your worries together in the coming weeks, anonymised as ever, and we can look at them properly: which are already happening, which are preventable, and what prevention looks like. PS. On Wednesday afternoon (15th July, 3pm UK) I'm on a webinar with L.E.K. Consulting: What Happens When AI Understands, Learns and Acts on Its Own? Rob Wild, who leads L.E.K.'s digital and AI work, hosts; Somnath Biswas, Head of AI Products at The AA, and Scott Breitenother, co-founder of the open-source coding agent Kilo, join me on the panel. It's exactly the territory above, from the opportunity side as well as the risk side: systems that remember, learn and act with growing independence, and what leaders should do now. [Register here](https://events.zoom.us/ev/Ai7Ul43jynrkzh35ueck7XltezmbNpQfd5s-aE8hw7-lxrmNPy4r~Av_DSgYIWzvz8Rdn8K4-1Z5Ovdhbv9-_L_33g4Z403574QCQbWu5QQj1yTkHji9_crGwQAX0spxim04e3oa6rvPaFg). Next Saturday I'll bring together your fears and worries, the visions of the future many of you sent in when I asked a few weeks ago, and the perspectives from that discussion. --- ## Edition #20: Look at the grass *4th July 2026* ## 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. [Image: Wimbledon centre court in 1982 and 2024. In 1982 the grass is worn up the middle and into the service area, the serve-and-volley path. By 2024 the forecourt is green and the only bare patch is a band along the baseline, marking the shift to the rally.] 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](https://steadman.ai/newsletters/david/the-ceo-gap.html). 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. [Image: Where the effort goes in knowledge work, 2022 versus 2026, shown as grass wear across three bands: framing, producing and reviewing. In 2022 the wear is heaviest across producing and framing. By 2026 producing has grown back green and the bare patch has moved to reviewing.] 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? --- ## Edition #19: A thousand small bargains *27th June 2026* ## 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](https://www.koganpage.com/skills-careers-employability/superskills-9781398628991), 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](https://economics.mit.edu/sites/default/files/2025-06/Expertise-Autor-Thompson-20250618.pdf), 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](https://www.cnn.com/2023/05/27/business/chat-gpt-avianca-mata-lawyers) 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](https://steadman.ai/newsletters/david/archive.html#email-2026-03-28), 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](https://www.bcg.com/publications/2026/when-everyone-uses-ai-companies-risk-critical-skills) 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](https://www.thesuperskills.com/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. --- ## Edition #18: Average by default *20th June 2026* ## 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](https://arxiv.org/abs/2606.01736) 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](https://www.sciencedirect.com/science/article/pii/S294988212500091X), 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](https://www.soundslikeme.com). 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](https://hbr.org/2026/03/research-using-ai-can-stifle-innovation-but-it-doesnt-have-to), 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](https://www.ft.com/content/9bbdf59e-ce46-4176-aab9-b45a3f49fc4e), 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](https://hbr.org/2023/09/good-judgment-is-a-competitive-advantage-in-the-age-of-ai) 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. --- ## Edition #17: Ride the bike *13th June 2026* ## 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](https://www.anthropic.com/news/claude-fable-5-mythos-5), at exactly double its predecessor's list price. Then, early this morning, the US government [pulled it](https://www.anthropic.com/news/fable-mythos-access) on national-security grounds and Anthropic suspended it for everyone, which is a story in itself ([see online](#extras-2026-06-13)). Developers using GitHub Copilot, Microsoft's coding assistant, [revolted](https://techcrunch.com/2026/05/30/what-a-joke-github-copilots-new-token-based-billing-spurs-consternation-among-devs/) 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. [Image: Claude's home screen this morning showing "Claude Fable 5 is currently unavailable"] I made the case [last month](archive.html#email-2026-05-02) 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](https://www.engadget.com/2190039/google-cuts-the-price-of-its-ai-plus-plan-and-doubles-the-storage/)), 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. [Image: Flat-rate pricing: a $200-a-month plan can absorb thousands of dollars of API-equivalent usage at current rates] 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. [Image: Ramp's AI Index: Anthropic's share of business AI spend overtakes OpenAI's] 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](https://x.com/kunchenguid/status/2063390768379924719). 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](https://www.cio.com/article/4178320/tokenmaxxing-when-ai-adoption-metrics-go-bad.html) 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. "Ride as much or as little, or as long or as short as you feel. But ride."EDDY MERCKX, THE GREATEST RACING CYCLIST OF ALL TIME 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](https://x.com/businessbarista/status/2064828549815849417), 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](https://techcrunch.com/2026/06/10/ai-pilled-firms-spend-7500-per-employee-each-month-on-ai/), 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](https://jellyfish.co/blog/is-tokenmaxxing-cost-effective-new-data-from-jellyfish-explains/), 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. [Image: Ramp's AI Index: AI spend per employee per month, rising at the top 1%, the top 10% and the median] *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. --- ## Edition #16: The open door *6th June 2026* ## 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? [Image: McKinsey: agentic software delivery needs about 50% less effort and 60% smaller teams, with the junior tester and business-analyst roles the ones that disappear.] Agentic software delivery, McKinsey estimates, takes about half the effort and sixty per cent smaller teams, with the junior tester and analyst roles the first to go. The counter-pressure these four hypotheses have to answer. 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. [Image: Most of today's jobs did not exist in 1940: a16z, employment by new versus pre-existing occupations, on Goldman Sachs data.] Most of the work people do today did not exist in 1940, the long-run case that technology keeps inventing jobs nobody could name in advance. (a16z, on Goldman Sachs data.) 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](https://www.linkedin.com/in/helenkfield), 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? --- ## Edition #15: How We Got Here *30th May 2026* ## How We Got Here ### What's on my mind I pulled [a book off the shelf](https://press.stripe.com/scaling) 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](https://www.dwarkesh.com/p/shane-legg) 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](https://claude.com/blog/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](https://quoteinvestigator.com/2012/01/24/future-has-arrived/), 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](https://jetpress.org/volume1/moravec.htm) 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](https://gwern.net/scaling-hypothesis) of this era had [dismissed it](https://www.dwarkesh.com/p/gwern-branwen); 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](https://arxiv.org/abs/1706.03762)." 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](https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models) on a model built from that paper. It was not the first to spot what it called [scaling laws](https://arxiv.org/abs/2001.08361), 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](https://epoch.ai/publications/how-much-does-it-cost-to-train-frontier-ai-models). 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](https://epoch.ai/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year). [ChatGPT, in November 2022](https://openai.com/index/chatgpt/), 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](https://www.federalreserve.gov/econres/notes/feds-notes/measuring-ai-uptake-in-the-workplace-20240205.html) used AI officially, despite [75% of knowledge workers](https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part) 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](https://openai.com/index/learning-to-reason-with-llms/), 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](https://www.dwarkesh.com/p/will-scaling-work) 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](https://epoch.ai/data-insights/ai-capabilities-progress-has-sped-up). Two curves, not one. [Image: Performance over time on the Epoch Capabilities Index. The non-reasoning fit flattens; the reasoning fit accelerates from late 2024.] *Source: [Epoch AI](https://epoch.ai/data-insights/ai-capabilities-progress-has-sped-up), 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](https://www.anthropic.com/news/3-5-models-and-computer-use), to click, type and act. Then the agent era. The unlock had been [named months before it arrived](https://www.dwarkesh.com/p/sholto-douglas-trenton-bricken): 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](https://www.anthropic.com/news/claude-opus-4-5). Agentic systems now reliably do the equivalent of [many hours of expert work](https://metr.org/blog/2026-1-29-time-horizon-1-1/) at a single press of a button. Every four months they do [roughly twice what they could before](https://metr.org/time-horizons/). 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](https://steadman.ai/newsletters/david/ai-from-whats-true-to-what-to-do.html), 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. [Image: Installing the tech is not the same as redesigning around it. US factories electrified for two decades, from 5% in 1900 to 53% in 1920, before the +5.3% manufacturing productivity surge of the 1920s. The gain waited on the redesign of the factory, not the installation of the machines.] *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](https://www.exponentialview.co/p/the-split-reality-of-ai-rising-productivity), 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.](https://www.lek.com/leadership/rob-wild) 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.* --- ## Edition #14: Kids these days *23rd May 2026* ## 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. --- ## Edition #13: What boards accept *16th May 2026* ## 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. --- ## Edition #12: Choosing is the work *9th May 2026* ## 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](https://openai.com/index/chatgpt/) 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? --- ## Edition #11: The bill and the harness *2nd May 2026* ## 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](https://moneywise.com/news/economy/las-vegas-mgm-grand-kills-buffet-on-may-31). 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](https://www.theregister.com/2026/04/16/anthropic_ejects_bundled_tokens_enterprise/) in mid-April. OpenAI, its rival, [took Codex, its coding agent, to pay-as-you-go](https://openai.com/index/codex-flexible-pricing-for-teams/) a fortnight earlier. GitHub, the Microsoft-owned developer platform, [moves every Copilot plan to usage-based billing](https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/) on 1st June. An Anthropic manager admitted its Pro and Max tiers have been [outgrown by today's workloads](https://the-decoder.com/anthropic-manager-hints-that-pro-and-max-plans-are-outgrown-by-todays-claude-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](https://a16z.com/llmflation-llm-inference-cost/). 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](https://x.com/emollick/status/2042088011748290750) 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](https://www.youtube.com/watch?v=2HMPRXstSvQ) 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](https://pubs.rsna.org/doi/10.1148/radiol.251395). [Ten years on, the US has six thousand more radiologists](https://www.apollo.com/wealth/the-daily-spark/the-radiologist-paradox) than it did then, and average pay is up roughly seventy per cent. [Image: US radiologists, 2015 to 2025. Hinton's 2016 'stop training radiologists' call is the dotted line. Both the headcount and average pay rise after it. Chart: Torsten Sløk, Apollo, 29th April 2026.] *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](https://www.lennysnewsletter.com/p/how-intercom-2xd-their-engineering) 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. --- ## Edition #10: Rise of the auditors *25th April 2026* ## 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](https://www.microsoft.com/en-us/microsoft-365/blog/2026/04/22/copilots-agentic-capabilities-in-word-excel-and-powerpoint-are-generally-available/) across Word, Excel and PowerPoint. Google [shipped agents](https://techcrunch.com/2026/04/22/google-turns-chrome-into-an-ai-coworker-for-the-workplace/) to 3.45 billion Chrome users. The UAE [committed to running half its government on agents](https://www.khaleejtimes.com/uae/government/sheikh-mohammed-announces-50-of-uae-govt-services-to-run-on-ai-agents-in-2-years) within two years. [OpenAI](https://openai.com/index/introducing-workspace-agents-in-chatgpt/), [Anthropic](https://www.anthropic.com/news/claude-opus-4-7) and [SpaceX](https://finance.yahoo.com/markets/article/spacex-strikes-60-billion-deal-for-the-right-to-buy-ai-coding-startup-cursor-143350832.html) 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](https://ajeygore.in/content/the-expensive-thing), a former CTO, wrote this month that when execution becomes free, verification becomes the expensive thing. [Martin Fowler picked it up](https://martinfowler.com/fragments/2026-04-02.html). 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](https://aleera.ai/), 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. [Image: Two team shapes. Left: everyone uses AI, nobody specialises. Right: three roles, all use AI, one specialises. One shape that isn't working. One that does.] *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. [Image: At scale: five Directors, one Builder, five Auditors. Eleven people. Most organisations have zero Auditors.] *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](https://simonwillison.net/2025/Feb/3/a-computer-can-never-be-held-accountable/) 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. [Image: IBM internal training slide, 1979. A computer can never be held accountable. Therefore a computer must never make a management decision. Forty-seven years later, more true, not less.] 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](https://fortune.com/2025/11/25/deloitte-caught-fabricated-ai-generated-research-million-dollar-report-canada-government/). --- ## Edition #9: The proxy break *18th April 2026* ## 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](https://ctext.org/zhuangzi/external-things), 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](https://www.gov.uk/government/publications/ai-cyber-threats-open-letter-to-business-leaders/ai-cyber-threats-open-letter-to-business-leaders-html) 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](https://arxiv.org/abs/2601.18353) 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](https://www.news.aakashg.com/p/ai-pm-interview-guide-2026) have moved here too. Candidates build a working prototype in 45 minutes while someone watches. [Zapier's hiring rubric](https://zapier.com/blog/raising-ai-fluency-bar-in-hiring/) 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](https://fortune.com/2025/10/07/deloitte-ai-australia-government-report-hallucinations-technology-290000-refund/), fabricated citations. [A CA$1.6m report for a Canadian province](https://fortune.com/2025/11/25/deloitte-caught-fabricated-ai-generated-research-million-dollar-report-canada-government/), 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. --- ## Edition #8: What a day can do *11th April 2026* ## 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](https://steadman.ai/newsletters/david/gen1-vs-gen2.html), 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](https://steadman.ai/newsletters/david/archive.html#email-2026-04-04): 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](https://steadman.ai/newsletters/david/ai-from-whats-true-to-what-to-do.html) 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. [[Image: Sequence: people, teams, organisation, new products and services. Build foundational capability in individuals first. Then embed it in team workflows. Then redesign organisational processes. Only then reach for the genuinely new. Each step depends on the ones before it.]](https://steadman.ai/newsletters/david/ai-from-whats-true-to-what-to-do.html) 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. --- ## Edition #7: What is your organisation actually for? *4th April 2026* ## 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](https://block.xyz/inside/from-hierarchy-to-intelligence) 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](https://en.wikipedia.org/wiki/Revealed_preference) - 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](https://svs.gsfc.nasa.gov/vis/a020000/a020400/a020412/jsc2025m000169_Artemis_II_Mission_Map_720.mp4), an orbit, after all, is not the absence of force. It is force put to work. --- ## Edition #6: The system and the surrender *28th March 2026* ## 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](https://audiencestrategies.com/case-studies/claude-code-and-viberate.html), 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](https://x.com/zackbshapiro/status/2036791156915290271) 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](https://steadman.ai/newsletters/david/gen1-vs-gen2.html)' agentic AI tool, [hasn't written a single line of code since November](https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens). 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](https://knowledge.wharton.upenn.edu/podcast/ripple-effect/how-ai-is-reshaping-human-intuition-and-reasoning-gideon-nave-and-steven-shaw/) 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. --- ## Edition #5: Reckoning and slope *21st March 2026* ## 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](https://bmmagazine.co.uk/in-business/pwc-ai-strategy-partners-no-place-automation-services/) 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. [Image: Slope over intercept] *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](https://www.anthropic.com/research/AI-assistance-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. --- ## Edition #4: The power and the care *14th March 2026* ## 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](https://x.com/Alfred_Lin/status/2031379148703408414) 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. [Image: AI and judgment: the 2x2 that matters. Source: Dan Hock] *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](https://hbr.org/2026/03/research-how-ai-is-changing-the-labor-market) shows that since ChatGPT launched, job postings in AI-exposed occupations have quietly dropped their skill requirements, and the trend is accelerating. [Image: Since ChatGPT's release, job postings in AI-exposed occupations have steadily dropped their skill requirements] 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. --- ## Edition #3: Extraction or expansion *7th March 2026* ## 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](https://www.anthropic.com/research/labor-market-impacts) that puts the gap between capability and reality into a single image. [Image: Anthropic spider diagram showing theoretical vs actual AI task coverage by occupation] 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. --- ## Edition #2: The hundred small things *28th February 2026* ## 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.](https://www.japantimes.co.jp/news/2026/02/24/japan/japan-ai-senryu-poetry-writing/) 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. --- ## Edition #1: The wonder and the weight *22nd February 2026* ## 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. [Image: METR Time Horizon: the length of coding tasks AI can handle autonomously has grown from seconds three years ago to longer than a working day] 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. [Image: Each dot represents 3.2 million people, coloured by their most advanced AI interaction] 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. ---