A weekly email for the AI-curious

Rise of the auditors

What's on my mind

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

Three things worth knowing

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

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

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

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

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

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

Eleven bits that didn't fit online →

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

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

Audit cold

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

Save one reusable AI workflow this week

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

What readers said

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

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

What's on my mind

The proxy break

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

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

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

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

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

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

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

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

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

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

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

I'd suggest two tests.

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

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

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

Use AI for breadth. Depth is ours.

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

Three things worth knowing

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

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

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

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

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

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

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

Fifteen bits that didn't fit online →

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

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

Let the model research you before you write its instructions.

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

Find out where your AI value actually sits.

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

What readers said

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

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

What's on my mind

What a day can do

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

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

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

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

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

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

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

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

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

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

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

Eleven bits that didn't fit online →

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Start with critique, not creation.

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

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

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

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

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

What readers said

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

Community voice

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

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

What's on my mind

What Is Your Organisation Actually For?

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

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

One out of hundreds. Something else is going on.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

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

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

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

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

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

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

Try this

Before you build anything, have AI interview you first.

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

Before you analyse anything, ask AI what looks weird.

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

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

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

What readers said

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

What's on my mind

The system and the surrender

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

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

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

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

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

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

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

Try this

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

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

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

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

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

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

What readers said

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

What's on my mind

Reckoning and slope

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

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

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

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

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

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

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

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

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

Try this

Mine your own email archive.

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

End every AI session the way a developer commits code.

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

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

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

What readers said

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

What's on my mind

The power and the care

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

1. Old code is fair game now.

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

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

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

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

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

Try this

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

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

Delete the headline. Ask AI what it should be.

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

Fix the instructions, not just the output.

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

What readers said

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

Extraction or expansion

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

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

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

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

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

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

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

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

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

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

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

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

Three things worth knowing

1. The software market is pricing in the collapse.

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

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

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

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

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

Try this

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

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

Know where your org sits on the AI tools landscape.

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

What readers said

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

Read the full letters and links readers shared below.

P.S. How I make this email

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

What's on my mind

The hundred small things

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

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

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

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

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

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

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

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

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

Three things worth knowing

1. A Fiction Worth Reading. Honestly.

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

2. The floor collapsed under junior roles.

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

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

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

Try this

Run your day through AI at six in the evening

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

Before you build training, check the settings menu

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

Push record. Think aloud. Send it to AI.

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

What readers said

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

Read the full letters and links readers shared below.

What's on my mind

The wonder and the weight

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

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

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

But there is a dark side.

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

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

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

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

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

Three things worth knowing

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

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

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

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

3. Freelancers are disappearing. The data is in.

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

Try this

Find your misfits (and back them)

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

Open Claude Code and build something

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

Measure outcomes, not logins

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