Ride the bike

Ride the bike

WHAT HAPPENED THIS WEEK * One activist letter froze a board's AI workspace overnight. The doctors are next.: From David's week helping a hundred non-executive directors: one board's AI workspace went dark the morning an activist investor asked for its contents to be made discoverable. The Conference Board's April survey finds two-thirds of directors use AI for board work while barely a quarter of executives call their board highly fluent, and the Medical Protection Society warned that doctors and the NHS could be sued over AI tools' mistakes, with the clinician left as the 'liability sink'; it wants AI reclassified as a product under the Consumer Protection Act 1987 so liability flows to developers. The governance frontier is moving from 'can the data leak' to 'who owns the answer when the model is wrong', and the CEO'd policy (every output checked, edited and owned by a named human) is the only good answer to both the letters and the writs. * Meta built a 'second brain' that 63,000 staff installed in three months. It started with one person.: Meta's analytics team reports that an internal AI tool one of its data scientists started has been installed by 63,000 employees within three months, with no top-down mandate and no transformation programme. One person built something useful and the rest of the company found it. The standing question for readers: what are you doing to encourage and enable this at your firm? * When cheap models do make sense.: Ethan Mollick argues for hierarchies in which smart models supervise cheap ones: the smart one checks the plan, the cheap one does the volume. Right for machine pipelines running thousands of low-stakes calls; for your own judgement work, the essay's time-saved maths says buy the best. The item is the deliberate counterweight to the essay's argument, marking where it does and does not apply. WHAT TO TRY * Ask the AI to write the marking scheme before it writes the answer.: Ben Yoskovitz, a non-developer who ships production software with AI, has the model write three to five specific pass-or-fail checks before any non-trivial task, approves them, and then has the AI do the job, grade itself against each check, cite the evidence and stop the moment one fails. Ninety seconds of discipline against the long loop of 'looks good' followed by 'wait, no it isn't'. * Plan in one session, then start a fresh one to build.: Tiago Forte treats the first AI session as planning-only when the goal is a concrete document or deck: work out the brief in chat one, never let it start drafting, then open chat two, paste the brief and build. A clean start gives a sharper model, the deliberate stop tests whether the brief holds together, and a crash in session two still leaves you with the brief. * Keep your AI project lean.: Someone at a training David ran asked why their Claude project had slowed over a few weeks; they had been piling reference files into it. The model reads everything in a project on every request, so book fifteen minutes on a Friday to prune, and never leave fat PDFs in there: have the model convert each one to plain text first, since PDFs burn far more reading capacity than the same content as text. Read the full edition with all links and sources [https://steadman.ai/newsletters/david/#edition-2026-06-13]

The open door

WHAT HAPPENED THIS WEEK * The CEO of a 350,000-person IT services firm says AI is hollowing out the middle, not the bottom.: Ravi Kumar, chief executive of Cognizant, told Fortune's COO Summit on 1st June that his company hired 20,000 entry-level graduates last year and expects to hire more in 2026, with new 'Frontier Business Operator' and 'Frontier Certified Engineer' roles defining AI-era work. He called job-extinction talk 'fearmongering' and argued AI thins middle management while entry-level and leadership roles persist. It's a direct counter to the consensus that entry-level work vanishes first — including the US Bureau of Labor Statistics data Edition 14 leaned on — and a real-world data point for the essay's bet on graduates. * The machine is writing the code now, and the gains are pooling at the top.: Tobi Lütke says one in eight pull requests merged at Shopify are now written by River, its in-house agent, not an engineer. Anthropic's own engineers ship roughly eight times the code per person they did before 2025. Cursor's developer report shows the output gap widening, with top developers pulling far ahead of the median. And OpenAI's Codex has passed five million weekly users, with non-developer adoption growing three times faster than developer adoption. The grunt of writing code is moving to the machine, the output is multiplying, and the reward is concentrating in the people who know what to ask of it. * Capability is outrunning even the best forecasters.: The Forecasting Research Institute asked expert forecasters and superforecasters how long a task a model would reliably finish by the end of 2026, measured on METR's time-horizon benchmark (about an hour and a half when the survey launched). All three groups put the end-of-2026 figure between three and four hours. Then, while the survey was still running, a frontier model in preview hit three hours and six minutes — already inside the range they'd picked for year-end. The forecast was overtaken before they'd finished making it. Cloudflare's Matthew Prince made the same public miss: bots passed humans in web traffic for the first time, years ahead of his own late-2027 estimate, though much of that is scraping rather than agents, so the figure is softer than it sounds. WHAT TO TRY * Ask the AI to orient itself in your folder before you ask it anything else.: Setting up a Claude Cowork project for a documentary filmmaker new to the tool this week, David's first prompt wasn't about the work. It was: 'Read the files and sub-folders, write yourself a little set of instructions for future chats. Write yourself a navigation guide.' Claude explored, then saved itself three memory files — a project overview, a folder guide, and a profile of the user. Every later chat in that project started smarter because it could re-read its own notes. * Don't say 'always allow' on the verbs you can't undo.: In a coaching conversation about agent permissions this week, the familiar pattern: three prompts in, most people click 'always allow' on everything just to stop the interruption. The discipline is to sort the verbs first. Reading, listing, searching — leave on auto. Deleting, sending, posting, spending — keep asking every time. Sorting the verbs first is what keeps one careless click from emptying a folder, sending an email you can't unsend, or running up a charge you didn't mean. * Talk longer on the call, so the AI can work for hours after.: On a client call this week the brief changed mid-conversation. David deliberately used more words than he otherwise would, narrating the bridge between the old direction and the new one. The over-explaining was really for the AI: context he was laying down for it to pick up later, once the transcript was in the project. The transcript plus 'go' did the work. Next time you're on a call that's being transcribed, elaborate a little more and narrate the why, not just the what. Read the full edition with all links and sources [https://steadman.ai/newsletters/david/#edition-2026-06-06]

How We Got Here

WHAT HAPPENED THIS WEEK * AI can now find software vulnerabilities faster than humans can patch them. Discovery is no longer the hard part; verification is.: A frontier model handed to fifty cybersecurity partners (Anthropic's Glasswing initiative) surfaced more than ten thousand critical or high-severity vulnerabilities in the systems it was pointed at. Cloudflare has roughly four hundred major bugs to work through; Palo Alto Networks shipped five times more patches than its usual release cadence. Maintainers have asked the developers to throttle the discovery rate because there are not enough security professionals to close the gaps before attackers find them. Software security used to be limited by how fast new vulnerabilities could be found. It is now limited by how fast humans can verify, disclose and patch them. * A general-purpose AI model has autonomously disproved a 1946 conjecture in geometry. Independent mathematicians have verified the proof.: OpenAI handed a general-purpose reasoning model a long-held belief tied to a 1946 planar unit-distance problem of Erdos, the prolific Hungarian mathematician, and the model produced a disproof. Other AI models have since solved further long-standing problems; the wrinkle is that the others were purpose-built for mathematics. OpenAI's was not. Machines now clear the tractable tail of problems fast, pushing the human frontier towards what still resists them. After AlphaGo beat the world's best human Go players in 2016, the skill of human Go players noticeably improved. Noam Brown, an OpenAI researcher who helped build its reasoning models, suspects the same pattern will play out in maths. And then, perhaps, in business. * Generative AI use among American adults has hit 58 per cent in four years. The personal computer took sixteen years.: The Federal Reserve's February 2026 Real-Time Population Survey of working-age adults puts overall adoption at 58 per cent, up from around 45 per cent in October 2024 but recently flat. Work use is 44 per cent. Non-work use is 51. Daily use sits at 14 per cent and saves an estimated 2.2 per cent of total work hours. Alfred Lin, a partner at Sequoia Capital, notes this is the penetration level the personal computer took sixteen years to reach: a four-fold acceleration on the closest analogue. The caveat is the plateau. The early-adopter phase is over. The hard part starts. WHAT TO TRY * Ask the model what else it needs to know: The most productive sessions David sees with senior leaders don't open with a clever prompt. They open with the user pasting context — role, situation, what they're trying to do — and then asking two questions. First: "what else do you need to know about me to help me well?" Second: "what could you do for me right now that I haven't asked for?" The first surfaces gaps the user wouldn't have spotted. The second produces use cases the user didn't bring. The blank-prompt paralysis dissolves. * Either AI challenges you at the start, or you challenge AI at the end. Don't skip the challenge.: A leader David sat with this week had let an AI output stand without pushing back. Two patterns work and one fails. You can challenge before the model starts: share your point of view and ask the model to challenge it, force it to surface holes and the strongest counter-argument. Or you can challenge after the first draft: force a rewrite, name what's wrong, make it earn the second pass. The pattern that fails — and the one David sees most — is read-and-accept. * Ramble into the microphone, let the machine find the structure: A managing director David coached this week outlined obsessively: 700 words of outline for a 1,500-word article. The cost of structuring before writing was eating his weekend. The fix was inverting the order. Pick up your phone, dictate the mess, paste the transcript into your model, ask it for the through-line. Structure becomes the cheap thing. Particularly powerful for executives who think by talking — more common at the top than people admit. Read the full edition with all links and sources [https://steadman.ai/newsletters/david/#edition-2026-05-30]

Kids these days

WHAT HAPPENED THIS WEEK * AI displacement now shows up in the US government data at both ends of the career ladder: A Bloomberg analysis of new BLS figures finds every one of the eighteen occupations the BLS classifies as AI-exposed has lost jobs over the past year, even as US payrolls grew 0.8% overall. Customer service representatives shed 130,180 jobs, 4.8% in a single year; interpreters down 24% over three years; credit authorizers down 26%. The exception that confirms the rule: medical secretaries up 15.8%, the cluster that needs a body in the room. The same picture shows up at the other end of the funnel. The Economist this month plotted US graduate full-time employment against AI exposure: computer science and information sciences graduates are down 10 to 15 percentage points since 2022; philosophy and psychology graduates held steady or gained. The displacement isn't just to the people already doing those jobs. It's to the people trying to start in them, and what they should be studying may not be obvious to anyone yet — a thread the essay returns to via Elliott's homework. * The UK's data regulator has put AI hiring tools on formal notice. Sixteen organisations have already had a letter: The Information Commissioner's Office issued formal guidance this week saying that AI-driven CV screening, candidate ranking, and video interview analysis without "meaningful human involvement at every consequential stage" may already breach UK data protection law. Sixteen organisations have been written to directly. The consultation closes on 29th May, six days after the edition lands. A concrete Monday-morning task for any leader running a hiring pipeline: get the full list of AI tools in use across the funnel, decide which involvements count as "meaningful" against the ICO's test, and put a response into the consultation. The window is genuinely short. * Salesforce will spend close to $300 million with Anthropic this year. Marc Benioff says the engineering productivity gains made it the easiest line in the budget: Marc Benioff disclosed that Salesforce is on track to spend close to $300 million with Anthropic over 2026, with most of the spend on coding, justified by engineering productivity gains of more than 30%. Separately, Anthropic announced a $200 million partnership with the Gates Foundation focused on global health. A Fortune 100 chief executive treating the model layer as a procurement line item, not a research expense. The bigger question is who in your firm is allowed to commit that kind of capital, against what kind of evidence, and how quickly. WHAT TO TRY * When the output goes wrong, shrink the task: Justin Skycak put it as a principle for skill acquisition this week: shrink the unit of practice until the mistake has nowhere to hide. The same rule applies to working with language models. Sprawling prompts produce sprawling failures you can't diagnose. Break the task into its smallest meaningful unit, run it, inspect the output, then rebuild. If you can't immediately see where it went wrong, your chunk is still too large. * Ask AI questions it can't possibly know the answer to: A marketing lead at a global firm told David this week she's running a five-minute stress-test on every AI tool she's thinking of trusting. She uploads her own data, asks the model to use only that data, then asks it questions she knows the data can't answer. Some models fabricate regardless ("53% of women in the northeast states feel..."). She's learned what its confident-but-wrong mode looks like before depending on it for an answer she can't independently check. Worth doing once on every tool you rely on. * Run your day past AI before you start it: A senior leader described her commute habit to David this week. She opens Claude, asks it to review her calendar and her email, then asks it to surface what she needs to read before each meeting, what's carrying over from yesterday, and which emails in her inbox need replies before the day eats her. Five minutes on the train, and the day is scoped from outside her own head. "Just a nice little daily habit," she said. Try it tomorrow. Read the full edition with all links and sources [https://steadman.ai/newsletters/david/#edition-2026-05-23]

What boards accept

WHAT HAPPENED THIS WEEK * The METR capability curve just went from one hour to one day. The unit of AI autonomy is now measured in human work-days, and the doubling holds on a log scale: METR, an AI evaluation lab, measures how long an autonomous task an AI can complete reliably. In early 2024 the answer was minutes. In May 2026, with Anthropic's forthcoming Claude Mythos Preview, it's sixteen hours of work a human would have done. The number isn't the headline; the curve is. From minutes to a day in twenty-four months, on a log scale that had previously held steady. If the next two years look like the last two, the unit becomes a week, then a month, then a year of human work at the press of a button. Plan accordingly. * Half of organisations have already redesigned core workflows around AI, and a fifth have built new business models. The gap framing misses the story: BCG's AI at Work 2025 survey of 10,635 employees across eleven countries reports that 72% of organisations are running generative AI tools, 50% claim to have redesigned end-to-end workflows around them, and 22% claim to have built new business models on top of them. Three and a half years after ChatGPT launched, a fifth of firms claim new business models because of AI. Half have rewired core workflows. Syed Ijlal Hussain, who surfaced the chart on X, framed it as a gap. David flips it: the 22% are doing what most boards he's working with haven't started. * Anthropic just passed OpenAI in US business AI spend. The strategy lesson is older than AI: pick an audience and serve them: Ramp's AI Index, built from anonymised spend data across its US business customers, shows Anthropic taking 34% of paid AI subscriptions in its May release, ahead of OpenAI on 32%. The first crossover. Anthropic's share has roughly quadrupled in a year. David's read: this was inevitable from early on. Anthropic stayed fixated on the enterprise user while OpenAI chased every consumer headline. Slow perseverance against a chosen audience won. The lesson isn't really about AI. Pick an audience. Set your strategy around their needs. Keep your head down and serve the people you said you'd serve. WHAT TO TRY * Hand over the context, not just the question: Experienced leaders have context and are short on time. AI tools convert context into time saved, but only if you hand the context over. A leader David spoke with this week made the leap from asking to delegating. "Here are the Q2 numbers, last quarter's board paper, and the three things the board flagged. Draft it." Same model, same minute. Asking gets you an outline. Delegating gets you a draft. * Build a personal skill, and add a rule to it every Sunday: A person David worked with this week reviews 100-page reports from their team on Sunday nights — typos, inconsistent language, logic gaps. A simple review skill in Claude now catches them. The compounding version is their own preferences layered on top: this market "is expected to grow" not "will grow"; never "dropping precipitously." Every time they catch a miss the model didn't, they open Claude and say "add this rule to my personal review skill." Whatever skill you build, the compounding habit is the same. Catch what the model missed. Add the rule. Trust the skill more next week. * Schedule a daily AI briefing. The use cases will follow: AI tools sit closed until you open them. That's a real reason senior leaders bounce off: not bad prompts, but a tool that requires you to think of the use case first. Scheduled tasks invert this. Every morning at three, David's reads his inbox from the previous day, prepares one-paragraph briefings for every meeting on the day's calendar, and emails a short summary that takes three minutes to read with breakfast. The tool changes from something you open to something that opens your day. Read the full edition with all links and sources [https://steadman.ai/newsletters/david/#edition-2026-05-16]