David's Saturday AI Thoughts

Anthropic's newest model costs exactly double its predecessor, one GitHub Copilot bill jumped from a flat $50 a month towards $3,000, and suddenly the invoice, not the model, is the story. David argues most organisations manage these bills exactly backwards: they celebrate the biggest token burners or cap everyone, and both approaches manage the number instead of the judgement. His maths says the gap between the cheapest sensible model and the dearest buys about 40 seconds of a manager's day. What he would do instead: a floor of five prompts a day for everyone, then a delegation budget run on trust, because a price with a budget behind it sharpens judgement while a price with a cap replaces it. Eddy Merckx supplies the moral: ride the bike. Plus three things worth knowing, three things to try, and what readers said about graduate hiring.

David finally does the thing he has spent years telling other people to do: he hires a graduate. Ethan joins for a placement year, taken on as an experiment, because the awkward truth is that a graduate today is more capable than ever and less needed than ever. David sets out four hypotheses for why a young person is still worth it: someone has to check and own what the machine makes, managing a machine is real work, it builds judgement fast, and an open door lets useful things in. He borrows Richard Hamming's open-door idea and makes the moral case for keeping the door open, while the front door into work has rarely been harder to push. Plus three things worth knowing, three things to try, and what readers said.

David walks through how AI got practically useful, grounded in Dwarkesh Patel and Gavin Leech's The Scaling Era. The through-line: the future of AI has shown up long before it shipped, every single time. Seeing wasn't the hard part; believing it enough to bet on it was. He revisits the early scaling-law moments that should have been obvious to outsiders, asks why they weren't, and what today's people who use language models in their work should be willing to bet on now. Pair with next week's Edition 16, written with Rob Wild at L.E.K., on where things go from here. Plus three things worth knowing, three things to try, and what readers said.

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 A... * 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... * 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 Salesf... 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 ap... * 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. Sh... * 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 wha... Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-05-23

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... * 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 employee... * 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... 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... * 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.... * 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 th... Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-05-16

What happened this week: * AI-adopting firms are growing headcount, not cutting it: A Goldman Sachs analysis circulating this week, charted by Callum Williams of The Economist, shows US firms that have adopted AI report net ... * Five percent, not fifty: the candid private-equity number: Pete Stavros, co-head of global private equity at KKR, told the Milken Institute conference last week that AI is improving portfolio compa... * Both AI labs went into private equity the same day: On Monday, Anthropic announced a $1.5 billion vehicle with Blackstone, Goldman Sachs and Hellman & Friedman. Engineers from Anthropic will embed ... What to try: * Push it harder, then skillify, then iterate: The simplest workflow upgrade David has coached this year, and it stacks. Push it harder: when the model gives you a perfectly reasonable answer, tell i... * Shadow your most AI-pilled employee for two days: Matt Stockton, an operator and investor, made the case this week. Find the rabbit-holed colleague (not the keenest, not the head of digital transfo... * Ask AI to build you an HTML slide deck instead of PowerPoint: Just ask. "Build me a slide deck on [topic] in a single HTML file." Most chat tools can do this now. Why bother? PowerPoint output from... Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-05-09

David builds the case that flat-rate AI pricing is dying and that the buyer's question is no longer 'how much will this cost' but 'where does the spending compound'. He opens at a Las Vegas buffet that closed on 31st May, then moves to the supplier-side news: three of the four biggest AI vendors switched pricing in the last few weeks (Anthropic stripped bundled tokens out of Enterprise seats in mid-April, OpenAI took Codex pay-as-you-go a fortnight earlier, GitHub moves every Copilot plan to usage-based billing on 1st June, and an Anthropic manager admitted Pro and Max tiers have been outgrown). He brings in two friends' worried voice notes from the buyer side: a friend in Tokyo asking what happens when bills go up five or ten times, and a partner at a professional services firm naming the outsourcing trap. He explains the supplier maths (unit prices falling roughly tenfold a year, his $200-a-month Max plan delivering $500 a day of equivalent API use, unsustainable) and the buyer maths (Jevons Paradox: cheaper energy made coal use rise, not fall). The radiologist is the modern Jevons: Hinton's 2016 'stop training radiologists' was right about the models and wrong about the radiologists. Ten years on the US has six thousand more of them and pay is up roughly seventy per cent. Punchline: the bill rises either way, the question is whether the spending compounds in the model (a utility cost) or in the harness, the layer of instructions, context and workflows that wraps the model (an asset nobody else can buy). Intercom doubled engineering velocity in nine months on exactly that bet. What happened this week: * AI adoption stalls one layer below the executive sponsor, at the line manager: Gallup data (Q4 2025) finds AI use correlates more strongly with managerial endorsement than with tool access. In firm... * The frontier-model leaderboard is now refreshing in weeks, not quarters: The Epoch Capabilities Index now shows GPT-5.5 Pro and Gemini 3.1 Pro above 155, up from GPT-4o's 128 in mid-2024. Seventeen... * Six VC firms, one investment thesis: Linas Beliunas read the published 2026 investment theses of six of the biggest venture firms side by side and found the same handful of AI bets in all of them: ... What to try: * Pick one tool, get fluent, then refine your harness: A leader David spoke to had spent weeks running the same task through ChatGPT and Claude side by side, then asking each to review the other. Gen... * Force yourself to change something on every AI output before you ship it: Came up at a senior training session this week, as the room debated when the 'check, edit, own' model breaks down. Increasi... * Skip the slides, build the page: In a senior strategy session this week, the most-praised artefact in the room was not a deck. It was a web page someone had built to walk teams through their thinki... Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-05-02

AI-native teams need three roles: Director, Builder, Auditor. Execution is cheap, verification is expensive. Most organisations have zero Auditors and are shipping nothing because nobody is named to check. What happened this week: * <10% of organisations scale agents beyond pilots (McKinsey) * GitHub paused Copilot signups / Uber blew 2026 AI budget / Goldman inference costs approaching headcount parity * 29% of employees sabotaging AI initiatives (Writer survey) What to try: * Ask is this the simplest version (Cantrill laziness) * Audit cold: different model, fresh context * Save one reusable AI workflow (Chrome Skills) Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-04-25

AI broke the old proxy (good writing = good thinking) but the new proxy ('sounds like AI' = no thinking) is equally unreliable. A friend's challenge prompted a deeper question: evaluate thinking, not wording. Two tests proposed: Quality (does the argument hold under pressure?) and Ownership (CEO principle). Fine-tuned models now preferred over human writing 62% of the time. What happened this week: * AI cover letters killed the signal: Freelancer.com study shows Goodhart's Law in action, better letters no longer predict better hires * Snap cut 1,000 jobs (16% workforce), AI writes 65% of new code, $500M annualised savings: substitution model has arrived * Passive AI delegation erodes confidence, pushing back strengthens it: 2,000-person study. Gartner: of 5.4hr saved, only 0.6hr reduces working time What to try: * Ask what keeps people awake at night, not how AI can help: surfaces real problems with AI solutions * Let the model research you before writing custom instructions: web search + self-portrait generates better instructions than manual writing * Find where your AI value sits: AI Value Map interactive tool, five questions on value allocation then five on capture Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-04-18

Team-level AI infrastructure can precede and contain individual training. The cost of encoding how a team works into shared reusable tools just dropped from hours to minutes with Gen 2 tools (Claude Code + transcripts). A small jewellery company built thirteen shared skills in a day. Step two doesn't just follow step one, it can contain it. What happened this week: * Claude Code now writes 4% of all GitHub commits, doubled in six weeks; Anthropic run rate $30B (up from $9B at end of 2025), Claude Code alone $2.5B; projected 20% of commits by December * Goldman Sachs quantified AI's net labour market drag: -25k jobs substituted + 9k augmented = 16k net monthly loss; entry-level-to-experienced wage gap widened 3.3pp. But CFO surveys put genuine AI ... * Meta's internal tokenmaxxing leaderboard: 85k+ employees, 60T tokens in one month, Zuckerberg not in top 250. Rewards orchestration over outcomes. Incentivise use yes, incentivise maxxing no What to try: * Start with critique, not creation. Brand voice evaluator was diagnosis-only; teams fear proofreaders less than replacements. Nobody fights the spellchecker * Ask what keeps people up at night, not what they want AI to do. First question surveys existing habits; second surfaces unmet needs. Almost nothing appears on both lists * Show your team how others use AI. 515-startup field experiment: case studies alone led to 44% more AI usage, 1.9x revenue, 39% less capital needed ('the mapping problem') Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-04-11

Organisations say they're production systems but behave like human systems. The revealed preference is togetherness, not efficiency. AI adoption reverts because training optimises for individual productivity while the real binding force is human collaboration. What happened this week: * Dorsey wants to replace org charts with AI world models (Block restructuring) * Mollick says de-weirding AI is a mistake; hidden AI use is the harder problem * Zapier raised the AI fluency hiring bar: slope not snapshot, accountability added What to try: * Have AI interview you before building anything * Ask AI what looks weird before analysing data * Let AI be the app: build skills not standalone software Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-04-04

A Wharton study of 1,372 people identified 'cognitive surrender': when AI produces an answer, people stop questioning it while recoding it as their own judgment. Accuracy drops from 45.8% alone to 31.5% with incorrect AI. The better the system gets, the harder it becomes to stay vigilant inside it. What happened this week: * Three CEOs (Coca-Cola Quincey, Walmart McMillon, Adobe Narayen) stepped down in one quarter citing AI transformation pressure; 38 years of tenure in one turnover, most since 1999 * Anthropic 5th Economic Index + HBR 2,500-employee study: experienced users (6+ months) treat AI as thinking partner, not productivity shortcut. AI may be skill-biased tech that compounds existing a... * Ethan Mollick: companies with zero AI failures aren't being ambitious enough. R&D-style experimental budgets need to reach HR, operations, finance What to try: * Don't fact-check AI in the same conversation: model defends its own chain. Start fresh, upload source materials cold for critique * Give your AI reviewer a persona with skin in the game: six senior-partner personas converged on the same systematic error a neutral reviewer missed * After every good session, turn it into a reusable skill: capture what 'good' looks like the moment you've achieved it, before memory fades Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-03-28

The gap between AI wonder and behaviour change. PwC's CEO says get with it or get out, but the real question is get with what. Jeremy Howard's slope-over-intercept frame: capability growth matters more than current output. Anthropic's research shows most coding tool users enter autopilot. Not all inefficiency is waste: some friction is load-bearing. What happened this week: * Centaur chess inverted: in 2005 amateurs with laptops beat grandmasters, by 2026 adding a human to an engine makes it worse. Carlsen deliberately limits AI prep to maintain self-generated understan... * Jensen Huang's benchmark: $500K engineer should consume $250K in AI tokens. But token spend is an intercept metric that says nothing about slope * RentAHuman: 600,000 sign-ups to a platform where AI agents hire humans for physical tasks. Inverts the usual displacement narrative What to try: * Mine your own email archive: pull months of correspondence on a topic, ask AI to synthesise the intellectual arc * End every AI session the way a developer commits code: one line documenting context for the next session * Use AI to teach you, not just to do things for you: Bloom's two-sigma tutoring now costs a subscription Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-03-21

The dual experience of AI acceleration: excitement and terror, said in that order. Top builders 3-5x more productive, median only 10-20%. The gap widens. Amazon outages show what happens when power outpaces care. Skill requirements dropping in AI-exposed jobs. The organisations that will matter are those building in care alongside speed. What happened this week: * Shopify CEO ran AI optimisation against 20-year-old Liquid engine: 53% faster parse/render, 61% fewer allocations. Legacy code isn't too embedded to improve, it's too embedded not to try * BCG/UC Riverside research (1,488 workers): AI productivity peaks at 3 tools then collapses. 'AI brain fry' causes 33% more decision fatigue, 39% more major mistakes. High performers hit first * ATMs didn't kill bank tellers, the iPhone did. Automating tasks within current structure creates adjacent roles. Redesigning the structure from scratch eliminates them (David Oks, a16z) What to try: * Simulate the toughest reader: create AI versions of board members from known priorities and past questions, run every document past them before a human sees it * Delete the headline, ask AI what it should be: if the model's headline differs from what the colleague wrote, it reveals a disconnect between message and evidence * Fix the instructions, not just the output: update custom instructions, project briefs, context files after every miss. Output matters today, instructions compound forever Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-03-14

Leaders deploying AI face a binary choice: extraction (cut costs from existing operations) or expansion (grow what the organisation is capable of). The apprenticeship pipeline paradox: if juniors never do the grunt work AI now handles, how do they develop the judgment that makes seniors valuable? Hiring as deliberate investment, not necessity. What happened this week: * Software stocks down ~30% since October while broader tech flat (Salesforce, Adobe, ServiceNow each down 25-30%). Market pricing in structural shift as AI enables smaller tools to replace enterpris... * Goldman Sachs 'AI-nxiety': 70% of S&P 500 discussed AI on earnings calls, only 1% quantified impact. Median reported gain 30%, concentrated in customer support and software development only * AI retroactively reclassified legal work: general-purpose Claude outperforms expensive legal AI products. Tasks billed at premium rates for decades revealed as procedural, not cognitive What to try: * Run AI and human on same task, focus on disagreements: 78% overlap in one test, but value was at the edges where only one method surfaced findings * Map your org on Gen 1 vs Gen 2 AI tools landscape: most orgs stuck on constrained defaults, need parallel strategies for the many (move to competent use) and the best (accelerate with agentic tools) Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-03-07

AI value sits not in dramatic one-off wins but in a hundred small daily elevations (better meeting prep, cleaner drafts, faster document scanning) that compound into transformation. Firms can't see it because they track big projects, not distributed micro-gains. The Japanese senryu poetry cancellation illustrates the risk: AI alone produces sameness, AI plus human steering produces something better. What happened this week: * Citrini Research fiction set in 2028: when AI makes expertise cheap, clients stop buying it. 'A lot of what people called relationships was simply friction with a friendly face' * Block cut from 10,000 to under 6,000 people while growing gross profit 24%. Y Combinator founders planning to eliminate all engineers below senior level. Market valued each eliminated Block role at... * Google Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 (double predecessor in 3 months). Model choice matters less; application layer and workflow matter more What to try: * Run your day through AI at 6pm: daily five-paragraph reflection from meeting notes, voice recordings, emails. Pattern recognition compounds over weeks * Before building training, check the settings menu: ask five people which model they use and whether they've changed settings. Answers reveal maturity better than any survey * Push record, think aloud, send to AI: skip the blank page for case studies, knowledge sharing, personal reflection Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-02-28

The tension between individual excitement about AI (senior leaders working weekends, doing a week's work in ten minutes) and organisational inertia (unchanged meeting cadences, team structures, adoption rates). 84% of the world has never used AI. What happened this week: * Technical barrier gone, domain expertise matters now: Wharton MBA students built companies in four days with AI tools, non-technical students went furthest * Apple chose Google over itself for AI features, paying a billion a year for Gemini: build vs buy resolved in favour of partnerships * McKinsey '25 squared': grow client-facing 25%, cut back-office 25% using AI to rebalance a $20B firm What to try: * Find your misfits: identify the one-in-ten AI enthusiasts, free them from reporting lines, give them mandate beyond old job description * Change the cadence: if AI makes execution faster, review more often (daily not every two days). Review cycle is now the bottleneck * Package around problems not platforms: 'this is your sales coach' drives adoption, 'look at our new AI tool' doesn't Read the full edition with all links and sources: https://steadman.ai/newsletters/david/#edition-2026-02-21