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  • Benedict Evans on Why AI Is Stuck in 1997: The Task vs the Job, Commodity Models, and Why the Jobs Apocalypse Is Overhyped

    Benedict Evans, the former Andreessen Horowitz partner and independent analyst behind the annual “AI Eating the World” presentation, sat down with Lenny’s Podcast for what the host calls the most rational take on AI you will hear this year. Instead of either doom or hype, Evans argues that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile, which means we are living through something closer to 1997 than to the singularity. The conversation moves through the jobs question, the difference between a task and a job, whether the model labs have any pricing power, the anti-AI backlash, and what people should actually do. You can watch the full conversation on YouTube here.

    TLDW

    Evans frames AI as a platform shift on the scale of the internet or mobile, with the crucial twist that almost nothing has been built yet, so we are in the 1997 moment where confident predictions about winners are usually wrong. He introduces his central tool, the distinction between the task and the job, to explain why “X percent of this profession is exposed to AI” studies are misleading, why the AI labs are paradoxically hiring forward deployed engineers and buying consultancies, and why accountants kept multiplying through every wave of automation (the lump of labour fallacy and Jevons paradox at work). On value capture he makes a deterministic bet that foundation models have no network effects, behave like a commodity, and will look more like cloud than like Windows, with the value moving up the stack to applications, much as it did in telecom, where a trillion-dollar industry grew data traffic thousands of times over while its stocks went nowhere. He covers distribution as the real moat, Apple Intelligence as the most compelling unshipped vision, the fuzzy anti-AI backlash (including the largely fake water panic and the very real harms of deepfakes), raising kids under radical uncertainty, and closes with the disarming admission that his own synthesis-heavy job is exactly the kind AI is currently worst at. His advice: presume radical uncertainty, dive in rather than sneer, and assume it will probably be okay.

    Thoughts

    The most useful thing in this conversation is a single question Evans keeps returning to: what is the task, and what is the job? A spreadsheet automated the arithmetic an accountant does, and the number of accountants went up for the next forty years. Claude Code can write the code, but deciding what to build, for whom, and why is the part nobody has automated. The reason the “this profession is X percent exposed to AI” studies feel hollow is that they assume a job is a neat stack of separable tasks. Evans argues, by analogy to the old expert-systems failure, that you simply cannot decompose a senior lawyer’s work that way. The 75-slide deck is the task. Walking your company, reading its politics, talking to your customers, and telling you the uncomfortable truth is the job, and that is what you actually paid McKinsey for.

    The boldest and most falsifiable claim is that the foundation-model companies look more like cloud than like Windows. No network effects means no winner-take-all, which means durable competition, which means commodity pricing and compressed margins, with the real value accruing up the stack in applications that nobody at the labs is going to build. His telecom analogy is the one to sit with. A trillion-dollar industry grew mobile data traffic by 1,500 to 2,000 times in fifteen years, and the stocks went nowhere for a quarter century, because it was a low-margin utility while all the interesting value moved to Apple and the people building apps on top. If he is right, the current token-burn economics, the person reportedly spending 1.5 million dollars a month on tokens, are the 2010 equivalent of a 50,000 dollar roaming bill, not the steady state. Evans flags openly that he could be completely wrong, which is the intellectually honest part and the part most forecasters skip.

    “It depends” and “it will probably be okay” sound like evasions, and Evans leans into that. But the 1997 framing is doing real work. The point is not that AI is small, it is that the things that will end up mattering have not been built, and that anyone confidently naming the winners today is repeating the 1997 mistake of betting on Excite over a search company with a weird logo. The discipline he is selling is to presume radical uncertainty and act anyway, because the alternative, declaring the whole thing slop and shouting about it online, buys a great feeling of moral superiority and nothing else. His repeated insistence that you can see the job that goes away but never the new job, because it does not exist yet, is the load-bearing idea under his optimism.

    The most disarming moment is the closing AI-corner answer, where the person whose entire brand is explaining AI admits he struggles to use it. His work is synthesis and precise information retrieval, and precise retrieval happens to be exactly what today’s models are worst at. He is, in his own words, the lawyer looking at VisiCalc: it is obviously transformative, and he just does not happen to make spreadsheets all day. That admission is worth more than any benchmark, because it locates the real variable. How much AI changes your life depends less on how good the model gets and more on whether your daily work sits on the part of the jagged frontier where it already works. That is a far more practical lens than arguing about whether AGI arrives in three years or thirty.

    Key Takeaways

    • Evans’s headline opinion is that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. Both halves of that sentence matter.
    • If you make the internet comparison honestly, we are roughly in 1997: very exciting, most of it does not work yet, most of what people will build has not been built, and it is unclear how any of it will end up working.
    • Adoption is spread across a very wide distribution. Even among teenagers, only something like 15 to 20 percent are daily active users and another 20 percent weekly, with the majority saying they do not use it at all.
    • That spread maps onto the “jagged frontier” question of where AI works, where it does not, whether you can predict where it will work in advance, and whether you can even tell after the fact.
    • Software developers are the accountants seeing VisiCalc: for them everything has already changed. Most other professions are watching, intrigued but unsure what to do with it.
    • The AI labs are investing heavily in forward deployed engineers, consultancies, and professional services. Evans jokes that a forward deployed engineer is an Accenture outsourced developer who lives in San Francisco.
    • Companies do not have spare people sitting around to reimagine every internal workflow, so reinventing a business around AI is itself a project that needs consultants, which is why the most cutting-edge labs are funding exactly the firms everyone assumed AI would kill.
    • The central framework: separate the task from the job. Sometimes the task is the job (the elevator operator pressing a lever), and automating the task ends the job. Far more often, the task is only part of the job.
    • Amazon gets you the SKU once you know which SKU you want. Knowing which one to buy is a different job. Claude Code writes the code, but knowing what code and what features to build is the job.
    • A McKinsey or Bain engagement is not really about the deck. The deck is the task. The job is walking your enterprise, understanding the politics, talking to your customers, and telling you the truth.
    • The Jevons paradox is just price elasticity applied to labour. Make something cheaper to produce and you usually do far more of it, not the same amount with fewer people.
    • Excel did not give investment bankers shorter hours. iPhone SDKs did not shrink the number of engineers even though Apple writes 90 percent of the code for you. The number of accountants rose through every wave of automation.
    • The lump of labour fallacy: since 1800, each technology automates jobs and unlocks new ones. You can always see the job that disappears and never the new job, because it does not exist yet.
    • Evans is wary of argument from authority on jobs. He wants Dario Amodei’s view on where models go in the next 6 to 12 months, not necessarily his theory of labour markets and comparative advantage.
    • The doomer scenario of every company buying ChatGPT and firing everyone in two weeks misunderstands how enterprises work. Enterprise sales cycles run 18 months or more. Nobody is ripping out SAP overnight. The full transformation takes 3 to 10 years, sector by sector.
    • AGI and superintelligence are being quietly redefined to mean whatever works now. Larry Tesler’s theorem: AI is whatever machines cannot do yet, because once they can, people call it just software.
    • We have no theory of human intelligence, no theory of why these models work, and no theory of how much better they will get, so everyone is vibes-forecasting. Even if progress stopped tomorrow, what exists is already transformative and will roll out for a decade.
    • On value capture, Evans argues models show no network effects, so no single one runs away with the market. Persistent competition plus little real product differentiation means little pricing power.
    • Sam Altman’s pitch of selling intelligence on a meter like electricity ignores the brutal margin structure of utilities. Your TV maker does not pay the power company a cut of your bill.
    • The telecom analogy: a roughly trillion-dollar mobile industry spends 15 to 20 percent of revenue on capex, grew data consumption 1,500 to 2,000 times since 2010, and its stocks went nowhere for 25 years because it is a low-margin commodity utility.
    • The elemental question: does the model do the whole thing, or does it need thousands of different apps built by different people? If it needs apps, the labs cannot build them all, just as Microsoft did not, so it looks more like AWS than like Windows.
    • If the product is a commodity, distribution becomes the moat. Google pushes Gemini through its surfaces, Meta sprayed AI across its apps and quietly ranked between ChatGPT and Gemini in usage, and incumbents with distribution have a structural edge.
    • Browsers are the warning: Microsoft used distribution to win the browser war, then it turned out winning browsers did not matter because the value was further up the stack.
    • Apple Intelligence, as shown at WWDC 2024, was the most compelling vision of a personal AI assistant Evans has seen. Apple could not ship it, but neither could anyone else, because tool-using on-device agents with no hallucinations across thousands of apps is genuinely hard.
    • The model is “the dumb thing underneath” that powers a feature. The same commodity model can sit beneath both Gemini on Android and Apple Intelligence on iOS while the products and distribution differ entirely.
    • The anti-AI backlash is a big fuzzy mess. Some is real (local electricity bills, deepfakes, real job anxiety), some is sort of true, and some is simply false.
    • The data-center water panic is largely fake. A Livermore lab study put US data-center water consumption at about 0.017 percent of US water use. Local well conflicts are planning problems, not data-center problems.
    • We have shockingly little hard data. The model labs do not publish meaningful usage numbers. There is no public daily active user figure for ChatGPT, so economists are reverse-engineering effects from government surveys.
    • Real new harms do appear with each wave. A teenager could not use Photoshop to make explicit fakes of every classmate and send them to the whole school in an afternoon. Now they can, and turn them into video.
    • The UK Post Office Horizon scandal (buggy Fujitsu software wrongly showing cash shortfalls, leading to prosecutions, bankruptcies, and suicides) is a reminder that every technology brings new ways to ruin lives, by malice or by accident.
    • You cannot reliably predict what gets exposed. In 1997 people thought taxis were safe from the internet and newspapers would be fine. The opposite happened. Today, “AI-proof” jobs like personal trainer may not be as safe as they look.
    • Uber and Airbnb show that similar-sounding companies can have very different market impact. Uber demolished and then grew the taxi market, while Airbnb’s effect on hotels was fairly marginal because business travel still wants a hotel.
    • Every new technology first lets you do the old thing but more, then unlocks things that were not possible before. Recorded music revenue is U-shaped: first “what if I do not pay 15 dollars for a CD,” then “what if 15 dollars a month gives me all the music there is.” Spotify is not an online music store, it is something else.
    • Coding was supposed to be one of the last things automated, and instead it is the most transformed role of all, which is itself a lesson in how badly we predict exposure.
    • Practical advice: do not stick your head in the sand. Dive in, submerge yourself, and come out understanding what you can do with it. Going into a shrinking job market announcing you will never use AI is not the right posture.
    • Evans’s honest coda: he struggles to find AI use cases because his job is synthesis and precise retrieval, the things models are worst at. He uses it for proofreading, images, redecorating his apartment, and dictation. He is the lawyer looking at VisiCalc.

    Detailed Summary

    AI is as big as the internet, and we are living in 1997

    Evans opens with the opinion he calls his most controversial: AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. To some in tech that sounds dismissive, as if he is underrating a once-in-history event. His reply is that smartphones and the internet were themselves enormous, and we are talking over the internet right now. The deeper point is the comparison’s timing. If this is like the internet, then it is like the internet in 1997: thrilling, but most of it does not work yet, most of what will be built has not been built, and nobody knows how the pieces will fit. His latest 80-slide presentation, he jokes, is essentially 80 ways of saying “we do not know,” which is partly facetious and partly the entire point.

    The jagged frontier and the wide spread of adoption

    Adoption is not uniform, it is a wide distribution. Some people in tech have bought clusters of Mac minis and stopped using Google, while most people outside tech who use AI at all touch it once every week or two. Even among 13 to 18 year olds, daily active use sits around 15 to 20 percent, weekly use adds another 20 percent, and roughly 60 percent say they do not use it. That spread maps onto what Evans calls the jagged frontier: whether a given task works, whether you can predict in advance that it will work, whether it is intuitive, and whether you can even tell after the fact. Software developers are the accountants who just saw VisiCalc, living in a clear before-and-after. Everyone else is somewhere on the curve, picking it up to varying degrees and a little puzzled about what it is for.

    Why the AI labs are buying consultancies

    One of the most counterintuitive trends is that the leading labs are pouring money into forward deployed engineers and professional services, the very category many assumed AI would erase. Evans’s explanation is grounded in how companies actually operate. Firms do not keep spare people sitting around to redesign stores, hunt down churn, or rebuild a tech stack, which is exactly why they hire Bain, BCG, McKinsey, Accenture, or Infosys when a big project appears. Reimagining every internal workflow around AI, then actually plugging vertical and horizontal systems together and retraining people, is itself a multi-month project requiring people you do not have. So the work gets outsourced, and the most advanced labs are funding the firms that do it. His joke lands the point: a forward deployed engineer is a statistician, or an Accenture developer, who happens to work in San Francisco.

    The task versus the job

    This is the spine of the conversation. Ask what the hard part of a job really is. Sometimes the task is the job: the elevator attendant’s whole job was driving the car, the task got automated, the job ended. Much more often the visible task is only a slice. Amazon gets you the SKU once you know which SKU you want, but knowing what to buy is a separate job. Claude Code writes the code, but deciding what to build, for whom, and how to take it to market is the job. A consulting deck is the task, while the reason you pay Bain is for them to walk your company, understand its politics, talk to your customers, and tell you the truth. Evans notes you can already generate a bad McKinsey deck with AI, and the LinkedIn grifters who do are missing that the deck was never the thing you were buying.

    Jevons paradox and the lump of labour fallacy

    The Jevons paradox is just price elasticity applied to labour: make something cheaper to do and you usually do much more of it. Excel did not hand junior bankers their Friday afternoons off, it expanded the work. iPhone developers write a fraction of the raw code because Apple wrote the drivers and file system, and there are not a tenth as many engineers, there are far more. The count of accountants climbed through adding machines, punch cards, mainframes, databases, ERP, spreadsheets, and cloud. The lump of labour fallacy is the broader version: since 1800 every technology has removed jobs and unlocked new ones, the removed jobs usually look bad in hindsight, the new ones tend to be better, and GDP keeps rising. You can always see the job that disappears and never the one that does not exist yet.

    The jobs question, Dario, and the enterprise sales cycle

    On the coming jobs apocalypse, Evans is cautious about argument from authority. Running an AI lab makes Dario Amodei worth listening to on where models go in the next 6 to 12 months, not necessarily on labour economics and comparative advantage. The doomer image of companies buying ChatGPT and firing everyone within weeks misreads reality: enterprise sales cycles run 18 months or longer, nobody is tearing out SAP overnight, and the full transformation will take 3 to 10 years, sector by sector, as people slowly work out what to do. He points to the lag in software itself. Many SaaS companies founded the day before ChatGPT launched could have been built a decade earlier, and were not, because the delay was someone realizing a problem existed and that this was the way to solve it.

    Redefining AGI and superintelligence

    Evans is skeptical of the moving terminology. He cites Larry Tesler’s line that AI is whatever machines cannot do yet, because the moment they can, people call it just software. Machine learning, image recognition, and sentiment analysis all got reclassified as not really AI once they worked, the same way jet airliners were once high technology and are now just planes. AGI is now often quietly redefined as doing some percentage of economically valuable work, which a 1975 mainframe also did, rather than anything about consciousness or a soul. Whether we reach human-level intelligence is, in his view, genuinely unknowable right now. The reassuring point is that you do not need to resolve it. Even if models hit a brick wall tomorrow, what already exists is transformative and will take a decade to deploy.

    Where the value accrues: commodity models and the telecom analogy

    Here Evans makes his most deterministic argument. Foundation models appear to lack network effects, so no single model runs away from the pack, competition persists, and product differentiation as users experience it is thin. Without differentiation or lock-in, where does pricing power come from? He skewers Sam Altman’s image of selling intelligence on a meter like electricity by pointing out that utilities have terrible margins and nobody pays the power company a cut of their TV. His telecom career supplies the analogy: mobile is a roughly trillion-dollar industry that spends 15 to 20 percent of revenue on capex, grew data traffic 1,500 to 2,000 times since 2010, and whose stocks went nowhere for 25 years because it is a low-margin commodity utility while the value sits up the stack with Apple and the app makers. If models are commodities and the real product is thousands of apps the labs will not build, the outcome looks like cloud, not like Windows.

    Distribution as the moat

    If the product is a commodity, distribution decides the winners. The web browser is the cautionary tale: the browser product is a thin wrapper around a rendering engine, tab browsing was the last real innovation 20-plus years ago, Microsoft used distribution to win, and then winning browsers turned out not to matter because the value was elsewhere. Now Google drives Gemini through its surfaces and Meta sprayed AI across its apps and, in survey data, sat between ChatGPT and Gemini in usage despite tech writing it off. An adequate product with great distribution and brand becomes a big deal, which is why OpenAI spent last year trying everything to build a flywheel before the giants defaulted everyone onto their own offering. The power of the default and sheer inertia do a lot of work.

    Apple Intelligence and the model as the dumb thing underneath

    Evans calls the Apple Intelligence segment of WWDC 2024 the most compelling vision of a personal AI assistant he has seen: tool-using, on-device, agentic, with no prompt injection or hallucinations across a standardized API spanning thousands of apps. Apple could not ship it, but neither could anyone else, because that is genuinely hard. The episode illustrates his framing that the model is “the dumb thing underneath” that powers a feature. The same commodity model can sit beneath Gemini intelligence on Android and Apple Intelligence on iOS, with different products, different distribution, and different decisions about what the feature should be. Apple has a billion edge-capable devices, while Google’s “coming soon to our most powerful devices” really means it will not work on most Android phones.

    The anti-AI backlash, water, and real harms

    The backlash, Evans says, is a big fuzzy mess of very different things. Some is tangible, like a higher local electricity bill in a small number of places. Some is essentially fake, like the water panic. He dug into a Livermore lab study putting US data-center water use at about 0.017 percent of national consumption. Local well conflicts are planning failures, not data-center failures. The jobs piece is genuinely unresolved, with charts pointing both ways and a youth employment slowdown that shows up regardless of degree or AI exposure. He stresses how little hard data exists, since the labs publish no meaningful usage numbers and there is no public daily active user figure for ChatGPT. He compares the moment to the social media backlash, compressed, where some fears were true, some half true, and some simply false. The real new harms are real, though: deepfakes let a teenager generate explicit fakes of an entire school in an afternoon, and the UK Post Office Horizon scandal shows how buggy software plus institutional denial can destroy lives.

    You cannot predict what gets exposed, and what to actually do

    Evans dismisses the O*NET-style exercise of scoring what percentage of each profession AI can do as deluded, the modern version of the expert-systems problem, where you try to describe a job as 700 logical steps and it never works. You cannot say a senior partner’s work is 17 percent automatable. The history of prediction is humbling: in 1997 people thought taxis were safe from the internet and newspapers would simply save on printing, and both were wrong. Coding, supposedly one of the last things to automate, became the most transformed role of all. Personal trainers might be next once your phone can watch your form. His closing advice is to presume radical uncertainty and act anyway: do not retreat into sneering moral superiority, dive in, internalize what the tools can do, and make yourself a great hire. He ends with a candid admission that his own synthesis-and-retrieval job is exactly what AI is currently worst at, so he is the lawyer looking at VisiCalc, sure it changes everything while not personally making spreadsheets all day.

    Notable Quotes

    “My most controversial opinion is that I think that AI is as big a deal as the internet or mobile, and only as big a deal as the internet or mobile.”

    Benedict Evans, stating the thesis that frames the whole conversation

    “If you’re going to make the internet comparison, it’s like we’re in 1997. It’s very exciting. Most stuff kind of doesn’t work yet. Most of the stuff that people are going to do hasn’t been built yet.”

    Benedict Evans, on why confident predictions about AI winners are usually wrong

    “You can’t look at a senior partner at a law firm and say, well, 17 percent of their work could be automated. This is horseshit.”

    Benedict Evans, on why O*NET-style job-exposure scoring fails

    “Claude Code can write you the code, but what code do you want? It can make you the features, sure, but what features do you want? Who’s your customer? What’s the right product for that customer?”

    Benedict Evans, drawing the line between the task and the job

    “There’s this quote from Sam Altman where he said we’re going to be selling AI intelligence on a meter like water or electricity, and you look at this and think, my dear sweet child, you need me to explain the margin structure of the utility industry to you.”

    Benedict Evans, on why model labs may lack pricing power

    “The model is just the dumb thing underneath that powers the feature. The model is the commodity that powers different decisions about what the feature should be.”

    Benedict Evans, on why value moves up the stack to applications

    “Every time we have a new technology it automates away a bunch of jobs, and then that automation unlocks a bunch of new jobs, and you don’t know the new job because it doesn’t exist yet.”

    Benedict Evans, on the lump of labour fallacy and 200 years of automation

    “Don’t stick your head in the sand and say I hate all of this stuff. That gives you a great feeling of moral superiority, but that’s not going to help. What helps is you diving into this and coming out understanding what you can do with it.”

    Benedict Evans, on what to actually do about AI right now

    “AI is good at stuff that computers are bad at, and bad at stuff that computers are good at.”

    Benedict Evans, quoting an observation that explains why he struggles to use AI in his own work

    This is a curated set of pulls, not a transcript. To hear the full argument in context, including the telecom and recorded-music charts and the lightning round, watch the full conversation on YouTube here.

    Related Reading

  • Dan Shipper’s Most Contrarian AI Predictions for 2026: Why the Job Apocalypse Is a Myth, SaaS Will Boom, PMs and Designers Win, and CLIs Are Already Over

    Dan Shipper, the CEO and founder of Every, returned to Lenny’s Podcast for round two of AI predictions. His last appearance produced one of the most prescient calls of the year: that non-technical people would build serious work inside Claude Code. He was unbelievably right. This conversation is the follow-up, a tour of his most contrarian forecasts for how AI is actually changing the way we work, who wins, who loses, and what almost every commentator is getting wrong about the next twelve to twenty-four months.

    TLDW

    Shipper argues that the AI job apocalypse is a myth, that SaaS is going to boom rather than die, that product managers and full-stack designers are the biggest winners of the agent era, that personal agents inside Codex and Claude Code will quietly replace the browser as the primary work surface, that every company will run a single shared super-agent in Slack instead of a fleet of per-user bots, that the CLI moment is already over, that pull requests are going to flood organizations from non-technical staff, that forward-deployed engineers who garden company agents become the new senior role, that GPT-5.5 still cannot match a real senior engineer on architectural judgment, that AI-generated internal writing is fine and probably better than what most humans produce, that CEOs and middle managers have not adapted yet but soon will be forced to, that the edge of AI lives wherever a curious human is using it rather than in San Francisco, and that the only durable strategy is to ride the models and keep playing with whatever ships next. The whole conversation balances aggressive AI bullishness with an equally strong bet on humans, on creativity, and on the unavoidable need for someone to care for every agent that gets deployed.

    Thoughts

    The most useful frame Shipper gives is that models commoditize yesterday’s human competence. Every time a frontier model crosses a new bar, the work that used to define seniority becomes cheap. The senior engineer who could carry a refactor in their head, the PM who could write a coherent strategy doc, the designer who could ship a polished landing page in a week. That competence is now frozen, codified, and available on tap. The interesting question is not whether models will keep eating tasks. They will. The interesting question is what humans do with the suddenly cheap raw material underneath them. Shipper’s answer is that humans climb the stack: they go up a level, find a new problem worth framing, and use the commoditized competence as feedstock for something that did not exist before. That treadmill is the actual engine of value creation, and it is why he can be simultaneously AI pilled and bullish on hiring.

    His SaaS take is the spiciest call of the episode and probably the most defensible. The crowd consensus is that agents will gut SaaS because an AI can just write the form filler, the dashboard, the workflow. Shipper points out the obvious counterfactual: agents do not reduce the number of people using SaaS, they increase it. A marketing lead who could never touch the data warehouse can now stand up a PostHog query through Codex. A founder who never opened Vanta can run a SOC 2 prep through an agent. The result is more users, more accounts, and a much fatter top of funnel for every horizontal tool. The second-order effect is even more interesting. When the SaaS tool runs inside the user’s agent, the user supplies the tokens. Vendor margins improve, not collapse. If he is right, the next two years are going to be brutal for the SaaS-is-dead thesis pieces and very good for the public software multiples.

    The PM and designer bet is where this gets personal for anyone in product. For a decade the bottleneck in shipping anything was engineering capacity. A PM with spiky product sense had to negotiate their vision through a roadmap, a sprint, a review, and a release. Designers had to convince an engineer that the third state of the empty screen was actually worth building. Both of those constraints are dissolving fast. A PM who can prompt Codex into a working prototype on Friday afternoon, then iterate it live in front of a customer on Monday, is doing the job of a small team. A designer who can ship a fully functional landing page in their own style, without negotiating with anyone, is suddenly the most leveraged person in the company. The scarce skill is no longer execution. It is taste, judgment, and the willingness to decide what is worth building. That has always been the real PM and design job. AI just stripped away the parts that were not.

    The quietest but most important prediction is that agents need humans, permanently. Every benchmark advance reveals a new layer of judgment the model cannot frame on its own. When the agent finishes the task, there is always a senior human who sees the deeper problem the model patched over. Shipper calls this gardening, and it is the basis for the new forward-deployed engineer role. The companies winning right now are the ones that put a real person next to every agent, watching what it does, course-correcting in Slack, and noticing when the output drifts. The dream of autonomous AI workflows is a stage in a journey, not the destination. The destination looks more like a thoughtful operator with a small cluster of agents they trust and constantly tend. That is a much more humane future than the discourse suggests, and it is the one Every is already living.

    The final advice, ride the models, sounds glib but is the single most actionable line in the episode. Most professional anxiety about AI dissolves the moment you actually use the newest model on real work. Most professional advantage accrues to the people who do that one thing consistently. The edge does not live in San Francisco where the labs build the things. It lives wherever a curious human meets a real workflow and discovers something the labs have not noticed. A PM in Iowa willing to try Codex on a Tuesday night can be further ahead than a research engineer who has only used the model on its evals. Pair that with Shipper’s closing motto, do things worth writing about and write things worth reading, and you have a pretty complete operating system for the next two years.

    Key Takeaways

    • The AI job apocalypse narrative is wrong. Models commoditize yesterday’s competence, then humans climb the stack and find new work to do with the cheap raw material.
    • Every has roughly doubled headcount in the last year despite being one of the most AI-forward companies in the world. The lived data point cuts directly against the doom thesis.
    • Shipper’s dual stance: simultaneously extremely AI pilled and very bullish on humans. He treats this as the only intellectually honest position right now.
    • Work will bifurcate. Companies will run one shared super-agent in Slack for everyone, and individuals will run their own personal agent inside Codex or Claude Code on their machine.
    • The personal agent inside Codex effectively becomes the new operating system. Instead of putting AI in the browser, you put a browser inside the AI.
    • The super-agent pattern is already real: Shopify has River, Ramp has its own, and Every runs Claudie inside Slack for internal consulting.
    • SaaS is not dying. Agents increase the user base of SaaS tools because non-technical people can finally drive them. Shipper would buy SaaS stocks today.
    • When SaaS runs inside an agent, the user brings their own tokens. Vendor margins improve because they no longer eat inference costs on every interaction.
    • The CLI era is already over. The magic was never the terminal. It was the AI plus the ability to see what the agent is doing. A good GUI captures the same benefits and more.
    • Pull requests are about to flood every company. Non-engineers can now ship code, run queries, and open tickets. Reviewing the output becomes the new bottleneck.
    • Open-source maintainers are already living in the future. Some receive thousands of agent-generated PRs per day and spin up thousands of Codex instances just to triage them.
    • Forward-deployed engineers are the new senior role. They live in Slack, garden the company’s agents, fix broken flows, and keep non-technical staff from doing damage.
    • Product managers with spiky product sense plus a little Codex fluency become extremely dangerous. Marcus at Every, formerly a PM at Axios, is the archetype.
    • Full-stack designers are the other big winner. They can build distinctive interfaces end to end without negotiating with engineering. The bottleneck on taste-driven product work disappears.
    • Designer hiring data has not yet caught up to the prediction. Shipper notes this and says check back in a year.
    • Sales is the role least changed so far. Top of funnel research has been turbocharged by agents, but the actual relationship and closing work remains human.
    • AI-generated internal writing is going mainstream and that is a good thing. Most humans are bad at strategy docs, quarterly plans, and PRs. AI drafts a coherent first pass that a human can refine.
    • Shipper says most of his email is now written by GPT-5.5 and Codex. He would honestly prefer the signature to say so.
    • Public writing, newsletters, and published essays still demand a human voice. Internal communication does not.
    • CEOs and middle managers have largely not adapted yet because their staff still does the work. That window is closing fast and will become an obvious career liability.
    • Your company will only go as far as your CEO goes in AI. The leadership ceiling becomes the AI ceiling.
    • Shipper’s senior engineer benchmark scores GPT-5.5 at roughly 62 out of 100. Real senior engineers sit at 85 to 90. Progress is real, but the gap on architectural judgment remains.
    • Models tend to patch problems locally instead of rewriting from first principles. A senior human still sees the deeper rework that the model avoids.
    • Every uses Notion-based agents to draft quarterly plans. The human edits, approves, and stands behind the output.
    • The hard rule on AI-generated communication: you have to read it and stand behind it before sending it. Pasting unread output is the only true no-no.
    • Every agent needs a human. Automation is a lie in the strong sense. The story of automation is the story of new and different humans being needed alongside it.
    • The reach test, organic daily usage, is the real signal that an AI product works. Benchmark scores are noisy. Daily reach is not.
    • Cursor’s SpaceX acquisition is a tell. Harnesses around models, not the models themselves, are where the strategic value is concentrating.
    • The edge of AI is not in San Francisco. It is wherever a real human meets a real workflow and discovers something the labs have not noticed yet.
    • A PM in Iowa willing to ride the models can be further ahead than a researcher in SF who only uses them on internal evals.
    • Ride the models. Use them for whatever you do. Try every new release the day it ships. That single behavior compounds faster than any other AI career strategy.
    • Shipper got bursitis, which he calls vibe coder elbow, from too much rapid agent-assisted coding while debugging his markdown editor Proof.
    • The closing motto for the year: do things worth writing about and write things worth reading.
    • Lenny will re-interview Shipper in roughly May 2027 to score the predictions.

    Detailed Summary

    Why The AI Job Apocalypse Is The Wrong Frame

    Shipper opens with the headline contrarian call. Benchmarks keep climbing. Models can now sustain seventeen-hour autonomous tasks at fifty percent accuracy. The pace is real and accelerating. None of that translates cleanly into mass unemployment. His mechanism: models codify yesterday’s human competence and make it cheap. The act of compressing past expertise into an API call is genuinely deflationary for the work it captures, but it is also raw material for the next layer of human work. He uses Every as his own data point. The company has roughly doubled in the past year despite being one of the most AI-forward outfits in media. Hiring goes up because agents create new categories of work that need humans, not because the agents fail. The discourse, he argues, is stuck modeling AI as substitution. The reality looks much more like leverage.

    The Bifurcation: Super-Agents And Personal Agents

    Work splits into two surfaces. The first is the shared super-agent that lives in Slack and serves the whole company. Shopify has River. Ramp has its own. Every has Claudie. Each is a single, trusted, gardened agent that anyone in the company can talk to. The pattern has converged on one shared agent rather than one agent per person because agents need human attention to stay useful, and a single shared instance pools the gardening cost. The second surface is the personal agent inside Codex or Claude Code that runs on your machine and reaches into your local environment, your editor, your files, and through an embedded browser into the web. Shipper calls this the new operating system. Instead of the old paradigm of putting AI inside the browser, you put the browser inside the AI. The agent sees what you see, follows what you do, and works on your stuff in your context.

    The SaaS Bet: Up, Not Down

    The SaaS-is-dead thesis was the consensus call of late 2025. Shipper takes the other side and would buy software stocks now. Three arguments. First, agents make SaaS accessible to people who never could have used it directly. The total addressable user base inside every company goes up. Second, the business model improves when the user runs the SaaS through their own agent, because the user supplies the tokens. Vendors stop subsidizing inference. Third, SaaS spend in his observable universe is up, not down, and is concentrating on the tools that play well with agents. He frames the prediction as a sound bite for the cycle: buy SaaS stocks, the apocalypse is dumb.

    The CLI Era Is Already Over

    For a moment in early 2026 it looked like everyone was migrating to the terminal because Claude Code was a CLI. Shipper says the moment is finished. The actual leverage was never the terminal. It was the model plus the ability to watch and steer an agent live. A great GUI captures every advantage of the CLI without the friction. His own engineering team at Every has mostly moved off the CLI as their primary surface and onto Codex desktop. He frames it bluntly: we speed ran the CLI era, it was nice, and now we are done. Tooling for the next two years will be visual, multi-pane, multi-agent, and built around the human watching the work unfold.

    The Pull Request Flood And The Rise Of Forward-Deployed Engineers

    Once non-engineers can ship code, run queries, and file changes through agents, the volume of incoming work explodes. Open-source maintainers already report receiving thousands of agent-generated pull requests per day. Inside companies, the same thing happens to data teams, ops teams, and any function that owns a review gate. The bottleneck shifts from creation to evaluation. The job that emerges to absorb the flood is the forward-deployed engineer. This is a senior person who lives in Slack with the company’s agents, fixes their context, sharpens their instructions, and prevents non-technical colleagues from making well-meaning but incoherent changes. Nitesh at Every is the example Shipper returns to. The model is the same one the labs use internally: pair every important agent with a real engineer who gardens it.

    PMs And Full-Stack Designers Win The Decade

    The two roles Shipper is most bullish on are product manager and full-stack designer. For PMs, the entire job of coordinating a team to translate vision into code collapses into a Codex session. A PM with strong product instincts and a little technical literacy can now prototype, iterate, and even ship. The example is Marcus, formerly a PM at Axios, who took a year to fully internalize AI and now ships faster than most engineers. For designers, the model is similar. The Friday-night-side-project designer who used to be stuck explaining a vision can now build the vision themselves, with their own taste fully expressed. The scarce skill in both cases is the same: judgment about what to build and the courage to decide it is good. Execution capacity is no longer the constraint.

    The Senior Engineer Benchmark And What Models Still Miss

    Shipper has built his own benchmark to test whether coding models can actually do senior engineering work. GPT-5.5 scores around 62 out of 100. Real senior engineers sit closer to 85 or 90. The gap is not in syntax or test pass rates. It is in the willingness to step back, see that a piece of code is fundamentally the wrong shape, and rewrite it from first principles. Models almost universally patch locally. They take the instruction at face value, accept the existing code as a constraint, and optimize within it. A real senior engineer ignores the prompt when the prompt is wrong. This is the durable moat for senior technical judgment, and Shipper expects it to remain visible for at least another year of model releases.

    AI-Generated Writing Goes Mainstream

    Internal writing inside companies is quietly becoming AI-first and Shipper thinks it should. Quarterly plans, status updates, PR descriptions, strategy memos, recruiting outreach, most internal email. He runs his own inbox through GPT-5.5 and Codex and says he would honestly prefer if the recipient knew. The point is not that AI is a better writer in some absolute sense. The point is that most humans are not very good at these specific genres, and the model produces a coherent, structurally sound first draft that a human can guide and approve. The constraint is honesty: you read it, you understand it, you stand behind it. Public writing, like the newsletters Every publishes, still demands a human voice. Internal communication does not, and treating it as if it did is a tax on the organization.

    The CEO And Middle Manager Lag

    Shipper points to a population that has largely escaped AI adoption: senior leaders and middle managers. They have staff to do the work, so they have not been forced to pick up the tools personally. He thinks this is the single largest pocket of latent disruption coming in the next year. Your company will only go as far as your CEO goes in AI, because every decision about where to deploy agents, where to hire, and how to restructure work flows downstream from leadership taste. A leader who has not personally lived inside Codex or Claude Code for a few weeks cannot make those calls well. Expect this to flip fast and to become a visible career liability for executives who do not adapt.

    Ride The Models

    The closing advice is the simplest. Ride the models. Use AI for whatever you actually do. Try every new release the day it lands. Most of the professional anxiety around AI dissolves on contact with the work, and most of the durable advantage in the field belongs to the people who do this one thing consistently. Shipper notes that the edge of AI does not live in San Francisco. It lives wherever a curious operator meets a real workflow and notices something nobody at the labs has yet. A PM in Iowa willing to spend a Tuesday night exploring Codex can find capabilities researchers have not surfaced. Pair that with his motto, do things worth writing about and write things worth reading, and you have most of an operating system for the next two years.

    Notable Quotes

    “The AI job apocalypse is not really a thing. I am super super bullish on PMs and full-stack designers.”

    Dan Shipper, opening his contrarian thesis for the conversation

    “I’m simultaneously extremely AI pilled and very bullish on humans. Automation is a lie. Every agent needs a human.”

    Dan Shipper, on holding both sides of the AI debate at once

    “What models do in general is they make yesterday’s human competence cheap. And so, it becomes commoditized. It’s not valuable anymore. What humans do is we go in there and we’re like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting.”

    Dan Shipper, articulating the core engine behind his anti-apocalypse thesis

    “I would buy SaaS stocks right now. The SaaS apocalypse is dumb. What agents do is increase the number of users of SaaS, not get rid of it.”

    Dan Shipper, calling the consensus SaaS-is-dead thesis directly wrong

    “We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.”

    Dan Shipper, on why the terminal-first agent moment is already done

    “Most of my email is written by GPT-5.5 and Codex right now. And I honestly would prefer it to say that it’s coming from GPT-5.5.”

    Dan Shipper, on the new etiquette of AI-assisted communication

    “The edge of AI is not in San Francisco. The edge of AI is wherever AI meets a real human doing something.”

    Dan Shipper, on where the actual frontier of the field lives

    “The only thing you need to do is ride the models. And that means use them for whatever it is that you do.”

    Dan Shipper, distilling his career advice for the next two years

    “Do things worth writing about and write things worth reading.”

    Dan Shipper’s closing motto, lifted from his own operating system at Every

    Watch the full conversation with Dan Shipper on Lenny’s Podcast here. The re-interview to score these predictions is scheduled for roughly May 2027.

    Related Reading

    • Every. Dan Shipper’s company and the live laboratory for almost every prediction in this conversation, including Spiral, Cora, and Claudie.
    • The Allocation Economy by Dan Shipper. The earlier essay that frames humans as managers of AI labor and underpins much of the gardening-the-agent thesis here.
    • Claude Code by Anthropic. The agent surface Shipper called correctly last year and one of the two environments he predicts will become the new operating system for work.
    • Codex by OpenAI. Shipper’s current daily driver and the visual, multi-pane agent environment he uses for almost everything from coding to email.
    • The Writing Life by Annie Dillard. The book Shipper makes every Every employee read, and the source of the company’s stance on writing as a tool for noticing the future.

  • Anthropic’s Growth Strategy Explained: $1B to $19B in 14 Months, Automating Experiments With Claude, and Why Old Playbooks Are Dead (Lenny’s Podcast Recap)

    TLDW (Too Long, Didn’t Watch)

    Amol Avasare, Head of Growth at Anthropic, sat down with Lenny Rachitsky to explain how Anthropic grew from $1 billion to over $19 billion in annual recurring revenue in just 14 months. He breaks down their internal tool called CASH (Claude Accelerates Sustainable Hypergrowth) that automates growth experimentation, why 50 to 70 percent of traditional growth playbooks are now obsolete, why the PM-to-engineer ratio may need to flip, and how Anthropic’s early bet on AI coding created a research flywheel that competitors are only now starting to copy. He also shares how he cold emailed his way into the job, why activation is the single hardest problem in AI products, and how he uses Cowork to detect team misalignment across Slack channels automatically.

    Key Takeaways

    1. Anthropic’s growth trajectory is historically unprecedented. Revenue went from $1 billion at the start of 2025 to over $19 billion ARR by February 2026. That 19x growth in 14 months dwarfs companies like Atlassian, Snowflake, and Palantir, which took 15 to 20 years to reach $4.5 to $6 billion ARR. The number Amol quoted was already outdated by the time the episode aired.

    2. Anthropic is automating growth experimentation with an internal tool called CASH. CASH stands for Claude Accelerates Sustainable Hypergrowth. The growth platform team uses Claude to identify opportunities, build experiments (mostly copy changes and minor UI tweaks so far), test them against quality and brand standards, and analyze results. Amol describes the current win rate as roughly equivalent to a junior PM with two to three years of experience, but notes it was not possible at all before Opus 4.5 and has improved significantly with Opus 4.6. Human review is still in the loop but decreasing week over week.

    3. Activation is the single highest-leverage growth problem in AI. The core challenge is capability overhang: models are improving so fast that users do not know what they can do. By the time you have tested and optimized onboarding for one model’s capabilities, the next model has already shipped with entirely new features that make your learnings obsolete. Anthropic addresses this by adding intentional friction in onboarding to understand who users are and funnel them to the right products and features.

    4. Anthropic indexes 70/30 toward big bets, the opposite of most growth teams. Traditional growth teams spend 60 to 70 percent of effort on small to medium optimizations. Anthropic flips that ratio because they believe the product value delivered two years from now will be 100x to 1,000x what it is today. In that exponential environment, micro-optimizations capture a negligible percentage of future value. Large strategic bets are where the leverage lives.

    5. The PM-to-engineer ratio may need to flip. Engineers are getting 2 to 3x more productive with tools like Claude Code, effectively turning a team of 5 engineers into the equivalent of 15 to 20. But PMs and designers have not seen the same multiplier. The result is that product management and design are “absolutely squeezed.” Anthropic is responding by hiring more PMs and deputizing product-minded engineers to act as mini-PMs on projects under two weeks. The counterintuitive insight: companies may need more PMs, not fewer, as AI accelerates engineering output.

    6. Cold emailing still works if you do it right. Amol got his job by cold emailing Mike Krieger, Anthropic’s Chief Product Officer (and co-founder of Instagram), at a time when no growth role was even listed. Key tactics: use a high-converting subject line you have tested over time, find personal email addresses instead of competing in crowded LinkedIn inboxes, keep the message extremely short, and follow up relentlessly until someone explicitly asks you to stop.

    7. PRDs are largely obsolete at Anthropic. Amol estimates that 60 to 80 percent of what his team ships does not have a formal PRD. For small projects, coordination happens entirely in Slack. For larger initiatives, he will sometimes throw his thoughts into Cowork five minutes before a kickoff meeting to generate a rough document. His default philosophy: if you can skip the doc and jump straight to prototyping or action, do it.

    8. The AI coding bet created a research flywheel. Anthropic’s deep focus on coding was not just a commercial play. A document written by co-founder Ben Mann in 2021, just months after the company was founded, laid out the case for focusing on AI coding because better coding models would accelerate their own researchers, which would produce better models, which would produce better coding tools, in a compounding loop. This is something competitors are only now starting to recognize and copy.

    9. Cowork is being used to detect organizational misalignment. Amol runs a weekly scheduled task in Cowork that uses the Slack MCP to scan conversations across the company and surface areas of potential misalignment. He describes cases where this caught teams about to do overlapping work or spin their wheels on conflicting priorities. He also uses Cowork to simulate coaching sessions with his manager, Ami Vora, by asking Claude to analyze her public writing and internal Slack activity and then deliver feedback from her perspective.

    10. Anthropic’s culture is its most defensible moat. Amol describes a culture where every single person is fully engaged, nobody is checked out, and there is radical transparency through “notebook channels” on Slack where anyone, including leadership, shares their thinking publicly. Employees openly challenge Dario Amodei in these channels after all-hands meetings. These notebook channels also serve a practical purpose: they become training data that helps Claude understand how different teams think and operate.

    Detailed Summary

    The Cold Email That Started It All

    Amol Avasare was not recruited through a job listing, a referral, or a sourcing pipeline. He cold emailed Mike Krieger, Anthropic’s CPO and the co-founder of Instagram, with a short pitch: he loved the product, thought Anthropic badly needed a growth team, and wanted to talk. At the time, Anthropic had no growth roles posted. They were just beginning to think about it internally, and the timing was perfect.

    Amol’s approach to cold email is methodical. He has a subject line formula he has refined over years of founder outreach that produces abnormally high open rates (he declined to share the exact copy). He targets personal email addresses rather than work inboxes or LinkedIn, where competition for attention is fierce. The message itself is brutally short: who he is, why he would be a fit, and a request to chat. His follow-up philosophy is to keep reaching out until someone tells him to stop. Krieger responded on the first attempt.

    What $1B to $19B in 14 Months Actually Feels Like

    From the inside, Anthropic’s growth does not feel like a victory lap. Amol describes it as the hardest job he has ever had, harder than being a founder and harder than investment banking. About 70 percent of his time goes to what the team calls “success disasters,” which are problems created by things going extremely well. All the charts are green and up and to the right, but the underlying infrastructure, processes, and systems are constantly breaking under the strain of hypergrowth.

    The revenue trajectory tells the story: $0 to $100 million in 2023, $100 million to $1 billion in 2024, $1 billion to roughly $10 billion in 2025, and already $19 billion ARR by the end of February 2026. Amol notes that at the end of 2024, Dario Amodei was pushing for growth targets that the team thought were impossible. Those targets were hit and exceeded. The internal culture has adapted accordingly. Linear charts are considered uncool. Everything is presented on a log-linear scale.

    Why Activation Is the Hardest Problem in AI

    The central growth challenge for AI products is not acquisition. It is activation: getting users to understand what the product can actually do for them. Amol frames this as a capability overhang problem. Models are improving so rapidly that even internal teams struggle to keep up with what is newly possible. If Anthropic employees have to carve out dedicated time to explore a new model’s capabilities, the average user is even further behind.

    The danger is that someone signs up for Claude, asks it about the weather, and walks away thinking that is all it does. The product development cycle for onboarding is also under strain: by the time you have run tests, gathered learnings, and shipped an optimized activation flow for one model generation, the next model has shipped with capabilities that make your work irrelevant.

    Anthropic’s approach borrows from Amol’s experience at Mercury and MasterClass. They add deliberate friction to the signup flow, asking users questions about who they are and what they want to accomplish. This allows them to route users to the right products and features. The data also feeds downstream into lifecycle marketing and ad targeting. Amol has seen this pattern work consistently across every company he has worked at: the right friction, applied at the right time, outperforms frictionless flows that dump users into a blank canvas with no guidance.

    The CASH System: Automating Growth Experimentation

    Anthropic’s growth platform team, led by Alexey Komissarouk (who teaches growth engineering at Reforge), has built an internal system called CASH. The name stands for Claude Accelerates Sustainable Hypergrowth.

    CASH operates on a four-stage loop. First, Claude identifies growth opportunities by analyzing trends, metrics, and past experiment results. Second, Claude builds the actual feature or change. Third, Claude tests the output against quality and brand standards. Fourth, Claude analyzes the results and gathers learnings after the experiment ships.

    Currently, CASH handles mostly copy changes and minor UI tweaks. The win rate is comparable to a junior PM with two to three years of experience. A senior PM would still do better. But the trajectory matters: this was not possible at all before Opus 4.5 launched, and results have improved meaningfully with Opus 4.6. Human approval is still required before shipping, but the amount of human time spent reviewing is decreasing week over week.

    The part that Claude still cannot handle well is cross-functional stakeholder management. Getting six people in a room to align on a decision remains a fundamentally human problem. As Amol’s head of design joked: “We will have AGI and it will still be impossible to get six people in a room to get aligned.”

    Why the PM-to-Engineer Ratio Might Flip

    This is one of the most counterintuitive insights from the conversation. The conventional assumption is that AI will reduce the need for PMs. Amol argues the opposite: companies may need more PMs, at least in the near term.

    The math is straightforward. Tools like Claude Code are making engineers 2 to 3x more productive. A team of 5 engineers now produces the output equivalent of 15 to 20 engineers in the pre-AI era. PMs and designers have seen productivity gains, but not at the same multiplier. The result is a bottleneck: one PM managing the equivalent output of 15 to 20 engineers worth of work, while also handling cross-functional coordination, stakeholder alignment, and strategic direction.

    Anthropic’s response is twofold. First, they are actively hiring more PMs. Second, they have formalized a system where product-minded engineers act as mini-PMs on any project that is two engineering weeks or less. The engineer handles everything: talking to legal, talking to security, managing stakeholders. The PM only steps in if things go badly off track.

    For larger projects, the PM remains squarely accountable. But the key insight is about leverage: if you are one PM managing 20 engineers, the highest-value use of your time is not shipping the 21st feature yourself. It is getting 5 percent better at guiding the team on what the right opportunities are and upleveling every engineer’s product thinking.

    The Coding Flywheel That Changed Everything

    Anthropic’s deep bet on coding was not obvious at the time. A document from co-founder Ben Mann, dated 2021, laid out the strategic logic just months after the company was founded. The argument was that investing heavily in AI coding would create a compounding flywheel: better coding models would help Anthropic’s own researchers write code more effectively, which would accelerate model development, which would produce even better coding tools.

    This early focus gave Anthropic a structural advantage that competitors are only now trying to replicate. It also explains why the company went so deep on B2B and enterprise use cases rather than chasing consumer attention. The commercial opportunity of coding was large on its own, but the internal research acceleration made it doubly strategic.

    Amol notes that this focus was partly born from constraint. Anthropic was the smallest, least well-funded player in the space for years. They did not have Meta’s distribution or Google’s cash flow or OpenAI’s first-mover advantage. That constraint forced extreme focus, which is a principle Amol applies broadly. He calls it “freedom through constraints.”

    How Amol Uses AI to Manage His Day

    Amol’s personal AI usage is extensive and worth documenting for anyone looking to see how a power user at the frontier actually operates.

    Every morning, a scheduled Cowork task reviews 20 to 25 charts across Anthropic’s products and sends him a summary of what needs attention. The false positive and false negative rates are improving week over week, giving him increasing confidence in delegating this monitoring.

    He uses Cowork to handle administrative tasks he hates: booking meeting rooms, first-pass email triage, filing expense reports in Brex and reimbursements in Benpass. None of this requires his attention anymore.

    For management, he runs weekly Cowork tasks that review what his direct reports have done, cross-reference their work against team OKRs and meeting transcripts, and surface feedback he should give them. He also runs a parallel task for himself, asking Claude to impersonate his manager Ami Vora based on her public writing and internal Slack activity, and deliver feedback from her perspective.

    Perhaps most powerfully, he runs a weekly misalignment detection task that scans Slack conversations across the company and surfaces areas where teams may be working at cross purposes. He describes cases where this caught potentially expensive coordination failures before they compounded.

    Notebook Channels and the Culture Moat

    Anthropic uses “notebook channels” on Slack, which function like internal Twitter feeds where employees share their thinking, priorities, and provocative ideas publicly. Everyone has one, from researchers to growth PMs to Dario Amodei himself. Employees openly disagree with leadership in these channels, and that is encouraged.

    These channels serve a dual purpose. First, they help scale cultural values and operating principles as the company grows rapidly. When Amol posts about “the importance of being comfortable leaving money on the table,” every new engineer on the growth team absorbs that principle. Second, and perhaps more importantly for the long term, these channels become structured context that Claude can reference. The HR team has even documented which internal documents Claude should reference for specific topics. Amol sees this as something every company will eventually need to do: share thinking in a structured way so that the AI agents running throughout the organization have the context they need.

    AI Safety as Commercial Strategy

    Anthropic is structured as a Public Benefit Corporation (PBC), not a standard Delaware C-Corp. This legally allows the company to optimize for public benefit rather than being bound solely to maximize shareholder value.

    Amol says the company has repeatedly taken significant commercial hits for safety reasons, including delaying product launches when safety risks were identified. He also makes a striking claim: what Anthropic says publicly about AI risk is actually a softer version of what they believe internally. The internal view on the potential downsides of powerful AI is more aggressive than the public messaging.

    From a growth perspective, Amol frames safety as a long-term competitive advantage. Growth teams at most companies try to squeeze every last dollar. Anthropic’s growth team is “very comfortable forgoing metric impact” to protect brand, quality, and safety. He argues this is how all the best products operate, and that as the stakes of AI get higher, Anthropic’s credible commitment to safety will become a moat.

    Advice for Thriving in the AI Era

    Amol’s advice for product managers and growth practitioners boils down to four points. First, stay on top of the tools. Try every new model release. Something that did not work three months ago may work now, and you will not know unless you go back and test it. Second, go deep on your unique spike rather than trying to be well-rounded. The PM who can also design is a unicorn. The engineer who thinks like a PM is a unicorn. Find your interdisciplinary edge and double down. Third, be radically adaptable. Amol estimates that 50 to 70 percent of how you operated in the past is now irrelevant. Clinging to old playbooks creates friction. Fourth, think in exponentials, not linear projections. If you are looking at the AI landscape through a linear lens, you will consistently underestimate how quickly things are moving.

    Thoughts

    This interview is one of the most information-dense conversations about growth strategy in AI that has been published so far. A few things stand out.

    The CASH system is the most concrete example yet of a company using AI to automate its own growth loop. The fact that it currently performs at a junior PM level is almost beside the point. What matters is the trajectory: it went from impossible to functional in a few months. If models continue improving at their current pace, this system will be operating at a senior PM level within a year. Every growth team at every AI company should be building their own version of this right now.

    The PM ratio insight is genuinely surprising and underreported. The default assumption in the tech industry is that AI will reduce headcount across all functions. Amol is making the case that in the near term, the opposite is true for PMs. Engineering output is exploding, and someone needs to direct all that output toward the right problems. That is a fundamentally human, organizational, political job that AI is not close to automating.

    The coding flywheel story is also worth highlighting because it shows the power of strategic focus in a world of unlimited possibilities. Anthropic had a generalist technology that could do almost anything, and they deliberately narrowed their focus to one vertical. That decision, made in 2021 before anyone knew what the market would look like, is arguably the single most important strategic bet in the company’s history.

    Finally, the notebook channels concept deserves more attention. The idea that employees should share their thinking in structured, searchable formats is not just a culture tool. It is an infrastructure investment for an AI-native future where agents need organizational context to be effective. Companies that build this habit early will have a significant advantage when agent-driven workflows become the norm.

    The uncomfortable subtext of this entire conversation is that Anthropic’s growth team, as talented as they clearly are, is riding a wave created almost entirely by the research team. Several YouTube commenters pointed this out, and Amol himself acknowledges it directly. The models are the product. The growth team’s job is to make sure users discover and adopt what the models can do. That is not a small job, especially at this scale, but it is a fundamentally different job than driving growth at a product that does not sell itself.