25% of OpenAI’s production engineers got 1 order: every project is on hold, and your only job is securing the company.

Then OpenAI aimed its peak-intelligence model at its own infrastructure and kept it running until the model stopped turning up bugs.

Greg Brockman, OpenAI’s co-founder and president, walked through that sprint on the No Priors podcast. He…

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Open this kit beside your next build session: 12 copyable agent briefs, a release checklist and 16 practice cases with grading criteria. Follow one product from an unverified idea to a pilot decision. You get the exact inputs, the checks and the cost formulas, so the next agent session has a concrete job and you know what evidence to ask for.

What you get in the full kit

• A product brief that separates customer evidence from assumptions before an agent starts building.

• A worked questionnaire assistant with a fictional source packet and 16 acceptance cases, including unsupported claims and attempted data leakage.

• Twelve agent briefs covering discovery, build handoff, independent review and the operating decisions around the product.

• A copyable trial log and release checklist that keep failed checks and unrun tests visible, plus the formulas for cost per accepted output.

• A release decision template, a rollback drill and a pilot review you can run with your first users.

Your subscription also includes

These are existing resources in the premium library. Pick the ones that fit the work you are doing:

• How Top Teams Review AI-Written Code for the review process around your agent-built changes.

• The 5-Person Team That Runs on Claude Code for eight systems you can adapt to a small team.

• Build Your Own AI Legal Team in Claude for a founder-focused legal drafting and review workflow. Qualified counsel still makes the legal calls.

• Which AI Plan Is Worth Paying For? We Priced 32 to Find Out for the subscription comparison and stack calculator.

• The Agent Workforce Kit: Anthropic’s 8 Metrics, a Role Radar and 12 Claude Prompts for deciding where agents fit in your team.

• The AI CEO Dashboard Kit: An Excel Model, 14 Checks and 10 Claude Prompts for the operating numbers behind the product.

• The AI-Era Fundraising Toolkit: 105 Verified Links, a Raise Command Center and 15 Claude Prompts for preparing a raise around the business you are building.

• Stop prompting. Start writing loops for reusable loop recipes and stop conditions.

• What actually moves the needle with Claude for the context-file setup and verification workflow.

• 13,486 AI Investors in One Workbook: The AI Corner AI Investors Database 2026 for the investor workbook, filterable by stage and country.

Eligible readers can try premium free for 7 days with the Start trial option below. If you prefer the annual offer, get 50% off your first year here. The trial and annual discount are separate offers; check the checkout terms before choosing.

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Elliot Lindberg emailed Lovable with a problem he had already run into: connecting a client’s website to Shopify.

He offered to work on the integration during an internship. In a public post, he later credited that email with getting him into the company at 17.

The useful part is how little the recipient had to imagine. Elliot had used the product for a real task. He could explain where it broke and propose work he wanted to do.

That is the starting point I would use for AI startup outreach: give someone a concrete reason to continue the conversation.

Below are real emails, with the useful decisions pulled out of each. Then comes the paid kit: copyable PDF templates and an editable XLSX workbook for turning your research into outreach you can stand behind.

Start with a problem you have actually touched

He names a real client use case and describes the experience he wished the product had. He also gives exact internship dates, so the founder knows what he is being asked to decide.

The email contains something valuable even if the company passes on him: feedback from someone who tried to use the product for a customer.

For an AI engineering role, I would adapt this with a small reproducible example of a failure. For a product role, I would show where a user gets stuck and a proposed acceptance test. A commercial candidate could bring a sample account brief built from public information.

Keep the work small enough that you can explain every decision. A focused example gives the team a place to begin assessing your judgment.

Give the recipient an easy handoff

Niraj Pant sent Evan Spiegel a short internship request in 2014. The visible reply thanks him for being brief and copies Snapchat’s lead recruiter.

Pant makes his experience easy to scan and finishes with an explicit internship request. Spiegel can route the email with a short reply.

That is a useful test for your own message. Could the person forward it to the relevant colleague without writing an explanation of what you want?

For an AI startup founder, the equivalent is a narrowly scoped pilot request. Say which workflow you want to test and what the buyer would need to provide. The recipient should be able to tell whether it belongs with them.

One caveat: these are selected success stories. They show what people sent, and they say nothing about your likely reply rate.

Take the method into your own inbox

The gap between an interesting example and a useful email is the preparation. You need a recipient whose work fits your request, plus evidence that supports what you say.

The paid section walks through that preparation for technical and commercial AI roles. It also covers founders approaching design partners or hiring candidates.

The Agent Workforce Kit pairs with this if you are designing the hiring process. Founders preparing investor outreach can also use the AI-Era Fundraising Toolkit.

Paid subscribers get the implementation kit:

• The editable XLSX outreach workbook, with qualification gates, follow-up review dates and a dashboard that separates replies from useful next steps.

• The copyable PDF template library, with 8 original outreach templates. It also includes follow-ups and reply handling. Every example is clearly labeled illustrative.

• The PDF proof packet, including a worked AI support-triage example and a blank version for your own role or product.

• The PDF practice materials, with 12 synthetic cases and expected routes so you can build a small, inspectable work sample.

Upgrade to paid

Upgrade to get the complete AI Startup Cold Email Kit and the worked playbook below.

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The LatAm AI Investor Map lists 378 investors that say they’re active in Latin America, with stage, HQ and thesis for each one. Premium members get the whole directory today, and every fund in it carries an AI conviction score.

I spent 2 days scoring it, because a list of names won’t tell you who writes AI checks. Only 22 of the 378 investors confirm they’re actively investing, and 331 carry no fund status at all. 5 list a latest investment, and all 5 are dated between November 2019 and January 2020.

Meanwhile, AI money in the region moved fast. Decade, a Brazilian AI-native wealth advisor, announced an $85 million seed in August, the largest seed round on record for a Latin American startup. a16z and QED co-led Carecode’s $4.3 million pre-seed for AI agents in healthcare.

So I turned the directory into a shortlist. Every fund got an AI conviction score from 0 to 5, with the rule for each point written down and a named source behind every AI deal.

The surprise is who drops out and who stays in. 11 rows mention AI or machine learning in the thesis, and once you remove a duplicate and a false match, 9 funds are left. Only 5 of them make the final 27.

Kaszek co-led a $12 million seed into Primero, a Mexico City company building enterprise AI agents, this year. Its stated thesis never mentions AI. Monashees opened a San Francisco office to sit closer to the AI wave, and its thesis is silent on AI too.

A fund’s thesis is what it wrote years ago. Its last 3 deals are what it believes today.

Premium members get the full map today:

• The 27 investors to email first, ranked, with stage and the deal behind each score

• The scoring rule, point by point, so you can reweight it

• Where the AI money sits by country, and the round plan for Brazil vs Mexico

• What the 2026 stage data means for the size of your next round

• The full LatAm AI Investor Map: all 373 funds, scored and filterable, with the 27 AI picks on their own tab

• A 2-week outreach plan built from the evidence column

What premium gets you

The AI Corner premium is $49 a month or $199 a year. Start with a 7-day free trial from the paywall below, or take 50% off your first year with the button.

• The full Business & Investing library, including the AI Investors Database with 13,486 investors

• Every Resources spreadsheet, kit and template

• The AI Agents builds, start to finish

• 3 new systems every week

One reply from the right fund pays for this many times over.

Get 50% off for 1 year

Download the full map

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Fear wiped Wednesday’s midterm rally in under an hour. The Nasdaq closed at 24,315, down 5.5%, and about 11.9% below its October 6 close of 27,599.79. That is a correction. The S&P 500 fell 4.4% to 7,353.73. The Dow sank 1,833 points, or 3.4%, to 51,523.06. Chips and memory led: SanDisk down about 22%, AMD and Micron about 17%, Nvidia about 9.2%. Meta closed near $607, down about 3.6%.

Fear wiped the midterm rally in under an hour.

When markets panic this hard, opportunity shows up for people who keep a cool head.

I invest as a scout and angel who watches both public tape and private decks every week. After a day like this, I skip the blanket mega-cap AI dip. I put capital where paid usage and short payback already show up.

Below the paywall I name the public tickers I am sorting after today’s rout, the application and vertical companies with real revenue numbers, the efficiency infra I still watch, and the private diligence checklist I run before writing a check, plus the 5 checks before you buy any AI dip this week.

Where I put capital after this rout

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He said it in a conversation with Sebastian Mallaby, author of the Hassabis biography The Infinity Machine, that Intelligence Squared published on October 15. Six days later he told WSJ Magazine, which named him its 2026 Innovator in Tech, that he now expects AGI within 2 to 3 years.

The company that sat on LaMDA is now the one where Sundar Pichai says Gemini 4 Argon left Google capacity constrained. This interview explains the miss behind that comeback, and the method Hassabis describes for hard problems works for a 10-person startup as well as it works for DeepMind.

I watched the full 14 minutes so you don’t have to. Here are the 10 lessons that matter.

1. A Bot That Is Wrong 20% of the Time Is “Doubly Tricky” for Google (the Trap of Being Too Close to Your Own Tech)

Hassabis sees the logic of waiting, then names the blind spot behind it.

“I think that’s an interesting thing of where you can be sometimes too close to your own technology.”

Hassabis says he had no part in the decision. Back then DeepMind ran mostly as an independent research group, and LaMDA belonged to the Google Brain side. He can still see the logic. Google Search exists to organize the world’s information, so a bot that gives the wrong answer 20% of the time is a far bigger risk for Google than for a startup. A startup, he says, “especially someone with someone like Sam leading it,” can “YOLO it.”

The phrase I keep coming back to is “too close.” The team that builds a model sees every hallucination up close, while users judge the product on the jobs it gets right. Anthropic faced its own version of this call when it limited the model that can hack anything to a small group of partners. If your brand rests on accuracy, fix the error rate in the system design first, as our agent reliability playbook lays out, and then let users tell you where the bar sits.

Try this: before you delay a launch, ask 5 users outside the team which error rate they would accept for each job.

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Hassabis is talking about the launch bar, and every AI founder hits the next one right after it: turning early AI users into customers who stay.

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2. ChatGPT’s Clunky Name Shows Nobody Knew How Big It Would Get

Hassabis reads the product name as OpenAI’s own forecast.

“ChatGPT is a programmer’s name. They would have named it something catchier if they knew how big it was gonna get.”

To be fair to his own side, Hassabis says, nobody knew how big ChatGPT would get, OpenAI included, and he adds that Sam Altman has admitted as much. The name gives it away. He says OpenAI thought of the launch as a research experiment, and as a startup with no existing products that relied on reliability, it could live with the mistakes.

A working title tells you how big the team thought the idea was. For a look at how OpenAI pitched its long-term ambition years before the chatbot, the 64 slides of its 2018 AGI plan are worth an evening.

Try this: name and staff your next experiment as if it might be the one that takes off.

3. Flawed Models Found Their First Jobs Where Errors Were Cheap

Users found the forgiving use cases before the labs did.

“It didn’t matter if it made some errors that weren’t to do with information seeking.”

What caught everyone by surprise, OpenAI included, was how quickly people found uses for models that still hallucinated a lot. Once millions of people had access, Hassabis says, they found low-hanging fruit like summaries and homework help, jobs where a wrong detail cost little. Demand for those early use cases turned out to be huge, and he says that is what showed the technology was mature enough to leave the lab.

That is the oldest pattern in AI products. The wedge is the job with the highest tolerance for error. Only 4.5% of Americans pay for AI, according to our breakdown of consumer AI spending, so the forgiving job that gets people coming back is where you earn the right to charge. For the build side, read the AI product-market fit playbook from OpenAI’s product lead next to PostHog’s 38 lessons from shipping 10 AI products.

Try this: rank your use cases by what a wrong answer costs the user and launch on the cheapest one.

4. Go Has More Positions Than the Universe Has Atoms, So Brute Force Loses

Hassabis picks 2 problems where checking every option is impossible.

“In Go, there are more possible board positions than there are atoms in the universe. So that means you can’t brute force and enumerate every option.”

Proteins are harder still. An average-sized protein has 10 to the 300 possible shapes, Hassabis says, so you can’t find the right fold by looking at every one.

DeepMind’s answer to both problems was the same. Train a neural network on what the domain already knows, from Go games to known protein structures, until it holds a model of how that domain works. His 2019 MIT deck on self-learning systems shows how long he has been betting on this approach.

The founder version is simple to run. Before you budget more compute for a problem, estimate how many options it has. When the count is astronomical, extra GPUs buy a slightly bigger slice of an impossible search, and our token cost playbook shows where that kind of spend leaks out of an AI bill.

Try this: write down the size of your search space before you choose the method.

5. Let the Model Pick What to Search, Then Write the Objective Function

A model shrinks the haystack, and a clear goal tells the search where to climb.

“The final thing you need is an objective function. You need to have a clear goal you can hill climb against.”

With a model in hand, the search gets smart. A Go position has around 100 possible moves, Hassabis says, and the model tells you which few are worth exploring, so the compute budget goes to the fruitful ones. He calls it a very generic solution for any problem you can frame as a needle in a huge haystack.

The goal is the last piece. Games make it easy, since you win or maximize the score, which is why DeepMind trained on Atari before Go. Science has clear goals too, like minimizing the energy in a system, and he says a surprisingly large share of problems can be framed this way.

The best agent setups today follow the same recipe. Give an agent a metric and you can let it optimize overnight, the idea at the heart of Karpathy’s autoresearch method.

Try this: state your product’s objective function in 1 sentence, and get the team to agree on it before you add compute.

Setting a goal your system can climb? Start with these:

• Google’s Agent Broke a 56-Year Math Record. Yours Forgets Yesterday

• Stop Prompting. Start Writing Loops

• The Harness Is the Product: 6 Decisions That Turn an AI Agent Into a Worker You Can Leave Alone

• Your AI Agent Is Going to Hallucinate at Scale

6. Hassabis Ranks Curing Disease First and Clean Energy Second

Asked to weigh benefits against risks, he gives a ranked answer.

“I think the number one thing we can apply AI to is curing terrible diseases.”

The question comes after a run of dark topics, from AI in warfare to cybersecurity fears. Hassabis answers that AI is the ultimate tool for science and medicine. Second on his list is helping the environment and finding new renewable energy sources, and he points out that this is where he has spent his own time, through AlphaFold and Isomorphic Labs.

A ranked list forces the trade-offs a vision statement lets you skip. Put your use cases in order and fund the top one first. If you invest in hard tech, our Physical AI Investment OS maps where the money sits across 12 layers.

Try this: write your top use case and your runner-up in 1 line each, and share both with the team.

7. Hassabis Says AI Could Bring a Second Renaissance Within 10 Years

He backs a 10-year forecast with a science group that’s nearly 10 years old.

“There’s almost no area of science that I think AI can’t help with. And I think if we do that right, in the next 10 years we’ll have almost a second Renaissance.”

DeepMind has had an AI for science group for nearly 10 years, Hassabis says, and that is where AlphaFold came from. Some rivals are only now setting up science teams. His group works on fusion, weather prediction, climate models and material science.

Sam Altman reached for the same word last month when he described the renaissance he wants AI to build. Big forecasts are easy to make, and a lab that shipped AlphaFold has earned a hearing for this one.

Try this: when you hear a bold forecast, check what the person making it has already shipped.

8. Even the Best Scientists Hit a Limit Without AI

Hassabis read the biographies of his heroes and saw a ceiling.

“There’s a limit to how much we can comprehend, I think, with just unaided, with our human minds, even with scientific method and other things.”

He grew up reading the biographies of scientists from Feynman to Turing and calls them all really smart. He also says he was born with an “insatiable curiosity” about the deep mysteries of the universe, like the nature of time and consciousness. From the start, AI was a 2-factor idea for him: an amazing tool for science, and an artifact that could help us understand our own minds.

If the greatest scientists hit a ceiling working alone, your best engineers will too. Pair them with AI on the parts no single person can hold in their head, and keep the judgment calls with people. Companies are already hiring for judgment as AI takes over execution, and The Agent Workforce Kit gives you Anthropic’s 8 metrics for running an agent workforce.

Try this: pick 1 problem too big for one person’s head and put an AI system on it this week.

9. DeepMind Worried About Safety From Its Start in 2010

He dates the concern to the founding year and explains why.

“We were worried about safety from the very beginning of DeepMind in 2010, and we still are now, because it’s general. So it can be used for anything.”

The technology is dual purpose, Hassabis says, and Google DeepMind knew it from day one. He says he would have done this work no matter what, because to him it is the most fascinating question and technology out there. The job for the industry, in his view, is to push the areas that are great for society, like medicine and energy, more quickly.

A date tells you more than a values page. Dario Amodei built a whole company around the same worry, and our read on his long game of safe AI covers how. The same dual-use logic applies to the AI features you ship, so run the AI app security checklist before launch.

Try this: ask every AI vendor you buy from when its safety work started and who owns it today.

10. Hassabis Loves the AGI Race and Still Wants International Cooperation

He enjoys the competition, then says something bigger sits behind it.

“I’ve got this gnawing feeling that there’s something much more important, much bigger than the commercial race, which is overall getting AGI safely over the line for humanity.”

Hassabis says the competitive part of him loves the “ferocious commercial battle,” leans into it and finds it helps him lead. He also says DeepMind is one actor among 5 or 6 leaders, with the Chinese labs in the mix, and that the story of the next few years is still to be written. He wants more cooperation on safety, while admitting that is very hard with today’s geopolitics.

When someone leading the race says the race leaves something out, it pays to listen. For your own planning, use our 4-scenario framework for AI policy so your roadmap holds up whichever way governments go.

Try this: add 1 slide to your next board deck on what your product does if AI rules tighten.

What This Means for You

The Hassabis method fits on an index card: a model of the domain plus a goal it can climb, launched first where users forgive mistakes.

• Founders: Write your objective function in 1 sentence before you pick a model, and launch on the job where errors cost the least. When you raise on that story, The AI-Era Fundraising Toolkit has the investor workflows ready.

• Investors: Ask each team how big its search space is and what error rate its users will tolerate. Test the answers against the 9 bottlenecks deciding who wins in AI and the payoff data in State of AI 2026.

• Operators: Price every search run against your agent’s ROI, and count the options before you add compute.

• Everyone else: Give an AI tool 1 task this week where a mistake costs you nothing, and check which AI plan is worth paying for before you upgrade.

The 5 Principles to Steal

1. Test Launch Bars Outside the Team. The people closest to the flaws are the worst judges of what users will forgive.

2. Pick Forgiving Jobs First. Let users show you where errors are cheap, then widen the scope.

3. Size the Search Space. Estimate how many options exist before you choose a tool or a budget.

4. Write the Goal Down. Every search needs 1 clear objective to hill climb against.

5. Date Your Safety Work. Ask each lab and vendor when it started and what it still worries about.

Watch the full conversation to hear how Hassabis connects Go and protein folding.

If this breakdown saved you the full watch, send it to one founder or investor who needs it.

Keep reading

• Muse Just Beat ChatGPT’s Launch 3-to-1. Here’s the Operating Manual Nobody Wrote

• OpenAI Declared the AGI Era. Then Greg Brockman Moved 25% of Its Production Engineers to Defense

• Anthropic Raised $580M With No Product. Here Is the Deck

• Your AI Agents Are Aging

Cover photo of Demis Hassabis by Arthur Petron, CC BY-SA 4.0.

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PostHog builds product analytics, and its 10 AI products reach nearly 100,000 people a month. In October 2026 it published 38 lessons from building them.

Every lesson gets the same format and weight, and PostHog’s own last lesson says to expect half of them to be wrong in a year. We sorted all 38 so you can see which ones to build on and which ones to revisit when the next model ships.

Sponsored by HubSpot for Startups

PostHog’s lessons get an AI product shipped. The harder part comes after launch, when you need to turn AI pilots into customers who stay.

IGNITION 2026 by HubSpot for Startups: the summit for AI-native founders, Nov 10 in San Francisco

• Built for AI-native teams: a curated room of 200, mostly Seed and Series A founders and GTM leaders

• Practical by design: mainstage talks from founders and investors, plus 1:1 clinics with GTM experts

It’s free for approved applicants, and applying through this link gets you priority review:

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The Short Answer

• Lessons that last: test every AI bet like a new product, and make agents reliable and transparent before adding capability.

• Lessons tied to today’s models: harness design, the ban on subagents and how context gets delivered. Expect these to change with the next model.

• Lessons that depend on your users: PostHog’s agent-first bet on an MCP server fits developers. Teams serving non-technical users should build the agent inside their app.

• The first question to ask: how does your AI feature beat a user opening Claude or ChatGPT?

Table of Contents

1. Decide Whether to Build an AI Feature at All

2. Choose Where the AI Lives: In Your App or Through an MCP Server

3. Build the Agent: Harness, Context, Cost

4. Earn Trust: Reliability, Showing the Work, Review Screens

5. Validate and Protect the Bet

6. FAQ

1. Decide Whether to Build an AI Feature at All

The first decision could save you from building anything at all.

Which jobs are worth handing to AI

Lesson 1’s point is you may not always need to add an AI feature in your product, especially when it could make your product worse by getting slower or unreliable.

For example, a document chat feature, which every AI tool can already do, isn’t worth adding. Not to mention, your users may already have their own AI agent or model that they trust.

If you’re not sure how it applies, lesson 3 gives 3 signs that a job is worth passing to AI (you only need 1):

1. Takes more than 30 seconds

2. Needs a language users don’t know (like SQL)

3. Users repeat the task at least 20 times

A good example of this is onboarding (lesson 4), like PostHog’s install wizard that they claim can do 2 hours’ worth of work in 8 minutes.

According to PostHog, users who make use of the install wizard sent their first event sooner, about 1.9 hours compared to 3.8 hours for those who don’t.

Wizard users are also more likely to pay: 14.2% of wizard users convert to paid, compared with 2.6% of others.

The catch here is that PostHog has no control test, or at least, doesn’t describe one.

Those using the wizard may already be familiar with the command line terminal. They may already be more technical and may have paid anyway, which can explain part of the gap.

How does this beat Claude?

Before you build anything, check whether users could just use a chatbot for the same job.

Lesson 10 indicates Claude as the default go-to, and so according to PostHog, you must answer the question “How does this beat Claude?”

For PostHog, the answer is context: the product’s features, users’ data, the tools, and even the skills that PostHog has written.

What to do:

• Check the AI feature against the 3 signs in lesson 3.

• If it hits none of the signs, don’t build it.

• Note what your AI feature has that a general chatbot like Claude or ChatGPT doesn’t have (like your users’ data or your own tools).

2. Choose Where the AI Lives: In Your App or Through an MCP Server

Let’s say your feature passes. Now you have to decide where users will actually reach it.

Why PostHog now says to build for agents first

Lesson 2 says to consider whether you need to build your own AI feature inside your app. Making it easy for other agents to use your product might be more worth it.

The standard method for AI tools like Claude Code and Codex to plug into a product and use it directly is through an MCP server.

PostHog ended up building both:

• PostHog AI (its own agent)

• The MCP server (letting other agents use PostHog)

According to their October 2026 lessons list, there are 5x more people who use the MCP server on a daily basis compared to PostHog AI.

PostHog studied 63 million MCP tool calls (90 days to September 16, 2026) and found out the following:

• About 7% of calls came from a chat window (third-party traffic)

• Running SQL and reading the data schema made up 32% of all calls

• In 30 days, agents created 252,000 charts using MCP, while people created 99,000 in the web app

The chart numbers might seem surprising, but they make sense since agents can make charts in seconds. The real question is how many of those 252,000 charts were actually used?

The SQL number also explains lesson 20, which says AI agents work better if you let them write their own SQL instead of forcing them to use preset tools.

When that advice won’t transfer

Going with MCP worked well for PostHog because its users are mostly software teams, people who are already working inside AI coding tools.

In PostHog’s March post, it said that a custom in-app agent is the better choice in 3 cases:

1. Your users aren’t engineers.

2. Compliance blocks outside agents.

3. You need full control of the experience.

For example, let’s say your users are salon owners.

They don’t use AI coding tools like Claude Code or Cursor, so building an MCP server for them would be pointless.

What to do:

• Find out if your users are already using AI coding tools.

• If yes, consider building an MCP server first, which is easier to build and maintain anyway (lesson 2).

• If not, or compliance or control is an issue, then go with the in-app route.

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3. Build the Agent: Harness, Context, Cost

PostHog admitted that half of the lessons in the list may become obsolete in a year. The lessons in this step are the ones most likely to change.

Why PostHog went through 3 harnesses

A harness is the software that connects a model to your product’s tools and data.

Lesson 11 details how PostHog AI went through 3 harnesses in a year, as described in their March 2026 post:

1. A coordinator that routed work to sub-agents

2. One agent loop with 44 tools written by hand

3. Anthropic’s Claude Agent SDK, plus PostHog’s own tools and skills

New models were one reason for the rebuilds (lesson 15). Cheap reasoning (OpenAI’s o4-mini) and reliable tool use (Anthropic’s Claude 4) made the old setup outdated.

PostHog’s advice is to not treat a harness as an advantage. I wrote an earlier post where I argued that the harness decides whether an agent works at all.

The harness affects quality (my point), but it won’t set you apart from the rest, which is PostHog’s point. Better to spend your effort on context.

Why more context beat better reasoning

Lesson 27 says that context failures are behind many agent failures.

A good example of this is PostHog’s install wizard. With 20+ PostHog products, 17+ SDKs, and 25+ frameworks, the possible setups are in the thousands, and the agent kept choosing wrong.

PostHog then added docs, source code, SDK reference, and example apps. This helped more than the reasoning and planning changes it had tried.

Lesson 28 says some context has to come from people. PostHog AI’s /init command researches your product and asks you to confirm the summary before saving it.

But even if the idea of giving the agent more context is correct, PostHog changed how it delivers that context. PostHog AI’s system prompt included every event and property in a user’s data.

PostHog replaced that list with a tool that the agent can call when it needs to look up an event or property (lesson 29).

Lesson 30 says that when the product changes faster than the docs, your agents will start working from outdated information.

PostHog now has agents that look at merged pull requests and open a doc pull request when needed to keep the docs current.

What a run costs, and why PostHog banned subagents

Lesson 25 says to learn your unit costs.

One wizard run cost PostHog an average of about $6.67 in February 2026. For PostHog, the better activation and retention of its own users are worth more than that.

PostHog’s February 2026 post has a section called “The subagent ban”.

A subagent is a helper agent to the main agent. It doesn’t share the main agent’s cache, so the subagent has to read everything again, adding to the cost.

They didn’t always finish either. PostHog shared an extreme case where the wizard spawned 5 subagents, then idled for 5 minutes waiting for the subagents to return.

2 of the subagents didn’t return, leading the wizard to run a kill command. That whole process took about 15 minutes, and it was all a waste. The real work then took about 3 minutes.

Lesson 26 can’t put it more simply: subagents cost more than you think. Across wizard runs, cost generally rises with the number of turns, from about $2 to about $11.

The same February 2026 post noted that the newer Sonnet 4.6 at that time seemed to handle subagents better. This lesson may not survive the next model.

I’m not telling you to be skeptical, but it helps if you remember what PostHog is selling.

For example, lessons 22 (AI observability) and 24 (tracking agent usage) match PostHog’s AI Observability and MCP Analytics products.

What to do:

• Expect to rebuild your harness, and don’t count on it as your advantage.

• When the agent fails, check its context first before working on the reasoning.

• Calculate your cost per run, and test the subagents again on current models.

4. Earn Trust: Reliability, Showing the Work, Review Screens

A working agent doesn’t mean people will rely on it.

Lesson 23 answers the question: capability or reliability? PostHog listed these early complaints about its AI:

• Inconsistent performance

• Unexpected failures

• Generic error messages

• Unclear limits

• No sign of uncertainty

Users weren’t asking for more features. As PostHog puts it, “capabilities are worthless if they aren’t reliable.”

Lesson 36 contains an example of when PostHog reversed its decision to hide the agent’s reasoning and failed tool calls.

PostHog’s reason was that it looked messy. But according to the users, that only made it hard for them to trust the AI. PostHog now streams everything to users.

Lessons 19 and 37 are about UI and how its role has changed now that AI agents do the work. Basically, more of the screens now are for approving, reviewing, and undoing the agent’s work.

What to do:

• Go through the complaint list first, starting with generic errors and unclear limits.

• Show users the agent’s reasoning and its failed tool calls.

• Add a place to approve, review, and undo what the agents did.

5. Validate and Protect the Bet

Validating an AI feature starts before launch and continues after it.

How to judge the bet, and who could beat it

Lesson 8 talks about how to validate AI bets by treating them like a new product.

• Before you build, decide what it’ll be judged on and how long it gets (its runway).

• Once it exists, ask your team how disappointed they’d be to lose it.

PostHog asked its own team right before launching a product. Only 23% said they’d be very disappointed, which was way lower than the 40% benchmark Sean Ellis set for product-market fit.

Lesson 9 is about writing down how an AI-native competitor would beat you, then funding that yourself.

PostHog’s example is Intercom, which it says “probably” pivoted so hard into Fin, its AI support agent, for this reason.

In Intercom’s case, an AI that answers the tickets means fewer human agents are needed. Fewer human agents mean fewer Intercom seats, and Intercom charges per seat.

How to protect it from prompt injection

Lesson 33 talks about prompt injection. It’s when someone hides instructions in text that an AI agent will read in hopes that the agent will obey as if the prompt came from you.

PostHog has a feature called self-driving which opens pull requests from product signals. Some of those signals might come from the public.

PostHog’s defense is an LLM classifier at the top of the pipeline, a separate model that drops signals that look malicious.

OWASP ranks prompt injection first in its 2025 and 2026 LLM Top 10 lists. It also says that mitigation doesn’t remove prompt injection entirely.

PostHog’s filter alone can’t solve this, and self-driving can open PRs without a human approving them.

What to do:

• Write the criteria and runway down before building anything, then run the survey on your team.

• Write down how an AI-native competitor would beat you, then fund your own version.

• Always treat public inputs as untrusted, especially if an agent can act without approval.

Anyway, if I had to pick 1 lesson to start with, I’d implement lesson 10 and ask how the AI feature could beat Claude. If it can’t, then you’re just building a worse version.

FAQ

What are PostHog’s 38 AI product lessons?

According to PostHog, they are lessons from building 10 AI products used by nearly 100,000 people a month, published in October 2026. They cover when to build AI features, agent design, cost, context, MCP servers and security.

Which AI product lessons will still hold next year?

The product lessons hold: validate each AI bet with written criteria and a runway, and favor reliability and visible reasoning before new capabilities.

Why did PostHog stop using subagents?

Subagents cost more than expected, since each one keeps a separate cache and rereads everything, and some went silent, which triggered retries. PostHog’s install wizard cost about $6.67 per run.

Should you build an MCP server or an in-app AI agent?

Build an MCP server when your users are developers who work in tools like Claude Code. Build an in-app agent when your users are non-engineers, when compliance blocks outside agents or when you need full control of the experience.

How do you make an AI agent reliable?

Give it rich, current context and show users its reasoning and tool calls. PostHog also treats the harness as replaceable as models improve.

Keep reading:

• Your GTM, run by agents

• Satya Nadella said every agent needs its own computer

• Your AI Agent Might Be Paying $11,000 a Month to Answer Yes or No

• Google’s agent broke a 56-year math record. Yours forgets yesterday

If this helped, send it to the founder or PM on your team who is shipping AI features.

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He frames the whole moment this way: “We are at early stages of the most consequential technology ever.”

The Alphabet CEO takes questions on Big Technology Podcast about compute, agents, AI-written code, safety standards and data centers in space, and several answers carry a number or a decision you can copy.

I watched the full interview and pulled out the 10 answers that matter most.

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1. Almost 25 Trillion Tokens Hit Gemini 4 Argon in 72 Hours and Google Stayed Capacity Constrained

Launch-week usage settles the argument before any benchmark chart does.

In the first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about 3x what Gemini 3.0 had in the first 72 hours.

Usage numbers end arguments that benchmark charts keep alive.

Google took a bit longer with this release than with its other models, since it wanted the testing and deployment done with care, he says. Even so, Google is capacity constrained as it serves the traffic.

My first check on any new model is tokens per day, because agent costs climb with usage and AI costs keep rising even as tokens get cheaper.

Plan capacity for launch week, since that week sets your ceiling.

2. Google Retired the AI-Written Code Percentage (Here Is What It Tracks Instead)

Volume alone flatters every team that ships a lot of code.

That number in our world today is like meaningless in the sense that every part of code we now touch is influenced by AI deeply.

When AI touches everything, the share stops telling you anything.

The host asked him to update a 25% figure, and he answers that Google now tracks newer metrics. In the 4 weeks since launch, he reports a 1/3 decrease in rollbacks, and he reads that as people writing code more productively and better.

By his account, people pointed the model at complex projects, including some touching millions of lines of code.

A falling rollback rate outranks any share-of-code figure for me, whether the diff comes from Claude Code or a person, because AI-generated code can look fine while churn climbs. Teams chasing the output half can start with context engineering.

Report output and rollbacks as one pair.

3. Gemini Merges Chat and Agent Tasks Into One Input as Agents Take a Growing Share

The mode switch between chatting and delegating disappears from the interface.

You don’t need to think about whether you’re asking a chat or a, agentic task, it’ll just kind of automatically do the right thing.

Hiding the chat-versus-agent choice removes friction early.

The Gemini app already shows chat and task, he says, and a steadily growing share of what arrives is agentic tasks, where last year most requests were chats.

Personal agents will work in the Gemini app, Google search and Gmail, running persistently in the background, and Google wants to get it right first.

Actual Gemini traffic is what sells me on the merge, so route each goal to whichever agent fits. Some jobs still fit a plain workflow, so check whether you need an agent.

Offer 1 entry point and route work behind it.

To build the background-agent version of this today, start here:

• Google just made agents 3x cheaper to run. Here is the playbook

• Brian Armstrong Runs 1,200 AI Agents at Coinbase. Here Is the Operating Model He Just Handed Every Founder.

• Claude Dispatch: The AI That Keeps Working When You Don’t

• Anthropic just solved the hardest part of building AI agents

4. Today’s Compute Came From a 2024 Decision (and Pichai Plans 2 to 3 Years Out)

Every capacity order is a forecast with a long fuse.

The compute we have today is a function of decisions I made in 2024.

One 2024 forecast still sets the ceiling today.

Google builds its own compute and its own data centers, he says, so lead times run long. It plans for what it needs 2 to 3 years out, and some decisions may look suboptimal 2 years out, depending on where the state of the art lands.

Everyone is short today, he notes, wishing for 2x what they planned. Dylan Patel did the math on the world’s compute by 2028, a second forecast to check against.

A capacity plan passes my test when each figure carries a logged error estimate, so the review in 2 years has something to grade.

Grade last year’s capacity call before you make this year’s.

Planning a build on that horizon? These 3 sit closest to your decision:

• Sam Altman: OpenAI’s Best 12 Months Start Now

• OpenAI’s $122B masterclass: 10 takeaways from Sarah Friar

• Jensen Huang handed you the AI roadmap. Here are the 10 moves that matter.

5. Frontier Compute Comes First at Google (and Every Other Business Plans Around It)

One rule decides the order of every compute request at Google.

The first principles we operate on is to making sure we have enough compute to invest in the frontier. And that’s non-negotiable.

A non-negotiable line settles every allocation fight in advance.

He traces the rule to 2015, when he set the company to be AI first, so AI now runs through every business from search to Waymo. Research and model development get compute first, and each independent business then plans from its own long-range projection, backed by demand signals and committed contracts.

To me, the order of operations is the part worth copying: settle the top priority first and let every team forecast around it. Pichai’s I/O 2026 keynote is the companion read.

Protect the 1 input your strategy depends on.

6. Google’s Cloud Business Gives Up Revenue So Research Gets Compute (and Some Constraints Help)

Every allocation has a loser, and Pichai names his.

I think our cloud business is, at any given moment in time, foregoing short-term revenues because we are making compute trade-offs to support frontier AI development.

Naming the loser out loud takes discipline.

That trade is already happening in some form, he says, and Google commits to investing what it takes to stay at the frontier. Some constraints help researchers too, he notes, since they push toward more efficient algorithms, maybe ones that use less memory.

I read that as a priority list with a price attached. Investors weighing the same trade can start with compute economics.

Name what you give up before you choose.

7. Pichai Calls the AI Buildout Different From Fiber (Here Is the Evidence He Cites)

A bubble comparison meets a 1-line rebuttal about idle capacity.

This is not like fiber. It’s not dark. We are using it.

Busy capacity is the strongest answer to a bubble claim.

Over a long enough timeframe, this level of build makes complete sense to him. For the plan itself he weighs demand signals, committed contracts, internal evals and model trajectories, and he calls the result a disciplined approach that is also confident and aggressive.

I’d stress-test that checklist against the biggest risks in the AI thesis before using it as a template for any build decision. Demand moves quickly, as AI spend shows.

Plan from demand signals and committed contracts.

8. Pichai Says Yes to the AI Overbuilding Question (and Still Backs Google’s Plan)

A direct question gets a yes, and the risk list comes before the confident close.

Am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes.

That confidence carries weight because the risk list came first.

Asked about overbuilding, he starts with a yes: constrained components can cause bottlenecks. External shocks to the economy count too, and he files them under macro risks.

I trust the confidence more because the failure points came first. My pre-build checklist opens with the bottlenecks, then asks what risk sits behind the forecast.

List your bottlenecks before you approve the build.

9. Pichai Points to Auditor-Style Evaluators for AI (and Says Regulation Will Play a Role)

Finance built standards and auditors; he points to the same toolkit for AI.

If you look at areas like financial controls, we have like accounting standards, we have independent auditors, et cetera, right?

The analogy fits because finance already runs on outside checks.

Worry is reasonable, in his view, and the right response is to ask early what more the industry can do. The next steps, he says, are better standards and independent evaluators, and regulation will end up playing a role.

My first question for any AI vendor is which outside party tests their model, since an internal eval grades its own homework. Buyers already hesitate on trust, and our Six AI Trends of 2026 breakdown tracks how fast regulation is landing.

Demand outside testing before you trust any eval.

If you ship AI to customers, read these before your next release:

• The US government switched off Anthropic’s most powerful model 3 days after launch

• Dario Amodei: AI Is Closer Than You Think (And Society Isn’t Ready)

• The AI model that can hack anything, and why you can’t use it

10. Google Dates Its Space Data Center Bet at 2 to 3 Years and Compares It to Waymo

A moonshot gets an honest clock from the CEO behind it.

You’re always, with all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way.

Honest timelines make ambitious bets easier to fund.

He recalls watching the Stanley car compete at the DARPA Grand Challenge in the desert, the car that became the basis for Waymo. By his clock, Waymo is almost 20 years in and space is 2 to 3, with quantum named as another long bet.

Sun-synchronous orbits offer 24-7 access to the most abundant form of energy, he says, and Google expects a hello world moment from the TPUs it sent up while he speaks.

The habit I’d borrow is stating a project’s age out loud, since patience in the world of atoms runs on long clocks.

Date your moonshot, then hold it to milestones.

What This Means for You

Plan capacity years ahead, and judge AI output by what survives rollbacks.

• Founders: Pichai ties today’s compute to decisions he made in 2024, so write down the capacity you need in 2 years and the demand signal behind each line. Read why lean AI startups are winning, then place a small first order this quarter.

• Investors: Ask each AI company when it ordered the capacity it serves today and what share goes to research first. Test the answers against Altman’s rules for the AI era and Andreessen’s AI moat argument before your next diligence call.

• Operators: Pair every agent rollout with a quality metric, the way Pichai reports more productive coding alongside 1/3 fewer rollbacks. Measure each agent’s ROI beyond tokens and time saved, and benchmark your scorecard against Anthropic’s 8 agent metrics.

• Everyone else: Hand 1 weekly task to an agent and track what breaks as well as what ships. Before you pay for a plan, see which AI plan is worth paying for, where we priced 32. Try code-free agents if you want a starting point.

The 4 Principles to Steal

1. Date every forecast. Write the expected miss beside each capacity number and grade it later.

2. Name your compute trade-off. Say what the frontier costs the rest of the business, the way Pichai does with cloud revenue.

3. Pair output with rollbacks. Volume flatters everyone, and graph engineering covers what a loop does with the 1 number it is given.

4. Layer checks around the model. Outside evaluators and standards catch what your own telemetry misses.

Long lead times reward the people who decide early. Make your next capacity call this week.

Watch the full interview on Big Technology Podcast for his answers on Anthropic and Apple too.

Keep reading:

• Anthropic just cut the price of frontier intelligence in half

• China just open-sourced Opus-level intelligence. Here is the playbook

• Google Open-Sourced the AI Agent Framework Behind Its Own Products

• Demis Hassabis Says AGI Arrives in 2 to 5 Years. Here Is the Full Picture

• Anthropic is closing in on a $1 trillion valuation. Dario won.

If this breakdown saved you the watch time, send it to one founder or investor who needs it.

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Claude Max costs $100 a month and comes with $100 a month in API credits. Claude Ultra costs $200 and comes with $200 in credits. If you already pay Anthropic for API usage, its top plans hand back their own price.

Pay for ChatGPT Plus at $20, Claude Pro at $20, Google AI Pro at $19.99 and Perplexity Pro at $17 a month billed annually, and you spend $76.99 a month on 4 assistants that cover much of the same ground.

On November 1, 2026 we checked 32 AI plans and 16 API models on each provider’s official pricing page. Then we built a calculator that takes your weekly usage and returns the cheapest stack that still covers it, the same kind of working Excel model as our AI CEO Dashboard Kit.

Premium members get the AI Plan Calculator: enter your weekly usage and see your stack and your yearly savings in 5 minutes.

Here is what the prices say:

1. The $20 tier carries most of the value for most founders.

2. The $100 and $200 plans pay off when you run Claude Code every day or build on the API.

3. A plan beats the API as long as your work fits inside its limits.

4. Caching and model routing cut token prices by 95% or more.

I ranked 18 of these plans by value for a founder. Claude Pro sits alone at the top because $20 a month includes Claude Code.

1. The $20 tier is where the value sits

Claude Pro includes Claude Code and Research for $20 a month, or $200 a year ($16.67 a month) on annual billing, per Anthropic’s pricing page. Our Claude playbook shows where that $20 pays off first.

ChatGPT Plus also costs $20 and includes Codex and deep research, billed monthly only, per OpenAI. Our GPT-6 Astra masterclass shows how to get more from each prompt.

Google AI Pro at $19.99 puts Gemini inside Gmail and Docs and adds 5 TB of storage, per Google’s plan page. If you already pay Google $9.99 for 2 TB of storage, the upgrade costs $10 more and brings Gemini 4 Argon with it.

xAI now sells Grok and Cursor together with X Premium in one subscription, from $8 to $200 a month. According to xAI, its $30 Premium tier carries the benefits of plans that cost $90 a month combined. Our one-person company manual shows how 3 SpaceXAI engineers started a company in 9 hours.

The free tiers are worth a look too. Kimi gives free users limited K3.1 Pro on the web, and our Kimi playbook covers setup and cost. The DeepSeek app is free.

The rule: pay for one $20 plan in the ecosystem you use most, and add a second plan only for a job the first one can’t do.

2. The $100 and $200 plans pay you back in credits

Claude Max gives you 5x Pro usage every 5 hours and every week. Ultra drops the 5-hour limit for verified accounts and gives 10x Pro usage every week. Both include monthly API credits equal to their price.

Google AI Ultra includes credits too, as Google Cloud credits: $40 a month on the $99.99 plan and $100 on the $199.99 plan.

Here is the math for a founder who spends $100 a month on the Claude API, say to run an agentic OS on Claude. Moving to Claude Max costs $100, the $100 in credits covers the API bill, and the total stays at $100 with 5x Pro usage on top.

ChatGPT Pro starts at $100 and tops out at $500 a month, with a $200 tier in between and monthly billing only, per OpenAI’s help center. OpenAI lists monthly API credits on Pro without publishing the amount.

3. A plan beats the API while your work fits inside it

API prices are public. Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens, so $20 buys 10M input tokens or 2M output tokens.

Say 4 of every 5 tokens you send are input. A blended million then costs $3.60, and $20 covers about 5.6M tokens a month at Sonnet 5.5 prices.

No provider publishes how many tokens its plans include. Claude says your limits depend on the model you pick and on how long your chats and files are.

So price your real tokens on the API only when you hit your plan’s limit every week, because below that the plan wins. Claude’s paid plans also let you keep working past a limit with usage credits billed at standard API rates.

4. Two settings cut token prices by 95% or more

The first is caching. On Claude Opus 5.5, a cached input token costs $0.20 per million against $4 for a fresh one, a 95% cut. OpenAI’s GPT-6.1 Sol shows the same gap at $0.10 against $2.

The second is routing. Claude Haiku 5.5 costs $0.10 per million input tokens, 40x less than Opus 5.5 at $4. Send classification and formatting to the small model and keep the large one for hard reasoning. Trimming what the agent sends pays too: NVIDIA cut agent API costs by about 50% by redesigning how its agent talks to the model.

The premium edition adds the other token savers with the math, including the price increases already on the calendar.

Keep reading:

• Claude Cuts Task Time by About 80%. Make It Run Your Week

• You Are Overpaying for Intelligence. Grok 4.5 Just Proved It

• Google Just Made Agents 3x Cheaper to Run

• Anthropic Just Cut the Price of Frontier Intelligence in Half

What 3 founders save

We ran 3 example founders through the calculator at US list prices, and the price sheet inside it links every source.

1. The solo founder coder pays $507 a month for 5 plans plus $150 of Claude API usage. The calculator moves him to Claude Ultra, whose credits absorb the API bill, and he saves $3,484 a year.

2. The non-technical CEO pays $107 a month for 5 plans that overlap. One Claude Pro plan on annual billing plus storage and X Plus brings her to $33, a saving of $884 a year.

3. The 10-person startup pays $1,341 a month across 4 vendors and the API. Claude Team seats on annual billing bring it to $927, a saving of $4,972 a year.

Inside the premium edition:

1. The AI Plan Calculator for Excel and Google Sheets: enter your weekly usage and get your recommended stack with your monthly and annual savings

2. The plan to pay for in 7 founder profiles, plus the 3 example founders broken down plan by plan with what to cancel first

3. The API vs plan breakeven test with token prices for 16 models, and 10 token savers with the math behind each one

4. The verified price sheet for 32 plans, with every source link

Get 50% off for 1 year. Premium also opens the full library: the Resources hub, every Claude & Anthropic playbook, the AI Agents builds and the Business & Investing deep dives.

Get 50% off

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“A few hundreds of dollars per seat, per month.” That’s what a Harvey spokesperson told Fast Company a bespoke seat costs, and large customers buy at least 100 seats for at least a year. Claude Pro costs $20 a month.

By our reading, that’s at least 90% less per seat: we take “a few hundred” as $200 or more, and $20 is 10% of $200. Harvey is built for law firms with hundreds of lawyers. A seed-stage startup has zero lawyers and a folder of PDFs someone has to read before Friday.

The other price tag is outside counsel. Clio puts the average US lawyer at $362 an hour, and business formation and compliance work at $400. One of those hours pays for 2 years of Claude Pro on the $200 annual plan.

Anthropic shipped legal plugins for Claude Cowork on January 30, 2026, with contract review and NDA triage among them. On February 3, Thomson Reuters shares closed down about 16%, per The Wall Street Journal.

Cheap only works with guardrails. In Vals AI’s legal benchmark, Harvey scored 94.8% on document Q&A against 70.1% for the lawyer baseline, and lawyers still won redlining, 79.7% to 65.0% for the best tool. Reading is where AI already wins. Judgment and drafting the final word still belong to a human.

A Stanford study found that even purpose-built legal research tools hallucinated on more than 17% of queries. Damien Charlotin’s database now tracks 2,241 court cases with AI hallucinations, and 884 of them involve lawyers.

So the rule for a founder’s AI legal team is simple: AI reads everything and quotes every line it relies on, then a lawyer decides the few things that can hurt you.

Try it on the next NDA in your inbox. Copy & paste this:

You are reviewing an NDA for [COMPANY]. We are the [DISCLOSER / RECIPIENT / MUTUAL] party.

1. Quote every clause you rely on, word for word, with its section number. If you cannot quote it, drop the point.
2. Check: mutual or one-way, definition of confidential information, standard exclusions, term and survival, residuals, non-solicit, non-compete, IP or feedback assignment, remedies, governing law.
3. Give one verdict: SIGN, NEGOTIATE or ESCALATE (a lawyer reads it first).
4. For every NEGOTIATE item, write: Replace "[exact quote]" with "[new text]".
5. Cite no law from memory. Turn any legal question into a question for my lawyer.

End with: "Prepared by AI for review. This is not legal advice."

That prompt handles one NDA. A startup signs NDAs, SAFEs, offer letters, customer MSAs and DPAs, files Delaware taxes and keeps a cap table that has to match all of it. Each of those documents needs its own checks and its own red flags.

Most founders paste each one into a chat and trust the confident answer that comes back. One invented clause in a customer contract or one missed 83(b) deadline costs more than a year of any AI plan.

So I built the kit I wanted for that moment. On the sample NDA inside it, the verifier script caught 1 invented quote out of 4 findings and 1 law citation with no source, before anything reached a lawyer.

Paid subscribers get the full kit today:

1. 10 ready-to-install Claude Skills, one for each legal moment a founder hits plus the checks that run on every task

2. An “opposing counsel” agent that attacks your redlines before the other side does

3. 3 scripts: a quote verifier, a cap table checker and a compliance calendar that exports to Google Calendar

4. A playbook template and an issue-list schema, plus a 4-level escalation matrix and a 1-day build plan

A few that pair well with this one: The Claude Prompts That Close Rounds (in Business & Investing), The Claude Code System That Replaces a 5-Person Team (in Claude & Anthropic), The Agent Workforce Kit (in AI Agents) and Build a One-Person Business With Claude (in Resources).

Get 50% off for 1 year. Premium also opens the full library: the Resources hub, every Claude & Anthropic playbook, the AI Agents builds and the Business & Investing deep dives.

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When we invested, one company in our portfolio was making around $100K a year. A year later it was at $7M ARR, and one of the major AI labs acquired it before it ever raised a Series A.

That company is one of the 53 we’ve backed with our own angel checks and through GR Angel, and 42 of them come from Y Combinator. In each of those rounds, the founder decided to let us in.

People often ask how two writers get allocations in YC rounds that fill in a few days. Below is our answer, and then how GR Angel will work in 2027.

Disclosure: we’re investors in every startup named in this article.

Founders choose who gets in

Every company accepted to Y Combinator gets the same deal: $500K, with $125K for 7% and $375K on an uncapped MFN SAFE (YC). YC does the first round of picking, and every company in a batch starts from the same terms.

Orange Collective puts the historical unicorn rate of an average YC batch at 6.5% (Orange Collective). The best rounds in each batch are oversubscribed, and the founders decide who gets an allocation.

That makes access the hard part of angel investing in YC. The founders we want to back usually have several investors competing for the same small allocation, and every one of them offers money on similar terms. Founders pick the investors who bring the most value beyond the check, and they remember who delivered for the last company they heard about. To get in, you have to show up with something specific, like customers and introductions to the next round’s investors.

Guillermo wrote about this in November 2025, when a16z set up a New Media team to help its founders get attention, and he summed up the market in one line: “Capital is abundant, attention is scarce.”

Why we spend most of our time on YC

Here is YC’s track record:

• More than 400 YC companies are valued above $100M, and more than 100 above $1B (YC).

• Over a dozen YC companies are public, and the combined valuation of YC alumni is over $800B (YC).

• Airbnb went public in 2020 at a valuation of over $100B, Coinbase in 2021 at $86B and DoorDash in 2020 at $39B, while Stripe is now valued at $107B (YC).

• The average YC Demo Day valuation is $40M post-money, so a company needs a 25x step-up to reach $1B (Orange Collective).

Y Combinator has funded more than 5,000 companies since 2005, and every three months more than 10,000 companies apply for a batch that typically accepts about 1% of them (YC).

Support continues long after Demo Day. YC says B2B and consumer companies often get their first 40 to 50 paying customers from the YC community (YC), and founders can book office hours with partners year round.

Our edge is attention

Between us we have 600K followers on LinkedIn and 400K newsletter subscribers, with 500M+ impressions a year. I write The VC Corner and The AI Corner, and Guillermo writes Product Market Fit and AI Market Fit.

When we back a company, we write about it and introduce the founders to the customers and funds that read us. Two results from that work are public. One newsletter feature led to a $1M investment offer for a founder, and one of my posts helped another startup double its ARR.

We also open doors. Our podcast, Compound, has hosted Mark Cuban, Substack co-founder Chris Best and Creandum founding partner Staffan Helgesson, and I’m an official scout for a16z speedrun.

Founders noticed. They started making room for us in oversubscribed rounds because they wanted that support.

Guillermo has a shorter version: “Money is a commodity, you can’t differentiate one dollar from another. Distribution is the moat.”

In 2025 we made 25+ investments with our own money, and in 2026 we said yes to a small group of outside investors for the first time. They asked to put more money in for the following year, and as of October 2026, $2M is already committed for 2027.

Some of the companies we’ve backed

We often join rounds led by tier-1 funds. Across the portfolio, our co-investors include a16z, Sequoia, Founders Fund, Coatue, General Catalyst, Peak XV, Lightspeed, Khosla Ventures, CRV and Kfund. Every figure below comes from the company or the press.

• HappyRobot. Pablo Palafox and his co-founders build AI agents for logistics. a16z led the company’s Series A in December 2024, and in August 2026 HappyRobot was valued at $1.2B (Fortune). We interviewed them:

• Replit. Amjad Masad and Haya Odeh’s company raised a $400M Series D at a $9B valuation in March 2026, led by Georgian with a16z, Coatue and Y Combinator participating (TechCrunch).

• QFEX. Two Cambridge math graduates are building a 24/7 exchange for traditional markets. They raised a $9.5M seed at a $95M valuation, led by General Catalyst with Paul Graham in the round (FinTech Global, March 5, 2026).

• Apollo Atomics. This MIT spinout is building nuclear reactors in a factory and closed a $31M seed led by FCVC, with Y Combinator in the round and Paul Graham among the individual investors (Pulse 2.0).

  

More of our 2026 YC investments and who else backs them:

• Arga Labs, a $10M seed, alongside investors including General Catalyst, BoxGroup, Emergence and SV Angel (TechCrunch).

• LightSprint, a $6.1M seed, alongside investors including 645 Ventures, January Capital, Rebel Fund and Kaz Nejatian of Opendoor (LightSprint).

• Didit, a $7.5M seed, alongside investors including Pioneer Fund, Robinhood Ventures, Orange Collective and Tomer London of Gusto (Didit).

  

• Prodigy Research, alongside investors including Paul Graham, Coinbase Ventures, DoorDash co-founders Andy Fang and Stanley Tang, and Rick Rubin (007 Venture Partners).

• Exosat, alongside investors including Paul Graham, Orange Collective, Robinhood Ventures and early investors in SpaceX and Anduril (007 Venture Partners).

• DeepReach, alongside investors including Robinhood Ventures and World Solver Syndicate (World Solver Syndicate).

• Karumi. Toni and Pablo build an AI agent that runs live, personalized product demos, and Rebel Fund is among its backers.

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If you know someone who wonders how small checks get into big rounds, send them this.

folk: from 2K to almost 500K users in four months

One of the fastest-growing companies we’ve backed is folk, the personal AI that Nozomio builds. It lives in your texts, on iMessage, WhatsApp and Telegram, in the same personal-agent category as Instinct and Muse.

Here is how its user count grew, based on numbers the company shared with us:

That’s roughly 250x in about four months. Nozomio is backed by LocalGlobe, BoxGroup, Y Combinator and 20VC, with angels including Paul Graham, Spotify’s Gustav Söderström and del.icio.us founder Joshua Schachter (Nozomio).

The relationship starts years before Demo Day

Karumi shows how early we meet founders, and the story starts with Guillermo.

About three years before Karumi’s YC batch, he judged an AI hackathon in Valencia and voted for a developer named Toni. A year later Guillermo was in San Francisco, sleeping on a friend’s sofa, when he found out Toni was working at a YC startup in the city. They met again in New York, where Toni and his best friend Pablo were thinking about starting a company. Guillermo spent months deciding whether to join them, and in his words, “I almost quit GoHub to do it.”

A few months later Toni and Pablo got into YC with Karumi. By the time they raised, Guillermo already knew them well, and we invested.

Our first exit came before a Series A

We’re keeping the names of the company and the acquirer private, and the figures at the top of this article are all we can share about the deal.

What founders can take from this

The question we hear most is why we see deals early. Our answer comes in 4 parts, and each one gives founders a test to run on any investor who wants into their round:

1. Founders know us before the round. With Karumi, the relationship started at a hackathon about three years before the batch. For founders: the investors you get to know before you raise are the ones who can move fastest once you do.

2. Our coverage moves numbers founders care about. A $1M investment offer and a doubled ARR each came from a single piece of content. For founders: ask every angel what they do after the wire, and for one company they helped with it.

3. Two people decide. Guillermo and I make every decision, which takes us from a first call to a wired check in under 24 hours. For founders: ask how many people sign off on a check and how long the last one took.

4. Most of our time goes to YC. Our best investments in 2025 came from YC, so we put most of our effort there. For founders: an investor who spends most of their week on companies like yours will understand your round faster.

How GR Angel invests in 2027

GR Angel is a private investment company. We invest directly or through private SPVs, and since 2026 a small group of private investors has invested alongside us.

How it works:

• Our investors commit their capital before the year starts, so the money for 2027 is in place before the first batch.

• Each company gets its own SPV, with its own documents.

• We all go into every deal together: the two of us and every investor in that year’s group.

• We back about 5 companies from each YC batch, roughly 20 in 2027, and we compete for the strongest companies in every batch.

• The same two partners decide, so founders get an answer within a day.

• After every check, the company gets coverage across our newsletters and introductions to customers and funds.

Get in touch

Accredited investors interested in co-investing alongside us can reach out at ruben@thevccorner.com. Details are shared privately, only with qualified investors, through the proper documents.

Founders in a YC batch, or applying to one: tell us in the comments what you’re building and which batch you’re in. We read every comment.

FAQ

What is GR Angel?

GR Angel is our private investment company. We are Guillermo Flor and Rubén Domínguez Ibar, and we invest in AI companies from every Y Combinator batch through direct investments and private SPVs.

How many companies has GR Angel backed?

Across our personal angel investments and GR Angel, we have backed 53 companies, 42 of them from Y Combinator, including HappyRobot, valued at $1.2B in August 2026 (Fortune).

Why does GR Angel focus on Y Combinator?

We focus on Y Combinator because our best investments in 2025 came from YC. Every YC company starts on the same $500K standard deal, and more than 100 YC companies are valued above $1B (YC).

How does GR Angel win allocations in oversubscribed rounds?

Founders let us in for our distribution, which reaches 600K LinkedIn followers and 400K newsletter subscribers, and for the introductions we make after we invest.

What is GR Angel’s plan for 2027?

We plan to back about 20 YC companies in 2027, roughly 5 per batch, through SPVs whose investors commit before the year starts. We all go into every deal together.

How can investors get in touch?

Accredited investors interested in co-investing alongside us can email Rubén at ruben@thevccorner.com. Details are shared privately, only with qualified investors, through the proper documents.

Keep reading

• Our GR Angel launch post on The VC Corner

• How Tech’s Quietest Super-Angel Built a Multi-Billion One-Person Venture Platform on The VC Corner

• The startup ideas YC wants built in 2026 on The AI Corner

• What YC’s New Startup Wishlist Says About the Future of AI on The AI Corner

Rubén and Guillermo

Portfolio figures combine our personal angel investments and investments through GR Angel. GR Angel is a private investment company. We are not a fund and we don’t publicly offer securities. This article is for information only and is not investment advice, an offer to sell or a solicitation of an offer to buy any security. Any investment is offered only to qualified investors through private documents. Startup investments are illiquid and high risk, and past performance does not guarantee future results.

Sources: YC Standard Deal · YC for Investors · YC Press · Y Combinator homepage · What Happens at YC · Orange Collective, The Unicorn Ratio · Fortune on HappyRobot · TechCrunch on Replit · FinTech Global on QFEX · Nozomio seed announcement · Pulse 2.0 on Apollo Atomics · TechCrunch on Arga Labs · LightSprint seed announcement · Didit seed announcement · 007 Venture Partners on Prodigy Research · 007 Venture Partners on Exosat · World Solver Syndicate on DeepReach

Getting the same quality of answer from an AI model has gotten about 13x cheaper every year since 2023, according to Epoch AI.

Yet only 37% of the respondents in McKinsey’s 2026 survey said AI had added anything to their EBIT (earnings before interest and taxes). A year earlier, the share was about the same.

The labs selling the models had a very different year. OpenAI and Anthropic’s combined revenue run rate went from about $30B to about $105B in eight months, per the State of AI Report 2026.

Falling prices and booming labs sit side by side with flat profits in the State of AI Report 2026. Nathan Benaich and Air Street Capital published it on October 8, 2026, as the 9th edition, with 244 slides.

I picked the 6 findings that help a founder or operator decide what to do next. The rest of the deck matters more to researchers and investors.

together with Plaid

AI got 13x cheaper, and most companies still can’t show the payoff. Everyone can buy the same models, so the difference comes from what you build around them, and financial services, where a mistake costs real money, is a good place to see how.

Everyone has the same AI models. The difference is what you build underneath them.

Read to find out:

• Why behavioral data beats point-in-time snapshots

• Why one agent for everything rarely survives production

• How AI governance helped one team ship 12X faster

Read the white paper

Table of Contents

1. What the State of AI Report Is (and Who’s Behind It)

2. The Price of AI Fell Off a Cliff

3. The Money Is Pouring Into 2 Labs

4. The “Human Skill Issue” and AI That Builds AI

5. Where AI Meets the Real World

6. What the Report Predicts for 2027

1. What the State of AI Report Is (and Who’s Behind It)

The report packs a year of AI news into a single free deck, written by a venture investor who backs some of the companies it covers.

It splits the past 12 months into 4 parts:

1. Research

2. Industry

3. Politics

4. Safety

It then closes with predictions for the next 12 months.

According to the report, only 3 labs are still in the race at the frontier:

1. Anthropic

2. OpenAI

3. Google

Anthropic leads on Artificial Analysis’s Intelligence Index, while Google leads on Arena, which ranks the answers people prefer.

Benaich also grades his own forecasts in public, misses included, which few forecasters do. Per the report, his strict hit rate is about 48%: 31 of 59 calls were fully correct before this year’s grading, plus 2 from last year’s list.

That track record is worth keeping in mind when you get to his 2027 predictions in section 6.

2. The Price of AI Fell Off a Cliff

Epoch AI puts the drop in perspective with older technologies. Electricity got cheaper over the hundred years up to 1973, and AI’s cost fell 54 times faster.

Computing and DNA sequencing came closer. AI still got cheaper 6 times faster than computing and around 4 times faster than DNA sequencing.

How fast the cost of a good answer dropped

Epoch AI’s analysis, published on September 22, 2026, tracked 5 benchmarks, from math and science to games. On each one, it measured the cheapest way to reach a fixed score.

Here are Epoch’s main findings on cost:

• The cost of reaching a fixed score fell about 47% per quarter since 2023. Over a year, that adds up to about 13x cheaper.

• The fastest drop happened right after a model first reached a new top score (about 66% per quarter).

• 2 years after a score was first reached, the decline slows to about 32% per quarter (about 4.7x per year).

• Math problems got cheaper fastest, at 50% to 52% per quarter. Game-based puzzles were slowest, at 39% to 43%.

Epoch’s example: in early 2025, an OpenAI reasoning model reached a set score on GPQA Diamond (a PhD-level science test) for about 30 cents per question.

About 18 months later, GPT-5.6 Luna reached the same score for $0.0004 per question. That’s about a 725x drop, based on Epoch’s unrounded estimates.

In everyday terms, Epoch’s 725x drop is like a $30 monthly subscription falling to roughly 4 cents.

Why your AI bill probably went up anyway

Epoch warns that most companies will capture only part of that saving.

Labs may tune models to do well on popular benchmarks, and a benchmark score measures something narrower than useful work.

The estimate also assumes users always switch to the cheapest model for each task, which few companies do in practice.

Then there are agents. One agent task can trigger dozens of billed model calls, from planning to checking its own output, so a lower price per call can still add up to a higher total bill.

We covered the same pattern in the nine bottlenecks in AI and the six AI trends from May: cheaper requests, bigger total bills.

For a small team, cheaper requests only show up in the budget when someone tracks how many requests the agents actually make.

3. The Money Is Pouring Into 2 Labs

Money keeps pouring into OpenAI and Anthropic, even while many of the companies paying them still can’t point to a profit from AI.

Where the revenue is landing

According to the report, OpenAI and Anthropic’s combined revenue run rate reached about $105B by late summer 2026, up from about $30B at the start of the year.

A run rate is the current monthly revenue multiplied by 12. It describes the pace of sales today, which can differ from the money actually earned in 2026.

Older public software companies had the opposite year.

When Anthropic added business plugins to Claude Cowork, nearly $285B of software market value was wiped out in early February 2026, per the report, which calls it the “SaaSpocalypse.”

The S&P 500 software and services index fell 26% from its October 2025 peak, per the same slide. The selling spread to cybersecurity stocks after Anthropic launched Claude Code Security on February 20, 2026.

By September 2026, software stocks had won that value back, per the same slide.

So far, the clearest winners are the 2 labs selling the models.

For a small company, the software sell-off matters because of the tools it pays for every month. Their makers may change prices or features as they react to AI.

What McKinsey found inside companies

McKinsey’s 2026 State of AI survey asked 1,719 respondents about AI’s impact:

• 80% said AI had made them more productive.

• 37% said AI had added anything to their organization’s EBIT, about the same as in 2025 (McKinsey).

• Only around 6% counted as “high performers,” meaning they attributed at least 5% of their EBIT to AI and called the impact significant (McKinsey).

Faster individual work and a company’s profit are 2 different things.

If everyone on a team saves an hour a day while the process, headcount and pricing stay the same, the saved hours rarely reach the profit line.

McKinsey’s high performers were more likely to redesign how the work gets done around AI, and to track whether those changes paid off.

Every company in that survey can buy roughly the same models. The payoff came from what each one rebuilt around them.

Seen that way, the 37% in McKinsey’s survey makes sense. The labs get paid the moment their models are used. Their customers only see profit after they change how they work.

together with Plaid

Want to see what that rebuild looks like in practice? Plaid’s white paper, Powering, building, and governing AI in financial services, shares what leaders at Brex, Databricks and Plaid learned taking AI to production, including how AI governance helped one team ship 12X faster. Read the white paper.

4. The “Human Skill Issue” and AI That Builds AI

Benaich puts the payoff gap down to people still learning how to set up and use AI.

Non-developers are catching up

In his launch essay, Benaich calls it a “human skill issue”: most of the gap comes down to knowing how to use AI and how to set it up.

He says the world needs a “Genius Bar for AI,” per the same essay, where people can get hands-on help choosing tools and setting them up.

There are signs that non-technical people are already catching up.

OpenAI’s data on Codex, its coding agent, shows that from August 2025 to early June 2026, individual non-developer users grew 137-fold and organizational ones 189-fold, per the report.

The fastest growth came from legal, sales, recruiting and marketing, all starting from small numbers.

The skill covers setup too: which tools the AI can use, what background it gets about the task and how it checks its own work.

A controlled study cited in the report tested exactly that. GLM-5.1 raised its success rate from 52.5% to 65.5% on 100 SWE-bench Verified coding tasks, and the only changes were to its setup.

In an ordinary team, the skill issue looks like everyone using AI on their own for quick drafts and answers, while the shared setup and the process stay exactly where they were.

If you want to see where your own team stands, our five-level diagnostic walks the whole team through it at once.

Claude now “leads” about a quarter of Anthropic’s model research

In August 2026, Claude “led” 26% of Anthropic’s model R&D work, with humans supervising, according to Anthropic’s R&D Automation Index. 6 months earlier, in February 2026, Claude led under 1%.

Anthropic also said about 30,000 agents were doing research and engineering work on its main internal platform at any one time in August (source).

Anthropic flagged a limit itself. Claude graded its own work, and when Anthropic compared Claude’s grades with its staff’s, they matched 59% of the time (source).

2 staff members grading the same task matched only 35% of the time, so even people find it hard to say who “led” a piece of work.

So the ~26% is best read as a ballpark, with “leads” in quotes. Anthropic built the index, ran it on its own teams and had Claude do the grading.

You can try a small version of this on one computer with Karpathy’s autoresearch project.

5. Where AI Meets the Real World

2 stories from the report show what happens when AI leaves the lab: agents that got real access, and data centers that ran into their neighbors.

How 700 agents ended up attacking Hugging Face

In July 2026, OpenAI ran a cybersecurity evaluation called ExploitGym with some safeguards reduced. By mistake, many of the agents were given impossible tasks.

Some started looking for ways to cheat the scorer. One found it could leave messages in a shared internal package cache.

According to METR and Redwood Research, about 1,200 agents used that message board between July 8 and 13, sending more than 70,000 messages and files.

About 700 of them took part in an attack on Hugging Face’s systems, mostly to learn how the scorer worked (METR).

OpenAI’s technical report records code execution on 41 of Hugging Face’s production dataset-server workers and 4 private code repositories downloaded, as summarized by METR.

METR reported that some agents recognized the attack was outside their task, and unethical, and joined in anyway.

When Hugging Face investigated, commercial AI services blocked its forensic requests because they contained attack commands. It used GLM-5.2, a Chinese open-weight model, instead, per the State of AI Report 2026.

For any business that gives agents access to its own tools and accounts, the lesson is practical: limit what each agent can reach and keep a log of what it actually does. Check that log before you trust the agent’s own account of its work.

Insurers have started to price this risk too, as we covered in Harbor’s $10M deck and memo on The VC Corner.

Why 71% of Americans don’t want a data center next door

In a Gallup survey run March 2-18, 2026, 71% of Americans opposed building an AI data center in their area (48% strongly and 23% somewhat).

In the same survey, 53% opposed a local nuclear plant (Gallup).

A follow-up Gallup poll in April asked opponents why. About half pointed to the resources data centers use, with 18% naming water and another 18% naming energy (Gallup).

Data Center Watch counted at least 45 projects, worth nearly $68B, that local opposition blocked or delayed in Q2 2026.

When the State of AI Report came out on October 8, 2026, Amazon, Alphabet, Microsoft and Meta had guided investors to about $733B in combined 2026 spending on data centers, chips and related infrastructure, up 79% from $410B in 2025, per the report. The final number will only show up in their year-end results.

This ties back to the price drops in section 2. Every model runs in a data center somewhere.

With dozens of projects blocked or delayed, there’s less room to run them, and prices may fall more slowly than the pace Epoch measured.

6. What the Report Predicts for 2027

Last year, the report got 2 out of 10 predictions fully right. Of the other 8, 5 were partly correct and 3 were wrong (State of AI Report 2026).

The correct calls were a major lab moving back toward open-source models and a Chinese lab reaching first place on a major leaderboard (Kimi K3 on Arena’s WebDev board in July).

The report makes 9 predictions for the next 12 months. These 4 affect ordinary businesses most:

1. Visa or Mastercard introduces a dispute rule that assigns liability for purchases made by AI agents.

2. A US state passes a law requiring businesses to accept cancellations and claims from customers’ AI agents.

3. An agent halves its failure rate on new tasks after a month of customer work, with the same underlying model.

4. US AI labs launch frontier cyberdefense products.

The last prediction on the slide reads “AGI 2027,” with no further detail.

I’d bet on the cyberdefense prediction. The Hugging Face incident already showed defenders struggling to get help from commercial AI.

“AGI 2027” is the one I doubt most, because the slide leaves it undefined and gives no way to check it.

If I were running a small team, I’d look at 2 things before the year ends: how much we spend on AI each month, and which of our actual processes changed because of it.

The models will keep getting cheaper on their own. The payoff only shows up once you change the work around them.

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FAQ

What is the State of AI Report 2026?

The State of AI Report 2026 is the 9th edition of Nathan Benaich and Air Street Capital’s annual review of AI, published on October 8, 2026 as a free 244-slide deck covering research, industry, politics, safety and predictions (State of AI Report).

How much cheaper is AI getting each year?

The cost of reaching a fixed benchmark score has fallen about 13x per year since 2023, or about 47% per quarter, according to Epoch AI’s September 2026 analysis.

Why aren’t most companies seeing profit from AI yet?

Only 37% of respondents in McKinsey’s 2026 State of AI survey said AI added anything to their EBIT, and McKinsey’s high performers were the ones that redesigned workflows around AI and tracked the results.

How fast did OpenAI and Anthropic’s revenue grow in 2026?

Their combined revenue run rate grew from about $30B at the start of 2026 to about $105B by late summer, according to the State of AI Report 2026.

A startup called Shift now pays more than 35,000 people to film themselves doing physical work so robots can learn it. Demand for that footage is roughly 10 times higher than a year ago, according to Encord’s head of robot learning.

The people selling it have a name: the “Datapreneur.” It’s the 4th meaning that word has had since 2021, and 2 of those 4 build real revenue.

So, what exactly is a “Datapreneur”?

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• What good data needs: the three requirements for duplex-ready training data, from the white paper by Voices’ CTO

• How to get it: custom datasets recorded as two-channel audio on a shared clock and fully consented for AI training

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The 4 datapreneurs at a glance

1. The 2021 data product owner: pays, because someone owns the outcome.

2. The 2023 data builder: pays, because someone owns the input.

3. The 2026 physical data operator: pays when access is exclusive, with a short shelf life.

4. The analyst with a side hustle: the version most people have seen, and the one that rarely pays.

Table of Contents

1. Where the Word Came From

2. The Version That Went Physical

3. What Physical Data Actually Sells For

4. The Version Spreading Through Data Teams

5. What the Machines Are Doing to Both Sides

6. The Only 2 Things That Have Ever Paid

1. Where the Word Came From

The first 2 versions were written by people watching a decade of data investment struggle to convert, and both landed on the same culprit.

The 2021 version, built inside companies

On 12 March 2021, Nadiem von Heydebrand published a piece in Towards Data Science with a blunt argument: enterprise data had a people problem, and the models were fine.

He had spent the 2010s watching organizations finance data labs and AI hubs. Then a stakeholder asked when the return would arrive, and the mood turned.

By his account, research firms put the failure rate at about 90% of data initiatives that would stay stuck before production.

His diagnosis was organizational.

Teams ran projects where they should have been building products. Desirability went unchecked, and the business case had no owner.

Output shipped on schedule, while the outcome belonged to nobody. His datapreneur is the person who signs up for the return and carries the loss when it fails to arrive.

The 2023 version, built from a career

2 years later, former Snowflake CEO Bob Muglia published The Datapreneurs with journalist Steve Hamm. Muglia describes the word as data and entrepreneur run together.

His datapreneurs are builders. He worked on SQL Server in its early days at Microsoft and later ran Snowflake, so the roll call is credible, even if the category is broad.

The part worth keeping is smaller and sharper. Muglia argues the real unclaimed asset is the semantic model of a business: the operating knowledge scattered across apps, dashboards, people’s heads and Slack threads.

Whoever bottles that owns something an agent would struggle to rebuild from a warehouse schema.

He expects data engineering to become business engineering for exactly that reason.

Hold onto both ideas. They come back at the end, because every version of this word that made money is built from them.

2. The Version That Went Physical

In July 2026 the word picked up a 4th meaning, a hyphen and, for the first time, a balance sheet with hardware on it.

The essay that spread it is “The Role of the Data-preneur” by Mahid, published on X on 2 July 2026. Worth knowing: Mahid co-founded The Open Robot Company, which sells capture rigs and field operations to exactly these operators, and the essay closes with a note from his company.

Access is the asset

The data-preneur in this telling is an operator who controls physical spaces where people do real work: warehouse crews, garment factory floors, restaurant kitchens, repair bays, farms.

The premise is that robots learn from demonstrations of physical work that are expensive to simulate, and the people standing next to that work know which parts are worth recording.

That local knowledge is the product.

It means knowing which tasks repeat, which ones fail, and which environment details change the outcome.

The serious version looks like a small operations company. It runs devices across sites where useful work already happens, with a buyer pipeline that says what to collect next.

The real business is keeping those devices productive.

Start here: our Physical AI Investment OS maps where this data layer sits in the wider robotics stack.

What a buyer is actually purchasing

What sells is closer to a skill package.

That means a task definition, synchronized camera streams, success criteria, failure examples, rights and provenance, and a delivery format a training pipeline can consume without a week of cleanup.

The rights layer comes first. Who owns the recordings and whether workers consented to resale decide whether the asset exists at all.

And the first pass over any submission increasingly runs through an agent, which means data has to be machine-legible before it can be persuasive.

3. What Physical Data Actually Sells For

The pricing logic here is unusually clean, and it transfers to almost any knowledge work sold to a sophisticated buyer.

2 ways data earns a premium

The first is distribution expansion. The data goes somewhere the buyer’s training set has yet to reach: a new task or a new environment.

The second is signal improvement. The buyer already has coverage there, and you bring back better evidence: cleaner geometry, synchronized viewpoints, force signals that plain video misses, tighter provenance.

Anything that satisfies neither is a commodity: the same task, in the same room, from the same angle.

Underneath both sits sample efficiency, which is the thing actually being priced.

If a buyer needs 10,000 weak demonstrations to learn a task and only 1,000 strong ones from a better setup, the second dataset wins on cost. It saves collection time and researcher attention.

The scale gap is real enough to justify the excitement.

By Instawork’s own estimate, the industry collected roughly 100,000 hours of robot training data in 2024 and 1 million in 2025, with 20 million projected for 2026.

By Instawork’s own math, even that is only about 0.04% of the way to closing what Berkeley’s Ken Goldberg calls the 100,000-year data gap.

Buyers already feel it. Encord’s customers once asked for 1,000 hours of annotated data and now want 10,000 or even 100,000, its head of robot learning told Business Insider.

Why every dataset has a shelf life

The trap sits directly underneath the opportunity.

Value peaks the moment a dataset expands the buyer’s coverage, then falls as soon as that territory is filled.

Buyers also want exclusivity on specific task and environment pairs, because a lab funding a workflow in one factory wants to keep it from competitors.

And the buyer pool is thin. Mahid’s essay puts serious, procurement-capable buyers of high-quality physical data at around 10, with no source given for the number.

A thin buyer pool plus decay on delivery is a demanding setup for an independent operator, and it is why this business rewards range over one good contract.

It already has casualties. Sam Padilla shut down his robotics data startup, Eidon AI, earlier this year after struggling with upfront costs and thin margins. His verdict: “a technical bottleneck does not necessarily imply a good business.”

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4. The Version Spreading Through Data Teams

The LinkedIn version has none of the above. It is the one most readers will have met first, and it has a real defect at its center.

5 income streams, 1 underlying skill

The script is familiar by now. Analysts who only run reports get automated away. Analysts who freelance, publish technical posts, sell a template pack and teach the occasional workshop stay safe.

It is presented as risk management. Look at what it is made of.

If a model can write the query, it can write the post explaining the query. If it can build the dashboard, it can generate the template pack that ships alongside it.

The workshop teaching intermediate SQL and pandas has a market for exactly as long as that skill stays scarce and expensive to acquire, which is the condition the advice itself says is ending. The freelance project is the same labor as the salaried one, on worse payment terms.

Diversification only reduces risk when the parts behave differently from each other. These 5 reprice on the same morning, for the same reason, because they are cut from the same material as the job.

An audience is rented

Every version of this advice ends at the same destination, which is building visibility.

Visibility is rented distribution. The landlord is a recommender system that changes its ranking whenever it likes, and the lease renews monthly on condition of producing more of whatever worked last quarter.

The particular patch being rented here is the most crowded ground in the field, because everyone receiving this advice gets the identical map.

Compare it with the physical operator, who at least owns the devices and the recordings. Those survive a platform update, while a following resets with every algorithm change.

5. What the Machines Are Doing to Both Sides

3 NVIDIA releases from 2026 set the boundary conditions for every version of this idea, starting with ASPIRE.

Software that finds its own skills

ASPIRE came out of NVIDIA GEAR with researchers from UMich, UIUC, UC Berkeley and CMU. It writes and repairs robot control programs from execution feedback and saves the fixes that work into a growing skill library.

On unseen long-horizon tasks it reports 31% success against about 4% for the prior coding agent.

Most headline results are in simulation. The updated paper adds a sharper one: with Claude Opus 5.5, ASPIRE reaches 99% average success across all 80 LIBERO-Pro tasks. Our read: as the models get stronger, each skill needs less human input.

Its sibling project, ENPIRE, runs the same kind of loop on physical robots, and reports agents reaching a 99% success rate on dexterous real-world tasks such as cutting a zip tie.

Less data, in the vendor’s own words

Cosmos 3 launched at GTC Taipei on 1 June 2026, combining physical reasoning, world generation and action prediction in 1 model.

NVIDIA’s own announcement describes it as a foundation for building physical AI systems with less data and lower training costs, and markets it for synthetic data generation.

Both things can be true together. Frontier models can cut the demonstrations needed per skill while total demand for real capture still climbs, because the number of skills being attempted climbs faster.

That is a defensible position, and it rarely gets stated, because stating it honestly puts the substitution risk in the same paragraph as the opportunity.

The identical logic runs 1 layer up. The tools making an analyst’s output cheaper are the same tools making the analyst’s side products cheaper, on the same schedule.

6. The Only 2 Things That Have Ever Paid

Strip the 4 versions back and the ones with a business underneath share exactly 2 features.

Proprietary inputs and accountable outcomes

The first is a proprietary input. Something the buyer can get only from you, like Muglia’s semantic model of a business or the operator’s access to a factory floor.

The second is accountability for an outcome. Von Heydebrand named this in 2021, and it has held up well. Somebody has to own the return and carry the loss. That role is scarce because it is unpleasant.

The version circulating on LinkedIn substitutes visibility for both, which is why it feels like a plan and behaves like a hobby.

The 2-week test for automation

For anyone deciding what to defend, the useful cut is specification. Tasks with a clean specification and a verifiable output are the exposed ones, because that shape is precisely what a model can be trained and graded against.

Routine queries and recurring reports make up most of what juniors do to earn a seat.

What survives sits upstream or downstream of the specification: knowing which question is worth asking, which table lies and since when, and which stakeholder will kill a finding on sight.

One honest note belongs here. How fast analyst roles are being displaced is still unmeasured. The mechanism is strong, but nobody has a reliable number for the timeline yet.

Which leaves 1 test worth running, and it has nothing to do with counting income streams. What do you own that a competent stranger with a frontier model and 2 clear weeks would struggle to reproduce?

Ownership is the bar: proprietary access to a dataset, a domain everyone else avoids, operators who trust you, a P&L line with your name against it.

If the honest answer is nothing, the fix is to go and acquire one of those things, and for most people the fastest route runs straight through the job the advice keeps telling them to look beyond.

Run it tonight: write down 1 input you own and 1 outcome with your name on it. If both lines are empty, that is your next 90 days.

The word has been invented 4 times. It has only ever meant 1 thing that pays: own the input, or own the outcome. Everything else is packaging.

If this saved you a LinkedIn course, send it to 1 analyst friend who needs the test.

Which of the 4 are you? Drop your answer in the comments.

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FAQ

What is a datapreneur?

A datapreneur is someone who builds a business from data they own or an outcome they are accountable for. The term was popularized by Nadiem von Heydebrand in Towards Data Science in March 2021.

Who coined the term datapreneur?

Nadiem von Heydebrand made the case for it in “Why the world needs datapreneurs” on 12 March 2021. Bob Muglia and Steve Hamm used it as the title of their 2023 book.

How do datapreneurs make money from robot training data?

They record real physical work in sites they control and sell it to robotics labs as training data. Shift, one supplier, pays more than 35,000 people to record tasks, according to Business Insider (15 Oct 2026).

How much robot training data exists?

Instawork estimates the industry collected about 1 million hours in 2025, with 20 million projected for 2026. Instawork puts that at about 0.04% of the way to closing the gap Ken Goldberg describes.

Is the datapreneur side hustle worth it for data analysts?

Rarely as a hedge, because every income stream rests on the same automatable skill. The durable versions own a proprietary input or an accountable outcome.

Your engineers now open more pull requests than anyone on the team can read. That’s the hidden bill of AI coding, and it rarely shows up in the demo.

This week Addy Osmani posted how the Claude Code team handles it. His X Article has more than 2 million views and 31,000 bookmarks so far. The full write-up, by Addy, Morgan Westlee Lunt and Qing Wang, lives on Claude’s developer blog, and it’s the most practical thing I’ve read on the topic this year.

Start with the numbers. According to the post, Claude writes about 90% of Claude Code’s own code. In Anthropic’s monorepo, changes landing per day went from about 2,500 in May to more than 30,000 in September.

Their AI code review process keeps review in place and moves most of the reading to machines. Humans get pointed at the risky stuff. And the team spends real effort on making any mistake fast to undo.

I paired it with the best research I could find on AI code quality and review time. What you get here is the general playbook for any team where agents write a growing share of the code, plus a 30-day plan to copy it at a startup.

Paid subscribers also get the AI Code Review Kit: review rules, 5 reviewer subagents, GitHub workflows, a PR template, a human checklist and a metrics script, ready to drop into a repo.

Key Takeaways

1. Review is the new bottleneck. Across 1,255 teams, Faros AI found that when teams move to high AI adoption, they merge 98% more pull requests, while time in review rises 91%.

2. AI-written code needs a closer read in specific places. In CodeRabbit’s study of 470 pull requests, AI co-authored changes carried about 1.7 times as many issues as human-only ones.

3. One gate became six layers of checks, with rollback under them. On the Claude Code team, merged PRs more than doubled while the revert share fell by over 40%.

4. Risk sets how many people approve. A docs fix can merge on automated checks alone, while the riskiest code needs two people. A bot’s approval never counts as a person’s.

5. Treat the AI reviewer like a product. Score it against your past incidents, and cap its rounds. One of their PRs ran more than fifty review rounds overnight before anyone stopped it.

Table of Contents

1. Why Code Review Became the Bottleneck

2. What Changes When AI Writes the Code

3. How the Claude Code Team Reviews Code

4. Who Approves What: Risk Tiers

5. How to Make an AI Reviewer Worth Reading

6. What Humans Still Own

7. The 30-Day Rollout

8. The AI Code Review Kit

1. Why Code Review Became the Bottleneck

Code review used to be cheap. In Google’s 2018 case study of its own process, the median change touched 24 lines and the median review wrapped up in under 4 hours. Google’s public review guide still treats one business day as the longest a reviewer should take to respond.

That rhythm assumed humans typed the code. AI changed the inputs.

More code, same reviewers

Faros AI pulled telemetry from more than 10,000 developers on 1,255 teams and compared each team’s lowest and highest AI-adoption quarters. When teams moved to high AI adoption, merged pull requests went up 98%, average PR size went up 154% and time in review went up 91% (Faros AI).

Tasks completed rose 21% and bugs per developer rose 9% in the same data (Faros AI). Company-wide delivery metrics like lead time and change failure rate stayed flat. The extra output just piled up in front of reviewers.

Google’s 2025 DORA report surveyed nearly 5,000 technology professionals and lands in the same spot. 90% of respondents use AI at work (DORA). AI adoption now goes with higher delivery throughput, and it still goes with lower delivery stability.

Even Anthropic hit the wall

Anthropic says code output per engineer grew 200% in a year. Before it built its own reviewer, only 16% of its internal pull requests got substantive review comments (Anthropic).

Here’s how I’d put it to a board. Your AI bill bought you more pull requests. Whether it bought you more shipped product depends on everything between “PR opened” and “running in production”.

2. What Changes When AI Writes the Code

Volume alone would be manageable. The trouble is that AI-written code tends to break in a few predictable places.

More issues per pull request

CodeRabbit ran its reviewer over 470 open-source pull requests, 320 of them AI co-authored and 150 human-only. The AI ones averaged 10.83 issues per PR, against 6.45 for the human ones (CodeRabbit).

Break it down by category and logic errors show up 1.75 times as often (The Register). Across all security findings the ratio is 1.57 times (The Register), and up to 2.74 times for specific issue types (CodeRabbit). In The Register’s coverage, the 2.74x case is XSS.

Error-handling gaps show up nearly 2 times as often, formatting problems 2.66 times and readability issues more than 3 times (CodeRabbit).

Keep the source in mind. CodeRabbit sells AI code review, and it guessed authorship from signals in each PR, so some of the “human-only” changes probably had AI help.

Duplicated code is rising

GitClear analyzed 211 million changed lines. Duplicated blocks of five or more lines rose about eightfold in 2024, and refactoring fell from about 25% of changed lines in 2021 to under 10% in 2024 (GitClear). If you review one thing first, check whether the new helper already exists somewhere.

“It feels done” is weak evidence

METR’s early-2025 trial found 16 experienced open-source developers took 19% longer with AI tools, while believing AI had sped them up by about 20%.

METR has since said that result is out of date. Its February 2026 update on late-2025 tools points to a speedup instead: an estimated 18% for returning developers and 4% for new ones. METR also calls that data unreliable, because developers who expected big AI gains increasingly opted out or held back tasks, so the true speedup is probably higher (METR).

My takeaway for review: people misjudge their own AI speed in both directions, so ask for proof the code ran.

The reviewer’s job shifts. Machines handle syntax and style well. Humans should spend their minutes on intent, fit with the system, duplication and proof that the code actually ran.

3. How the Claude Code Team Reviews Code

Credit where it’s due: this section summarizes the post by Addy Osmani, Morgan Westlee Lunt and Qing Wang. The framing and the startup lessons are mine.

They rewrote the question

Review has always had two purposes. It catches bugs before users do, and it spreads knowledge of the codebase.

For the bug-catching part, the team dropped the old test of whether a human read every line. Their test is which check, human or machine, is best placed to catch what could go wrong with this specific change (claude.dev).

That pulled review forward in time. They describe it as working “more like spellcheck while you write”. By the time a person looks, Claude has usually reviewed and fixed the change at least once.

They measure human time

The target: most PRs merge with at most one human involved, and that human can be the author. Reverts and incidents must hold flat (claude.dev).

The dashboard tracks human approvals per PR at the median and the 95th percentile, plus human comments and how long a PR sits waiting on a person. Reverts and incidents are the guardrail. If they climb, you saved review time by spending it on firefighting.

6 layers of checks, with rollback under them

The post’s own diagram shows six layers of checks sitting on top of a rollback safety net (claude.dev). Here they are, in my words:

1. Prove it works before the PR. Claude must run something, a test suite or a screenshot against the design, and show the output. Then a fresh subagent reads the diff. Boris Cherny, who created Claude Code, says this feedback loop will “2-3x the quality of the final result”.

2. An AI reviewer on every PR. Several agents hunt bugs in parallel, and a separate group votes before anything gets posted. Every finding carries a tag (Important, Nit or Pre-existing) so authors stop getting blamed for old bugs.

3. Security review with teeth. Open bug or security findings block the merge until someone fixes them or closes them with a reason.

4. CI that blocks. Lint, types, tests on every platform and a build. The PR template makes you write how you tested and how to undo the change.

5. Human approval sized to risk. Zero, one or two people, depending on the change. Section 4 covers it.

6. Merge queue, flags and staged release. Every check reruns against main with the change applied. New behavior goes to Anthropic staff first, then to a latest channel, with stable trailing behind.

Under all six sits fast detection and rollback. Flags switch off without a deploy, a weekly on-call rotation watches, and each incident review asks which layer should have caught the problem (claude.dev).

My favorite line in the post comes from a colleague of the authors: slowing down lowers your error rate “but mostly because you’re doing less of everything”. The team bet on getting faster at spotting and fixing problems. I think that’s the right bet for any startup.

4. Who Approves What: Risk Tiers

This is the piece most startups can copy fastest. It’s a written policy plus a few GitHub settings.

The Claude Code team uses three tiers (claude.dev):

1. Low, like docs, copy, internal-only features and additive changes behind a flag: zero human approvals once automated review and security checks pass.

2. Medium, like ordinary product work and sensitive spots such as caching, the auto-updater or system prompts: one person.

3. High, the riskiest code plus a short list of paths that always need two people: two people.

The rules around the table do most of the work:

• The change picks the tier. A classifier reads the diff and assigns it, and a second one asks whether this particular change deserves another reviewer anyway. CI workflow files always get a person.

• Fast lanes are for trusted authors. Only people on a repo’s trusted list, mostly the owning team, get the lighter paths.

• A bot’s approval never fills a human slot. AI can clear a low-risk change. That’s the ceiling.

• Whoever prompted the agent owns the PR. They count as one of the required reviewers.

• An approval covers one commit. A new agent push, or a merge conflict resolved by hand, means approving again.

At a 10-person engineering team, those five rules map almost one to one onto a CODEOWNERS file and GitHub branch protection. You could have them live by Friday.

That’s the system. The paid half shows how to run it on your own repo: tuning an AI reviewer so people actually read it, and a week-by-week rollout with the numbers to track. It comes with the AI Code Review Kit, a zip you drop into any GitHub repository:

• REVIEW.md and CLAUDE.md templates, plus 5 reviewer subagents and a /pre-pr-review skill

• 3 GitHub Actions workflows, including AI review with a 3-round cap and diff-only re-review

• A PR template, risk tiers, CODEOWNERS and branch-protection settings

• A human reviewer checklist, an incident eval template and a metrics script for approvals per PR, wait time and revert share

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Sam Altman last read a line of code roughly 12 months ago. If he ever writes some again, he expects it to feel like a “Revolutionary War reenactment.”

Then he says the line that matters more: “we should be shooting more for a new renaissance than a new industrial revolution.” The standard AI pitch ends with a cure for cancer, and he hates it, because in his telling it quietly swaps human agency for material abundance.

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Altman, OpenAI’s CEO, sat down with Matthew Berman for a 45-minute interview, published on Oct 21. He covers a cure candidate he hopes for within a couple of years and the moats he still trusts once every lab has a great model.

I watched the full interview so you can skip it. Here are the 10 moments that matter, each with its clip.

The ground we covered earlier:

• How he runs his inbox

• Falling intelligence prices

• Build costs

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On October 8 in The VC Corner I shared 80 seed AI funds with a dated deal in the last 180 days. That list is a sprint plan for seed funds in a 180-day window.

A raise needs the whole map. Your lead might be a seed fund, and your Series A might come from the venture arm of the company that becomes your biggest customer.

So I built the map.

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The AI Corner Investors Database holds 13,486 investors who back AI companies, sorted by type into five tabs:

1. VC Funds: 6,649 venture funds, micro VCs, accelerators, solo GPs, venture studios and family offices

2. Angels: 2,933 individual angels and fund partners, including 62 syndicates and angel networks

3. Corporate VC: 979 corporate venture arms and strategic investors

4. Private Equity: 223 private equity and growth firms with AI activity

5. China: 2,702 China-based investors of every type, kept in their own tab

Every investor appears once. We merged records by website domain and, where a website was missing, by name, so a fund that shows up on ten public pages takes up one row.

The stage coverage is where this gets useful for a founder. 7,933 investors outside China are recorded investing at seed, and 3,080 at pre-seed:

Look at the corporate column. 610 corporate venture arms are recorded writing seed checks into AI. Founders tend to leave corporates for later rounds. In AI, a strategic investor at seed can bring a first enterprise customer along with the check.

Recency matters as much as stage. 2,594 of the venture funds carry the date of their latest AI deal, and 274 of them announced one in the last 30 days:

Those 274 funds are the partners with the freshest reason to take a meeting this month. 487 funds that write pre-seed or seed checks closed an AI deal in the last 90 days.

Before a row went in, it had to survive the checks. Public investor lists look clean until you open the rows, and here is what the checks caught:

1. A “top AI angel” whose 68 recorded AI deals all went into a company he founded, every one led by him. Removed.

2. Another “AI angel” whose highlighted deals were a grocery store and a school. Removed.

3. A “seed AI” deal that turned out to be a $10.5M Series A in a mobile gaming studio.

4. A ranking of “top private equity firms in AI” from March 2024 that included venture firms.

5. 202 people filed as funds, from well-known operators to AI researchers. They now sit in Angels, where you’d look for them.

6. 29 investors typed only as “Investor”. They’re now pension funds, sovereign wealth funds, family offices and private equity firms, each placed in the right tab.

Every focus line in the database is written by us from the investor’s own public description, in one short sentence on what it backs and at which stages. Where the public description was too thin to summarize, the cell stays empty.

Every one of the 13,486 rows passed those checks.

Premium members get the full database today:

1. The AI Corner AI Investors Database 2026 in Excel: 13,486 investors across five tabs, with stage flags, focus, notable AI investments, latest AI deal and public profiles

2. Europe and Spain views: 2,045 European investors and 132 in Spain, ready to filter

3. The China tab: 2,702 investors, 1,125 of them with an AI deal in the last 180 days

4. The 20-minute filter recipe that turns 13,486 rows into your first 50 names

5. CSV files for every tab, ready for your CRM or a Claude prompt

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Tomorrow at 6:07 AM, Claude Code can have the 3 decisions that matter most waiting for you in Slack.

You set it up once with a single command and a prompt, and because it runs in Anthropic’s cloud, your laptop can stay closed all night.

That’s System 1 of the 8 in this playbook, and its full setup is free below. The other 7 take over the rest of the work a 5-person software team does on repeat.

By the end, you will have:

• The Morning Briefing set up tonight, with the exact prompt and a test run

• The org chart of a Claude Code team, with what each of the 8 systems covers

• The 4 platform facts that make this work on a founder’s budget

• The monthly cost of the full stack, next to what five developers earn

The work a 5-person team does on repeat

Every morning someone triages the inbox and the calendar while two or three engineers ship features. Someone else watches each deploy and tracks what competitors charge, and the founder stitches it all together and makes the calls.

• Claude Code can take the repeatable part of that work, while you keep the judgment calls and the customer conversations.

Four facts make it practical:

• Cloud scheduled tasks run your prompts on Anthropic’s infrastructure, on a timer or on a trigger, so they keep working when your laptop is closed. They’re in research preview on Pro, Max, Team and Enterprise plans.

• Claude Haiku 5.5 costs $0.10 per million input tokens on prompts up to 100K tokens, and Anthropic says it pairs well with Opus 5.5 and Sonnet 5.5 as a subagent on coding work.

• Claude Opus 5.5 takes the plans and the hard calls at $4 per million input tokens and $20 per million output tokens.

• Auto mode is the default permission mode for new sessions on Pro, Max and Team plans. A classifier approves routine actions, which suits sessions that keep going while you’re away.

Here is how the team fits together:

The 8 systems:

• The Morning Briefing: 3 ranked decisions waiting in Slack when you wake up

• The Parallel Feature Factory: 4 features in flight, watched from one agent view

• The Self-Healing Deploy Loop: a go or no-go after every production deploy

• The Migration Workflow: hundreds of files moved by subagents that check each other

• The Always-On Research Desk: competitor signals logged daily and a memo every Sunday

• The Self-Improving Skills Library: every correction you make twice turned into a skill

• The Phone-First Founder OS: every session steered from the Claude app

• The 5-Person Team Stack: all 7 wired together with one settings file and one router

The complete setup for System 1 comes next, and it is free, so you can have it running by tomorrow morning.

SYSTEM 1: The Morning Briefing (free)

You wake up to the 3 decisions that matter most today, already ranked in Slack.

When to use. You lose the first hour of the day to triage across inbox, calendar, PRs and your project board.

What you need. A Pro or Max plan and 5 connectors added at claude.ai/customize/connectors: Gmail, Google Calendar, GitHub, Linear and Slack. Cloud tasks use these claude.ai connectors. MCP servers you added with claude mcp add stay on your machine, so add the claude.ai version of each one.

Step 1: Create the task. In any Claude Code session:

/schedule weekdays at 6:07am, run the morning briefing

Claude asks follow-up questions about the schedule and the prompt before it saves the task. Keep the 7 minutes past the hour: runs scheduled exactly on the hour can start several minutes late.

Step 2: Paste the prompt.

You are my chief of staff. Every weekday at 6:07:

1. Read unread email from the last 18 hours. Rank by revenue impact.
2. Read today's calendar. Flag meetings that are missing an agenda.
3. List open PRs older than 24 hours and what each one blocks.
4. Check Linear for anything marked urgent since yesterday.

Write a briefing of 200 words or less:
- The 3 decisions I need to make today, each with one recommended action
- Counts for inbox, meetings and PRs
Send it to me as a Slack DM. Quote numbers exactly as they appear.

Pick Sonnet 5.5 in the model selector. Claude uses the model you pick on every run.

Step 3: Trim the connectors. A new task includes every connector on your account, and Claude can write through any of them during a run. Open the task at claude.ai/code/routines and keep only the 5 this briefing reads.

Step 4: Test it tonight.

/schedule run

Open the run from the task’s page. A green status means the session started and finished cleanly, so read the transcript to check the briefing itself. If a run comes back empty, ask Claude:

/schedule why did my morning briefing do nothing this morning?

Output.

GOOD MORNING. 3 THINGS THAT MATTER TODAY.

1. 11:00 call with [Name] at [Company]. Their last email
   flagged pricing and they added 3 seats last week.
   Likely upsell. Lead with the [X] tier.
2. PR #347 has waited 36 hours and blocks Thursday's deploy.
   Review by noon or move the deploy.
3. [Competitor] shipped [feature] yesterday. 2 prospects
   asked about it last week. Decide: parity or positioning.

INBOX 12 need replies (top 3 ranked) · MEETINGS 6, 2 missing an agenda · PRS 3 open, 1 critical

Two notes. Scheduled runs draw on your plan’s usage the same way an interactive session does. And if the briefing needs files that only live on your laptop, use a Desktop scheduled task and keep the machine awake.

What the full stack costs

One founder can run all 8 systems on Claude Max 20x at $200 a month, which includes $200 a month in API credits. Five software developers at the US median wage cost $679,900 a year in salary alone.

To be fair to the people in that comparison: the stack covers the repeatable work in those 5 roles. Judgment and the customer call at 11 AM stay with you.

System 1 is a single scheduled task. Systems 2 to 8 need scaffolding around them, and the paid part ships that scaffolding as files you paste into your repo.

Below the paywall:

• The first 60 minutes: a 6-step checklist from install to System 1 running in the cloud, with a timeline (Exhibit 3)

• The starter kit, 5 files: a complete CLAUDE.md with a path-scoped rule, a settings.json with 6 deny rules and 3 hooks, the protect-files.sh hook script, the repo folder tree and a team-standards review skill

• Systems 2 to 8, each with a “when to use, setup, output” card: 3 more SKILL.md files, 1 subagent file, 1 deploy API call and 4 scheduled prompts

• The model routing sheet with API prices for all three Claude 5.5 models (Exhibit 4)

• The map of all 8 systems with the trigger, machine, features and model for each (Exhibit 5)

• The 30/60/90 day build order and the 8 Skills to write first if you only have one weekend

A paid year of The AI Corner is $199. This link takes 50% off your first year until November 1, so the full kit costs $99.50: about half of one month on the Max 20x plan that runs it.

Get the full playbook at 50% off

The first 60 minutes

Block one hour on your calendar. By the end of it, System 1 runs in the cloud and your repo holds the CLAUDE.md and guardrails that Systems 2 to 8 build on.

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That’s what the top 1% of paying AI users put on their own credit cards, against $25 for the median payer. A top-1% payer spends about 36 times what the middle payer does.

Olivia Moore at a16z published the number in the seventh edition of the Top 100 Consumer AI Apps, the first to rank products by revenue as well as traffic. The money ranking and the traffic rankings disagree on 29 names.

Her summary of the market: “the top 10% of users are driving this market.” Elena Burger hosted the 49-minute conversation with Olivia and Josh Elman.

I watched the full conversation so you don’t have to. Here are the 10 parts that matter.

1. The $903 Tier: Who Pays for Consumer AI

About 50% of Americans say they use AI, and about 4.5% pay for it.

“The top 1% user is spending $903 per month personally on their personal credit cards on AI. And the median is spending $25.”

Olivia Moore, a16z

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The number that caught my attention came three days earlier. micro1 has committed $1B over the next twelve months to buying data from companies, per The Next Web, with financing from Citi and Hercules Capital. It also pays tens of thousands of experts to train AI models, so two groups can collect: companies sitting on years of operational records, and professionals with deep expertise. This guide covers both, with the links to apply:

Apply as a company

Apply as an expert

Why micro1 is buying

The public web shows finished work. AI agents need to see how work gets done inside a company: the back-and-forth on a deal, the exception a support lead approves, the QA check before a release ships. micro1 turns that material into reinforcement learning environments, simulated workplaces where agents practice everyday workflows. In micro1’s own words, “Real business operations involve incomplete information, competing priorities and exceptions that require judgment.”

The bidding has already gone public. When bankrupt Spirit Airlines put its operational data up for sale, micro1 submitted a $12.5M late bid for part of it, against Google’s winning $10M offer.

Investors are paying up for the model too. micro1 priced its Series B at a $4B valuation after passing $500M in run rate, eight times its valuation a year earlier, in a round led by Dream Ventures with Microsoft, Javelin, Flybridge, G Squared, Up Partners and LG. Its customers include frontier AI labs, Microsoft, Amazon and the robotics company 1X, per Forbes.

The team behind those numbers is tiny. micro1 sits at No. 15 on the Lean AI Leaderboard, which still lists it at $250M in revenue with 80 employees. At the figures Ansari shared in October, about $5.6M of run rate per person, it would move into the top ten.

micro1 shares the top of the market with three rivals. Mercor reported a $2B gross run rate in June, per Dealroom, and Handshake’s AI training business reached nearly $1B in April, per The Information via Dealroom. Surge AI’s latest public figure is $1.2B in 2024 revenue, per Sacra.

For companies: $100K to $2M+ for your operational data

Your company’s SOPs, Slack history and project records now have a market price. micro1 buys them through its Enterprise Data Partnerships program, and it looks for three kinds of material:

▫️ Operational documentation: SOPs, knowledge bases, internal docs, CRM data, project histories and QA processes

▫️ Decision-making patterns: how your teams weigh information and make operational calls

▫️ AI performance feedback: your people’s feedback on AI outputs in day-to-day work

micro1 works across software and engineering, finance and accounting, customer support, sales, legal and contracts, plus operations and logistics.

What it pays

micro1 prices each deal on the quality, uniqueness and value of the data: $100K+ for a qualified partnership, $500K+ for large datasets or ongoing work across several teams, and $1M+ for highly unique proprietary data. Its company referral page puts the top of the range at $2M+, and Fast Company reported offers from $100,000 to $2 million.

Who qualifies

Companies with 30+ employees and established documentation, written mostly in English. US companies get priority, followed by the UK, Canada and other Western markets, and micro1 says it considers other regions too.

How it works

1. Apply through this company link. You start with a short assessment of what data you hold.

2. micro1 evaluates the fit. If there’s a match, it schedules a discovery call on your data and your privacy requirements.

3. You complete an AI interview and onboarding.

4. You sign the agreement. It sets the approved data scope, the anonymization and redaction rules and how long micro1 keeps the data, and you review representative samples before micro1 uses anything.

5. You get paid at the tier your data qualifies for, and your company keeps ownership of the underlying data.

Get an offer

How to get the best price

▫️ Lead with long-horizon workflows. Buyers value data that shows how work unfolds over weeks most, per Fast Company, so project histories, QA trails and decision records come first.

▫️ Package several teams together. micro1’s $500K+ tier covers ongoing participation across multiple teams and business functions.

▫️ Set the scope before any data moves. micro1 removes personal information, can rewrite datasets synthetically so they keep the workflow and lose the direct ties to original records, and processes each customer’s data in an isolated pipeline.

▫️ Bring legal and HR in early. Slack and email archives carry your employees’ words, and in Europe identifiable messages count as personal data under GDPR.

For experts: $20 to $200 an hour

In micro1’s 36 newest listings on September 11, pay ran from $20 an hour for transcription to $100 to $200 an hour for physics PhDs, according to Second Talent’s review of micro1’s job board. QA and software roles reached $175 an hour, finance and tax $170, and attorneys earned $90 to $110.

Across all pipelines, the average runs $60 to $70 an hour, and expert pipelines in finance, medicine and law pay $170 to $180, according to the Lean AI Leaderboard’s March profile of micro1. Ansari has also posted that micro1 pays $50 to $90 an hour to annotate videos of robots doing tasks, per Forbes.

How it works

1. Pick a role on micro1’s expert page. Roles cover medicine, law, finance, engineering, software, physics, product and creative work.

2. Take the AI interview. micro1’s AI interviewer builds an exercise from the job description and your skills: a lawyer applying to an M&A pipeline gets a live contract redlining task, for example.

3. Get matched to a pipeline and get paid hourly or per task, fully remote.

Three things raise your odds:

▫️ Apply where your credential is strongest. The interview simulates the actual job, so a specialist role you can do in your sleep beats a generalist one.

▫️ Treat the interview like a client call. It runs on camera, and a proctoring model scores every session live, so a private, well-lit room and a steady connection matter.

▫️ Bring your colleagues. Expert referrals now account for 60%+ of micro1’s new hires, per the Lean AI profile, and each one you refer earns you a bounty.

Find your role

Refer and earn

micro1 also pays the people who send it good companies and good experts. Refer a company that micro1 approves for a data partnership and you can earn up to $50K, per micro1’s company referral page, paid to your micro1 wallet once micro1 onboards the company or accepts its data.

Expert referrals pay a set amount per role once micro1 selects the person you referred and they complete 10 hours of work, per micro1’s referral docs. The Lean AI profile put those bounties at $200 to $3,000 per hire, and you can refer as many people as you like. You can register as a referral partner at refer.micro1.ai.

Ansari closed his October post with a bet: “data is on its way to becoming the largest industry humanity has ever seen.” If your company has the records, apply here. If you have the expertise, find your role here.

Sources: Ali Ansari on X · Forbes · The Next Web · Fast Company · micro1 data partnerships · micro1 company referral program · micro1 referral docs · Lean AI Leaderboard · ARR Club · Dealroom on Mercor · Dealroom on Handshake · Sacra on Surge AI

FAQs

How much does micro1 pay companies for their data? micro1 pays $100K+ for a qualified Enterprise Data Partnership, $500K+ for large datasets or ongoing work across several teams and $1M+ for highly unique proprietary data. Its company referral page puts the top of the range at $2M+.

What data does micro1 buy from companies? Operational documentation such as SOPs, knowledge bases, internal docs, CRM data, project histories and QA processes, plus records of how teams make decisions and human feedback on AI outputs. micro1 uses it to build reinforcement learning environments that simulate real business operations.

Which companies qualify for micro1’s data partnerships? Operationally mature companies with 30+ employees and established documentation, mostly in English. micro1 prioritizes US companies, then the UK, Canada and other Western markets.

How much do micro1 experts earn? In micro1’s 36 newest listings on September 11, 2026, pay ranged from $20 an hour for transcription to $100 to $200 an hour for physics PhDs. The Lean AI Leaderboard reported an average of $60 to $70 an hour across pipelines, and $170 to $180 in expert finance, medical and legal pipelines.

How does the micro1 referral program work? Referrers earn up to $50K for each company micro1 approves for a data partnership, paid once micro1 onboards the company or accepts its data. Expert referrals pay a set amount per role after micro1 selects the referred expert and they complete 10 hours of work, and referrers can submit unlimited referrals.

Then he says the line that matters more: “AI is the ultimate democracy.” Airbnb has the same tools your team has, so the gap sits with the person at the keyboard.

Chesky, Airbnb’s co-founder and CEO, sat down with the Silicon Valley Girl podcast seven years after its host ran up to him at LAX. In 34 minutes he covers who AI amplifies and who it leaves behind, plus the notebook he fills every day.

Go-to-market works the same way. Your competitors buy the same software you do, so the gap shows up in whichever part of the funnel is weakest.

together with HubSpot for Startups:

Most founders can tell you when growth slowed. Far fewer can point to the part of the funnel causing it, because one or two soft spots drag everything else down and they’re hard to see from the inside.

HubSpot for Startups built a free GTM audit for exactly that, using what it learned helping 35,000+ startups grow:

You drop in your URL, and it reads your website and pricing, plus your social presence, tech stack and competitors, before you type a word. Then you answer a few questions about your channels and pipeline, and where things feel stuck.

▫️ A score for every dimension of your GTM, so you can see which one is holding the rest back

▫️ A prioritized fix list built for your stage and tech stack

▫️ A 90-day roadmap that tells you what to fix first

It takes about 10 minutes, and it’s free:

Run the free audit

1. Everyone Has the Same AI Tools, So Why Is the Gap Growing? (The Amplifier Effect)

Everyone had the same tools. A few people still pulled far ahead.

“AI is like an amplifier. It makes the good people better, and the people that weren’t as good, it’s less of a multiplier for them.”

Chesky learned this as a designer. Everyone had the same tools back then, he says, and the very best people still got the most out of them.

AI repeats the pattern with higher stakes. Codex and Claude Code sit in every company’s toolbox, Airbnb’s included, and he says the performance gap widens because the tool rewards curious people who can iterate without limit.

He offers himself as evidence: he couldn’t code when he started out 19 years ago, and today he can because he can prompt. That is the gap widening among builders inside 1 career.

Execution got cheap. Taste and judgment did not, so the question moved from whether you can build it to what you build, whether you should, and whether it matters.

2. How Airbnb Ships 80% More Features With AI (Why Token Leaderboards Backfired)

A new service used to take Airbnb 1 year. Now it takes weeks.

“We look at product velocity. How fast are you shipping? How many features are you shipping? How quick are you shipping them? Are they high quality? And are they successful?”

Chesky credits 2 causes for the 80%. AI ended hand-coding as the default, and the culture changed with it, because people have to use the tools and use them correctly.

Early this year, he says, CEOs measured AI adoption by tokens burned and posted leaderboards to shame the low scorers. He says leaderboards “might have been a good thing to do for a couple months” to get people using AI, then calls them a long-term strategy that was not going to work. Then everyone asked why the AI bill kept climbing.

His replacement metric is velocity: what shipped, how fast, how good, and whether it worked. Effort is the easiest number to inflate, and customers never see it. OpenAI’s product lead makes a related case: AI-native companies out-build teams 10x their size.

Steal the 5 questions and drop them into your next sprint review.

Planning to ship at that speed? Start here:

▫️ Ship your first AI agent in a day

▫️ Sam Altman: 3 months of work now takes 7 minutes

▫️ How to 8x Your Code Output Using Context Engineering

3. Agent vs Chatbot: What Airbnb Built Instead of a Chat Box

Airbnb’s new AI search feels like chat and works like an app.

“A chatbot is a user interface. And I think because most agents are chatbots, we conflate the two.”

Chesky separates 2 things most of us blur. An agent is an application that understands you and knows you, and a chatbot is the interface for talking to one. Most agents ship as chatbots, so people blur the two, and he calls agents the future of Silicon Valley.

Travel exposes the gap in 3 ways: text is hard to compare, shoppers want to browse instead of type, and a typical group of about 3 travelers needs to share results, message the host, and watch listings change when a filter flips.

Airbnb’s answer is an AI search toggle. You describe a weekend in Paris with a certain view and architecture, and it prefilters the results while the familiar filters stay in place. Summaries written for each guest, a listing page you can question, and a comparison tool sit around it, and hosts will get an app that coaches them on pricing.

The product lives in the application around the model, the case behind 6 decisions for agents. Ask whether you need an agent at all first, then see how 3 founders shipped managed agents.

Build the application first and let chat be one door into it.

4. Chat Is a Jet Engine on a Bicycle (What to Build Instead)

Plenty of AI products put a jet engine inside a chat window.

“A lot of entrepreneurs are trying to fit the most powerful technology in human history into a messaging platform. It’s like putting a jet engine on the back of a bicycle.”

Chesky calls Instinct and Muse the beginning of consumer and agentic AI, then rejects messaging as the right interface for most work. His advice to founders: try audio, or go further out, because most people are visual and TikTok beats Twitter for that reason.

He runs the experiment on Airbnb itself. Social networks became broadcast platforms, he argues, so Airbnb is building a travel graph where you add friends and family, see who visited Chicago and where they stayed, and ask what’s good. People want recommendations from the ones who know them over celebrities and writers.

Interfaces are up for grabs everywhere. Zuckerberg is placing a $14B bet that glasses replace the phone, and Satya Nadella’s answer is a computer for every agent, where most builders hand theirs a chat box.

Chat handles a question and an answer well and strains when the job is browsing, comparing, or deciding with other people. Ask what your user would rather see or hear before you ship a chat box.

5. How a Masseuse in Tulum Gets a Global Audience on Airbnb

Airbnb sells small businesses the 1 thing they can’t build alone: an audience.

“This photographer would never have known how to broadcast to all these guests from around the world.”

Chesky builds the case with 2 examples he flags as hypotheticals, down to the made-up location. A masseuse in Tulum lives on word of mouth from locals, until a listing puts her in front of every traveler headed to town. A Paris photographer who shoots families at the Eiffel Tower sells professional photos in 30 minutes, at a price he describes as reasonable.

The menu is already long: chefs, photography, car rental, airport pickup, luggage storage, baby gear, laundry, ski rental, and Instacart groceries that know your address and guest count. He expects dozens, then hundreds, of services to plug into a booking.

The logic lives in the long tail. Ride sharing has Uber and food delivery has DoorDash, yet most services lack a global brand, and the few with an app sit outside Airbnb’s system.

Airbnb supplies the audience, and a small business or 1-person company supplies the service. Own the service, rent the audience.

6. Airbnb Tests Every New Service in 1 City First (How It Decides What to Launch)

Airbnb usually tests a new service in 1 city before it goes anywhere else.

“We usually do a test in 1 market, 1 city. If it works, then we bring it to other markets.”

Dozens of services sit in test at once. Ski rental is in test in Italy, by Chesky’s recollection, and surfing gear is the next idea he floated.

The interviewer asked for a launch bar, maybe 10% of guests using a service. Chesky skipped the number and described intake: a contact button at the bottom of the page, an apply button for services hosts, and a promise to take every lead. Floral arrangements for a romantic trip are moving through that pipeline now, and if Airbnb needs more time on your idea, it launches eventually.

Call it validation by distribution: let supply walk in, test in 1 city, then widen. That is an MVP with a boundary, and product-market fit shows up in what the test teaches.

It spares you the $40K of features nobody wanted and tells you early when the market is rejecting the idea. I’d take 1 city and an answer over 10 cities and a guess.

7. Airbnb’s CEO Has 100,000 Ideas a Year and Calls 99,000 of Them Bad

Airbnb was 1 idea in a pile of bad ones. He still builds the pile.

“When you critique your ideas, you edit your thinking and you’re afraid. And bad ideas sometimes are one step away from a good idea.”

The interviewer credits “the best way to have a good idea is to have a lot of ideas” to Linus Pauling, and Chesky stretches it to drawings: the best way to make a good one is to make a lot of them. The design firm IDEO used to run an exercise where everyone sticks Post-it notes on a wall and nobody critiques anything at first, devil’s advocates included.

By his own count, “not exaggerating,” he has around 100,000 ideas a year, and 99,000 are bad. Outsiders see only what ships, a textbook case of survivorship bias.

Chesky cites Eminem as someone who wrote constantly. Rick Rubin, who produced Eminem, built a career on taste alone, and taste needs a pile to pick from. Start yours with 100 agent ideas or 70 startup ideas from YC.

8. The Notebook System Airbnb’s CEO Uses to Filter 99% Bad Ideas (Bold, Review, Repeat)

99% of what he writes is useless. A 4-step filter finds the rest.

“At the end of every day, I bold the things that jump out at me.”

Chesky fills a notebook with about 1,000 words a day, roughly 100 lines of 1 or 2 sentences each. The system runs like this:

1. Write down every thought, unfiltered.

2. At the end of each day, bold the 5 to 10 lines that jump out.

3. At the end of each month, have AI read the bolded lines.

4. Let AI also read the lines you dismissed, spot patterns, and build a short list to iterate on.

The count wobbles mid-answer: 100,000 ideas a year becomes about 30,000 after the notebook math, and the 99% ratio holds. A good idea returns, he adds, because a month later you write it down again.

His notes read as borderline nonsense to anyone else, and he accepts that because only the survivors get seen. Treat the setup as a small self-improving loop: volume feeds the filter, and the filter picks what earns another round.

Building your own notebook-plus-AI loop? Start here:

▫️ Garry Tan Runs YC at 400x His 2013 Output. His AGI Is a Folder of Markdown Files

▫️ I built a second brain in 10 minutes. Here is the exact stack

▫️ Your Obsidian Vault Finally Has a Model That Can Read All of It

9. Cap Your Feed at 10 Minutes a Day and Do Deep Work on a Big Screen

By Chesky’s estimate, a phone holds your attention for about 15 seconds.

“The bigger the screen, the longer your attention span.”

He confesses to a habit he says nearly everyone watching shares: too much time on social media, watching what other people ship. In his words, “we’re all like kids chasing soccer balls,” copying fast fashion as new launches pile up.

His budget is 10 minutes a day on competitors and the latest AI developments, instead of the 2 to 3 hours, or 17 to 25 trips into X, YouTube, or Instagram, most of us burn. His own ideal, which he says he follows less than he wishes, is 1 session of 20 to 30 minutes on the platform, then done, then uninterrupted time with the phone off.

The phone, he argues, trains your dopamine to multitask. A whole IMAX film, by contrast, holds people in their seats.

Our piece on phones, comfort, and disconnection shows how technology turned freedom into paralysis, so put your deep work on the biggest screen you own.

10. It Is Easier to Build a Company Now Than 20 Years Ago (And 50 to 100 More Anthropics Are Coming)

Chesky wishes he were 26 again.

“It is so much easier to build a company now than it was 20 years ago.”

Outside Silicon Valley, he says, AI feels like something happening to people, changing their jobs and displacing them. He concedes some of that fear is true, then says everyone has the power now and anyone with an idea can make something.

His forecast is 50 more Anthropics, maybe 100, plus millions to tens of millions of small companies, because 1 to 3 people can now start a whole business. If AI is a window at all, he says, it lasts 1,000 years, since technology that doubles every year for 10 years is a 1,000x.

Chesky doesn’t know what he would build at 26, but he would look at education, storytelling (you could build an “AI Disney”), and health. Andrew Ng, who taught 8 million people AI, calls AI terrible for learning, so read his case before you bet on education.

If AI still feels intimidating, he says, jump in. He felt nervous about Claude Code, asked a strong employee to tutor him, and points to YouTube for anyone without one. Here is how to use Claude like a power user.

Ready to build? Start here:

▫️ How to Build Three AI Agents Without Writing Code

▫️ The 5-agent sales team you can build this weekend

The Amplifier Playbook

AI hands every company the same tools, so the volume of your ideas and the focus you protect decide who AI amplifies.

▫️ Founders: Run every new idea as a 1-city test and take every inbound lead while it runs. Judge the test on features shipped and how many succeed. Pick the city this week.

▫️ Investors: Ask each team how it measures AI: tokens spent, or features shipped and how many worked. Chesky’s 5 velocity questions double as a diligence list. Add them to your next call.

▫️ Operators: Retire the token leaderboard if you run one, and put the 5 velocity questions in its place. Pair every tool rollout with training on using it correctly. Start this quarter.

▫️ Everyone else: Keep a notebook and bold 5 to 10 lines each night. Cap your feed at 10 minutes a day and do deep work on your biggest screen. Start tonight.

The 5 Principles to Steal

1. Measure shipped value. Count features shipped and how many succeed, ahead of tokens burned. A token count tracks effort and velocity tracks results, so ask Chesky’s 5 questions in every sprint review.

2. Match interface to task. A chat box is 1 option; test whether your user wants to browse, compare, or talk. Airbnb kept its filters and controls and added AI search on top, so copy that move before you replace anything.

3. Test small, then scale. 1 market, 1 city, then widen. Let the test teach you what to fix, and take every inbound lead while it runs, the way Airbnb does for new services.

4. Write more, filter daily. Bold the best lines every night and let AI sort the month. About 99% of the notes get cut and the good ideas return on their own, so keep the volume high.

5. Protect your attention. Cap the feeds and give deep work the biggest screen. Budget 10 minutes a day for news, then do the work on a computer, where attention span stretches.

Same tools, different habits.

Pick the habits.

Watch how Chesky decides what Airbnb launches, from the Tulum masseuse to the 1-city test, in the full interview.

If this breakdown saved you 34 minutes, send it to one founder or investor who needs it.

Interfaces, agents, and product speed

▫️ OpenAI’s Product Lead Reveals the 4-Part Framework for AI Product Strategy

▫️ Stop Blaming the Model. Fix the Architecture

▫️ Brian Armstrong Runs 1,200 AI Agents at Coinbase. Here Is the Operating Model He Just Handed Every Founder.

Ideas, notes, and focus

▫️ Your voice is the only AI moat that compounds. Here is how to clone it into Claude in a weekend

▫️ How to 10x any AI skill using Karpathy’s Autoresearch method

▫️ What Stealth Startups Know That Others Don’t

Building a company in the AI era

▫️ How Lovable hit $400M ARR in 14 months with 146 people and almost zero paid ads

▫️ The Billion-Dollar Startup Formula: Why AI-Driven Small Teams Are Beating Giants

▫️ Zero Ads. Zero VC. $230M ARR. The Story of Magnific