Sequoia recently told the market that the next trillion-dollar AI companies will sell work, not software. Instead of charging for software seats, they will complete the work itself and capture a share of the much larger global labor budget.

A few weeks ago, I broke down why that thesis is so compelling.

Then Andrew Côté published a rebuttal that exposes the uncomfortable weakness inside it:

“Using AI for a task trains AI to do that task, so every services niche eventually becomes a niche the model owns outright.”

That is not always technically literal. Enterprise providers may not train on customer data, companies may self-host models, and proprietary workflows do not automatically flow back to foundation-model labs.

But economically, the direction is the same: as foundation models improve, more of the value created by thin AI service layers becomes commoditized.

That breaks the simplest version of Sequoia’s thesis.

So where does the durable value go?

I pulled apart both theses to map where the money may actually go next, including physical markets where a single industrial process can represent hundreds of billions of dollars in annual activity.

In this issue you’ll find:

1. Why “sell work, not software” may train its own killer

2. Software investing is close to over

3. The five enormous atoms markets Côté is pointing toward

4. How AI and robotics could bring software-like leverage to physical industries

5. Who is already building the atoms version of this thesis

6. What founders and investors should start, back or partner with before this becomes consensus

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1. Why “sell work, not software” trains its own killer

The flaw in Sequoia’s “sell work, not software” thesis is captured in Côté’s line:

“Using AI for something trains it to do that task.”

Again, this should be understood as an economic principle, not always as a literal description of model training.

A customer interaction does not necessarily become a training example sent directly to OpenAI, Anthropic or Google. But every successful AI workflow reveals what can be automated, which tools are required, where humans intervene and what a correct output looks like.

That knowledge spreads.

Model providers improve their reasoning and tool use. Competitors replicate the workflow. Open-source models catch up. Customers learn to perform more of the process themselves. What was once a differentiated service gradually becomes a native model capability or a standard feature.

Côté’s conclusion is that “every possible niche where you can use AI to do something better eventually becomes a niche occupied by AI.”

That does not mean every AI services company disappears. It means the service itself is not automatically the moat.

An AI services business that wraps a foundation model and sells its output may initially capture attractive margins. But its advantage often comes from a temporary gap between what the customer can do directly and what the company can orchestrate on the customer’s behalf.

That gap closes on the model’s schedule, not the startup’s.

Sequoia’s own feed illustrates the direction. Promoting Factory AI’s Matan, the firm wrote that almost every token today begins with a human asking a model to do something, but that within 12 to 24 months, 90% of tokens could be machine-initiated.

That human typing “hey, go do this” is precisely the human-in-the-loop signal that teaches the model to remove the human. Sequoia frames the shift from human-initiated to machine-initiated tokens as the bull case for AI labor. Read it Côté’s way and it is the countdown clock on every services wrapper: the closer we get to 90% machine-initiated, the less any middle layer between customer and model gets to keep.

So run this test before you build or back an AI services company: what remains defensible after the foundation model has ingested a year of your usage? If the answer is “our workflow,” “our prompts,” or “our fine-tune,” you are selling the training data for your own obsolescence. If the answer is a proprietary distribution channel, a regulated license, or a physical asset the model cannot replicate, you have a real business. Most services plays fail that test.

2. Software investing is close to over

Côté’s second claim lands harder than the first: “Software investing is over. Software was never more than 2% of the economy anyway.” The trillion-dollar software franchises are already public and obvious. Betting on a “next generation” of $1T pure-software names is betting on 2% of GDP to keep minting the outcomes that a saturated category already produced.

This reframes the Sequoia thesis as a fight over the wrong prize. Sequoia is right that the labor budget dwarfs the software budget, which is exactly why “sell work” sounds bigger than “sell seats.” But labor-as-software still lives inside the services trap from section one: it captures labor spend only until the model absorbs the task. Côté’s move is to skip the trap entirely and go where the spend is both enormous and physically defensible. As he puts it, “the hard business of technology lies ahead, which is remaking the physical world. The other 98% of the economy.”

The number that reframes everything is his growth claim: “Almost all economic growth is in the future.” Software’s growth is largely behind it; the category matured and the winners were crowned. The physical economy’s AI-driven growth has not started. If you are allocating for the next decade rather than trading the last one, a mature 2% category is a strange place to concentrate.

Hardware itself has not suddenly become as scalable as software. What has changed is the intelligence controlling the hardware: AI models, simulation, digital twins and more adaptable robots can be developed once, improved with data and reused across multiple machines and factories.

That means a company can increase output without increasing engineers and workers at the same rate, because one team can supervise more equipment and software updates can improve an entire installed fleet. The economics become more scalable, but materials, energy, machinery, maintenance and deployment costs remain.

3. The five $10T atoms markets

Côté names the destinations explicitly: “Chemicals, Metal, Energy, Transportation, Healthcare.” His scale claim is the part investors keep underweighting: “Each market is 10x the total size of all software. These are not $1T company valuations, they are $10T gross annual revenue markets.”

The unit distinction matters. A $1T valuation is a stock-market number for a handful of names. A $10T gross annual revenue market is the recurring flow of money through an entire sector every year: the actual dollars changing hands for chemicals, steel, electrons, freight, and care. Côté is telling you to stop hunting for the next trillion-dollar market cap and start hunting for a slice of a multi-trillion-dollar annual revenue river that has never had software-grade tooling pointed at it.

He is careful to include the reason these markets have been ignored, giving two in his words: “Lower annual growth rates” and “Lower leverage on value production.” Chemicals and steel grow at GDP-like rates, not SaaS rates, and each unit of value historically required proportional physical input, with no software-style leverage where one engineer serves a million users at near-zero marginal cost. That is exactly why generalist VCs skipped them, and exactly why they are uncontested now.

The bet is that AI plus robotics breaks both constraints at once. Remove the growth ceiling by unlocking demand that was capped by cost and capacity, and add software-style leverage by letting a handful of models and autonomous machines run processes that used to require armies of operators. Do both, and a GDP-growth, low-leverage industry starts to behave like software while sitting on a revenue base ten times software’s size.

For a builder, the actionable move is to pick one of the five and go one layer deeper into a single process inside it, not the whole sector. “Chemicals” is not a strategy. “AI-controlled continuous-flow synthesis for one class of specialty chemical” is a wedge into a $10T sector where nobody has your tooling.

4. How a $400B industrial process gets software-like margins with AI plus robotics

The most quotable and most testable claim Côté makes is the unit-economics one: “You will see software like margins and growth rates in physical industries where the TAM for a single industrial process is $400 billion, where demand is extremely price sensitive and the addressable market is massively under-estimated.”

Unpack “extremely price sensitive,” because it is the engine of the whole thesis. In physical markets, demand is capped by price. When AI plus robotics collapses the cost of a process, you do not just win share, you unlock demand that never existed at the old price. That is how a mature-looking market turns out to be “massively under-estimated”: the visible TAM was measured at today’s cost, and cutting the cost expands the market underneath you.

The software-like margin comes from replacing per-unit human labor with a fixed model-and-robotics cost that then serves rising volume at near-zero marginal increase. That is the same leverage curve that made software gross margins 80-plus percent, now applied to a process that currently runs at industrial margins because every unit of output needs proportional human operation. Automate the operation and the margin structure migrates from steel toward software while the revenue base stays the size of steel.

The tell that this is real and near sits in Côté’s own most recent posts. On July 28 he noted “the kind of visual models you can fit on a drone” (@Andercot): the concrete detail that the perception models needed to run physical processes now fit on cheap edge hardware. The bottleneck was never the ambition, it was models small and cheap enough to sit on a machine in a plant, on a truck, or on a drone. That constraint is lifting now.

The builder takeaway: underwrite the demand unlock, not just the cost cut. Model what happens to volume when your process cost drops 40%. If that cut unlocks 3x the demand, you are not competing for a fixed pie, you are creating the market and you get to price it.

5. Who is already building the atoms version: ATOMS, hard tech, defense, energy, and the playbook to copy

The bits-to-atoms trade is not a forecast; capital is already rotating. Travis Kalanick’s ATOMS is the loudest signal that a top-tier operator is treating the physical economy, not another app layer, as the next platform. Around it sits a widening cluster: industrial AI applying models to plants and supply chains, hard tech building the machines, defense tech where physical autonomy gets funded and battle-tested, and energy, the input every other atoms market depends on.

Côté’s own timeline puts him inside the perception-and-robotics layer of this wave. His July 28 note on drone-scale visual models, referenced above, is a direct read on where the buildable edge is right now: put capable models on cheap physical hardware and point them at a process.

Travis Kalanick's direct quote on what he's building:

"CPU manipulates bits to compute information. Robots and AI manipulate atoms to compute the physical world."

That's not a metaphor. It's a thesis.

Uber was the proof of concept — touch glass, car arrives. Atoms as a programmable layer. Kalanick calls it "sci-fi at the time." It shipped in 2010.

His new company, Atoms, is the follow-through: take that same logic from ride-sharing into every factory, warehouse, and supply chain that still runs on unoptimized physical processes.

The framing matters because it changes the competitive landscape entirely. If the physical world is just an atoms-based computer, then robotics isn't automation — it's infrastructure. The same reclassification that made servers into cloud computing and phones into platforms.

Kalanick's been thinking about this since Uber. That's a long time to sharpen a thesis before building the company around it.

Côté raises the cultural stakes to make the urgency concrete: “Walk through the world with nostalgia and appreciation, you are seeing the last time in history where everything that has been designed and made was wholly made by humans” (@Andercot, July 28 2026). Strip the poetry and it is an investment thesis: the transition of physical making from wholly-human to AI-and-robot-assisted is happening now, which means the winners in each atoms vertical are being seeded in this window.

The playbook to copy from the ATOMS-style plays is consistent. Pick one physical process. Own the machine or the deployment, not just the model, so the foundation-model owner cannot commoditize you. Sell into a price-sensitive market where a cost cut unlocks new demand. Keep the defensible asset physical or regulated, the exact opposite of the services wrapper that trains its own killer.

Defense and energy are the two verticals where this is furthest along, because both fund physical autonomy directly and both are politically insulated from the “just wrap a model” competition. If you want to see the atoms playbook running with real budgets today, those are the two rooms to be in.

6. The builder and investor moves: what to start, back, or partner on before this is consensus

Start here if you are building: pick one industrial process inside chemicals, metal, energy, transportation, or healthcare, and own a physical or regulated asset in it, not a workflow on top of a model. The section-one test is your filter. If a year of your usage would train a foundation model to replace you, do not build it. If your moat is a deployed machine, a plant contract, a license, or a distribution channel the model cannot touch, build it now while the category is uncontested.

Back this if you are investing: companies whose defensibility is physical and whose demand is price-elastic, so a cost cut expands the market instead of just shifting share. Underwrite the demand unlock Côté describes, the “massively under-estimated” addressable market that only appears once cost drops. A $400B single-process TAM with elastic demand and a physical moat is a fundamentally better shape than a labor-as-software play sitting inside a $10T sector but exposed to model commoditization.

Partner on the sensing-and-robotics layer that every atoms vertical needs. Côté’s repeated point about “visual models you can fit on a drone” (@Andercot, July 28) marks the enabling technology: edge-deployable perception models are the shared infrastructure for chemicals, metal, energy, transportation, and healthcare automation alike. If you cannot own a full vertical, own the perception stack that all five need to run.

The one thing to avoid: the pure AI services company whose entire value is completing a knowledge task with a wrapped foundation model. That is the trade Sequoia is talking the market into, and it is the exact business Côté’s one line dismantles. Every task it completes is a training example for its executioner, and the timeline to obsolescence is set by the model owner.

The move this week: take one AI services idea you were excited about and rewrite it as an atoms play. Same customer pain, but instead of selling the completed knowledge work, ask what physical process sits underneath it, whether a model on cheap hardware could run that process, and what asset you would own that the foundation model never can. Answer all three, and you have found a business on the right side of the bits-to-atoms line before it becomes consensus.

What do you think?

Cheers,

Guillermo

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Anthropic just deleted over 80% of Claude Code’s system prompt for Opus 5, and the model got MORE intelligent, not less.

Fresh off the Opus 5 launch, Claude Code creator Boris Cherny sat down with YC at Startup School 2026 and laid out the exact playbook for building products when the model keeps outrunning them.

We went through the full 36 minutes and pulled out every stealable move (the ablation method, SKIP - not a real violation, higher-level prompting, dynamic workflows that spawn thousands of agents) so you can rebuild your AI product for Opus 5 without watching a second of it.

In this guide you’ll find:

1. Why Opus 5 Runs for Weeks With No Scaffolding and Ignores Prompt Injection

2. The Delete Playbook: Cut 80% of Your System Prompt and Rebuild It Line by Line

3. Product Overhang: The Model Capabilities Startups Aren’t Shipping

4. Give Claude Harder Tasks Than You Think It Can Do: Bun’s Zig-to-Rust Rewrite in 11 Days

5. Verification Is the Skill Nobody Gets Right: The Two-Week Prompt That Live-Blogged Itself

6. Spawn Thousands of Agents From One Prompt: Dynamic Workflows and Self-Maintaining Codebases

👋 Just a quick note before you continue!

If you are serious about building or investing in AI, you can subscribe here:

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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Cherny frames the launch with one data point: Anthropic took Arc AGI to 30%, “before the best score was in the low single digits or low teens.” Everything below is his account of how to build products for a model moving that fast.

1. Why Opus 5 Runs for Weeks With No Scaffolding and Ignores Prompt Injection

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SPOILER: SAM BELIEVES WE’VE REACHED SINGULARITY

If your company looks like a startup from 10 years ago, you are already in trouble.

Two weeks before thi

s interview, Altman met a startup that had rebuilt an entire office productivity suite from scratch. Documents. Presentations. Spreadsheets. The kind of product that, until recently, would have required a full team and at least a year of development.

They built it in two weeks.

That is how dramatically the startup baseline has changed.

But most founders are still following the old playbook: hire a large team, spend a year building version one, and launch another “AI agent for vertical X.”

Altman believes that approach may produce revenue but it probably will not produce the defining companies of the AI era.

The biggest opportunities are not in applying today’s models to obvious workflows. They are in building products for the models that will exist two to four years from now.

And according to Altman, almost nobody is thinking far enough ahead.

In a 70-minute conversation with Ti Morse, he explained why startups now have an unprecedented advantage, why the most popular AI startup idea is probably the wrong one, how he identifies founders who will survive, and why he believes we may already have entered the singularity.

We watched the full interview and extracted the five most important lessons for founders.

In this guide, you’ll discover:

1. Why a startup that looks like it did 10 years ago is now “in bad shape”

2. The bet almost every founder is making and why it probably will not define this era

3. Why you should build for the AI model you will not have for another two years

4. The founder skill that cannot be taught, only earned through painful repetition

5. How Altman can tell within minutes which founders are likely to survive

👋 Just a quick note before you continue!

If you are serious about building or investing in AI, you can subscribe here:

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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And you, are pro open models or against it?

Leave your opinion here!

Almost 200 Silicon Valley companies, including Y Combinator and Proton, just told the Trump administration that banning Chinese open-weight models would make “hundreds of companies instantly die.” On the same day, 25 firms led by Nvidia, Microsoft, Meta, and Palantir sent their own letter warning against “premature restrictions,” while OpenAI and Anthropic, both gearing up for IPOs, pointedly refused to sign.

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We read every letter, both op-eds, and the actual model releases from the past two weeks, then turned it all into a founder’s playbook for building a model layer that survives whatever Washington decides.

In this guide you’ll find:

1. Why 200 founders say a Chinese-model ban kills their companies overnight (and whether yours is exposed)

2. The cost-discipline playbook: how to match model to task so your unit economics survive at scale

3. Who signed, who refused, and what the OpenAI/Anthropic no-show tells you about the coming lock-in

4. The self-hosting escape hatch: building a model stack no policy shift can shut off

5. The 2026 open-weight shortlist: which models to build on right now

👋 Just a quick note before you continue!

If you are serious about building or investing in AI, you can subscribe here:

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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1. Why 200 founders say a Chinese-model ban kills their companies overnight (and whether yours is exposed)

If your product runs any high-volume task on a downloadable Chinese model like Qwen or Moonshot because frontier API prices would wipe out your margin, a ban targets your unit economics directly. That is the specific mechanic behind the warning from Suhail Doshi, founder of AI infrastructure startup Particle, who told Business Insider on July 22 that “there’ll be hundreds of companies that instantly die” if Washington blocks downloads of Chinese open weights (businessinsider.com).

The letter came from the newly formed Little Tech Association, which sent its appeal to Trump, Commerce Secretary Howard Lutnick, and OSTP Director Michael Kratsios. It names the models at issue specifically, Moonshot AI and Alibaba’s Qwen, and targets the downloadable weights rather than API access. The founders’ core argument, in their own words: “American leadership requires two things: world-leading American open-weight models and continued access for U.S. builders to open models already available worldwide.” They asked for “targeted safeguards” instead of “broad prohibitions” (politico.com).

Their sharpest practical point is that a ban does not stop proliferation, it only handicaps the domestic side of it. The weights are already downloaded, mirrored, and running on servers worldwide. A U.S. prohibition removes them from the legal stack of American startups while leaving them freely available to everyone else, foreign competitors included.

Run the test on your own product this week. List every model call your app makes, tag each one by provider and by whether the task is high-volume or genuinely hard, and flag anything that would break at frontier pricing. If a Chinese open-weight model is carrying your cheap, high-frequency path, you are in the group Doshi is describing, and the next four sections are your contingency plan.

2. The cost-discipline playbook: how to match model to task so your unit economics survive at scale

Open weights are a survival issue because of cost discipline, not ideology. Microsoft laid out the exact principle in its July 24 essay: open weights “let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else.” Microsoft calls that discipline “what will make AI economically sustainable as its use scales into the billions of everyday tasks” (microsoft.com).

Google’s July release cadence is the clearest live demonstration of tiering. In three days it shipped three models at three price-performance points: Gemini 3.6 Flash for general quality work, 3.5 Flash-Lite explicitly “for scaling repetitive use cases like sorting tickets and extracting data,” and a locked-down 3.5 Flash Cyber restricted to governments. The Flash-Lite demo compared it head to head with 3.5 Flash on high-volume tasks:

The lesson: even a frontier lab does not run everything on its best model. It routes ticket-sorting and data extraction to the cheapest tier and reserves the expensive one for hard problems. If a $2T company enforces that discipline, a seed-stage startup burning $8 a call on frontier tokens for classification tasks has no excuse.

Build a routing layer this week: classify each request by difficulty, send the bulk of your volume to a cheap open-weight or Flash-Lite-class model, and reserve frontier calls for the small share of requests that genuinely need them. That single move turns a negative-margin AI feature into a positive-margin one, which is why the ban debate is really a margins debate.

3. Who signed, who refused, and what the OpenAI/Anthropic no-show tells you about the coming lock-in

The signatures tell you who wins under a ban and who loses. The 25-company letter against “premature restrictions” that would “stifle competition” was signed by the infrastructure and diffusion layer, Nvidia, Microsoft, Meta, and Palantir, and reported by CNBC on July 24 (cnbc.com). These companies make money when AI diffuses widely: more chips, more Azure seats, more open Llama downloads, more deployments.

OpenAI and Anthropic did not sign. CNBC notes both are “gearing up for potentially massive IPOs,” and that is the whole story. Closed frontier labs benefit when cheap open substitutes are removed from the legal market, because it pushes every high-volume task back onto their paid APIs. TechCrunch put it bluntly on July 20 in a piece titled “OpenAI is scared of open-weight models” (techcrunch.com). For them, a ban is not a safety measure, it is a moat.

The strongest signal of where the infra giants stand came from Jensen Huang’s first-ever post on X, where the Nvidia CEO shared the letter and argued that open models let organizations “inspect, modify and run AI locally while reducing dependence on foreign providers” (via Stocktwits and Digg). When a CEO breaks a lifetime of X silence for one issue, that issue is existential to his business.

The takeaway for founders: your incentives are aligned with the infra camp, not the frontier labs. Read any pricing or lock-in argument from a lab racing to IPO as self-interested, and design as if the closed labs will not protect your margins, because they have publicly declined to.

4. The self-hosting escape hatch: building a model stack no policy shift can shut off

The one architecture no policy shift can shut off is self-hosted open weights running on your own infrastructure. Once you have downloaded the weights and deployed them in your environment, no ban, price hike, or API deprecation reaches into your stack. Mistral built its enterprise pitch around exactly this, arguing on July 3 that the value from “data, workflows, feedback, and models” should accrue to the enterprise “rather than to model providers” (x.com).

On July 21, Mistral expanded its Microsoft partnership specifically to give “enterprises and regulated industries frontier AI they can control,” pairing open weights with the compute to run them (x.com). A self-hostable model is insurance against every scenario in this fight, including the ones nobody is predicting yet.

Mistral’s Robostral Navigate, released July 8, is the template for a specialized, self-hostable open model. This 8B robotics navigation model runs on a single ordinary RGB camera, hits 76.6% success on the R2R-CE unseen benchmark, and beats multi-camera systems by 9.7 points while using far less sensing. It is small enough to deploy anywhere and good enough to be state of the art at its job.

The concrete move: identify the one or two highest-volume tasks in your product, find an open-weight model in the 7B-to-30B range that handles them, and stand up a self-hosted deployment behind your routing layer as a fallback path, even if you keep hitting an API today. Hugging Face’s “Local AI 201” guide, posted July 21, is a starting point for the mechanics (x.com). If you can flip a config and route around any single provider, no letter to Trump can touch your business.

5. The 2026 open-weight shortlist: which models to build on right now

Build your default stack on models you can legally keep no matter what Washington decides, which means American and European open weights first, with Chinese models isolated behind a switch.

Mistral (French, self-hostable). The safest control-layer bet. Frontier-class enterprise models plus specialized releases like the 8B Robostral Navigate, all deployable in your own environment, backed by the expanded Microsoft compute partnership announced July 21. Use it for anything where control and data residency matter (x.com).

Meta Llama (American, open). The reference open-weight family from a company that signed the anti-restriction letter, which means a strong incentive to keep releasing weights. The default for general-purpose self-hosted workloads that need broad ecosystem support and tooling.

Google Gemini variants (American, tiered API). Not open weights, but the cleanest cost-tiering options: 3.6 Flash for quality, 3.5 Flash-Lite for repetitive high-volume tasks like ticket sorting and data extraction, and 3.5 Flash Cyber restricted to governments. Use these to fill the price-performance tiers your self-hosted stack does not cover (x.com).

Moonshot AI and Alibaba Qwen (Chinese, at risk). The exact models named in the ban debate. They frequently offer the best price-to-performance available, which is why founders depend on them and precisely why they are the legal risk. If you use them, isolate them behind your routing layer so a single config change swaps in a Llama or Mistral equivalent the day a rule lands. The New York Times argued on July 23 that these Chinese open weights are the underlying strength of China’s AI strategy and that “America should take cues” (nytimes.com); treat them as valuable but expendable in your architecture.

Structure the whole stack so no single model is load-bearing: an American or European open-weight default, a tiered API layer for cost, and any Chinese model quarantined behind a switch. Do that, and whatever Washington decides in the coming weeks becomes a config change instead of an extinction event.

And you, are pro open models or against it?

Leave your opinion here!

NOW, He’s Back

He broke down the exact fundraising script, expansion rule, and physical-world framework he used to build Uber and is now reusing to build Atoms.

We pulled out the stealable plays (the uncapped-anchor auction that took Uber’s Series B to a $375M pre, the 98% beachhead rule, and the “atoms computer” pitch) so you can run them without watching all 92 minutes.

In this guide you’ll find:

1. The ‘uncapped anchor’: how Kalanick ratcheted Uber’s Series B from 2x to a $375M pre

2. When the lead backed out: what a $210M reset taught Kalanick about fundraise credibility

3. Why Kalanick blames a missing board seat for losing food to DoorDash at 5% share

4. The ‘atoms computer’: how Kalanick reframes real estate, manufacturing, and logistics into one pitch

5. The stealth tradeoff: how 8 years of silence built a cultural moat but killed the talent pipeline

6. The beachhead rule: why Kalanick won’t start a new business until the last one runs itself at 98%

Subscribe now

👋 Just a quick note before you continue!

If you are serious about building or investing in AI, you can subscribe here:

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

Try premium free for 7 days. Or get 50% off this week only.

Get 50% off forever

Read more

• LEARNING OF THE DAY: RETARDMAXXING, if Marc Andreessen is doing it, why shouldn’t you? Learn what it means here!

YC just told founders in one line that the last three years of AI startups are already obsolete: “that’s what most startups from 2023 to 2025 built, tools that help people do their jobs.”

Their Summer 2026 Requests for Startups is a map of where the next wave of money goes instead, and the total addressable market they’re pointing at (services spend) is many times larger than all of software.

We read the full RFS and broke down all six bets into what to build, why the timing just flipped, and which incumbents are exposed, so you can act before the batch does.

In this issue you’ll find:

1. The 2023-2025 playbook is dead: why YC is done funding copilots and wants companies that sell the service

2. The 4 outsourced services YC will fund you to replace (and why outsourced means easiest to kill)

3. Company Brain: the missing infrastructure layer every AI startup is quietly blocked on

4. Why the science bets (agriculture, discovery engines, personalized medicine) all became fundable in the same 12 months

5. The stealable timing signal: how to read an RFS for when a market just opened

If you are serious about building or investing in AI, you can subscribe here:

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

Try premium free for 7 days. Or get 50% off this week only.

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Over 100 YC startups are now hiring Forward Deployed Engineers, up from basically zero three years ago, and nobody can tell you exactly how the model actually works.

So we went through Bob McGrew’s 51-minute breakdown of the FDE playbook he ran as Palantir’s head of product and pulled out the full mechanics:

• when to use it

• who to hire

• how field hacks become durable product

• how to price for outcomes without sliding into consulting

McGrew was an early engineer at PayPal, an early executive at Palantir and most recently Chief Research Officer at OpenAI, where he led ChatGPT, GPT-4, and the o1 reasoning model. He was in the room when the FDE model was invented. His most surprising advice to founders copying it: don’t.

The whole thing is a trap that’s dangerously easy to turn into a low-margin consulting firm.

We previously broke down the first decade of this model in The Palantir Origin Playbook (2003–2013): how the CIA became an alpha customer, how Gotham was deployed through embedded teams and how software and consulting were fused from day one. This guide picks up where that history ended. Instead of retelling the origin story, it turns the model into an operating playbook for AI founders.

Inside you will find:

1. When the FDE Model Is a Moat and When It’s a Trap: The No-Incumbent Test

2. 2. How Palantir Lands Its First Customers: Build a Demo, Then Turn Services into Product Discovery

3. How Field Hacks Become Product: The Gravel Road, the Ontology, and the Loop That Keeps You Out of Consulting

4. Who to Hire: Domain Rebels and Prototypers, Not Loyalists and Craftsmen

5. Price the Outcome, Clear IT: How AI Startups Go Zero to Eight Figures in a Year

6. The Metrics That Prove It’s Working, and Why the Adoption Gap Is the Whole Opportunity

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Most AI products being launched today rent intelligence from the same handful of GPT and Claude APIs. That makes them fast to build, but it also leaves them with similar capabilities, permanently variable costs and limited control over the technology at the centre of their product.

This week, Mira Murati’s Thinking Machines released Inkling, a 975B open-weights model that companies can fine-tune through its Tinker platform without building their own GPU infrastructure. Bridgewater has already used the platform to train an open model on its financial knowledge and reportedly outperform proprietary models on financial reasoning.

Fine-tuning is becoming accessible enough for startups to own the intelligence they sell. Founders can now build specialized models, license them, deploy them inside enterprise environments and create businesses that were previously reserved for well-funded AI labs.

In this issue, I break down:

1. How owning the model can give startups better margins, control and defensibility

2. Why open weights turn AI intelligence into an asset your company can own

3. How startups can now train specialized models without becoming AI labs

4. Why Inkling’s capabilities matter for products, customers and unit economics

5. 10 business ideas unlocked by accessible fine-tuning

6. How to decide whether owning a model should become your startup’s advantage

Hey, welcome to AI Market Fit!

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↳Alex Karp’s Playbook for Which AI Companies Survive

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↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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1.How owning the model can give startups better margins, control and defensibility

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Investors will tell you your pitch makes total sense, then still say no, and most founders never figure out why.

The man who raised over $70 billion across a 30-year career (flagship funds at General Catalyst, now leading fundraising at Lila Sciences) has the answer: they believe you, but they don’t trust you, and belief and trust are not the same thing.

We broke down his full 52-minute masterclass with Patrick O’Shaughnessy into the exact frameworks John Kim used to move that capital, so you can run your next raise on the thing that actually unlocks a check.

In this guide you’ll find:

1. Belief vs trust: why investors say yes to logic and still don’t wire the money

2. The hard reelect number: why your first close caps your total raise (and why friends-and-family is the most expensive money you’ll take)

3. How General Catalyst built consensus one LP at a time (and why committees never fund contrarians)

4. The 3 laws of fundraising: differentiation, trade-offs, and the conversion math that turns a raise into effort

5. The psychology of every meeting: the drama triangle, the repeatable phrase, and being the Secretary of State

6. What to steal: 5 moves for your next raise

Want to learn all about the AI Opportunity before it’s mainstream?

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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In the last 12 months Anthropic has pulled at least a dozen senior people out of OpenAI, Google DeepMind, Microsoft, xAI, AWS, and the chair of Berkeley CS, and the densest cluster of them all landed on one team: Compute.

Everyone saw the headlines one at a time. Nobody laid the full year of hires side by side to read the pattern.

So we did, one person per section, tracing where each came from and what team they joined, then pulled out the single bet the whole roster is making so you can position ahead of it.

The short version: this is not prestige signaling ahead of an IPO. It is a targeted acquisition of the three inputs to recursive self-improvement (frontier research, raw compute, and the enterprise and government distribution that pays for the compute).

The celebrity researchers are the least revealing hires. The compute and go-to-market operators are the tell.

In this issue you’ll find:

1. Karpathy: Claude starts helping build Claude

2. Jumper: Anthropic turns science into a product category

3. Blomfield: Compute becomes a founder-level operating problem

4. Nordeen: The race to activate physical capacity

5. Fontoura: Extracting more intelligence from every cluster

6. Nelson: Scaling is not only brute force

7. Carlson: Winning institutional permission and demand

8. Boyd: Turning compute into a reliable internal platform

9. The Pattern: Anthropic’s intelligence-production flywheel

Want to learn all about the AI Opportunity before it’s mainstream?

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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And yet almost none of these robots have done a single hour of paid work.

In the same week, Figure, 1X, Unitree, and Boston Dynamics each dropped a new demo, and we broke down all four side by side against real deployment, funding, and pricing data.

The result is a map of four completely different bets, and the one layer where the real money is actually heading.

In this issue you’ll find:

1. The four bets, decoded: what each of this week’s demos is really signaling

2. Demo vs deployment

3. The price war America is losing

4. Boston Dynamics’ quiet cheat code

5. What to steal: where the real opportunity is (the deployment OS, not the robot)

   ---

Want to learn all about the AI Opportunity before it’s mainstream?

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

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One more thing: the perks page is not static. We update it every week with new deals, so it’s worth checking back regularly.

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The creator of Claude Code hasn’t written a single line of code in 2026, and says 100% of the code at Anthropic is now written by the model, with zero manually written code anywhere at the company.

In Sequoia AI Ascent, Boris Cherny laid out exactly how he got there, from shipping 150 PRs in a day off his phone to running dozens of autonomous loops that babysit his codebase.

I pulled the full playbook: the workflow, the moats AI kills, and the hiring shift, so you can run it yourself without watching the whole thing.

In this guide you’ll find:

1. Why Claude Code was useless for 6 months on purpose (the pre-PMF bet founders should copy)

2. The exact workflow behind 150 PRs a day: loops, sessions, and hundreds of parallel agents

3. The 2 startup moats AI just killed, and why disruption is about to go 10x

4. The org change that is Anthropic’s real edge (not the models)

5. Who wins the next decade of software

6. The Claude Code Playbook: 10 Things Founders Can Do Today

Want to learn all about the AI Opportunity before it’s mainstream?

Here’s a good place to start:

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

Try premium free for 7 days. Or get 50% off this week only.

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🛠️ Claude’s Creator Coding Playbook

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That single bet reframes everything: intelligence is not the bottleneck, cost is.

I broke down the full 68-minute conversation between Naval, Garry Tan, and two founders (Daniel Francis of Abel Police and Farbood Nivi of A-LIST) and pulled out every concrete move they are running right now, so you can act on it without watching it.

In this guide you’ll find:

1. 💸 The cost play: how they drove per-user AI from $100/month to $2.84

2. 🧠 The personal leverage stack: six Codex agents

3. ⚠️ Why compounding errors decide the winner

4. 🇨🇳 The China AI thesis

5. 📘 The CEO operating manual for the AGI era

6. 🚀 What to build in 2027

PS: Here are 4 other resources you should check

↳Alex Karp’s Playbook for Which AI Companies Survive

↳19 great new details about Cursor’s rise

↳Perplexity’s CEO 2026 AI Pitch

↳The First 48 Hours of Claude Fable 5

Subscribe now

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Palantir went from zero to a roughly $500 billion company doing exactly what today’s AI labs are promising, and its CEO Alex Karp just said the quiet part out loud: “somehow it’s not working, but we’re not allowed to say it publicly because we’ll look stupid.”

In a 21-minute interview he laid out which AI companies live, which die, and why the real threat to winners is nationalization, not competition.

We pulled every framework, sales tactic, and moat from the full talk and turned it into a positioning playbook, so you don’t have to watch it.

In this guide you’ll find:

1. The ‘de-masturbatory’ test: how Karp separates real enterprise AI value from token-maxing theater

2. Taste plus money: the un-scalable moat Karp says every AI startup underprices

3. Karp’s reverse sales tactic: why Palantir sends prospects to competitors first

4. The three-tier code framework (primitives, FDE code, free code) and where LLMs actually win

5. The three-year moat: why copycats can’t catch structural knowledge stores

6. The real killer: why nationalization, not OpenAI, ends the winners (and how bragging about AI layoffs speeds it up)

👋 Just a quick note before you continue!

Building great AI systems and agents is quickly becoming one of the most important advantages a company can have. So we’ve been building non-stop and this will become a much bigger part of AIMF and PMF going forward.

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• Claude for AI SDR: The Complete Guide

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• The Claude Code Founder Guide

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The move behind it wasn’t a new sales team. It was a product decision to make every feature a tool its cloud agents could call, so the agents started teaching users the rest of the product.

We pulled Harvey’s own Q2 charts and the co-founders’ breakdown to reverse-engineer the exact growth, product, and hiring plays, then mapped where the $11B-valued leader has left the legal AI market wide open for you.

In this guide you’ll find:

1. The product decision that drove +$100M NNARR in one quarter

2. Why 53% DAU/MAU is the real growth engine

3. The 4 executive hires Harvey made to go from $100M to $11B

4. The infrastructure bet most founders skip

5. The legal AI market map: where Harvey wins, where Paxton and GC AI win

6. Your opening: the 3 unclaimed positions in legal AI

Attio is the AI CRM that compounds revenue around the clock.

It gives your team the foundation for moving fast, staying sharp, and scaling without breaking. Every customer signal, from emails and meetings to product usage, billing, support, and agent activity, is synced from day one, giving you a live picture of every account.

With Attio, you’ll get:

• Revenue agents, workflows, and automations across every stage of your motion

• Instant answers to anything you Ask Attio about your business

• Access to your customer data wherever you work, through Attio’s MCP, API, and App SDK

30,000+ teams use Attio to drive more revenue at scale. Are you one of them?

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Three years ago I started writing on Substack. Everything changed from there

I didn’t have a master plan, just a habit of putting my thinking on product, founders and venture into the open, one post at a time.

What began as a newsletter slowly compounded into an audience, the audience compounded into relationships and those relationships compounded into new opportunities.

Today the writing and the investing are inseparable. My channels are the value-add I bring to the companies I back and I wouldn’t have access to many of those companies without the channels. Later it became my thesis.

Venture capital is going through a structural shift. Capital has been commoditized and money on its own no longer wins the best deals. To get into a competitive round you have to bring something the founder genuinely needs and increasingly that something is distribution. Building a product has never been faster or cheaper, so the market is crowded and noisy and getting heard has become the hard part.

I built my model around exactly this. My large-scale, founder-centric media platforms are a proprietary distribution layer that I put to work for the companies I back. Over three years it turned into 50+ investments, most of them next to top tier funds.

Inside you will find:

1. Why the VC Market Is Structurally Broken

2. The Three Pillars of My Distribution Engine

3. From Distribution to Access: The Portfolio

4. Backing Founders With Distribution, Not Just a Check

5. What Three Years Taught Me About the Real Moat

Today’s issue is free!

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1. Why the VC Market Is Structurally Broken

Over the past decade, venture returns have become increasingly concentrated. A small number of top-tier funds consistently capture a disproportionate share of the outcomes, while the majority of funds compete for a much smaller slice of the value, often fighting over lower-quality opportunities.

The dynamic is driven by a simple structural imbalance:

• Capital has become abundant.

• Truly exceptional founders and companies remain scarce

When the scarce resource is the founder and not the money, the power flips. The best founders are now in a position to choose their investors rather than the other way around, and in that world brand and distribution become the primary drivers of access.

Firms like a16z, Sequoia, Founders Fund, Accel and Index Ventures keep winning allocations not only because of the capital they provide, but because they offer a differentiated value proposition on top of it.

a16z made this explicit by building a media and distribution layer around its capital, positioning itself as a launch partner rather than simply a financial backer. They just published a wrap up of their first year into new media. Read it here!

That reframes the whole competitive landscape.

Venture is no longer only about deploying capital efficiently. It is increasingly about controlling access, attention and narrative and the firms that understand this are the ones quietly winning the rounds everyone else only reads about.

2. The Three Pillars of My Distribution Engine

If capital is commoditized, then access has to be earned through distribution.

So I focused on building large-scale, founder-centric media with global reach, designed to consistently attract high-quality founders, operators and investors. The distribution layer rests on three pillars:

1. Daily newsletters

2. A large social presence (Linkedin)

3. Recurring interview series with some of the most relevant operators and investors in tech

Newsletters, roughly 1M views per month combined:

• Product Market Fit: 77k+ readers, 350+ posts published

• AI Market Fit: 43k+ readers, 100+ posts published

Brands that have sponsored the newsletters include Attio, Framer, Deel, Polymarket, Evertrace, Nova and Granola.

Linkedin Profile: 250k+ followers, generating 2 to 4M impressions per week, a direct and recurring touchpoint with the ecosystem

Interview series, direct and on-the-record conversations with leading founders and investors, including:

• Staffan Helgesson, Co-founder, Creandum

• Chris Best, Co-founder and CEO, Substack

• Mark Cuban, Investor and Entrepreneur (Shark Tank)

• Spencer Skates, CEO and Co-founder, Amplitude

• John Santora, CEO, WeWork

• Robert Herjavec, Investor and Entrepreneur (Shark Tank)

• Anna Piñol, Partner, NFX

• Martin Mignot, Partner, Index Ventures

• Anish Acharya, Partner, Andreessen Horowitz (a16z)

• …

The strategy itself is simple to describe and hard to execute. Identify high-signal insights and resources, distribute them consistently and ahead of the curve and build long-term trust with a global audience of founders.

Do that for long enough and it compounds into a distribution advantage that becomes very difficult to replicate.

3. From Distribution to Access: The Portfolio

The portfolio is the proof point of the thesis: high-signal content opening the door to competitive rounds, consistently alongside top-tier funds and accelerators like a16z, Y Combinator, General Catalyst, Lightspeed, Index and others.

Companies I invested in:

• Replit: AI-powered browser IDE to build and deploy software instantly (a16z, Coatue, Prysm Capital, Google AI Futures Fund)

• HappyRobot: Enterprise AI workforce platform for logistics and supply chain (a16z, Y Combinator)

• Peec AI: Analytics for brand visibility in generative AI search (Singular, Antler, 20VC)

• Throxy: Vertical AI agents automating the full B2B sales funnel (YC W25, Base10 Partners)

• Cubic: AI code-review platform flagging bugs and technical debt in PRs (YC X25, Peak XV)

• Karumi: AI agent delivering interactive product demos on live calls (YC F25, Rebel Fund)

• Momentic: AI-native automated software-testing platform (YC S23, General Catalyst, Standard Capital)

• Azimov: AAA game engine plus multimodal ML for AI-native character IP (a16z Speedrun, NFX)

• Veritus Agent: Voice-first AI agents for consumer lending (Crosslink, Threshold, Emergence Capital)

• Agaton: Agentic platform turning sales conversations into revenue intelligence (Inception Fund, Alstin Capital, seed+speed)

• QFEX: 24/7 global exchange for equities, commodities and FX (YC W25, General Catalyst)

• Agemo AI / Codewords: AI agent platform running business workflows from plain language (Visionaries Club, Firstminute Capital, Sequel)

• Quiver AI: AI lab for vector design and visual code (SVG) generation (a16z)

• Nozomio: Context layer for AI agents, indexing code, docs and sources (YC W25, CRV, LocalGlobe, BoxGroup)

• Flai: 24/7 AI voice and text workers for car dealerships (YC S24, First Round Capital, Toyota Ventures)

• OrangeSlice: Agentic sales enrichment from real-time buying signals (YC S25)

• Keystone: On-call AI engineer that triages alerts and drafts PRs (YC S25, True Ventures, Pear VC)

• Mito AI / Mito Films: AI-native collaborative video creation platform (Lightspeed, KFund, Kibo Ventures)

• Frontnow: GenAI virtual shopping advisor for e-commerce, 117 languages (Peak, PROfounders Capital, Identity Ventures)

• InfiniteWatch: Customer-interaction intelligence monitoring 100% of web sessions (Base10 Partners)

• Omnia: Brand-visibility platform for LLM search engines (Visionaries Club, KFund, Baobab Ventures)

• Zalos: Computer agents automating finance ops inside enterprise systems (YC F25, 20VC, Cohen Circle, 14 Peaks)

• GETASAP Asia: Tech-enabled retail distributor, 8-hour delivery across SEA (YC W25, General Catalyst, Pioneer Fund)

• Novoflow: AI voice employees automating clinic operations and EHR workflows (YC X25)

• Modern Relay: Context-graph infrastructure for enterprise AI (Point Nine, Emerge VC, Amino Collective)

• Minerva Intelligence: AI-native accounting firm for SMBs (YC X25, Amino Capital, Goodwater)

• Didit: Modular identity and fraud-verification infrastructure (YC W26)

• Sellraze: AI reselling assistant pricing and listing items via camera (YC F25)

• Pally: AI relationship manager unifying messaging across WhatsApp, iMessage, X and email (YC W24, Pioneer Fund, Founders Inc)

• YouShift: AI-native OS automating hospital shift scheduling (YC W25, Harvard Ventures, Xfund)

• Fenrock AI: AI back-office agents for banks, starting with compliance (YC W26)

• Gru Space: Space infrastructure, building the first hotel on the Moon (YC W26, NVIDIA GPU Ventures)

• ETN (European Tech Network): European live-streaming tech media network, “Europe’s TBPN” (Angel investors)

• Cua: Open-source “Docker for Computer-Use Agents” (YC X25, Peak XV)

• Klavis AI: Open-source, hosted MCP integration platform (YC X25)

• Hyperspell: Memory layer for AI agents across Notion, Drive and more (YC F25)

• Imagine AI: B2B content pipeline for executive teams (YC F25)

• April: Hands-free AI voice executive assistant (YC S25, Scale Asia Ventures)

• Slashy: AI work assistant executing tasks across Notion, Sheets and more (YC S25)

• Floot: AI full-stack app builder for non-coders (YC S25)

• Hera: AI motion designer generating animations from text (YC S25)

• Fernstone: AI-native full-stack insurance brokerage for businesses (YC F25)

• Grade: API for performance-based payroll for workers and AI agents (YC W26)

• Selfin: AI bank that learns behavior and manages money automatically (YC F25)

• Clova: “Cursor for video editing”, full edits from a text prompt (Founders Inc)

• Gojiberry AI: AI go-to-market agents for B2B teams (YC Spring 2026)

• Lightsprint: Collaborative product development with cloud agents (YC Spring 2026)

• PerfectBit: “Correct by construction” AI training data, verified against physics simulators, scientific databases and formal proof systems (YC Spring 2026)

• Trellis: Agents that run short-term rental operations end-to-end (YC Spring 2026)

• Thomas: “The first AI founder”, a virtual human that runs his own companies (YC Spring 2026)

• Arga Labs: Real-world sandboxes to test agents and agent-facing software (YC Spring 2026)

• Apollo Atomics: The most compact nuclear reactors (YC Spring 2026)

• Standout: Agentic hiring marketplace (YC Spring 2026)

• Akkary: Autonomous Customer Operations (YC Spring 2026)

• Movoa AI: Founder ex-11X, Ari (stealth)

• Nota (stealth)

• HeyL (stealth)

• Litmus Labs (stealth)

• Theorema Inc (stealth)

4. Backing Founders With Distribution

Capital is only part of what I bring. Over the past year I’ve worked with teams directly on distribution and growth, on product launches, visibility and early momentum, including with companies like Vanta, Synthesia, ElevenLabs and HappyRobot, where the channels amplified their reach and positioning at moments that mattered.

The outcomes are measurable. Millions of views generated across the channels, more inbound demand, stronger investor momentum, and in several cases faster revenue growth at an early stage.

That is the part most checks can’t replicate and it is exactly why distribution is becoming a core component of venture value creation rather than an optional layer sitting on top of it.

5. What Three Years Taught Me About the Real Moat

Three years in, the model works. Writing every day brought me closer to founders, and that proximity is what now gets me into rounds I’d otherwise never see.

The newsletters and the following aren’t marketing wrapped around a fund. They’re the reason founders pick up the phone.

The plan from here is simple: keep growing the audience, get to the best founders earlier and back more of them. Capital has become easy to find. The trust and attention behind these channels took three years to build, and that is the real moat.

If you’re a founder building something I should see, or an investor starting to treat distribution as your edge, this is the thesis I’m betting the next three years on.

Cheers,

Guillermo!

The Renaissance was a period of major cultural, artistic, scientific, and intellectual growth in Europe that lasted roughly from the 14th century to the 17th century.

The word Renaissance means “rebirth” in French because it marked a renewed interest in the ideas, art, and learning of ancient Greece and Rome.

What where the main ideas of the reinassance?

• Humanism:
  people should study human achievements, reason, and potential, not just religion.

• Individualism:
  individuals could achieve greatness through their talents and efforts.

• Classical Learning:
  admiration for and study of ancient Greek and Roman ideas.

• Curiosity and Inquiry:
  questioning old beliefs and exploring science, nature, and the world.

• Realism in Art:
  portraying people and nature as they actually appear.

What technological development pushed the Reinassance?

The key technological development was the printing press (invented by Gutenberg around 1440), which helped spread Renaissance ideas quickly across Europe. Before that, books had to be copied by hand, making knowledge expensive and rare.

• Before the printing press, knowledge was scarce, expensive, and controlled by a small elite.

• The printing press dramatically reduced the cost of accessing and spreading knowledge.

• This accelerated learning, science, innovation, and eventually transformed society.

Similarly:

• Before AI, creating knowledge-based work (writing, coding, research, design, analysis) required significant human expertise and time.

• AI dramatically reduces the cost of producing and applying knowledge.

• This could accelerate innovation, education, scientific discovery, and economic productivity across society.

If the printing press democratized access to knowledge, AI democratizes the ability to create and use knowledge.

The Renaissance directly transformed these major fields:

1. Art – painting, sculpture, architecture

2. Literature – poetry, drama, prose, vernacular writing

3. Education – curriculum, universities, classical studies

4. Philosophy – humanism, ethics, political thought

5. Science – observation, experimentation, astronomy, anatomy

6. Mathematics – geometry, perspective, practical applications

7. Medicine – anatomy, surgery, medical research

8. Technology & Engineering – mechanics, military engineering, inventions

9. Printing & Publishing – mass dissemination of knowledge

10. Religion – biblical scholarship, challenges to Church authority, groundwork for the Reformation

11. Politics – statecraft, diplomacy, political theory (e.g., Machiavelli)

12. Economics & Commerce – banking, accounting, trade networks

13. Geography & Exploration – navigation, cartography, overseas exploration

14. History – critical study of historical sources

15. Music – new forms of composition and notation

16. Law – renewed study of Roman law and legal scholarship

17. Linguistics & Languages – study of Greek, Latin, and vernacular languages

The role of apprenticeship workshops in the Renaissance is massively underrated. They were essentially the AI labs, startups, and universities of their time—all combined into one.

What was a workshop?

A workshop (bottega in Italy) was run by a master like Verrocchio, Ghirlandaio, or Bellini.

Inside you had:

• Apprentices (young learners)

• Journeymen (experienced workers)

• Masters (elite creators)

• Patrons (customers funding projects)

They worked together on real commissions every day.

Why did they accelerate the Renaissance?

1. They compressed learning

Before workshops, knowledge was often scattered.

A 12-year-old apprentice could learn:

• Drawing

• Anatomy

• Geometry

• Engineering

• Sculpture

• Business

all in the same place.

Leonardo da Vinci entered Verrocchio’s workshop around age 14 and absorbed multiple disciplines simultaneously.

2. They enabled knowledge transfer

Every generation built on the previous one.

Think:

Master
  ↓
Senior apprentice
  ↓
Junior apprentice
  ↓
Next generation master

Ideas didn’t disappear when someone died.

3. They created talent density

Florence had dozens of elite workshops within walking distance.

The best people constantly interacted.

This is similar to:

• Silicon Valley

• OpenAI + Anthropic + DeepMind

• Y Combinator

Talent clustered geographically.

4. They mixed disciplines

A Renaissance workshop wasn’t just art.

A workshop might work on:

• Architecture

• Sculpture

• Engineering

• Military devices

• Stage design

• Painting

This cross-pollination generated new ideas.

Leonardo is the perfect example:

• Artist

• Anatomist

• Engineer

• Inventor

because workshops encouraged interdisciplinary thinking.

5. They created competition

Masters competed for prestige and commissions.

When one innovated:

• Better perspective

• Better anatomy

• Better engineering

everyone else had to improve.

Innovation spread rapidly.

Now, who’s building the Florence of the AI reinassance?

Cheers,

Guillermo

Some years later, they’ve invested in companies like Anduril, Etched, Cognition, SpaceX, OpenAI, Anthropic, Seronic, Modal.

Some days ago they announce their $100M new fund.

This is what you should learn about this:
Why a16z believes attention is becoming more valuable than capital.

1. The investment thesis behind Jake Paul’s new oversubscribed growth fund

2. Capital Is Abundant, Attention Is Scarce

3. Why AGI Makes Intelligence a Commodity, Relationships Win

4. The Arbitrage: Charisma vs. $100 Million of Paid Attention

5. Proof of Concept: The Most-Viewed Amateur Boxing Event Ever

6. Why Only Three Brands Survived a Decade at the To

7. Followers Aren’t the Moat. Monetization Is

8. Anti-Fragile by Design: Why the Cancellable Are the Lucky Few

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Three years later, 95% of their users treat Cursor primarily as an agent, and the company is now training a model as big as GPT-4 and Opus, from scratch, on 10 to 20x more compute than it has ever had. This is the story of how a product company talked itself into becoming a frontier lab, told in its own words at the Compile keynote.

Everything below comes from a single 27-minute keynote (delivered by Cursor co-founder Michael and his team.

In this newsletter you’’ll learn:

1. Why four nervous programmers who almost didn’t build Cursor

2. Cursor quickly went from assistant to agent

3. How Composer 2 learned to retrain itself every five hours

4. Agents at work: 6 million automation runs and Amplitude’s 20,000-component migration

5. The big bet: a from-scratch model as big as GPT-4, on 10-20x more compute, shipping in weeks

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Attio is the AI CRM that compounds revenue around the clock.

It gives your team the foundation for moving fast, staying sharp, and scaling without breaking. Every customer signal, from emails and meetings to product usage, billing, support, and agent activity, is synced from day one, giving you a live picture of every account.

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