When people talk about the one-person, billion-dollar company, they picture a software prodigy in a hoodie shipping an AI app from a bedroom. That picture is wrong.

The first one-person, billion-dollar company will be a completely normal company.It might be a media company. A construction firm. A law firm. A consulting practice. A logistics broker. A recruiting agency. It will sell the same thing its competitors sell, to the same customers, at roughly the same prices. If you looked at its website, nothing about it would scream “AI.”

What will be different is everything underneath. The company won’t sell AI — it will run on AI. The founder will use AI the way a traditional founder uses a headcount budget: as the labor force that executes marketing, sales, operations, customer support, finance, and product delivery.

This distinction matters more than any other part of the thesis. AI companies compete in the most crowded, best-capitalized market in history, against OpenAI, Anthropic, Google, and ten thousand venture-backed startups. A normal company supercharged by AI competes against sleepy incumbents who still run their business on email, spreadsheets, and payroll. The arbitrage isn’t in building AI. It’s in applying AI where nobody has bothered to apply it yet.

Sam Altman has been predicting this moment for years:
“In my little group chat with my tech CEO friends, there’s this betting pool for the first year that there is a one-person billion-dollar company, which would have been unimaginable without AI, and will now happen.” — Sam Altman, via Fortune

He’s also framed what the company actually is — not a person with a laptop, but a person commanding compute:

“One person + 10,000 GPUs could build a billion-dollar company.” — Sam Altman, in conversation with Garry Tan

Notice what’s embedded in that framing: the founder isn’t the workforce. The GPUs are.

I’ve been working and researching about this topic for more than 3 years. Here’s my reflections:

1. Why first solo unicorn will not sell AI

2. The 1PBDC founder profile

3. The 1PBDC org chart

4. The 1PBDC real constraint

5. The real question most people aren’t making

6. How to start building it now

We’ve got a special offer for premium access.

As a premium subscriber, you get access to the full archive:

• 160+ playbooks

• 65+ success stories

• 30+ pitch deck collections

• 25+ investor resources

We’re already 70,000+ founders and operators inside.

You can try it free for 7 days.

Get 7 day free trial

1. Why first solo unicorn will not sell AI

Read more

Pocket FM added $250M in net new ARR last year to cross $500M, and it did so while EBITDA-profitable, a claim almost no AI-content company can make.

Here’s the through-line before you read a word of tactics: Pocket didn’t win by making content cheaper with AI.

It won by finding the one metric whose tiny movement cascades through the whole business, then rebuilding the company around manufacturing that number. Steal that discipline, not the category.

In this guide you’ll find:

1. How Pocket FM Cut CAC 30% While Increasing Ad Spend 8x

2. The 3-Year Experiment That Turned 25 Minutes of Usage Into 2.5+ Hours

3. The International Growth Playbook That Increased Retention 50%

4. How a $1M Bet Became a $15M ARR Business in 60 Days

5. The Decision That Stalled a $200M ARR Company, Then Added $250M in One Year

6. 5 Growth Systems From Pocket FM You Can Copy This Quarter

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Salesforce sells one agent (Agentforce) in eight different ways:

• Foundations, free

• $2 per conversation

• Flex Credits, $500 per 100,000 credits, with every action consuming a different number of them

• $5 per user per month, bundled with credits

• An add-on at $125 per user per month

• Another add-on at $150 per user per month

• Agentforce 1 Editions, from $550 per month

• $2 per Help Agent resolution

Eight public options on one product, from the company that taught the industry how to price software.

Pricing was never easy. Put an AI inside the product and the old model stops working, and nothing has replaced it yet.

The question of what to charge for, when the product does the work instead of helping a person do the work, has no settled answer yet.

👇 Last time I wrote about how much to charge and what belongs in each tier. You can find it here 👇

Today’s issue is about choosing the pricing model itself.

With an agent in the product, one model stops being enough. There are two choices underneath it:

1. What you charge for: access, activities, outputs or verified outcomes

2. How the customer pays for it: subscription, pay as you go, prepaid credits, committed spend.

Almost every AI product now combines them.

So most teams guess, and they anchor the guess to their token bill, which is the one method guaranteed to underprice them. On AI products the median target gross margin is now about 50%, against the 75% to 85% SaaS was built on. At those margins the guess is not a rounding error.

Inside you will find:

1. Why One Pricing Model Stopped Being Enough

2. How to Set Up the Claude Agent

3. How to Decode What Your Competitors Actually Charge For

4. How to Cost One Unit of Work Before You Price It

5. How to Choose the Combination That Fits Your Business

6. How to Set the Credit Weights and the Price per Credit

7. How Many Credits to Include and What Happens When They Run Out

1. Why One Pricing Model Stopped Being Enough

Per seat had a good run because for twenty years it was approximately correct. Software helped a person do a job, more people doing the job meant more value delivered.

An agent breaks the chain in the middle. The product no longer helps a person do the job, it does some fraction of the job. So founders go looking for a replacement, and this is where the confusion starts, because two different questions get answered with the same word.

The first question is what you charge for.

• Inputs: tokens, compute, storage. What the infrastructure providers sell.

• Access: a seat, a license, a workspace. What SaaS sold for twenty years.

• Activities: an action run, a document processed, a query executed. What most usage-based AI products sell today.

• Outputs: a completed piece of work, delivered. A drafted contract, a reconciled invoice, a resolved ticket.

• Outcomes: a verified business result. A ticket the customer never reopened. A recovered chargeback.

Value alignment goes up as you move down that list, and so does the measurement burden.

The second question is how the customer pays. Subscription, pay as you go, prepaid credits, committed spend, top-up packs. And more importantly:

Credits are a payment mechanism, not a pricing metric.

You do not choose between “outcome pricing” and “credit pricing.” You choose outcomes as the metric and credits as the way they get paid for.

→ Which means most AI products need a stack rather than a single model:

The rest of this guide is how you fill in each row of that table for your specific business, with numbers you can defend.

2. How to Set Up the Claude Agent

The rest of the issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Open AI just shipped GPT-6 Astra, its most capable model.

OpenAI put GPT-6 Astra against a competitive Excel modeling challenge. It finished the task roughly four times faster than the human competitor who won it.

The more important number is 57.9%.

That is Astra’s score on Terminal-Bench 4.0, a benchmark designed to test whether a model can take control of a real terminal, work through a multi-step task, and actually finish it. GPT-5.6 Sol scored 37.3%.

GPT-6 Astra is capable of taking on much longer, more complex tasks than previous models and carrying them through with far less supervision.

For a founder, that means you can delegate real work.

Inside you will find:

1. The 3 founder workflows Astra actually earns its price on (and the agency invoice each one replaces)

2. The credit-management playbook:when Astra is worth $50/M output

3. The crazy things founders are already shipping

4. How to Install ChatGPT Work and Run Your First Astra Task

5. How to Pick the Right Model and Stop Burning Credits

6. Four prompting rules from OpenAI’s own docs that stop Astra from wandering (and wasting)

7. The 10-Minute Plugin Setup That Makes Everything Else Work

8. The Prompt Library: 10 Founder Jobs You Can Delegate Today

9. How to Give Astra Your Company Brain in Five Minutes

10. Build Two Schedules and One Trigger

11. How to Build the Founder OS Dashboard

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Before you sell 10% of your company, check whether someone is willing to give you the money without taking any of it.

Most founders default to VC because it’s the funding market they can see. But underneath there is an entire layer of startup capital that most founders barely touch: grants, prizes, government programmes, R&D incentives, cloud credits, fellowships and other non-dilutive funding.

The problem is finding the right programs, figuring out whether you qualify and surviving the application process.

Inside you will find:

1. The Full Database

2. The Setup That Powers Every Prompt Below

3. How to Let Claude Interview You Before You Look at a Single Programme

4. How to Make Claude Pick the Programmes You Actually Qualify For

5. How to Know If a Call Is Worth Your Forty Hours

6. How to Get the Application Written Through a Second Interview

7. How to Catch Every New Call Before the Deadline

+150 Sources Across 9 Categories

The full database includes 150+ funding opportunities across 9 categories:

• Government Innovation Grants

• R&D Tax Credits & Cash Refunds

• Revenue-Based Financing & Customer Capital

• Venture Debt & Growth Loans

• Prizes, Competitions & Fellowships

• Cloud, AI & Infrastructure Credits

• Corporate & Platform Programs

• Philanthropic & Mission Capital

• Public Procurement & Innovation Contracts

For every opportunity, the database tells you:

• Funding source → who is offering the money

• Type of funding → grant, prize, credit, contract, R&D incentive, financing, and more

• Country / region → where you need to be based or operate

• Stage → from pre-seed to growth

• Funding amount → minimum and maximum available

• What it actually is → a plain-English explanation of the programme

• Key requirements → who can apply and what you need to qualify

• Deadline / cadence → rolling, annual, or fixed application windows

• Time to money → roughly how long it takes to get funded

• Best for → the type of startup most likely to benefit

Why Non-Dilutive Capital Changes What You Raise and When

Keeping your shares is the argument everyone already knows. Three others matter more.

1. Price. A seed round raised on a story gets priced like a story. The same round raised eighteen months later, with the engineering funded by a grant and the infrastructure funded by credits, gets priced on a working product with customers on it. What you save is the gap between those two prices.

2. Timing. Grant committees do not care that the market cooled. Tax authorities have no investment thesis. This is the one line in your funding mix that behaves identically in a bad quarter.

3. Signal. Innovation agencies run technical diligence most seed funds cannot staff. Winning one is something a lender will price and an investor will notice.

What Claude Does With This List

There is still one problem. Even with everything organized, 150+ opportunities are too many to review manually.

That is where Claude comes in:

• interviews you first. Before you look at a single programme, Claude asks you the twenty-odd questions that determine eligibility across every scheme in the list

• cuts the list down to what you can actually win. It runs your profile against all the sources and sorts them into eligible, ineligible and unclear

• reads the call documents so you don’t have to. Point it at a specific programme and it extracts the real questions, the published scoring grid and the weights and the profile of who has won before

• it re-interviews you to write the application. The second interview is narrower and harder: it asks you the questions the evaluator will ask, in the evaluator’s order, and turns your answers into the application.

Let’s begin!

1. The Full Database 👇

This full database + the Claude agent is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Unlock the full database with a 7 day free trial!

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Read more

“You can go from zero to 15 mil ARR in about four months with like two or three people and like hundreds of agents, like, you know, a few hundred skill files. Building these agentic systems today, I think all you’re doing is you’re seeing where the bottlenecks are. How do I just instruct the agent to create a piece of software or a markdown file that blows away that roadblock?”

Find the bottleneck. Hand it to an agent. Repeat. That is the job.

Where are your bottlenecks? Answering that with numbers instead of opinions is a six figure consulting job. MBB call it an AI transformation assessment.

Here are the 14 skills that produce the same assessment inside Claude for free. Every one of them runs on a real framework:

1. Boundary Setting (SIPOC + Value Stream Mapping, Rother & Shook, 1999)

2. Process Recovery (Process Mining, van der Aalst; IEEE Process Mining Manifesto, 2011)

3. Cost and Time Baseline (Time-Driven Activity-Based Costing, Kaplan & Anderson, HBR, 2004)

4. Tacit Work Elicitation (Critical Incident Technique, Flanagan, 1954 + Contextual Inquiry, Beyer & Holtzblatt, 1998)

5. Task Decomposition (Task-Based Framework, Autor, Levy & Murnane, QJE, 2003)

6. AI Suitability Scoring (SML Rubric, Brynjolfsson & Mitchell, Science, 2017; Brynjolfsson, Mitchell & Rock, 2018)

7. Automation Design (Levels of Automation, Sheridan & Verplank, 1978; Parasuraman, Sheridan & Wickens, IEEE, 2000)

8. Constraint Analysis (Theory of Constraints, Goldratt, 1984)

9. Roadmap Sequencing (Weighted Shortest Job First, Reinertsen, 2009)

10. Benefits Case (Benefits Realization Management, Ward & Daniel + Amdahl’s Law)

11. Behaviour Specification (Given-When-Then, Dan North, 2006 + ISO/IEC/IEEE 29148:2018)

12. The Agent Contract (Design by Contract, Meyer, 1986; Hoare logic, 1969)

13. Failure Design (FMEA + Pre-Mortem, Klein, HBR, 2007)

14. The Acceptance Bar (Service Levels and Error Budgets, Beyer, Jones, Petoff & Murphy, 2016)

Inside you will find:

1. How to Set Up the Assessment Inside Claude

2. How to Map the Way Work Actually Happens

3. How to Score What AI Can Actually Take Over

4. How to Turn the Scores Into a Sequenced Roadmap

5. How to Turn the Decision Into an Agent Specification

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

We’ve invested in tens of startups that have raised over $100M together.

And after years of writing about fundraising on this newsletter, the useful part ended up spread across dozens of editions.

So we decided to build the ultimate guide to fundraising.

Inside you will find:

1. How to Pitch to Investors

2. How to Find a Cofounder

3. How to Get Customers

4. How to Create a Competitive Advantage

5. Why VCs Say No to Seed Rounds

6. Why You Should Write a Newsletter for Investors

7. The San Francisco Founder Guide

8. Fyxer Fundraising Playbook: Series A to B in months

9. A full pack of resources you can use:

   • 90+ Pitch Decks from the Most Succesful Startups

   • 8 Investor Databases with 6,600+ Entries

   • The Notion Data Room Template

   • The Investor Update Template

   • The Excel Financial Models

Why This Is Valuable

None of this is theory. Every chapter is something you can apply to a round you are running in the next ninety days.

• What makes you fundable before you pitch: the cofounder decisions that hold for four years, the customer strategy that works in a crowded market, and the competitive advantage you can build out of what you already have.

• The materials: the three-part pitch framework, the cap table thresholds and the valuation math investors screen for, the data room structure with the Notion template and the Excel model, and 90+ real decks sorted by the stage each one was raised at.

• The process: the fourteen objections VCs use to say no at seed and what to do about each, the nine-section update that keeps your investors working for you after the round closes, and the seven steps Fyxer AI used to raise $40M in under six months.

• The lists: eight investor databases and 6,600+ entries across family offices, angels, seed VCs, corporate funds, LPs and accelerators, filterable by stage, region and sector, with the contact paths included.

The full guide is available exclusively to paid subscribers!

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Welcome back to Product Market Fit 🔥

Today I’m sharing a curated set of AI pitch decks from frontier labs like OpenAI and DeepMind to early-stage startups backed by top-tier investors.

Some of these companies are already category leaders. Others are just getting started.

But that’s exactly the point.

This is a rare chance to study how both giants and early teams think, position, and pitch when they’re trying to build something that doesn’t fully exist yet.

1. Why This Matters

Most people look at pitch decks to copy slides.

That’s not the value.

The real value is understanding:

• how great founders frame a problem before it becomes obvious

• how they simplify complex technology into a clear narrative

• how they justify why this moment matters now

And in AI, this matters even more.

Because many of the things that feel “normal” today were just bold claims inside these decks a few years ago.

2. What You Will Find Inside

+ other Pitch Decks from: Aisera, Arga Labs, Artificial Societies, Axle Health, Charta AI, Code Four, Epiminds, Feroot AI, Gojiberry AI, Gradient, Hedra AI, Lightsprint, MITO AI, Nas.com, Nexad AI, Octave, Omnia, Onti, Sazabi, Thomas, Trellis…

Inside this collection you’ll find:

• Frontier AI companies like OpenAI, DeepMind, Anthropic, Mistral AI, ElevenLabs and Synthesia shaping the direction of the AI industry across foundation models, voice, video and enterprise adoption

• Agent infrastructure and AI workflow layers such as Nozomio, Aisera, Artisan, Boardy, Arga Labs and Lightsprint, showing how AI systems are becoming more autonomous, contextual and embedded in day-to-day work

• Vertical AI applications across healthcare, security, public safety and enterprise operations, including Axle Health, Charta Health, Feroot, Code Four, Sazabi and Scribe

• Creative and media platforms like MITO, Hedra, FLORA AI and Synthesia, building new categories around AI-native content creation, design and video production

• GTM, marketing and AdTech platforms such as Octave, Letter AI, Nexad, Omnia and Epiminds, showing how AI is reshaping sales, brand visibility, advertising and distribution

• Early-stage AI startups from YC and emerging ecosystems, including Gojiberry, Trellis, Thomas and others, offering a view into where the next wave of AI-native companies may emerge.

Y Combinator just backed its first AI founder.

Not a founder who uses AI → An AI that is the founder, works for itself and was given one instruction: make money.

3. Why These Decks Give You an Edge

1. You learn how top founders think (not just what they build) → You see how they go from insight → narrative → company.

2. You understand what great positioning actually looks like → Clear problem, sharp angle, strong “why now”.

3. You get a preview of where AI is going → Before it becomes consensus.

If you’re building in AI, these are worth studying.

This database is available exclusively to paid subscribers!

If you’re not subscribed yet, you can unlock this and our full library (200+ startup decks, 10,000+ investors, AI Agent playbooks and fresh high-signal content every week) by upgrading below:

Get 7 day free trial

Here are some examples:

🤖 AI Agent Templates

↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks

↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library

↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools

↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

4. The 40+ AI Pitch Decks

Read more

His argument is that systems deployed seven to ten years ago were built for a threat environment that no longer exists, that AI-powered attacks now run at machine speed, and that the gap is structural rather than a patching problem (👉 Full Post Here).

That is a CISO’s problem. Here is the same shift at founder scale.

The attacks that reach an eight-person company were always the cheap ones: phishing, credential stuffing, keys harvested from public repos. What changed is that running them now costs almost nothing, so they get pointed at everyone instead of at targets worth a human’s time. Verizon’s 2026 Data Breach Investigations Report counts fifteen attack techniques already boosted by generative AI, 31% of breaches starting from software vulnerabilities, and 48% involving ransomware.

You have no trillion dollars of architecture to rethink.

By the end of the weekend you will know, with evidence you can point a finger at:

• which ex-employee still has live access, and to what

• how many people could take over your company today

• which tool can read every document in your Drive

• whether a stranger can send an invoice as your domain

• which live keys are in your history, your repo, your frontend, and your Slack

• how to rotate them without taking production down

• whether user A can read user B’s data by changing a number in a URL

• which vulnerable dependencies production actually touches

• how long a restore really takes, and how much data you lose

• what to do in the first fifteen minutes when it happens

Each of those is a section, and every section moves through the same six steps: what Claude needs, what you run, what you paste into Claude, what Claude returns, how you fix it, and how you verify the fix held.

Inside you will find:

1. How to Build Claude a Model of Your Attack Surface

2. Who Still Has Access to Everything You Own?

3. Can Someone Send an Invoice in Your Name?

4. Where Your API Keys Are Leaking Right Now

5. How to Make Claude Review Every Pull Request

6. How to Verify Your Authorization Boundaries on Staging

7. Do Your Backups Actually Restore?

8. The One-Page Runbook for the Day It Happens

Launch your site faster than ever with Framer - now with Agents that make every part of your workflow effortless

Framer is the pro website builder for creators, teams, and businesses that care enough to get every detail right. Now with Agents that work alongside you across the full website workflow: designing new pages, managing your CMS, editing content, and adding SEO all without leaving the tool where the real site lives. Trusted by teams at companies like Miro and Perplexity.

Agents bring speed and scale; you bring taste, judgment, and control. Together you can:

• Build and iterate faster: generate sections, refine layout and styling, and ship updates in minutes.

• Scale content with confidence: update CMS collections, localize copy, and keep metadata consistent across pages.

• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

Launch Your Site With Framer Today!

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

236 companies got into YC’s Summer 2026 batch. 151 of them (64%) are AI-native, with agents alone accounting for 52 companies (22%).

We analyzed all 236, sector by sector, and pulled out where founders piled in, where the real white space opened (Industrials jumped from 12.8% to 23.7% of the batch), and the handful of companies building AI-native replacements for entire regulated industries.

This is the map, so you can pick your lane before demo day tells you it’s already taken.

In this guide you’ll find:

01. The batch by the numbers: 236 companies, 64% AI-native, and the Industrials surge nobody’s talking about

02. The crowded default: 89 companies packed into agents and AI infra, and what that means for you

03. Own the P&L, not the tool: the AI-native companies replacing whole regulated incumbents

04. Atoms are back: how robotics and defense hard tech took over the batch’s fastest-growing slice

05. What Summer 2026 tells founders to build (and where it’s already too late)

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Where? In a research report sitting on their own website: 37,657 real conversations, the actual questions people bring to Claude when they need to decide something in their life, sorted into nine verticals with real percentages and real sub-clusters attached.

Everyone read it as an AI safety story. It’s a market map.

This is the closest thing to free customer discovery you will ever get from a company that isn’t yours. Nine verticals, ranked by volume, stakes and how often the model gets it wrong, each one telling you exactly what a stranger typed into a chat box because there was nowhere else to ask.

Inside you will find:

1. Why This Data Is Better Market Research Than Anything You’d Pay For

2. The One Number That Tells You Where Not to Build

3. The Second Filter: What Happens When Claude Gets It Wrong

4. Health and Wellness: The Biggest Cluster, and the Narrowest Wedge

5. Professional and Career: Why the Obvious Idea Is Already Crowded

6. Relationships: The Vertical Everyone’s Afraid to Touch

7. Personal Finance: The Advice Gap Between What to Do and How

8. The Five Smaller Verticals Worth a Second Look

Taskrabbit runs on Attio, the agentic CRM. Here’s what that adds up to:

• 4x faster deal updates across sales team

• Strategic deals reclaimed from lost email data

• 90% team adoption overnight, up from sub 50%

Ready to try now?

Try Now!

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Peter Thiel co-founded PayPal.

He wrote Zero to One, one of the most quoted startup books ever printed.

He wrote the first outside check into Facebook, turning $500,000 into more than a billion dollars and backed the founders who built Palantir and SpaceX.

Across two decades of doing this from both sides of the table, as a founder and then as an investor, he has said almost nothing in public about what actually makes a pitch work.

Except once. Running Founders Fund, he sat down and wrote an example deck himself. An actual slide-by-slide pitch, built around a fictional weight-loss scale called SOMA, with every rule he thinks a fundraising story should follow baked directly into it.

It’s dated 2012. The company inside it never existed. And founders still pass it around today, even though the logic underneath it never expired.

📚 I went through all eleven slides, flagged exactly where a 2026 investor would stop reading and built the fix for each one directly into a prompt: hand it to Claude and get a ready, Thiel-style pitch deck with every one of those fixes already built in.

Inside you will find:

1. The Logic Behind the Deck

2. What Limits It in 2026

3. The Full Deck, Slide by Slide

4. How to Run It in Write Mode and Review Mode

5. How to Run It in Write Mode and Review Mode

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Someone has to run the operating rhythm, catch what falls between teams, keep the org structure sane and close the gaps before they turn into emergencies and until there’s budget for that seat, the founder is the one doing it.

The problem isn’t effort. A COO’s job isn’t one thing you get good at in the margins of your day, it’s a set of functions that all need attention on a schedule. Do all of that on top of the job you already had, and you’re capped, not because you’re not capable of it, but because there is only one of you and the role needs full attention on all of it at once.

Claude can run this layer instead. 14 prompts, one for each function above, each engineered to execute the job end to end with no supervision once you hit send:

1. Weekly Operations Brief

2. Ops Health Dashboard

3. Quarterly OKR Review

4. Meeting Follow-Through Tracker

5. Priority Arbitration Prompt

6. Project Portfolio Tracker

7. SOP Documentation Prompt

8. Change Management Rollout Prompt

9. Org Design Health Check

10. Performance Review Cycle Prompt

11. Vendor & Tool Stack Audit

12. Contract & Renewal Calendar

13. Risk & Compliance Checklist

14. Business Continuity Audit

Inside you will find:

1. How to Run the Operating Rhythm

2. How to Make Sure Nothing Falls Through the Cracks Between Teams

3. How to Turn What Actually Happens Into Process Everyone Follows

4. How to Keep the Org Itself Healthy

5. How to Close Every Operational Gap Before It Costs You

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Glean just crossed $300M in top-line revenue seven years in, and its founder gave up his real playbook in a five-minute hallway interview over lunch.

Arvind Jain, the 2x unicorn founder behind Rubrik and Glean and a decade-long Google search engineer, broke down who to hire first, when to raise, what investors actually bet on, and how Glean quiewetly became the layer enterprises use to cut AI costs by close to 6x. We went through the entire conversation question by question so you can steal the moves without watching a single minute.

In this guide you’ll find:

1. The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)

2. Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea

3. We asked about Uber burning its AI budget: is enterprise AI ROI actually real?

4. The use cases nobody predicted, and who actually buys Glean

5. Openrouter getting acquired by Stripe 💰

6. Will AI erase all jobs as Dario Amodei says?

7. The OpenAI panel: a 3-layer playbook for deploying agents safely

8. The Enterprise AI Playbook by Glean

1. The origin story: ‘Google for your work life’ (and 20 engineers before anyone else)

We opened with the classics: how did you end up here, who do you hire first after raising your first round, and how do you communicate the vision to so many engineers?

The wedge was a problem Arvind lived, not one he researched. At Rubrik, the enterprise data security company he co-founded, fast growth quietly wrecked internal productivity. “Everybody would complain about not being able to find things inside the company or connect with the right people to get help,” Arvind told us. He had spent over a decade building search systems at Google, so the fix was obvious to him: a real search product for all of a company’s data and knowledge. That became Glean in early 2019. Before that, he grew up in India and moved to the States in the ‘90s for grad school, and he’s been in the tech industry — and based in San Francisco — ever since.

His answer to “who do you hire first” was one word: builders. “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” Arvind said. Office, devices, food, early customers, he handled all of that himself so the product team could stay heads-down until the product hit maturity.

He could hire 20 engineers around one idea because the idea fit in a sentence. “It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you,” Arvind said. A pitch this simple removes the friction of explaining what you’re building. But he flags the harder half: people have to believe it will be big. “They’ve got to feel that it’s going to be something big.”

The steal: if you can’t recruit engineers around your idea in one sentence, the idea is too complicated to fund and too complicated to build. Pressure-test your mission on a non-technical friend this week. If they can repeat it back, you can hire around it.

2. Arvind’s fundraising rules: raise fast, dilute early, and know investors bet on you not the idea

Next we asked when a founder should raise at all — bootstrapping versus raising money — and whether raising was easier the first time at Rubrik or the second time with Glean.

Arvind’s capital strategy is deliberately aggressive, and it’s built to protect focus, not valuation. “I like to go big and go and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” he told us. His reasoning is operational: he does not want to spend every month calculating whether the company can afford one more engineer. Raising more removes that recurring distraction.

He directly rejects the most common counterargument. “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that,” Arvind said. His view is that the drag of under-capitalization costs more than the dilution.

The deeper insight came from our second question: raising was definitely easier the second time, and the reason is about what investors are actually buying. “Investors are actually never looking to invest in great ideas. They’re actually, they’re looking to invest in people,” Arvind said. His logic: any early idea gets transformed, refined, probably changed into something completely different once you talk to customers, so investors are underwriting your persistence, not your pitch deck. A track record signals you’ll “stay on the mission” and not give up.

He also reads the current market as unusually generous. “There’s a lot of capital that is actually waiting to be deployed” on AI ideas, Arvind said, so founders “should feel really, really, really comfortable” they’ll be able to raise. Note this is his read, not a hard figure.

The steal: frame your raise around the person and the persistence, not the perfect idea, and if you can raise enough to stop thinking about hiring math for 18 months, take it. Run the raise as a distraction-elimination decision, not a valuation-optimization one.

3. We asked about Uber burning its AI budget: is enterprise AI ROI actually real?

We put the reported case of Uber burning its 2026 AI budget in four months without seeing ROI directly to Arvind. He didn’t dodge it. “It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now,” he said.

The clearest value, per Arvind, is basic knowledge-seeking that makes every employee “more productive, more knowledgeable,” and “that’s not in contention.” The catch: “it’s hard to measure. The fact that, you know, people can work faster and, you know, oftentimes, you know, that doesn’t translate into like your P&L statement.” The companies seeing real results are larger ones with intentional programs, good metrics, and systematic process-by-process adoption — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Reality, he said, is “like any other technology where you have to provide a lot of love and care and investment.”

That’s why Glean sells a long-term engagement, not a switch you flip. “We bring our platform so you immediately get those productivity savings, you get a great coworker, but then the real business value, like, you know, we spend many, many months understanding, you know, their business and helping them build those agents and get them to production level of quality,” Arvind said.

How Glean is using this to win. Arvind’s Glean:GO keynote turned the ROI gap into Glean’s entire product pitch. He named the failure mode most enterprises are living: employees now work for the AI instead of the reverse. “Today employees spend a lot of time coaching AI agents and are feeling overburdened with that. This is bot sitting. We are spending too much time working for AI as opposed to AI working for us,” he said. Botsitting — employees feeding context, supervising, and cleaning up after AI agents — has become a significant drain on worker time: Glean’s Work AI Institute, surveying 6,000 full-time digital workers, found employees spend 6.4 hours a week on it, more time than they spend using AI to actually produce work. In the keynote he laid out the three pains every enterprise names: struggling to bring the right context to agents, AI costs running higher than they can afford, and the proliferation of AI-written documents (SLOP), skills, and agents that are hard to manage and secure. Every one of those pains maps to a Glean feature.

On the OpenAI panel he was blunt about the near-term math: “AI is often becoming more expensive than the business value that it is delivering in the short order... We need to make sure companies don’t run out of money before that happens.” Positioning Glean as the platform that closes that gap — cheaper, better-contexted agents — is the commercial answer to the ROI question we asked.

The steal: stop selling (or buying) instant ROI. Set the expectation that production-quality agents take months of context-building, and instrument the specific business processes where you can actually measure cost-per-task before and after. Pick one high-cost, high-human-involvement process and commit to a multi-month build rather than spraying pilots.

4. The use cases nobody predicted, and who actually buys Glean

We asked four go-to-market questions in a row: have customers used Glean in ways you never imagined, who do you reach out to in the organization, do customers build the agents themselves or do you send in your engineers, and where are your customers?

Because Glean is horizontal, the entry point is the person who owns company-wide AI. “Typically we’ll start with the CIO. You know, the CIO typically thinks about how to bring company-wide AI technologies,” Arvind told us. From there, Glean expands to departmental leaders when the value is concentrated. “Sometimes, you know, we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot.”

The footprint is mid-to-large enterprises, weighted to the US. “About 70% of the business is probably US and then 30% is the rest of the world right now,” Arvind said, naming Europe, Asia, Canada, and Latin America as the rest. (Those percentages are Arvind’s stated figures.) Named customers on stage at Glean:GO included General Motors (helping engineers design cars faster), McCarthy (construction management), and Navita (patient support), showing how far the horizontal platform stretches across industries.

The use cases prove the “chief of staff” positioning, and Arvind admits almost none were planned. “Almost all use cases are things that we didn’t imagine people would actually use our product for,” he said. The surprises: a performance-management agent that “writes the first drafts” of annual reviews for individuals and managers, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” His mental model: “since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”

Who builds the agents once Glean is in? The customers do, with Glean enabling them. When we asked whether Glean sends in its own engineers, Arvind said customers build the agents themselves, backed by Glean’s “training and enablement programs,” and “sometimes we actually bring our partners to help build agents for our customers.”

The steal: for a horizontal platform, sell the platform to the CIO and the sharpest single-department win (CRO/sales) in parallel, then let unpredicted use cases pull expansion. Ship analytics that surface which unplanned workflows customers invent, because those are your next feature roadmap and your next case study.

5. Is Glean just a model router? The context moat and the 81% cost cut

Our spiciest question: OpenRouter — reportedly being acquired by Stripe — what do you think of that? It got the most strategically important answer of the whole interview.

Arvind said he’s very familiar with them: “They were one of the first model routing companies, but more sort of model access company than routing.” Glean does some of the same things OpenRouter did, but mostly in the enterprise context for large enterprises, and the business is very different: model access “is actually only one part of our overall solution.” That’s why Arvind resists being called a model router even though Glean does model access. “Largely, like, you know, we think of Glean as the context and intelligence company,” he told us, describing the goal as “bringing the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”

How Glean is using this to win. The “context company” framing isn’t marketing — it’s the entire thesis, and it rests on one number: structured data is a rounding error of what a company actually knows. “Structured data and data lakes capture only 5 to 10% of all enterprise knowledge,” Arvind said in his Glean:GO keynote, and documents like playbooks and SOPs only go so far. “Much of the real know-how of how work happens lives in people’s heads.” (The 5-10% figure comes from his talk; treat it as his framing.) The good news, per Arvind, is that the other 90-95% “does surface in emails, messages, meeting notes, exception approvals” as people do their regular work.

Capturing that is the moat, and Arvind draws a hard line between Glean’s approach and the connector-first competition. Glean’s context engine “combines advanced retrieval with a powerful enterprise graph” that maps the people, customers, teams, projects, and the relationships between them. “That deeper understanding of your business, how it operates, is what Glean sets itself apart from the industry’s typical approach of delivering shallow context via MCP,” Arvind said, adding: “Glean is the only context and intelligence company that truly understands your enterprise.” Glean CPO Emrecan Dogan sharpened the same point in the company’s Glean:GO press release: “Models are getting better and more interchangeable. Understanding the enterprise is not. We designed Glean around that reality in 2019.”

And context is exactly how Glean wins the cost war. The headline number from Glean:GO: “Glean saves 81% on token costs compared to Claude, with Glean response preferred 78% of the time,” Arvind said. (Percentages are Arvind’s own from the keynote.) Independent observers noticed the token gap too; Rohan Paul flagged the 1.3M vs 4.4M split on X.

As we put it in the interview, “the cost is even like 6 times or close to 6 times less than other options like Claude Cowork.”

The waste Glean attacks is architectural, not technical. “Most systems still point the most expensive models at the simplest of the tasks. That’s not really a limitation of AI, it’s an architecture choice,” Arvind said. Glean’s Pareto frontier analysis makes the spread concrete: across 1,000 enterprise tasks and 37 model/reasoning configurations, the cost spread between the cheapest and most capable options is dramatic for modest quality differences. There is no single best model, so routing is the point. Auto-routing uses a small specialist model to pick the right reasoning effort per task.

The second half of the savings comes back to context. “Quality context stops AI from having to reconstruct it at runtime, which burns tokens and hands back answers it can’t use,” Arvind said. Better context means fewer tokens and better answers at the same time. The delivery layer is the AI Gateway, which pairs model choice with governance: closed and open models routed through one place with Glean Protect controls, so customers can pick models that meet their data retention and regulatory requirements.

The steal: the durable asset in enterprise AI is the years-long graph of how work actually happens, not a thinner MCP connector to the same systems. Then audit which model is handling your simplest, highest-volume tasks — if a frontier model is answering questions a mid-tier model could answer, you’re paying the 36x spread for a 22% gain you don’t need. Build (or buy) a routing layer that matches task difficulty to model cost, and measure it in dollars per task, not tokens.

6. Will model companies eat every startup idea (and what happens to jobs)?

We closed the interview with the two questions on every founder’s mind: Dario Amodei’s much-quoted prediction that AI will erase 50% of white-collar jobs in the next 5 years, and what Arvind would say to founders starting a company right now.

On jobs, he’s a clear skeptic of the doomsday number. “It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.

On founders, Arvind spends real time reassuring people who fear the model companies will eat everything. He hears it constantly: “Every idea they come up with, you know, it feels like, you know, you can just use an AI model or the model companies, you know, just come and do that.” His worry is that founders “start to give up too soon” and never build.

His counter is that change is the entrepreneur’s raw material. “AI changes, you know, if anything, you know, that AI changes, it actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs,” Arvind said. And he doesn’t think the model layer can cover the surface area: “Model companies won’t be able to meet even 10% of all the needs, you know, that the market is going to have.”

The OpenAI panel added the sharpest version of the upside case. Alexander predicted a step change in what one person can do: “We are now going to enter a situation where the most productive individuals could be running unicorn companies, and folks who are less productive are, well, where they are today.” His prescription: hire high-agency people and structure teams to unlock their creativity, then scale it. Alexander framed it as an old pattern, not a new one; since the history of our species, someone who is good at using tools is faster and more productive than someone who is not.

The steal: pick a specific workflow the model companies will never prioritize (your 10%+) and build the context and product around it. Then staff for leverage, hire high-agency people who can each drive AI across multiple functions, because the ceiling on individual output just jumped.

7. The OpenAI panel: a 3-layer playbook for deploying agents safely

The panel with OpenAI’s Alexander produced the cleanest framework in all three sources for deploying agents without spooking the security team. It starts with a simple rule: an agent acting for you inherits exactly your permissions, no more. “If they’re running on behalf of me, these agents should not be given access to enterprise data and information that I did not have rights to see or use,” Arvind said, adding that they “should not have the ability to make any changes into the system that I could not make.” Then you scope further, because an agent is built for a specific set of tasks and shouldn’t wield all the powers you personally hold.

The third case is the new one: independent agents with their own identity. “Take your customer support team. Maybe you have 10 people in the team today and now this agent is the 11th member of that team,” Arvind said. Once an agent has its own identity rather than impersonating a person, you need a distinct permissions and guardrails model so it behaves like a good teammate, “adding positive value and not actually escalating and, you know, fighting with the humans inside.”

Alexander distilled the whole thing into three layers. “One is the control of what the agent can do. The second is the observability of that. And then lastly, starting to shift away from agents always impersonating us as people and having more agents that have a specific role with exactly the right permission set for that workflow, and that’s an independent agent.” His rollout advice was to start narrow and expand: connect agents to the most important context first, then add more incrementally. “Your security team will be very happy if you add one and then you add the other one. You can actually move really fast if you do it incrementally.”

This connects straight back to the ROI question from our interview: the enterprises that actually reach production-quality agents are the ones whose security teams never got a reason to say no. Glean’s three-layer model is how it sells “safe to deploy” alongside “cheaper to run.”

The steal: deploy agents in exactly this order. Mirror the user’s permissions, scope down to the task, then graduate the most valuable workflows to independent agents with their own identity, observability, and guardrails. Give your security team an incremental rollout they can approve one connection at a time, and you’ll ship faster than a big-bang launch that gets blocked.

The full interview Q&A

Every question we asked Arvind in the 20-minute interview, one by one, with his answers (lightly cleaned of filler words; the sections above expand on each theme).

Q1. How did you end up here? What’s your personal story as a founder?

Glean started in early 2019 while he was still co-founder at Rubrik, where fast growth had quietly wrecked internal productivity: “Everybody would complain about not being able to find things inside the company or connect with the right people to get help.” Before Rubrik he spent over a decade at Google building search systems, so the fix was obvious to him: “We should actually build a really good search product for all of our enterprise data and knowledge. And that’s how Glean got started.” Personally: grew up in India, moved to the States in the ‘90s for grad school, and has been in tech — and based in San Francisco — ever since.

Q2. You just raised your first round — who do you hire first?

“Hire software engineers, people who are going to build the product, because right now that is all we need.” Everything else — office, devices, food, finding early customers — he handled himself: “The first 10 people that we hired at Glean, they were all engineers. Maybe even the first 20, I don’t remember hiring anybody outside of R&D,” until the product reached a certain level of maturity.

Q3. How do you communicate the vision of the product to so many engineers?

“Simple ideas are easy to communicate. So if you think about Glean’s idea, it was actually very, very straightforward. It’s a Google for you in your work life. It’s a place where you go and ask questions when you’re looking for information or answers and we’ll bring the right information back to you.” The second half is belief: “They’ve got to feel that it’s going to be something big.”

Q4. Bootstrapping versus raising money — when should a founder raise?

“I personally have chosen the path of raising capital quickly... I want to focus all of my attention and energy into building the product. I don’t want to continuously think every month about, okay, well, how much can we invest? Can we really hire one more engineer or not? I like to go big and go big fast. So if you can, then I would recommend that, just raise capital. What’s the downside?” And on the classic objection: “Sometimes people feel that, oh, is it too early to dilute the company? And I don’t really believe in that.”

Q5. Was raising money easier the first time or with Glean?

Definitely easier the second time: “Investors are actually never looking to invest in great ideas. They’re actually looking to invest in people.” Ideas get transformed once you talk to customers, so investors underwrite persistence — “do you have the persistence to stay on that journey?” A track record answers that. He also notes today’s market is unusually founder-friendly: “There’s a lot of capital that is actually waiting to be deployed on those ideas.”

Q6. Dario Amodei says 50% of white-collar jobs could be erased in 5 years. What do you think?

“It’s a very bold statement and obviously I don’t think there’s any kind of deep analysis or research that was done to come up with the number 50. The fact that the number is 50 means it’s more figurative... But I kind of disagree with that regardless. I don’t think people are going to be replaced with AI in the sense that there are no jobs for us anymore. I think we’ll all have jobs and I think the nature of our jobs is going to change” — the same way his own work has continuously evolved in tech.

Q7. Uber reportedly burned its 2026 AI budget in four months without ROI — do enterprises actually see ROI from AI?

“It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now.” The clearest value is basic knowledge-seeking — “every employee in the company becoming more productive, more knowledgeable” — “but it’s hard to measure. The fact that people can work faster oftentimes doesn’t translate into your P&L statement.” Larger companies with intentional programs, good metrics, and systematic process-by-process adoption are seeing results — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Glean’s model is built for that reality: immediate productivity savings up front, then “many, many months understanding their business and helping them build those agents and get them to production level of quality.”

Q8. Have you found any use case you didn’t think of when you started?

“Many. Actually, almost all use cases are things that we didn’t imagine people would actually use our product for.” His list: a performance-management agent that “writes the first drafts” of annual reviews, customers “responding to RFPs” with AI, teams “reviewing legal contracts with AI,” and salespeople using AI “to figure out what messages to send to which one of their accounts.” Why it works: “Since Glean is so horizontal and it connects to all enterprise systems, it sort of functions as your chief of staff.”

Q9. Who do you reach out to to sell Glean in the organization?

“Because we’re horizontal, typically we’ll start with the CIO. The CIO typically thinks about how to bring company-wide AI technologies. And sometimes we do work with departmental leaders too, because for example, for salespeople, for CROs, we do a lot — there’s a lot in Glean that can help you truly improve the productivity of your sales team.”

Q10. OpenRouter is reportedly being acquired by Stripe — what do you think of that?

“I’m very familiar with them. They were one of the first model routing companies, but more sort of model access company than routing. And Glean does some of the same things that OpenRouter did, but we do it mostly in the enterprise context for large enterprises. But our business is actually very different. Model access is actually only one part of our overall solution. Largely, we think of Glean as the context and intelligence company” — the goal being to bring “the collective human intelligence and the know-how, the knowledge and experience of your enterprise in one place and then make it available to AI systems.”

Q11. For your agent builder — do customers build the agents themselves, or do you send in your engineers?

Customers build them, with Glean enabling rather than staffing: “We also have training and enablement programs. And in fact, sometimes we actually bring our partners to help build agents for our customers.”

Q12. What’s the main location of your customers — your go-to-market, roughly?

“We have customers worldwide. About 70% of the business is probably US and then 30% is the rest of the world right now. So we are in Europe, we are in Asia, in Canada, Latin America. And typically we work with mid-sized to large enterprises.”

Q13. To close — anything you’d like to share with founders starting a company right now?

He hears the same fear constantly: founders feel “every idea they come up with, you can just use an AI model or the model companies just come and do that... Is there any value to ideas left? What I fear and what I worry is, will people start to give up too soon and not become entrepreneurs?” His answer: “AI changes, if anything, actually creates more opportunity for entrepreneurs. Because it brings change, and whenever there is change, that is great for entrepreneurs. And model companies won’t be able to meet even 10% of all the needs that the market is going to have.”

What to steal

1. Recruit and fund around a one-sentence mission. If you can’t explain your product in the length of “Google for your work life,” it’s too complex to hire engineers around. Test yours on a non-technical friend this week; if they can repeat it, it’s tight enough.

2. Raise to eliminate distraction, not to optimize valuation. Arvind’s rule is to raise enough that you never spend a month calculating whether you can afford one more engineer. Frame your pitch around your persistence, because investors bet on people, not ideas.

3. Sell (and expect) months, not minutes. The “botsitting” tax is 6.4 hours a week per worker (Glean’s Work AI Institute) because vendors oversold instant magic. Commit to one high-cost process, build production-quality agents over months, and instrument the before/after so value actually reaches the P&L.

4. Sell horizontal platforms to the CIO, expand through surprise. Start with the company-wide AI owner, win one sharp department (CRO/sales) in parallel, then let the use cases customers invent — performance reviews, RFPs, contract review — pull your expansion and write your roadmap.

5. Own context, not the model, and route by task difficulty. Structured data is 5-10% of what a company knows; the moat is the years-long graph of how work actually happens, surfaced from emails, messages, and meeting notes. Then match model cost to task difficulty: the 36x spread for a 22% quality gain is pure waste, and Glean’s benchmark shows routing plus context can cut token costs 81% while winning 78% of comparisons.

6. Deploy agents in three layers so security says yes. Mirror the user’s permissions, scope to the task, then graduate the best workflows to independent agents with their own identity and observability. Incremental rollouts ship faster than big-bang launches that get blocked — and they free high-agency people to run what used to take whole teams.

Hope this was valuable!

If it was, subscribe to PMF and share it to other founders!

Onwards!

Grok Bot is out and, of course, it creates bots: one agent per task, each with its own cloud computer, its own logins to your real tools, and a job that keeps running whether you’re watching or not.

If you’re a founder, you already know exactly where the week goes. Sales outreach that never stops needing new leads. Support tickets asking the same three questions on repeat. Marketing content that has to ship every week whether or not you have time to write it. Invoices that need chasing and numbers that need reconciling.

A market that keeps moving while you’re heads down on product. None of that needs to be you. It needs an agent chartered for exactly that job.

📚 In this guide, five bots with ready-to-paste prompts to replace these functions:

• Sales

• Support

• Marketing & Brand

• Research

• Finance & Admin

Plus, the charter template that keeps every bot on a leash, the prompts to teach one a task and turn it into a schedule, and the group chat prompt to run more than one of them on the same goal.

Inside you will find:

1. What Grok Bot Actually Is

2. How to Write a Charter That Lets a Bot Run Without You

3. How to Teach a Bot a Task Once and Have It Run Forever

4. The Five Agents for a Founder

5. How to Put Bots in One Room and Give Them a Goal

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Most AI founders target problems models already handle roughly a fifth of the time.

Google’s Chief Scientist Jeff Dean argues that’s exactly the bet that gets you killed: 20% success means the capability is already arriving and the general model is likely to catch up within six to twelve months.

Drawing on his full conversation at YC Startup School, this issue breaks down Dean’s:

• actual selection filter

• durability test

• three moats general models structurally cannot copy

• context-engineering plays any founder can run today with nothing but an API, so you can build for where capability is going, not where it already is.

Inside you will find:

1. Why Agents Will Soon Run for Days

2. The Napkin-Math Bet Behind the TPU

3. Context Engineering Without GPUs

4. Keeping Agents on the Rails

5. The 1% Rule for Picking Problems

6. Three Moats General Models Can’t Copy

7. Specs and Taste Are the Scarce Skill

8. The Evaluator That Ate Six Months

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Two brothers ran three X accounts to a reported 69.8M impressions, 321K likes and 319K bookmarks.

How? Using Grok Bot behind the ideas, the posts and the replies.

Grok Bot is the latest AI agent platform created by xA. Inside it you create bots, individual agents that each have a name, one job and a profile carrying their standing rules. They share a persistent managed Linux cloud VM with a browser, a filesystem and a terminal, and each one:

• keeps its logins

• learns a workflow by watching you do it once

• re-runs that workflow on a schedule while your laptop is shut.

Above all, it sits inside the only ecosystem with a first-party search tool for X, which xAI describes as performing keyword search, semantic search, user search and thread fetch.

Why does that combination matter more on X than anywhere else? Because most channels ask you to build an audience before they give you distribution, while X hands you distribution first and lets the audience form afterwards. It is why inbound customers, hires and warm investor intros still tend to arrive there first

The catch has always been that knowing what is already working on X, every single day, is a full-time job. That is the job you are about to hand over.

Inside you will find:

1. Why Grok Reads X Better Than Any Other Model

2. How to Set Up the Bot That Runs Your Account

3. How to Find the Posts Worth Repackaging

4. The Two Plays That Turn a Source Into a Post

5. The Weekly Routine That Learns From What Worked

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

Halfway through his conversation with Harj Taggar at Startup School 2026, Patrick Collison told a stadium full of founders that the lean startup doctrine, the one that taught a generation to find a tiny niche and iterate out from it, is running out of room.

A few minutes earlier in the same conversation, he had described spending almost two years building Stripe before it ever launched publicly. Same founder, same hour on stage, two stories that sound like they contradict each other.

They don’t, but the reason they don’t is the actual lesson.

Lean Startup isn’t really dead. But one of the assumptions we’ve attached to it probably needs updating: that learning cheaply means you have to think small.

Below you’ll find the two moments from the interview that matter, what Eric Ries’s original doctrine was actually solving for and two prompts you can run in Claude this week:

• one to check whether your niche is genuinely divergent or just a smaller, safer version of something crowded

• one to find the narrowest real door into the ambitious thing you actually want to build

Inside you will find:

• The Doctrine Eric Ries Actually Built

• Why Patrick Collison Says the Assumption Broke

• The Twist Buried in Stripe’s Own Origin Story

• The De-Correlation Test

• The Production-User Safety Net

Powerful models are only as useful as the company context they can access!

Make enterprise AI actually work with Glean, the context layer that connects every model, tool and workflow.

Glean connects your organization’s knowledge, people, permissions and activity to give AI the understanding it needs to deliver relevant, secure and work-ready results.

With one governed context and intelligence layer across tools like Claude, Cursor, Copilot, and custom agents, Glean helps your company:

• Give AI the right context: connect knowledge across 275+ enterprise applications while preserving permissions, metadata and access controls

• Get more value from every model: improve retrieval, reduce unnecessary tool calls and use approximately 30% fewer tokens than off-the-shelf MCP tools.

• Stay flexible as AI evolves: access 35+ models (including Claude, GPT and Gemini) without rebuilding your context layer or locking your company into one provider

Try Glean Now!

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 80,000+ founders and operators inside!

Let’s begin!

Read more

In a talk at YC, Tom Blomfield walks through both of his companies, GoCardless and Monzo, and what emerges is a repeatable playbook: the word-of-mouth engine, the launch-in-months approach, the reason he chose the hardest possible problem on purpose.

And, in the same breath, the part investors never want a founder to run: the burnout math.

I broke the whole thing down so you can steal the growth moves and recognize the warning signs before they hit you.

Inside you will find:

1. How a Tarpit Idea Became GoCardless

2. Why Blomfield Left a Company He Loved

3. How Monzo Hit a Million Customers With Zero Ad Spend

4. Why Picking a Harder Problem Is a Growth Channel

5. What Going All-In Actually Cost Him

6. The Burnout Math Investors Don’t Want to Hear

7. What to Steal From Blomfield

Launch your site faster than ever with Framer - now with Agents that make every part of your workflow effortless

Framer is the pro website builder for creators, teams, and businesses that care enough to get every detail right. Now with Agents that work alongside you across the full website workflow: designing new pages, managing your CMS, editing content, and adding SEO all without leaving the tool where the real site lives. Trusted by teams at companies like Miro and Perplexity.

Agents bring speed and scale; you bring taste, judgment, and control. Together you can:

• Build and iterate faster: generate sections, refine layout and styling, and ship updates in minutes.

• Scale content with confidence: update CMS collections, localize copy, and keep metadata consistent across pages.

• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

Launch Your Site With Framer Today!

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

Not subscribed yet? Try it free for 7 days and unlock this guide, plus our full premium library:

Get 7-Day Free Trial

We’re already 70,000+ founders and operators inside!

Let’s begin!

Read more

We went through the full 109-minute conversation with David Senra and pulled out every mechanic of how Kalanick ran his raises, from anchoring low to building a live demand curve, so you can run the same process on your next round.

In this guide, you’ll get:

1. How Kalanick Turned a $6B Offer Into $17.5B

2. The Fundraising Process That Created the $11.5B Upside

3. QED vs. Super Cycle: The 2 Pitch Modes

4. The 5-Room Auction That Drove Uber’s Valuation Up

5. How to Build a VC Demand Curve

6. The Kalanick Fundraising Tracker

Taskrabbit runs on Attio, the agentic CRM. Here’s what that adds up to:

• 4x faster deal updates across sales team

• Strategic deals reclaimed from lost email data

• 90% team adoption overnight, up from sub 50%

Ready to try now?

Try Now!

Everything below comes from one source: Kalanick’s own account of his Uber raises, given to David Senra across a 109-minute sit-down recorded this month. These are his conclusions, in his voice, with the exact numbers he cited. Treat them as his playbook, not verified fact, and adapt the mechanics to your own round.

Read more