Most founders start a raise the same way: ten tabs open, a couple of old spreadsheets and a few names copied from LinkedIn posts.

Investor data is scattered across directories, fund websites and newsletters, each in its own format. So you end up with a long list of names you can’t sort. You can’t see who invests at your stage, who writes the cheque you need or who covers your sector, and the same email goes out to everyone.

So we put it all in one place.

The Capital Directory brings together +5,000 investors, angels, funds and LPs, and every profile carries stage, sector, geography and cheque size. Raising a $500K Pre-Seed for an AI startup in Europe? Four filters take you from 3,570 investors to the 227 who match.

From there you can:

• search by name, thesis, sector or city

• save a shortlist and export it as a CSV

• find the LPs that back venture funds, if you’re raising a fund

👇 Here’s what’s inside. The link is at the end! 👇

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Why You Need It

Raising a round means answering three questions about every investor before you write a single email:

• Do they invest at your stage?

• Do they write a cheque the size of your round?

• Do they cover your sector and your country?

Every profile in the directory carries stage, sector focus, geography and average cheque, plus LinkedIn, X and website links where they are public. That’s how you get from 5,000 names to the 30 that fit your round in a few minutes.

The Full Map

The directory has four tabs, one for each type of capital, plus a Saved tab for your shortlist.

Investors: 3,570 Profiles

Partners and individual investors who write direct startup cheques.

It covers partners at Insight Partners, a16z, Bessemer, Accel, Index, Sequoia, General Catalyst and Benchmark, along with thousands of micro-fund GPs and solo investors who are much harder to find on Google.

Angels: 75 Profiles

Operators and founders investing their own money, mostly at Pre-Seed and Seed. Among them: Lenny Rachitsky, Kunal Shah (CRED), Nicolas Brusson (BlaBlaCar), Christian Reber (Wunderlist, Pitch), Dharmesh Shah (HubSpot), Sachin Bansal (Flipkart), Roxanne Varza (Station F) and Nalden (WeTransfer).

Venture Funds: 1,360 Profiles

Firms, accelerators and angel groups investing directly in startups: Sequoia, a16z, Founders Fund, Y Combinator, Antler, Techstars, Seedcamp, Point Nine and more than a thousand smaller funds.

Limited Partners: 81 Profiles

The tab maps 81 of them, including Harvard Management Company, MIT Investment Management Company, GIC, the European Investment Fund, Bpifrance, KfW Capital, HarbourVest, Horsley Bridge and Cendana Capital, each with its type and location.

👇 Get the Capital Directory 👇

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Here’s a good place to start:

🤖 AI Agent Templates
↳ The Playbook McKinsey Charges $2M For — 12 skills that turn Claude into a McKinsey-level consulting engine.

📈 Startup Playbooks
↳ The $100M GTM Playbook — how the fastest-growing AI startups built their sales and marketing machines.

🎯 The Deck Library
↳ +40 AI Pitch Decks (Ultimate Collection) — the decks behind the fastest-growing AI startups, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List (+ the Claude Outreach System) — family offices actively writing cheques, plus the Claude system to reach them.

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BREAKING: Hyphenate just raised $45M in Series A funding to bring AI agents to corporate finance.

The round was led by Nexus Venture Partners.

Hyphenate is building an autonomous finance platform where AI agents execute workflows across revenue, treasury, and accounting, reducing manual work for finance teams.

The numbers behind the raise:

• 86x revenue growth in 12 months

• 11x customer growth over the same period

• 90% of routine finance work handled by its agents across the platform

Customers already include Groupon, Synthesia, and Mini Melts.

Announcement Here

Harvey has raised nine rounds in less than four years. The last one, in September, valued it at $15.5B.

It isn’t the only one moving this fast:

• OpenEvidence went from $1B to $15B in 19 months, across five rounds

• Legora raised $550M twice in seven months, the second time this week

• Cognition went from $26B to $48B between May and September

These companies raised because their products grew faster than anything investors had seen. But who they let in first, when they went back to market and how much of the company they sold were decisions the founders made.

Full document at the end!

Refer a friend

Inside you will find:

1. The Fastest-Raising AI Startups:

   1. Cognition Find out how a launch video became a $2B valuation, and how it got to $48B + a16z’s Investment Thesis by Marc Andreessen

   2. Cursor See how a seed from OpenAI’s fund turned into $29.3B in 25 months, and a $60B exit + a16z’s and Accel Investment Thesis

      1. Harvey Learn how nine rounds, almost always with a new lead, took it from $5M to $15.5B + Sequoia’s Investment Thesis

   3. Sierra Find out how two founders with a track record started at $1B and reached $15.8B + ICONIQ’s Series C Investment Thesis

   4. Legora See how a Stockholm team raised $1.1B in 2026 alone and filled its last round with pre-IPO investors + ICONIQ, GC, Bessemer, Accel, Menlo’s Investment Theses

   5. OpenEvidence Learn how a repeat founder raised five rounds in 19 months, with a different lead almost every time + Sequoia’s Investment Thesis

   6. Lovable See how Lovable went from a $1.8B Series A to $13.3B in 13 months + Creandum, Accel & Menlo’s Investment Theses

   7. ElevenLabs Find out how a first round at a $9M valuation became $22B, with tender offers in between + a16z and ICONIQ’s Investment Theses

   8. Mercor Learn how three 22-year-olds went from $250M to $10B in 13 months + General Catalyst’s Investment Thesis

   9. Decagon See how it tripled its valuation in seven months, and why it is now talking to buyers + Accel, a16z, Index’s Investment Theses

2. The Fundraising Playbook for Your Next Round Learn the moves to copy, from the first check to growth rounds.

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Revolut made £120K in revenue in 2015. Last year it made £4.5B.

It started as a prepaid card for people tired of losing money on exchange rates abroad. Today it has 68 million retail customers and £1.7B in pre-tax profit.

• 2015: launched with a free card and the interbank rate

• 2017: Premium, Business and crypto all launched in the same year

• 2021: first full-year profit

• 2023: £1.8B ($2.2B) in revenue and £438M in pre-tax profit

How did it get there? Every year Revolut gave the same customers a new reason to use the app:

• Foreign exchange brought them in

• Current accounts made them open it every day

• Subscriptions, trading and crypto added revenue on top

• Local IBANs, deposits and credit turned a travel card into their main bank account

We went through eight years of Revolut's accounts and annual reports. Each chapter below takes one milestone on that curve, from £1M to $2B, and breaks down the products and growth moves that got Revolut there.

Refer a friend

Inside you will find:

1. Path to £1M: Win the First 300,000 Customers With One Cheaper Exchange Rate See how a free card and the interbank rate pulled in 300,000 users while every one of them lost money.

2. Path to £10M: Launch Every Paid Product in a Single Year Find out how Premium, Business, euro IBANs and crypto all shipped in 2017, and how crypto access became a referral reward.

3. Path to £50M: The Free-Card Referral That Tripled Daily Signups Learn why giving a friend a free card beat cash rewards, and how Metal built a 60,000-person waitlist.

4. Path to £100M: Add Stock Trading and Go From 3.5M to 10M Customers See how trading, Apple Pay and new countries tripled customers while partnerships became a managed channel.

5. Path to £200M: How Revolut Grew 45% When Travel Stopped Find out how a travel card kept growing through the pandemic with no marketing spend.

6. Path to £500M: How Crypto Made Revolut Profitable See how FX and Wealth reached 55% of revenue in the first profitable year, and what the auditors flagged.

7. Path to £1B: How Revolut Replaced the Crypto Revenue It Lost Learn how cards, subscriptions and interest filled the gap when FX and Wealth revenue fell by £79M.

8. Path to $2B: How Interest Income Grew From £83M to £500M See how deposits, credit and local accounts turned Revolut into its customers’ main bank.

9. The Revolut Playbook in 5 Moves Take the five moves and apply them to your own roadmap and distribution.

This issue is available exclusively to paid subscribers!

Premium subscribers get the full Revolut breakdown plus our whole library of company growth playbooks.

A few of the most loved:

1. The Cognition Playbook: From Debunked Demo to $1B ARR
   An Inside Breakdown of How Cognition Found Its First Customers, Bought Windsurf in a Weekend and Reached $1B in 24 Months

2. The Bending Spoons Playbook: From $40K to an $18B IPO
   How a Failed Startup From Milan Learned to Buy Product-Market Fit and Win Every Deal It Bids On

3. How Glean Grew From $100M to $300M ARR in 15 Months
   The $60K-to-$5M Land-and-Expand Loop and the “Cut Your AI Bill” Pitch That Beat Microsoft

4. The $100M GTM Playbook
   How the Fastest-Growing AI Startups Built Their Sales and Marketing Machines

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Before AI, getting from $1M to $100M in under two years was a record. Today ten startups have done it, six of them in a year or less.

So we took the 10 startups that reached $100M ARR the fastest (AI labs excluded) and broke down how they sold. For each one, we looked at:

• How many people they had in sales and marketing

• Which GTM motions brought in the revenue

• Which tools their teams run on

• What they did at each stage, from the first customer to $100M

The pattern? Creators and the product do the selling for much longer than they used to. Then every one of these companies builds a sales team anyway.

Refer a friend

Inside you will find:

1. How Top AI Startups Reached $100M ARR

   1. Higgsfield Find out how a 150-person creative team and paid creators got it to $100M with no paid ads.

   2. Lovable See how 45 people reached $100M through the product, the community and launch after launch.

   3. Manus Learn how an invite-only launch and a 2 million waitlist turned into $100M in eight months.

   4. Emergent See how 100+ creators a week sold an app builder to people who can’t code.

   5. Replit Find out how four sales reps handled a company that grew from $2.8M to $150M in a year.

   6. Cursor See how a code editor grew 10% a week on product alone, then built an enterprise sales org.

   7. Legora Learn how lawyers selling to lawyers, plus a Jude Law campaign, built the fastest direct-sales company.

   8. ElevenLabs See how voice actors, an API and a 20x quota turned inbound into $100M.

   9. Sierra Find out how paid design partners and outcome-based pricing got Sierra into the Fortune 1000.

   10. Clay See how Clay runs its own GTM, with 60% self-serve revenue and zero cold outbound.

2. AI Startups’ GTM Tools See which tools show up across the GTM job posts of these companies.

3. The Playbook Stage by Stage Learn what to build between $0 and $1M, $1M and $10M, and $10M and $100M.

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Four things stand out:

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✍🏻 Guest post by Sky Yang, co-founder and CEO of Imagine AI (YC F25), a company we backed. Imagine AI builds a content agent for B2B teams and runs the free benchmark this data comes from 👇

At the median B2B company, 76% of the reactions it gets on LinkedIn each week go to the people who work there. The company page gets the rest.

We tracked 223,551 posts from 1,051 B2B companies and their teams, refreshed every day, to see what separates the companies that get heard. What we found:

• Companies with 4+ people posting get about 7x the reach per page follower of companies where one person or nobody posts

• Execs with the same size audience still end up about 7x apart

• Multi-image posts get 2.11x an account’s typical post, text-only gets 0.71x

• The most common kind of post, the industry take, is also the weakest

The problem? Most teams know their buyers, investors and future hires spend time on LinkedIn. Almost nobody knows what to post or has the time to do it, and copying someone’s one viral post doesn’t help, because luck decides a lot of which post breaks out.

Refer a friend

Inside you will find:

1. How to Measure Your LinkedIn Without Fooling Yourself Learn why your best post is mostly luck, and the one number to track instead.

2. Why Your People Get 3x the Reach of Your Company Page See where three quarters of a company’s LinkedIn attention comes from.

3. How to Get 7x the Reach From the Same Audience Find out how many people should post, and from which departments.

4. What to Post: The Formats and Topics That Double Your Reactions Compare every format and topic against the same account, and see what the top quartile does differently.

5. How Often to Post (and Why Filler Posts Cost You) Find the cadence that gets the most weekly reach, and what a weak post does to the next one.

6. How to Get Free Reach From Your Customers and Investors See why only 12% of company ties on LinkedIn go both ways, and how to use it.

7. The Four Habits Behind the Companies That Win (+ the Free Benchmark) Get the full playbook in four rules, see what the top of the leaderboard does, then check where your company lands.

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• Build faster: Create, refine, and publish production-ready pages in minutes.

• Scale confidently: Manage content, localization, and metadata across your site.

• Stay in control: Review every change, collaborate in real time, and publish when ready.

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1. How to Measure Your LinkedIn Without Fooling Yourself

Every account has a typical post: the number of reactions an ordinary post of theirs gets. We work it out from each account’s own history with a geometric mean, a kind of average that stops one huge post from pulling the number up.

That number matters because single posts swing a lot. For the median founder or exec, two posts in three land between the typical post divided by 2.42 and multiplied by 2.42.

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Cluely had made going viral its entire growth strategy. Our idea was to bring that distribution mindset to investing: back exceptional founders, then help them reach millions of potential customers and investors.

Twelve months later:

→ A portfolio that has raised $1.2B+.
→ Companies including HappyRobot, Replit, Nozomio, Throxy, Apollo Atomics, Gru Space and QFEX
→ Alongside investors including a16z, Sequoia, Founders Fund and General Catalyst
→ 1 Exit: Portco went from $10Kmrr to 7M arr in close to 1 year

Today, we’re publicly introducing what we’ve been building: GR Angel.

Here’s how it started, what we’ve accomplished and where we’re taking it.

1. Before GR Angel, We Were Two Angels With an Audience

Rubén and I came to angel investing after working in venture capital.

I had been an M&A lawyer at PwC, founded a B2B SaaS company, and worked at GoHub Ventures. Rubén had worked at Mundi Ventures before leading investments and media at Itnig Capital.

Alongside those careers, we built our publications and audiences. Through that work, we developed relationships with founders, operators and investors across the startup ecosystem.

When we started writing angel checks, we put those assets to work for the companies we backed.

We shared their launches, featured their products, and introduced them to people who could help. One newsletter feature led to a $1 million investment offer for a founder. A single post from Rubén helped another startup double its ARR.

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

As we shared our investments and the companies’ progress, several private investors approached us. They wanted access to the opportunities we were finding and asked to invest alongside us.

We opened the door to a small group. That was the beginning of GR Angel.

2. Our Edge: Distribution and Network Access

The founders we want to back often have several investors competing for a place in their rounds.

Our job is to give them a compelling reason to choose us and deliver after we invest.

We bring two things.

Distribution: Getting Founders in Front of Millions

I write Product Market Fit and AI Market Fit. Rubén writes The VC Corner, The AI Corner and The Founders Corner.

We both have 2 of the biggest Linkedin accounts in Europe + now some of the fastest growing X accounts.

Together those publications reach hundreds of thousands of founders, operators and investors, generating tens of millions of impressions every month.

For a portfolio company, that creates an audience for product launches, a way to reach potential customers, and visibility with investors ahead of its next round.

We can help a founder explain what they’re building and get it in front of people who could buy it, work on it, or invest in it.

Network Access: Opening the Right Doors

Our work in venture capital, our publications and our investments have built relationships across the startup ecosystem.

We use those relationships to make targeted introductions: a potential customer, an experienced founder who has solved a similar problem, or an investor who could be relevant for the next round.

We’re also expanding that network through Compound, our podcast, with guests including Mark Cuban, Substack co-founder Chris Best and Creandum founding partner Staffan Helgesson. Ruben is also an official scout for a16z Speedrun.

The value is in connecting founders to the right people at the right moment and giving those people a reason to pay attention.

Distribution and network access help us earn allocations. After we invest, we put both to work for the founders.

Our investing now focuses on the top AI companies coming out of Y Combinator. YC gives us a concentrated place to look. Our relationships and reach help us compete for the opportunities we want.

3. The Companies Behind the Numbers

Across our personal angel investments and GR Angel, we have backed 53 companies, including 42 from Y Combinator. Together, those companies have raised $1.2 billion+.

These figures cover our combined portfolio, including investments made before GR Angel existed. Here are some of the founders and businesses behind it.

HappyRobot: AI Agents for Logistics

Pablo Palafox, Luis Paarup and Javier Palafox are building AI agents for logistics—the calls, emails and messages that keep freight moving.

The company reached a $1.2 billion valuation following its $150 million Series C. Its customers include DHL, Kuehne+Nagel and Uber.

Replit: Making Software Creation Accessible to Everyone

Amjad Masad and Haya Odeh built Replit to let anyone create software. Its AI agents have accelerated that ambition: describe what you want, and the platform helps build it.

Replit grew ARR from $10 million to $525 million in 18 months, passed 50 million users, and raised a $400 million Series D at a $9 billion valuation in March 2026.

Peec AI: From Zero to $10 Million ARR in 16 Months

As people move from searching Google to asking AI, brands need to understand whether they appear in the answers.

Marius Meiners, Tobias Siwonia and Daniel Drabo built Peec AI to solve that problem. The company reached $10 million ARR in 16 months, with 3,000+ customers, including Zalando, Hugo Boss and Chanel. It raised a $21 million Series A in November 2025.

QFEX: Backed by General Catalyst and Paul Graham

Annanay Kapila and Joshua Wharton are building a trading platform for continuous global markets.

QFEX raised a $9.5 million seed round at a $95 million valuation before generating revenue, with General Catalyst and Paul Graham among its investors.

Nozomio: Better Context for AI Coding Agents

Arlan Rakhmetzhanov, one of the youngest founders we’ve backed, is building infrastructure that helps AI coding agents access the information they need to work effectively.

Nozomio raised a $6.2 million seed round led by CRV, with Paul Graham and Gokul Rajaram investing alongside us.

GRU Space: A Hotel on the Moon

Skyler Chan is pursuing one of the most ambitious ideas in the portfolio: building the first hotel on the Moon.

The company reports $500 million in letters of intent and has attracted 1 billion+ views. It’s an early, highly speculative bet—and an example of the scale of ambition we’re willing to back.

More Companies to Watch

The portfolio also includes:

• Quiver AI: an $8.3 million seed round led by a16z.

• CodeWords (Agemo): €7.6 million raised, with the CEOs of Miro, ElevenLabs, Personio, Zalando and Supercell among our fellow angels.

• Momentic: AI-powered software testing, with a $15 million Series A and General Catalyst among its backers.

• Throxy: $6.2 million raised with Base10.

• Apollo Atomics: an MIT nuclear spinout that raised a $31 million seed round.

• Karumi: personalized product demos, backed by Rebel Fund.

Across the broader portfolio, our co-investors include a16z, Sequoia, Founders Fund, Coatue, General Catalyst, Peak XV, Lightspeed, Khosla Ventures, CRV and Kfund.

Explore the full portfolio →

4. Why We’re Launching Publicly Now

A year after our first private investors joined us, they wanted to increase their commitment.

They had seen the companies we could access, how we supported founders, and how the portfolio was developing. They encouraged us to grow GR Angel and bring in more capital.

That is why we’re taking this next step.

We’re raising. We’re opening conversations with additional private investors who want to invest alongside us, allowing us to write larger checks into our highest-conviction opportunities.

We’re hiring. We’re looking for exceptional people across sourcing, content and operations to support the investment activity and the distribution behind it.

We’re meeting founders. If you’re building an exceptional AI company, we want to hear what you’re working on and where our audience and network could help.

We started by investing our own money. Investors asked to join us. A year later, they pushed us to think bigger.

Today, we’re launching GR Angel publicly, with the same commitment that got us here: invest in founders, put them in front of millions, and open doors that help them grow.

Hope this was valuable!

Cheers,
Guillermo

Portfolio figures combine our personal angel investments and investments through GR Angel. GR Angel is a private investment company, not a fund. This newsletter is for information only and is not investment advice or an offer to sell securities. Past performance is not indicative of future results.

Mercor just mapped 29 major AI benchmarks against 923 occupations in the US economy. What they found is a massive blind spot:

90% of American working hours are spent on tasks with little or no benchmark coverage.

Think about that.

AI labs spend billions making their models better. But much of that improvement is measured on a surprisingly narrow set of jobs.

• Lawyers: 18 of 22 tasks tested.

• Legal secretaries: 1 of 14.

• Court clerks: 0 of 30.

• Freight forwarders: 0 of 31.

• Medical records specialists: 0 of 17.

And here’s why this matters for founders.

AI labs use benchmarks to find where models fail, build training data around those failures, and make the next generation better.

That creates a feedback loop: the jobs that get tested attract more targeted improvement. The ones that don’t are easier to overlook.

And that’s where vertical AI startups have an opening!

While the big labs optimize for the same benchmarks, founders can build specialized agents for the workflows nobody is measuring: customs declarations, medical approvals, court filings, construction permits, insurance claims.

Some are already turning these overlooked tasks into million-dollar businesses.

Inside you will find:

1. Why AI Only Gets Better Where It Gets Tested See the loop that makes models improve on some jobs and stand still on the rest.

2. Freight and Customs: How Evos Hit $1M ARR in Four Weeks Find out why the people who move goods across borders are almost untested.

3. Healthcare Paperwork: How Forus Went From $1B to $3B in Four Months Learn why the faxes, approvals and medical records around the doctor are wide open.

4. Courts and City Halls: The Legal Jobs With Zero Coverage See why clerks and city staff get none of the attention lawyers get.

5. Loans, Claims and Appraisals: How Vesta Grew Revenue 12x Find the money jobs the labs skip once you go below the analyst.

6. Engineering and Permits: How Solvera Booked $1M in Three Months Learn how startups sell finished drawings and designs to the people who build things.

7. Special Education: The 12-Month Wait Startups Are Going After See why special education has zero tasks tested and what founders are building there.

8. The Trades: How UpSmith Makes Money for Plumbers and Electricians Find out why the office of a trade business is the easy part to automate.

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1. Why AI Only Gets Better Where It Gets Tested

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Of the +3,200 Pre-Seed investors in our database, more than 2,000 are accelerators and incubators. Only 545 are venture funds.

Most founders raising a first round don’t know this. They build a list of famous VCs, send the same deck to all of them, and wait.

The problem is that “investor” covers very different people:

• A fund that has to return 3x to its own investors

• A corporate that wants a partner for its product roadmap

• A family that invests its own money on its own timeline

• A program that gives you a small cheque in exchange for a cohort and a demo day

Each one decides in its own way, writes a different cheque and asks for different things.

So we took our full database of +7,000 investors and sorted every single one by type.

Why You Need It

Filtering by stage only tells you if an investor can write the cheque. How they decide depends on what kind of investor they are.

• A corporate VC will ask how your product fits their business

• A family office wants to know who you are and whether you’ll still be around in ten years

• An accelerator will look at the team and the speed of execution, and barely at the numbers.

Send the same pitch to all three and it won’t work.

Knowing the type also tells you what to expect after the yes:

• A government fund comes with paperwork and reporting

• A venture studio wants a big share of the company because it helped build it

• An angel network can take months to move, then close the round in a single evening.

The better you understand who is on the other side of the table, the fewer meetings you waste.

The Full Map

The database contains +7,000 investors from Pre-Seed to Series A, organized into 8 tabs, one per type.

Each entry includes:

• name

• type

• preferred round

• headquarters country and city

• website

• key people

Here is what you’ll find in each tab:

VC Funds: +3,000 Investors

The largest group, and the one founders already know. It includes everything from micro funds and solo GPs to firms like Sequoia, Index Ventures, Accel, Balderton and Creandum.

They dominate Seed (2,032 of 2,957 investors) and Series A (470 of 615).

A VC invests other people’s money, so every deal has to be able to return the fund. The main question behind every meeting is whether your company can become very large.

Lead with the size of the market and the speed of your traction. Find the right partner in the key people column before you write: a cold email to the partner who covers your sector beats a generic contact form.

Accelerators & Incubators: +2,000 Investors

Almost all of them sit at Pre-Seed (2,017). Techstars, Plug and Play, Founder Institute and Antler are in here, along with hundreds of local programs.

They usually invest a small amount for a slice of equity, then give you a cohort, mentors and a demo day in front of investors.

Applications are open and they judge the team first. Apply to several at once and pick the one whose network matches your market.

Family Offices: +400 Investors

Families and wealthy individuals investing their own money: Bezos Expeditions, Emerson Collective, Novo Holdings, Otium Capital and hundreds of smaller offices.

Most of them come in at Seed (372). This tab also holds private holding companies, which is how many German angels invest (through a GmbH), so the German list is long.

With no fund cycle, they can wait longer for a return and often follow on in later rounds. They decide slowly and rarely answer a deck blast.

A warm introduction works best, and so does a story that connects your company to the family’s own business or interests.

Corporate VCs: +380 Investors

The venture arms of operating companies: GV, M12, Intel Capital, Salesforce Ventures, Samsung Next and AbbVie Biotech Ventures, plus banks, telcos and insurers.

They matter most at Series A, where they make up 91 of the 615 investors.

A corporate invests for the return and for the strategy: a new product line, a supplier, a future acquisition. Show them how your product helps their business, and ask early whether the investment comes with a commercial deal or restrictions on who else you can sell to.

University Funds & Programs: +270 Investors

University-backed funds, incubators and alumni networks: Oxford Science Enterprises, Cambridge Enterprise, Imperial Enterprise Lab, CDTM and the MIT $100K competition among them.

Most of them (235) are at Pre-Seed.

The first filter is usually your connection to the university: you studied there, you work there, or your technology was born in one of its labs. If you have that link, start here.

These programs open doors to research, talent and grants that a normal fund can’t.

Public & Government Funds: +250 Investors

State, regional and development-bank money: Bpifrance, the UK Innovation & Science Seed Fund and dozens of regional funds across Europe, the Middle East and Asia.

Most are at Pre-Seed (156) and Seed (84).

They invest to grow the local economy, so being based in the right place (or willing to open an office there) counts as much as the product.

The money is often cheap and non-dilutive in part, but the process has forms, deadlines and reporting. Start early and pair them with private investors, who they often like to match.

Angels & Networks: +200 Investors

Individual angels, angel clubs and syndicates (Keiretsu Forum, Band of Angels, Business Angels Switzerland, Aligned Syndicate), plus equity crowdfunding platforms like Republic, Wefunder and OurCrowd.

They split between Pre-Seed (80) and Seed (122).

Angel networks run pitch events: you apply, get selected, pitch to a room and close with whoever says yes. It takes one champion inside the group to move everyone else. Crowdfunding platforms work best when you already have users or customers who want to own a piece of the company.

Venture Studios: +120 Investors

Studios and company builders that co-found startups with their own team and money, like Atlantic Labs and Swanlaab Venture Factory.

Most are at Pre-Seed (86).

A studio gives you a team, a process and capital from day one, in exchange for a large share of the company.

They fit best before you have a co-founder or a product. Read the terms carefully: a cap table that starts with a big studio stake can make your Seed round harder to raise.

👇 Get the All-in-One Investor Database 👇

There is still no shortcut to fundraising.

But you shouldn’t send the same pitch to a family office, a corporate and an accelerator and expect the same answer.

We’ve sorted all +7 investors by type and stage, so you can start from the right category, pick the right names and write to each one in the way they decide.

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👇 Here’s the full list

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Railway started 2026 at around $20M in ARR. It is now about to cross $100M.

It has 40 employees and one founder.

• A $100M Series B in January, led by TQ Ventures

• More than 2 million developers on the platform

• $90M of its own data center hardware

• No sales team until last year

Railway is a cloud where developers, and now their coding agents, deploy apps and databases without touching servers. Many developer platforms sit on top of AWS or Google Cloud and pay their prices. Railway runs on servers it bought, so its margin grows with usage.

Its founder? Jake Cooper left Uber in 2020 and set up a box of databases for his side projects. He posted it on Twitter asking who he should talk to about a job. He got zero job offers and a few angel checks.

Inside you will find:

1. Why Two Co-Founders Is the Worst Number Find out why Jake never looked for a co-founder and how he gets outvoted on purpose.

2. How Railway Survived Losing $500K a Month See why he killed the free tier and brought it back only once Railway owned its hardware.

3. Solve the Problem Before You Look at How Stripe Did It Learn the innovation tokens rule for deciding what to reinvent and what to buy.

4. Say No to 90% of Ideas With One Question: Why Now? Copy the one-page abstract every Railway initiative needs before it reaches planning.

5. Do the Job Manually Before You Hire for It Build the role spec from work you’ve already done, and see how Railway keeps its people.

6. The Six-Week Onboarding: Trust, Clarity, Judgment Copy the plan that took a new hire to owning the entire edge network within a year.

7. Write Values That Check Each Other See how Railway’s four values work as brakes on each other, and what one of them cost.

8. Move One Brick a Week, 52 Times a Year Set up the weekly rhythm and the 36 offline hours behind Jake’s biggest decisions.

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Not just one AI assistant. A whole team of AI agents, each with its own role, tools, and responsibilities. And you only need to talk to one of them.

That’s what Grok Bot now makes possible.

You can have a Chief of Staff managing your AI team, with specialists handling sales, projects, data, emails, and recruiting.

They:

• communicate

• assign tasks

• share updates

• and only involve you when a decision needs your approval.

And it’s already happening.

At SpaceXSI, a five-person team shipped 100+ pull requests a day, coordinated by a Grok Bot managing hundreds of coding agents.

Inside you will find:

1. Why One Grok Bot Can Now Manage All the Others See how the Primary Bot finds work, routes it to specialists and checks in only when needed.

2. How to Draw Your Bot Org Chart Before You Hire Let a Bot map your team’s recurring work and propose which Bots to hire.

3. How to Hire Your Lead Bot and Teach It to Delegate Set up the Primary Bot that takes every request and sends you one report a day.

4. The Five Specialists Worth Hiring Under It Hiring prompts for an Account Bot, a Project Bot, a Data Bot, an Inbox Bot and a Recruiting Bot.

5. How to Share Your Bots With the Team and Decide Who Approves What Publish Team Bots in Slack, add Managers and set approval lines for email, payments and bank data.

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Today I’m sharing a curated list of the angel investors behind many of the biggest AI funding rounds of 2026.

Cognition raised $2B at a $48B valuation. Lovable doubled to $13.3B. Fireworks AI, Together AI and Baseten raised more than $3.8B between them. And a new group of companies came out of stealth with some of the best-known founders in tech on their cap tables.

So we went through 20 of the most notable AI companies that raised in recent months and for each angel we included the essentials you need to research them or plan outreach:

• Name

• Role

• LinkedIn profile

• Company backed

So instead of just knowing who these angels are, you can also quickly understand why they matter, what kind of operator or founder they are and which breakout AI companies they’ve backed.

If you’re raising, these are the people who wrote personal checks into the companies everyone wants to back today.

👇 Let’s dig in! Below are the companies covered in the database 👇

Lovable

AI software-development platform that lets anyone build and deploy apps by describing them in plain language.

Funding: Raised a $400M Series C at a $13.3B valuation in August 2026, led by Menlo Ventures with EQT’s Scaleup Europe Fund as co-lead, and with Balderton, Tencent and Kaszek joining. Lovable has raised about $952M in total.

Angels in the database: 26

ElevenLabs

AI audio platform building models for speech generation, dubbing, music and conversational voice agents.

Funding: Raised a $500M Series D at an $11B valuation in February 2026, led by Sequoia. In September 2026, a $300M employee tender offer doubled its valuation to $22B.

Angels in the database: 19

Scuba

Shared “context engine” that helps teams and their AI agents understand how a company works, built by former Slack executives Noah Desai Weiss and Dolapo Falola.

Funding: Came out of stealth in October 2026 with $34M across two rounds: a pre-seed led by Accel and a seed co-led by Accel and IVP.

Angels in the database: 12

Cognition

Developer of Devin and owner of Windsurf, AI coding agents used by enterprise engineering teams.

Funding: Raised a $2B Series E at a $48B valuation in September 2026, led by a16z with Accel, Founders Fund, General Catalyst and Avenir. The round came four months after a $1B raise at $26B.

Angels in the database: 10

OpenEvidence

Clinical AI platform that gives physicians evidence-based answers sourced from medical literature.

Funding: Raised $250M at a $15B valuation in September 2026, co-led by a16z and Byers Capital, with hospital systems participating. It follows a $250M Series D at $12B in January 2026.

Angels in the database: 8

LiveKit

Open-source infrastructure for real-time voice and video AI. ChatGPT’s voice mode runs on it.

Funding: Raised a $100M Series C at a $1B valuation in January 2026, led by Index Ventures with Salesforce Ventures, Altimeter and Redpoint. In September 2026 it acquired Loophole Labs to expand its agent infrastructure.

Angels in the database: 8

Meticulous

Testing platform that checks every code change before it merges, including the ones written by AI agents.

Funding: Raised a $15M Series A in October 2026, led by Chemistry with participation from Menlo Ventures.

Angels in the database: 8

Baseten

Inference platform companies use to run AI models in production.

Funding: Raised a $1.5B Series F at a $13B valuation in June 2026, led by Altimeter, Conviction and Spark Capital. In September it was reported to be in talks at about $26B.

Angels in the database: 7

Supabase

Open-source Postgres platform behind a large share of the apps built with AI coding tools.

Funding: Raised a $500M Series F at a $10.5B valuation in June 2026, led by GIC. In October 2026 it added $150M led by GIC with CapitalG and agreed to acquire the database startup Turso.

Angels in the database: 7

Together AI

Cloud platform for training, fine-tuning and running open-source AI models.

Funding: Raised an $800M Series C at an $8.3B valuation in July 2026, led by Aramco Ventures with Vista Equity Partners, General Catalyst, Nvidia and Salesforce Ventures. Total funding is about $1.33B.

Angels in the database: 7

Fireworks AI

Inference platform that serves and fine-tunes open-weight models for production workloads.

Funding: Raised a $1.5B Series D at a $17.5B valuation in July 2026, co-led by Atreides, Index Ventures and TCV, as its annualized revenue passed $1B.

Angels in the database: 4

Mercor

Marketplace that hires vetted experts to train and evaluate AI models for labs like OpenAI and Anthropic.

Funding: Raised a $350M Series C at a $10B valuation in October 2025, led by Felicis. In July 2026 it was reported to be raising about $500M at $20B, with annualized revenue above $2B.

Angels in the database: 4

Periodic Labs

AI lab founded by Liam Fedus (ex-OpenAI) and Ekin Dogus Cubuk (ex-Google DeepMind), training models on data from its own labs to discover new materials.

Funding: Launched with a $300M seed led by a16z in September 2025. A Series A at a $7.5B valuation was reported in mid-2026, though the company has not announced it.

Angels in the database: 4

Rogo

AI platform for investment banks and finance firms.

Funding: Raised a $160M Series D at about $2B in April 2026, led by Kleiner Perkins. In September 2026, Barclays, BNP Paribas, Citi, MUFG and Société Générale invested about $30M.

Angels in the database: 4

Factory

AI software-engineering platform building autonomous “Droids” for enterprise development teams.

Funding: Raised $200M at a $5B valuation in September 2026 from Blackstone, Khosla Ventures, Sequoia, Insight Partners, NEA and others, tripling its valuation in five months. Total funding is over $400M.

Angels in the database: 3

Mecka AI

Pays people to wear motion sensors while doing everyday tasks, then sells the data to companies training humanoid robots.

Funding: Raised a $60M Series B in October 2026, led by Sequoia with Nvidia, M12, Qualcomm Ventures and Samsung. Total funding is over $120M.

Angels in the database: 3

Proximal

Turns real agent traces into evaluation environments and training data that show where frontier models fail.

Funding: Came out of stealth in September 2026 with a $15M seed at a $300M valuation, led by General Catalyst.

Angels in the database: 3

Rillet

AI-native ERP and accounting platform.

Funding: Raised a $100M Series C at a $1B valuation in August 2026, led by ICONIQ with Sequoia and a16z, its third round in 14 months. Total funding is $208.5M.

Angels in the database: 3

Hone

Builds AI “Engines” that are assigned a business outcome, like cutting vendor spend, and run the work for weeks or months.

Funding: Raised a $60M seed at a $285M valuation in October 2026, five months after founding, co-led by Benchmark and Index Ventures.

Angels in the database: 2

Sabi

Baseball cap with brain sensors that turns imagined words into text and commands for AI assistants.

Funding: Raised a $50M seed in October 2026, led by Khosla Ventures with Accel, Initialized and DST Global. Forbes reported a $600M valuation.

Angels in the database: 1

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AI agents are starting to approve payments, change prices, and sign contracts. But what happens when an agent makes a $1M mistake? Who’s responsible? And who’s paying for it?

That’s the problem Harbor is solving!

🌎 Today, Harbor emerged from stealth with $10M in seed funding from Base10, 8VC, Abstract, NFX, and others. We’re proud to be investors too!

👇 We’re sharing the full Seed Pitch Deck + One-Pager behind the $10M raise 👇

Founded by four Stanford alumni, Harbor is already working with StackAI, HappyRobot, Crossmint, leading banks and law firms, and one of the world’s largest insurance brokers.

Here’s the opportunity: traditional insurers have decades of data to price human mistakes. AI agents have almost none.

→ Harbor is building the insurance layer for the agent economy: monitoring agent decisions, preventing costly mistakes, and insuring the remaining risk.

This could become essential infrastructure for every company deploying AI agents.

Inside you will find:

1. Why Insurers Can’t Price an AI Agent

2. How Harbor Turns Agent Behavior Into a Price

3. Why Owning the Risk Standard Is the Moat

4. The Road to $1M ARR: Selling Cover Where Agents Are Sold

5. The Team Behind Harbor

6. The $10M Seed Pitch Deck + One-Pager

Framer is the AI website builder for teams that care about every detail. Agents work directly in the canvas, helping design pages, manage CMS content, write copy, add SEO, and audit sites.

• Build faster: Create, refine, and publish production-ready pages in minutes.

• Scale confidently: Manage content, localization, and metadata across your site.

• Stay in control: Review every change, collaborate in real time, and publish when ready.

Trusted by teams at Miro and Perplexity.

Launch Your Site With Framer Today!

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1. Why Insurers Can’t Price an AI Agent

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It took seven months and a team of under 200 people.

For most people, it’s the first time an AI does the work for them.

The person behind it? Alexandr Wang, co-founder of Scale AI and Meta’s Chief AI Officer since 2025. This week he explained how Muse was built:

• A first prototype ready in two weeks

• A spreadsheet of 100+ behaviours that decided the launch date

• Memes from his own X account as the main marketing

• More than 200 researchers reporting directly to him

Inside you will find:

1. Build Where Your Company Has an Unfair Advantage Learn why Meta stayed out of the coding race and bet on personal AI.

2. Ship a Prototype in Two Weeks, Then Spend Seven Months Making It Work See how the first Muse demo reached the board in days and what the rest of the year was for.

3. Decide What “Ready to Launch” Means Before You Build The 100-row spreadsheet that decided when Muse shipped, and how to build yours.

4. Why Great Products Need One Point of View How Muse stayed under 200 people and kept every PM from adding their own feature.

5. Your Ads Should Show What the Product Does for the Customer Why the big AI labs’ campaigns missed, and the screenshot test every consumer product must pass.

6. How the Founder’s Memes Drove Muse’s Growth The posting strategy behind “make Meta cool again” and why the internet rewards risk.

7. Get Users to a Wow Moment, Then Make Every Task a Little Bigger Find out how the “trust fall” turns first-time users into daily users.

8. When the Company Is Broken, Rebuild It From Scratch What Meta did after Llama 4, and the small flat team Wang built in its place.

9. Do Every Job at Your First Company, Then Become the Coach How Wang went from running every decision at Scale to managing 200+ direct reports at Meta.

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Most founders who try to copy this flood their account with hundreds of creators and thousands of near-identical talking-head videos, then watch Meta cluster and suppress the reach to zero.

We went through Hector Serrano's full breakdown of the program he built as WisprFlow's content lead (that went super viral on x) , pulled the exact numbers, and cross-checked the Tribe performance model and WisprFlow's current pricing against first-party sources.

What came back is not a tool review. It is a copyable 30-day rebuild of a paid ads engine that runs on culture, not spend.

In this guide you'll find:

1. Why performance-based UGC kills the old creative risk model

2. The payment model: pay 7% of ad spend, not per video

3. The smart retainer: how to recruit top creators before you have any track record

4. Start with 4 to 5 creators, not hundreds, or Meta will suppress you

5. Where to actually find your first creators: the Instagram keyword search method

6. Treat creators like a coached sales team, not a content vending machine

7. The scaling cadence: 5 to 50 in four months by doubling, then coasting at 40 to 60

8. How to scale hiring without lowering the bar: referrals and exclusivity

9. The one thing that separates leaderboard creators: relentless experimentation

10. What to steal: your 30-day rebuild checklist

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

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↳ +40 AI Pitch Decks (Ultimate Collection) — the best-funded AI startups of 2026, slide by slide.

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1. Why performance-based UGC kills the old creative risk model

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As every ambiotius founder, I’ve been fascinated by the story of Adam Neumann and WeWork since I first knew about it (I even interviewed the current WeWork CEO, a legend btw).

A company valued at $47 billion filed an S-1 showing $3 billion in losses over three years, roughly $219,000 lost for every hour it operated, and then collapsed in weeks.

After that, I’ve followed Adam’s new business, Flow and was waiting to see what he’d do next. Call me naive, but I believe he’s out there trying to build one of the greatest companies in the world.

So when he published this interview, I knew I had to dissect it.

So I went and found the most valuable insights fron the interview, and broke them to you.

In this guide you’ll find (sorry if it’s too long)

1. The 95% claim against $3 billion in losses: how Neumann frames his own collapse

2. The money wound: why a brutal childhood became the engine that built and nearly broke him

3. The hole a number never fills: why chasing valuation is a founder’s trap

4. The $300,000 arbitrage that became WeWork: Green Desk’s exact unit economics

5. Dream to the stars, execute the basics: the two-places-at-once operating rule to become a legendary founder

6. How to know who are your real friends

7. First principles on a $90,000 budget: the flooring quote cut from $45,000 to $12,000

8. One building to two a day: when the company outgrows the founder

9. Power comes from influence, not control: the leadership rule Neumann learned too late

10. The unit economics almost nobody reports: WeWork was profitable the moment it stopped growing

11. A $300M ask became a $3B offer signed in under 30 minutes: the Masa deal that changed everything

12. WeWork’s core financial mistake: long leases against short commitments

13. The walk downtown where ego replaced mission: the real beginning of the end

14. Benioff’s rejected advice: IPO at a discount instead of taking SoftBank

15. Build from your truth, not the customer’s wish list: the Henry Ford rule

16. Choose investors by their worst-day behavior: the 6-year filter

17. The $20 billion buyout the board turned down chasing $32 billion

18. Forced public before he was ready: the S-1 that said $219,000 lost per hour

19. If you stab the king, kill him: the super-voting-share playbook that took him out

20. The $460 million personal guarantee and the partner who answered on the worst day

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🎯 The Deck Library
↳ +40 AI Pitch Decks (Ultimate Collection) — the best-funded AI startups of 2026, slide by slide.

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Mark Cuban started his first company with $500 from his first customer.

He had just been fired from a software store in Dallas and was sleeping on the floor of a three-bedroom apartment he shared with five friends. He told the customer that if the software didn’t work, he would wash their car.

What came after:

• MicroSolutions, sold to CompuServe in 1990

• Broadcast.com, sold to Yahoo for $5.7B in 1999, with Cuban still owning a third

• The Dallas Mavericks, bought for $285M in 2000

• Cost Plus Drugs, launched in 2022 with 111 medications

What would he build today? We asked him in Las Vegas last October. His answer was AI, repeated six times.

Since then he has given founders the same playbook in every interview: learn agents, walk into small businesses and charge for the work they never get to.

Inside you will find:

1. Get Paid by Customers Before You Raise From Investors See why Cuban treats cash as more important than revenue and raising money as an obligation.

2. Know Your Business Better Than Anyone Trying to Kill It The learning habit he built reading software manuals at night, and still runs today with agents.

3. Bet on the Ideas People Call Stupid Learn how to tell a contrarian bet from a bad idea, and why you only have to be right once.

4. Sell Trust and Let Your Customers Do the Marketing The trust formula behind Cost Plus Drugs, and the cookie company making $12M in profit with zero ads.

5. Hire People Who Cover Your Weaknesses His startup rules on teams, the day his partner told him he was a jerk, and why star hires rarely save a company.

6. Use AI to Show Up With the Work Already Done Build the 12-minute business plan and the weekly price report, and pass the test he now runs on every founder.

7. Start Where Small Businesses Need AI Most The $100-an-hour agent business he would start at 20, and the Rebel Cheese agent that recovers $50K a month.

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1. Get Paid by Customers Before You Raise From Investors

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This year, Alphabet, Amazon, Meta, Microsoft and Oracle will spend about $780 billion building AI capacity.

Only 2% of S&P 500 companies track what AI does for them over time.

The rest of a16z’s new State of Markets report points the same way:

• 69% of the S&P 500 already runs AI in production

• the top 1% of AI users spends about 8x more than the top 10%

• 86% of US venture dollars went to AI companies this year

• startups that raised recently still grow as fast as they did in 2021

What it means for you? Companies are spending on AI faster than they can prove what it returns. The startups that close that gap for their customers are the ones winning budgets and rounds right now, and the data shows exactly where they sit.

Inside you will find:

1. How to Sell AI Your Buyers Can Measure See why 69% of large companies run AI but only 2% track it, and build that metric into your product.

2. Why Your First Customers Should Be Power Users Find out how the top 1% of AI users pulls away from everyone else, and how to find them in your market.

3. How to Cut Your Inference Bill While Compute Gets More Expensive Learn the caching, routing and fine-tuning moves that keep margins healthy as GPU prices rise.

4. Which Software Still Gets Paid, and the Path Your Company Should Take See why cyber and vertical software held up, and how to choose between growth and margins.

5. The Numbers You Need to Raise in 2027 Compare your growth, margin and runway with the benchmarks investors are using today.

6. Where a16z Is Looking to Invest Next Find out the seven areas a16z is focusing on, with the startups already winning in each.

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1. How to Sell AI Your Buyers Can Measure

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Bill Gates and Paul Allen sold Microsoft’s first product before they had written it.

The founders behind the biggest companies of the last 50 years all got their first customers with moves that look small, a bit awkward and impossible to scale:

• Steve Jobs called Bill Hewlett at 12 and asked him for parts

• Brian Chesky flew to New York to photograph his hosts’ homes himself

• Facebook took 60% of Harvard in 10 days before going anywhere else

• Patrick Collison walked out of a cinema to fix one user’s error

• Jensen Huang told Nvidia’s first big customer that his chip didn’t work, and still left with $5 million

The problem? Because none of this scales, most founders skip it. And these are the moves that bring in the first 100 customers.

Inside you will find:

1. Why Steve Jobs Says Most Founders Never Get a Yes
   See how a phone call at 12 got Jobs his first job at HP, and why most founders never make that call.

2. How Airbnb Got Its First 100 Hosts to Love It
   Find out why Chesky and Gebbia flew to New York to meet every host, and what they did once they got there.

3. How Microsoft Sold Its First Product Before Writing It
   Learn how Gates and Allen closed MITS with a letter, an all-nighter and a demo that worked on the second try.

4. Why Facebook and PayPal Started With Tiny Markets
   See how winning 60% of Harvard and 30% of eBay power sellers set up two of the biggest companies ever built.

5. How Stripe Turned Its First Users Into Evangelists
   Find out why every user error at early Stripe rang the whole team’s phones, even at the cinema.

6. How Nvidia Kept Sega After Failing Them
   Learn how Jensen Huang told his only big customer the chip didn’t work and still left with $5M.

7. How to Make Selling a Daily Habit
   See why Kalanick and Neumann think fear and comfort stop most founders, and how to keep knocking on doors.

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1. Why Steve Jobs Says Most Founders Never Get a Yes

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Bending Spoons started in 2013 with about $40.000, the money left over from a failed startup.

On July 1, 2026, it listed on Nasdaq at $29 a share, a valuation of about $18.4 billion. Since then:

• it closed the acquisition of Airtable, at an enterprise value of $1.285 billion

• it agreed to buy Miro, at $1.355 billion

• it reported $704 million of revenue in a single quarter, up 126% in a year

What they do? Bending Spoons buys software products that already have millions of users, rebuilds them with its own engineers and tools, and keeps them forever. Evernote, AOL, Vimeo, WeTransfer, Eventbrite and Airtable all belong to it, and it has never sold a significant business it bought.

Most startups spend years looking for product-market fit, the moment a product finds a market that really wants it. Bending Spoons buys products that already have it, and makes money by running them better than their previous owners could.

Inside you’ll find:

1. How Bending Spoons Decided to Buy Product-Market Fit Why a failed AI startup and $40,000 convinced three engineers to stop betting on luck.

2. How a $10K App Grew Into a $4B Revenue Machine The first acquisition, the debt ladder, and the Grindr deal that almost took over the company.

3. How They Win Every Deal They Bid On The pricing model and why the highest price always wins.

4. What Happens the Day After They Buy a Company Evernote from 350 people to 20, AI agents fixing bugs, and how they handle layoffs.

5. The Team Behind Bending Spoons The founders and the rules they run on: no titles, no bonuses, every job up for grabs.

6. Why Nobody Has Copied Bending Spoons Why private equity structurally can’t, and the numbers that show the gap.

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1. How Bending Spoons Decided to Buy Product-Market Fit

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New business creation on Stripe is up almost 2x in a year.

That jump is bigger than the one Stripe saw during the COVID lockdowns. And since Stripe now handles a bit more than a quarter of all new Delaware incorporations, it is close to a picture of the whole market.

The new companies are also doing better:

• Average revenue per new company went up slightly, even with twice as many of them

• 20% of Stripe Atlas startups charged their first customer within 30 days in 2025, up from 8% in 2020

• The time to reach $1M, $10M and $100M in recurring revenue has roughly halved

The question? What to build!

Patrick Collison answered it in a conversation with Replit’s founder. He would start trying things right away, go after industries still running on clunky 1990s software and look at what is popular among young people but still low status.

Inside you will find:

1. Why This Is the Best Time in History to Start a Company See the Stripe data behind the surge and why Collison’s first rule is to try anything.

2. How to Find the 1990s Software Still Running Your Industry Let Claude audit the incumbent software in any vertical and score where it fails.

3. How to Spot Markets That Are Popular but Low Status Find the behavior young people pay for that incumbents still ignore.

4. Why the AI Labs Won’t Eat Your Startup Learn where value goes when intelligence gets cheap, and test your idea against the labs.

5. How to Build a Moat With Hamilton Helmer’s 7 Powers Check which of the seven powers your idea can reach, and at what stage.

6. Why the Long Tail Is the Safest Place to Build Size a niche big enough to pay you and too small to attract big competitors.

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1. Why This Is the Best Time in History to Start a Company

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