From top insights and reports to new funds, VC jobs, resources, and the hottest deals, here’s everything you need to stay ahead 👇

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In-Depth Insights 🔍

• Grok Bot Builds an Always-On GTM Team 🤖
  A SpaceXAI GTM lead connects Grok Bot to Salesforce, Gmail, Slack, and other tools to automate recurring sales workflows around the clock. Specialized bots handle meeting prep, prospecting, account monitoring, slide creation, and call coaching using routines, memory, and learned writing styles. [Krista Letz]

  

• The Claude Prompt Stack for Fundraising 🤝
  A founder-focused prompt stack covers investor research, objection handling, data-room audits, and term-sheet review before a fundraising process begins. Its core prompt loads company context upfront and pushes Claude to challenge assumptions like a senior investor instead of producing generic answers.

  

• Sarah Guo’s Bet on the Application Layer 🎯
  Conviction’s Sarah Guo is betting that the largest AI opportunities will sit above foundation models, despite capital concentrating heavily in a few model companies. The thesis is that major labs cannot build every application themselves, but concentrated portfolios also leave Conviction exposed if that assumption proves wrong.

  

• A Free Tool for Finding the Right VCs 💸
  VC Corner’s matching tool ranks 20 potential investors from 293 active VCs, angels, and family offices based on stage, sector, geography, check size, and thesis. The shortlist feeds into VC Pitch Conf on September 17, where qualifying startup tickets include 20 guaranteed seven-minute investor meetings.

  

• Guillermo Rauch on the Software Factory 🧠
  Vercel CEO Guillermo Rauch argues that abundant AI intelligence makes the internal software factory more important, leading Vercel to consolidate hundreds of agents into one company-wide agent called V. He also sees distribution shifting from product-led growth toward agent-led growth, with agents increasingly discovering and buying software for their users. []

  

• Grant Radar Finds Non-Dilutive Capital 🔍
  VC Corner’s Grant Radar matches founders with grants, SBIR programs, EIC funding, R&D tax credits, and cloud credits based on region, sector, and stage. It scans 156 active US, UK, and EU programs and returns a ranked shortlist with application links in roughly 10 seconds.

  

• How SaaS Companies Are Positioning Around Claude 🥊
  An analysis of 50 SaaS and AI-native companies finds that most treat Claude as infrastructure rather than a direct competitor, with 34 shipping MCP servers to integrate with it. Only 12 companies name Claude directly, while four positioning strategies are emerging for vendors deciding whether to integrate, differentiate, or compete. []

  

📢 Want to get in front of +500k founders and investors?

For sponsorship opportunities across this newsletter and LinkedIn (300k followers), email: ruben@thevccorner.com

Interesting Reports 📊

• Private Equity’s Zombie Problem 🧟
  More than 2,500 US PE-backed companies have passed the traditional five-year exit window, leaving $860B in NAV tied up in aging funds. Cov-lite debt delays defaults, while continuation vehicles and looming 2028-2029 maturities give GPs and lenders fewer clean exit options. [PitchBook]

  

• VC’s 2026 Winners Are Being Measured by Outcomes 🏆
  Accel leads global and North American VC rankings, while Index Ventures takes Europe based on follow-ons, exits, and CAGR. The rankings favor firms that turn capital into realized outcomes, not those simply writing the most checks. [PitchBook]
  

• Fundraising Prompts Are Commoditizing the Analyst Layer 📚
  A new library of 375 prompts covers investor mapping, raise readiness, outreach sequencing, and objection handling across eight fundraising workflows. As research becomes easier to automate, the harder advantage moves to judgment, timing, and relationships that prompts cannot encode.

  

Recently Launched Funds 💸

• Reach Capital closed its fifth venture fund at $265M to back early-stage companies applying technology and AI across learning, health, and work.

• GD1 held a NZ$56.7M first close of its second venture fund to invest in Pre-Seed through Series B companies across deep tech, connected hardware, health tech, SaaS, and marketplaces.

• Third Mind is raising a $25M first venture capital fund to invest in early-stage technology companies.

• BlueHill Capital closed its inaugural venture fund at $41.8M to back Seed and Series A companies across energy, semiconductors, defence, space, robotics, and advanced materials.

• Odyssey Ventures secured a £10M commitment toward its $50M Discovery I venture fund, targeting seed and pre-seed AI and DeepTech companies.

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

4 months ago · 45 likes · Ruben Dominguez

VC Jobs 💼

• Allocator One (Vienna, Austria): VC Manager (apply here)

• Photon Ventures (Hilversum, Netherlands): Senior VC Associate (apply here)

• Dragonfly (New York City, NY): VC Investor (apply here)

• Samsung Next (Mountain View, CA): VC Investor (apply here)

• 3M Ventures (San Francisco, CA): VC Manager (apply here)

• Ananda Impact Ventures (Munich, Germany): VC Manager (apply here)

• Novo Holdings (Copenhagen, Denmark): VC Associate (apply here)

• Backtrace (Munich, Germany): VC Lead (apply here)

• Horizon (San Francisco, CA): VC Associate (apply here)

• Aha (Dallas, TX): VC Principal (apply here)

Hottest Deals 💥

• Higgsfield raised $400M in Series B funding to scale its AI-powered video creation platform and accelerate product development. (read more)

• Gravis Robotics raised $200M in Series A funding to scale its autonomous construction technology and expand its physical AI platform. (read more)

• Also raised $150M in Series D funding to accelerate the development and deployment of its autonomous transportation technology. (read more)

• Velaura AI raised $110M in Series A funding to advance its AI and semiconductor technology platform. (read more)

• Rillet raised $100M in Series C funding to expand its AI-native ERP platform and serve more finance teams globally. (read more)

• ABCuro raised $66M in Series D funding to advance its clinical-stage biotechnology programs and therapeutic pipeline. (read more)

• Smack Technologies raised $61M in Series B funding to accelerate its technology platform and expand its national-security applications. (read more)

• Hopscotch Primary Care raised $53M in Series D funding to expand its technology-enabled primary care model. (read more)

• Network Bio raised $50M in funding to advance its biotechnology platform and drug-discovery programs. (read more)

• Vals AI raised $40M in Series A funding at a $400M valuation to build AI evaluation and benchmarking infrastructure. (read more)

• Trajectory raised $40M in Series A funding at a $300M post-money valuation to advance its AI technology platform. (read more)

• Leal Therapeutics raised $30M in the second close of its Series A funding to advance its therapeutic pipeline. (read more)

• Renata Medical raised $25M in Series D funding to advance its medical-device platform and commercial expansion. (read more)

• Flip raised $25M in new funding to accelerate growth and expand its technology platform. (read more)

• Ours Privacy raised $15M in Series A funding to expand its privacy-focused technology platform. (read more)

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✅ The Best 23 Accelerators Worldwide for Rapid Growth

✅ AI Co-Pilots Every Startup & VC Needs in Their Toolbox

This is a guest article by .

He teaches VC and PE classes at the Stanford Graduate School of Business and leads the Stanford GSB’s Venture Capital Initiative. He publishes venture capital research, investor rankings and fundraising guides for founders in his newsletter.

Every founder raising from venture capitalists has heard the same names: Sequoia, Andreessen Horowitz, Benchmark. But one number should shape your fundraising strategy more than any of them. By my estimates, about 5% of venture capitalists have generated roughly 90% of the industry’s profits. If you are choosing whom to take money from, the question that matters most is whether your investor sits in that 5%. Fame is an imperfect proxy for it.

Until now there has been no transparent, fully data-driven way to answer that question. The list the industry leans on, the Forbes Midas List, is largely a black box. When we tried to reverse-engineer it from its own disclosed methodology, even our best-fitting replication left 49 of its own top 100 absent from the actual list. Among investors who appear on both our ranking and Midas, the correlation is only about 0.27.

So together with Blake Jackson I built an alternative: the 2026 Strebulaev-Jackson Venture Ranking, drawn from more than 230,000 investments by nearly 13,000 venture capitalists across more than 5,000 firms over a 30-year window. Every point traces back to a specific investment in a specific company on a specific date. We apply no editorial judgment, make no manual adjustments and do not rely on firms submitting their own numbers.

This article contains the complete top 100, the methodology behind it and, because a ranking alone will not get you a good investor, what to do with it once you have it.

The VC Corner

The VC Corner is where founders stay ahead: sharp news, fundraising strategies, battle-tested playbooks, and the tools to grow.

By Ruben Dominguez

What the score actually measures

Six factors drive it. They are worth reading closely, because between them they reward the behavior a founder should want from an investor.

1. Valuation, honestly discounted. Two companies can both be described as worth $100 billion when one figure is a public market capitalization and the other a private post-money valuation. These are not the same number, because the preferred stock VCs buy carries downside protections that common stock lacks. My work with Will Gornall put the average overstatement for unicorns near 50%. We discount private valuations uniformly.

2. Dilution. A 10% stake at the first round is not a 10% stake at exit. Two companies can both sell for $1 billion, but if one raised four rounds along the way, its early investors were diluted round after round. We track each investment’s ownership down through every subsequent round.

3. Net profit. Turning $10 million into $2 billion is a different achievement from turning $1 billion into the same $2 billion. Subtracting the cost of every investment rewards capital efficiency and penalizes spraying large checks to manufacture a few headline wins. In our data, roughly three-quarters of investments returned negative net profits.

4. Value add. Investors who lead rounds and take board seats contribute more than passive check-writers, and we award additional points for those roles.

5. Human-capital decay. A VC’s skill, network and judgment depreciate if not continuously exercised. We discount each investment by time elapsed, using a half-life of three years: a dollar of value created in 2022 is worth about fifty cents in 2025, and a quarter if it was created in 2019. This keeps the ranking current, rewarding investors who are good now rather than those resting on a single brilliant bet from two decades ago.

6. Credit between firm and individual. Investors move between firms, so when a partner who made their best deals at one firm decamps to another, both firms deserve some credit. We split it, giving a quarter to the firm where the investment was made and three-quarters to the firm where the partner works now. That reflects academic evidence that most return variation traces to individuals rather than institutions.

The 2026 results: top firms

Sequoia tops the ranking with 10,158 points. Andreessen Horowitz is second with 8,292. Accel, DST Global and Tiger Global round out the top five. Two names in that top 20 deserve a second look from any founder building a target list. Parkway Venture Capital, founded in 2019, sits at 19 on the strength of Figure AI. Notable ranks 20th. Neither is a household name, and both outrank firms whose brands are far better known.

Rank, firm, headquarters, year founded, ranking score and top deal. The top deal is the single highest-scoring investment for that firm under our methodology, not necessarily its most famous or highest-valued holding.

Scores are integer-rounded. On geography: 62 of the top 100 firms are headquartered in California, 19 in New York, and six each in Massachusetts and Texas. Whatever broadening has happened at the seed and angel level, the institutional core of US venture has not left its historical home.

Four patterns in this table matter more to a founder than the order itself.

The gap between good and famous is an order of magnitude

By rank 10 the score has dropped to roughly 3,000, less than a third of Sequoia’s. By rank 100 it is 245. The top firm scores about 41 times the hundredth.

Venture’s power law, usually described at the level of individual deals, applies just as forcefully to the firms themselves. For a founder, that means the difference between a top-decile investor and a merely well-known one is an order of magnitude, and worth real effort to close.

Counting unicorns tells you almost nothing

The ranking is not a unicorn-counting contest. SV Angel has backed roughly 139 unicorns yet ranks 31st. Insight Partners has invested in about 124 and ranks 28th. Felicis has some 58 unicorns to its name and sits at 74th. Meanwhile DST Global, with 62 unicorns, ranks 4th, and Thrive is 8th with 47.

How can a firm with a fraction of the unicorn count rank far higher? Because the methodology rewards value actually captured rather than the number of top deals. A firm that wrote a tiny, heavily diluted check into a company that later became a unicorn gets less credit for it: the dilution adjustment shrinks the stake, the net-profit adjustment subtracts the cost, and a small early position in a crowded cap table can be worth much less by exit. A firm that took a large, concentrated, board-level position in a smaller number of winners gets a great deal of credit. Expressed as points per unicorn, the spread runs from around six for the highest-volume names to more than eighty for the most concentrated.

For founders, that spread reveals which investors commit and stay involved, and which are writing many small checks in the hope that one or two of them land.

Your best investor may be one you have never pitched

The oldest firm in the top 100, Bessemer, traces its venture capital roots to the 1970s. The youngest, Inflection Ventures, was founded in 2022. Eight of the top 20 predate 2000, a testament to how durable a genuine venture franchise can be. Yet twenty-one of the 100 firms were founded in 2015 or later, and several rocketed up on a single recent, fast-appreciating bet.

A cluster of life-sciences firms earns a place on therapeutics rather than software: OrbiMed (27), Atlas Venture (38), ARCH (49), Versant (61) and Sofinnova (75), all ranking on concentrated, capital-efficient bets. Crypto-native firms such as Paradigm (34), Pantera (76), Multicoin (84) and Polychain (94) rank on a different opportunity set again. The methodology does not privilege any sector; it measures value created, net of cost and decay, wherever it occurs.

If you are building in a specific space, the best-performing investor for you may be a specialist who will never appear near the top of a generalist media list.

AI is already rewriting the top of the table

For 23 of the 100 firms, the single highest-scoring investment is a frontier-AI or AI-infrastructure company. That is nearly a quarter of the list, anchored to a wave of companies most of which did not exist, or were tiny, five years ago.

The decay factor means this reshaping happens in real time. Firms that placed early, concentrated bets on the leading AI companies, such as Thrive on OpenAI, Menlo on Anthropic and Lightspeed on Mistral, are rewarded immediately rather than years after the fact.

What a ranking cannot tell you

Somewhere out there, a founder is already pasting this table into a spreadsheet, sorting by rank and preparing to email firms one through one hundred in that order. I understand the impulse. Ranked lists are seductive precisely because they appear to do your thinking for you.

But if there is one thing you should not do with this list, or with any list that Ruben or I or anybody else publishes, it is exactly that. A ranking is an argument about what counts as good, compressed into a single number. The order is the least interesting part of it. What matters is the reasoning underneath, whether that reasoning matches what you are actually trying to build, and what your own research turns up when you apply it to your own situation. Read the methodology, disagree with the parts you want to disagree with, then go and do the work yourself.

A ranking can narrow your list, but three things should drive the final choice, and no score fully captures any of them.

1. Think about fit, not only rank. The ranking applies one yardstick to many playbooks. Crossover and hedge-fund-style firms such as Tiger Global (5), DST Global (4), Dragoneer (23), Altimeter (24), Coatue (29) and Greenoaks (44) tend to take large minority stakes with few or no board seats. Sequoia and Benchmark run deeply involved, board-heavy models. Sutter Hill (26) has effectively incubated companies from scratch. These models all score well, because they all create value, but from the founder’s chair they feel nothing alike. A founder who wants an engaged thought partner and a founder who wants capital plus autonomy should be targeting different firms from the same top 100. A high rank cannot tell you which of the two you are dealing with.

2. Diligence the partner, not the logo. One of the most striking facts in our data is that fully half of our top 100 firms have no partner in our individual top 100. Firm strength and individual strength are far from the same thing, which is precisely why our methodology splits credit between them. The brand does not sit on your board; a person does. Ask who specifically will work with you, what else they are carrying and how long they have been at the firm. Then reference-check that person with founders they have backed, including the ones whose companies did not work out. How an investor behaves in a down round is the information you most need, and no ranking, ours included, can supply it.

3. Remember that this is closer to a marriage than a transaction. An investor is your partner for seven, ten, sometimes fifteen years. That is longer than many marriages and considerably harder to exit. You can sell your house, change your product or replace your team. You cannot easily remove an investor from your cap table or your board. That asymmetry should make you slow down at precisely the moment a competitive round pressures you to speed up.

The value-add factor exists in our ranking because involved investors demonstrably contribute to outcomes. But the same board seat that opens a door can block a sale, override a strategy or replace a chief executive. So ask not only whether an investor will help you win, but whether you want this person in the room on your worst day. A slightly lower-ranked investor who is genuinely aligned with you will beat a higher-ranked one who is not.

How to use this list

1. Screen for performance. The 5% who generate 90% of the profits are worth real effort to reach, and the table above tells you where they are.

2. Filter for fit. Stage, sector, check size and the model of involvement you actually want.

3. Go one level deeper, to the individual. The firm gets you the meeting, but you will be working with one person for years afterwards.

4. Reference-check the downside. Talk to founders whose companies struggled, not only the trophy logos on a firm’s website.

What comes next

We are extending the same six factors to individual investors rather than firms, and there the contrast with conventional wisdom is sharper still: More than half of our top 100 individual VCs appear nowhere on the 2026 Forbes Midas List. We will also extend the rankings internationally, release industry-focused rankings starting with biotech and AI, incorporate verified submissions from firms and investors, and publish the time series back roughly 25 years so the rise and fall of franchises can be seen directly.

Subscribe to get them first here.

FAQs:

Q: Which VC firm performs best?
A: Sequoia leads the 2026 Strebulaev-Jackson Venture Ranking with 10,158 points, ahead of Andreessen Horowitz at 8,292 and Accel at 4,576. The score measures value captured after dilution, cost, and time decay.

Q: How much better is a top VC firm than an average one?
A: The top firm scores roughly 41 times the hundredth-ranked firm. By rank 10 the score has already fallen to under a third of Sequoia’s, which shows venture’s power law applies to firms rather than only to deals.

Q: Does backing more unicorns mean a VC performs better?
A: No. SV Angel has backed around 139 unicorns and ranks 31st, while Thrive has backed 47 and ranks 8th. The ranking rewards value actually captured after dilution rather than the number of billion-dollar logos.

Q: How concentrated are venture capital returns?
A: The top 5% of VCs have generated roughly 90% of the industry’s profits, which makes whether your investor sits inside that 5% the decision rather than a marginal one.

Q: Do you need a decades-old firm to be a top VC?
A: No. Twenty-one of the top 100 firms were founded in 2015 or later, so a decade of concentrated, well-timed positions is enough to rank alongside firms with fifty-year histories.

Q: Which AI companies do top VC firms name as their best investment?
A: Twenty-three of the top 100 name a frontier-AI or AI-infrastructure company as their single highest-scoring deal. OpenAI is named by four firms, xAI by three, and Anthropic and Perplexity by two each.

Q: Where are the best VC firms based?
A: Of the top 100, 62 are headquartered in California, 19 in New York, and six each in Massachusetts and Texas.

One gets $1,000 in credits. The other gets $300,000.

The difference is a single field. One of them had an investor add the fund ID.

Microsoft, Google, Anthropic, and OpenAI all gate their top tiers the same way. Nobody tells founders this, so most apply solo, take the entry-level number, and never learn what they walked past.

Built with VC Pitch Conf, we mapped 95 compute credit programs. 23 pay out with no investor involved. For the other 72, one introduction is worth six figures.

What’s Inside

▫️ Stage and format, pre-seed through Series A, and whether the credit is raw compute, GPU hours, or API tokens

▫️ The investor unlock, the exact intro route for each program and how much it adds

▫️ Review time, validity, and expiry, so nothing sits unclaimed past its window

▫️ The watch-outs, including the credit cliff: auto-billing starts the second your credits run out

▫️ Apply links straight to each program’s form

Start with the 23 that need no investor if you are bootstrapping or raising later. The rest tell you exactly which intro is worth asking for.

Why This Is the Most Underused Money in the Stack

Six figures of infrastructure, and it costs no equity, no board seat, no valuation conversation.

The catch is structural. The biggest tiers sit behind a relationship, so a founder who has never been told this applies through the public form and gets the public number. Then builds a year of runway assumptions on it.

The credit cliff is where it gets expensive. Credits run out, auto-billing starts the same day, and a startup burning $400 a month in compute wakes up burning $12,000. Every expiry date in the map exists because of that.

Pair it with the 80+ non-dilutive funding sources already in the library and you have most of the equity-free capital map in one place.

Where the Intros Come From

The map came out of a collaboration with VC Pitch Conf, a virtual 1:1 pitch conference on September 17. The connection is direct: the intros that unlock the top credit tiers come from investors who already know you.

With a pitch ticket:

▫️ 20 guaranteed 1:1 pitches, 7 minutes each, matched by industry, stage, and geography

▫️ 2.5 hours, non-stop, replacing months of cold outreach

▫️ 200+ VCs, angels, and family offices, all actively deploying

Free for everyone:

▫️ The full content track, keynotes and panels

▫️ Free entry for investors, plus co-investor, LP, and family office matchmaking

The keynote lineup

▫️ Where $B+ investors are putting venture dollars right now — Karen Page @ B Capital ($12B+ AUM), J Bradley Hilton @ Hilton Family Office, Candice Beaumont @ Salsano Group

▫️ What founders should send before the first meeting — Haley Bryant @ Hustle Fund (750+ deals)

▫️ The 2026 bar: what “fundable” means at pre-seed, seed, and Series A — Tim Suzman @ Pioneer Fund (920+ deals), Bill Liao @ SOSV (800+ deals)

▫️ How to pitch family offices vs VC funds vs accelerators — Shambhavi Mishra @ Antler (1,800+ deals), moderated by Shaun Gold @ OpenVC

▫️ How to find your moat and become investable — Igor Ryabenkiy @ AltaIR Capital ($600M AUM)

▫️ What should AI startups build now to raise venture money — Andrew Brackin @ Gradient ($1B AUM), Ray Wu @ Alumni Ventures

The Compute Credit Map

95 programs. 23 with no investor required. Apply links on every row.

Free for premium subscribers, alongside everything else:

The 4 VC Corner Libraries

▫️ Investor Lists — 10,000+ verified check-writers, including 200+ AI angels and 2,500 SaaS angels

▫️ Pitch Decks — 300+ that raised, including 26 that raised $400M

▫️ Financial Models — the 13-week runway system, the SaaS model, the SAFE calculator

▫️ Resources — every playbook, including what top VCs look for in 2026

One claimed program covers a year of subscription many times over.

Cancel anytime. First subscribers keep 50% off for life.

Get 50% off forever

The Compute Credit Map:

Read more

Weeks of Crunchbase tabs and half-updated portfolio pages, then 200 emails to discover most of those firms stopped writing checks in 2024.

The VC Corner

The VC Corner is where founders stay ahead: sharp news, fundraising strategies, battle-tested playbooks, and the tools to grow.

By Ruben Dominguez

There is a free tool that skips all of it. Enter your stage, geography, and up to three sectors. Get your top 20 matches, with check sizes and theses attached.

👉 Find your top 20 matches — free, no signup

Why These 20 Are Different

Two filters run underneath, and both matter more than list size.

▫️ They are active. Every investor shown has done at least 2 deals in the last 12 months. That one filter removes most of what makes a scraped list useless.

▫️ They match your profile. Stage, sector, and geography, across 5 stages, 21 sectors, and 11 regions.

You also get what most lists leave out: check size and thesis on every card. A $25K angel and a $15M fund are different conversations, and knowing which one you are looking at saves you the email.

You can browse all 293 manually if you would rather work the full set.

See your matches!

Then Pitch Them, September 17

The tool exists because of the event behind it: VC Pitch Conf, virtual, September 17. Your matches are the investors you get paired with there.

With a startup ticket:

▫️ 20 guaranteed 1:1 pitches, 7 minutes each, matched by stage, sector, and geography

▫️ 2.5 hours, non-stop. No travel, no cold outreach, no intro requests

▫️ 293+ investors attending, all actively deploying

Free for everyone:

▫️ 4 hours of keynotes and panels with a16z Speedrun, Antler, SOSV, B Capital, Plug and Play, Entrepreneurs First, 500 Global, and Alumni Ventures

▫️ Free entry for investors, plus 2 hours of co-investor, LP, and GP matchmaking

Around 1,000 founders and investors are joining. Investors also get the full event deal flow, roughly what a team sources in three to six months.

What to Have Ready Before You Pitch

Seven minutes rewards preparation more than any other format. Everything below is inside the premium libraries.

The 4 VC Corner Libraries

▫️ Investor Lists — 10,000+ verified check-writers, including 200+ AI angels, 2,500 SaaS angels, and family offices cutting pre-seed checks

▫️ Pitch Decks — 300+ that raised, including 26 that raised $400M and Rippling’s $200M memo

▫️ Financial Models — the 13-week runway system, the SaaS model, the SAFE calculator

▫️ Resources — every playbook, including what top VCs look for in 2026 and what they check in diligence

The 5 AI Corner Libraries

▫️ Claude & Anthropic — every Claude system and setup

▫️ Prompting & Context Engineering — the prompt packs and frameworks

▫️ AI Agents — build, deploy, and run agents end to end

▫️ AI Tools & Models — what to use, what it costs, what works

▫️ Business & Investing — AI applied to money and operating

FAQs:

Q: How do you find the right VCs for your startup?

A: Filter by stage, sector, and geography, then confirm the investor has deployed capital recently. A free matching tool returns your top 20 from 293 investors, all with at least two deals in the last 12 months, showing check sizes and theses.

Q: How do you know if an investor is still actively investing?

A: Recent deal activity is the cleanest signal. Many published lists include firms that stopped writing checks years ago, so a two-deals-in-twelve-months filter removes most dead entries before you send a single email.

Q: What is VC Pitch Conf?

A: A virtual 1:1 pitch conference on September 17, 2026. Each startup ticket includes 20 guaranteed seven-minute pitches with investors matched by stage, sector, and geography.

Q: Is the VC matching tool free?

A: Yes, with no signup. You enter your stage, geography, and up to three sectors and see your top 20 matches with check sizes and investment theses.

Q: Can investors attend VC Pitch Conf for free?

A: Yes. Investor attendance is free, including two hours of co-investor, LP, and GP matchmaking plus access to the event’s full deal flow.

You just raised a $2M pre-seed round.

Congratulations.

Now, you’re probably about to make the most expensive mistake of your startup’s life.

Right now, a senior engineer in San Francisco costs between $250,000 and $450,000. That is up to 20% of your entire runway gone before you’ve even validated your product. Yet, most founders hire anyway.

The money is there, the pressure is on and waiting feels like losing.

But the hiring playbook we’re all running is dead. It was written for a market that closed in 2022.

Today, the leanest startups are hitting $1M ARR with just one or two engineers.

They aren’t rushing. They’re leveraging AI, hiring globally and ditching the tired “work for equity” pitch. Most importantly, they realize that hire number one isn’t just a headcount decision.

They write the DNA of your company culture.

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Table of Contents

1. The Assumption That Is Burning Your Runway

2. Where the Best Engineers Actually Come From

3. The Process Is Part of Your Pitch

4. The Equity Pitch Is Broken. Here Is What to Say Instead.

5. What You Are Actually Evaluating in 2026

6. Hire One Is a Cultural Template, Not a Headcount Decision

1. The Assumption That Is Burning Your Runway

There is a question founders almost never ask before posting their first engineering role.

What specifically cannot get built or maintained without this person, given the tools available today?

It sounds simple. In practice, most founders skip it entirely, because the assumption sitting underneath it has felt so obviously true for so long that it stopped being examined.

The AI Multiplier Changes the Math

That assumption is now worth examining. GitHub’s 2024 research found that developers using AI assistance completed tasks 55% faster than those working without it.

That figure understates the real-world impact for strong engineers, who are not just moving faster on individual tasks but collapsing the architecture, debugging and iteration cycles that used to require multiple people into what one person can now manage alone.

Some of the most capital-efficient seed-stage companies operating right now have one or two engineers and meaningful ARR.

A configuration that would have been implausible in 2020.

None of this means avoiding engineering hires. It means the threshold for when a hire becomes genuinely necessary has moved and founders who have not recalibrated that threshold are hiring earlier than their actual constraints require.

The Hidden Tax of Moving Too Fast

Premature hires do not just cost money. They create coordination overhead, fragment ownership and introduce management burden at the exact moment when speed and clarity matter most.

The founders getting this right are staying lean longer than their instincts tell them to, using that constraint to get sharper on what they actually need and hiring one exceptional person instead of two adequate ones when the moment arrives.

The difference in output between those two configurations consistently surprises founders who have only ever operated the other way.

2. Where the Best Engineers Actually Come From

Harj Taggar, who built Triplebyte and observed thousands of early-stage engineering hires from the inside, is unequivocal about the sourcing hierarchy.

Personal network first, by a margin that makes every other channel feel secondary.

Not because other channels cannot work, but because the information advantage that comes with hiring someone already known is irreplaceable at the stage where a single bad hire can genuinely determine whether the company survives.

Mining Your Network the Right Way

Most founders agree with this in principle and dramatically underapply it in practice.

The mental scan happens fast.

The conclusion that nobody relevant is available arrives within a few minutes and the founder moves on to job boards.

What has not happened is the actual work. Making the calls, sending the messages, sitting down with the best engineers in the network and making a direct ask.

Then, crucially, asking each of those people the same follow-up question, who are the best engineers you know and treating every referral with the same seriousness as a warm investor introduction.

The founders who close their first great engineering hire in sixty days almost always did it this way.

The Global Talent Unlock (and Its Caveats)

Once the network is truly exhausted, the geography unlocked from remote work normalization remains dramatically underused.

World-class engineers in Warsaw, Medellín, Tbilisi, Nairobi and Manila have spent years building production systems, contributing to open source and competing globally.

According to Stack Overflow’s 2024 Developer Survey, over 40% of professional developers now work fully remotely, a figure that has held steady since 2022 and reflects a permanent structural change in where talent is accessible.

The talent in these markets has compounded fast and the compensation levels that are life-changing for engineers there extend the runway without compromising on quality.

The honest caveat is that distributed hiring fails when treated as pure cost optimization.

In a remote setup, you’re not just looking for technical ability but for async communication discipline, written clarity and the capacity to operate independently across time zones.

Strong technical skills don’t guarantee any of that and those qualities need to be evaluated explicitly.

3. The Process Is Part of Your Pitch

Most founders treat the hiring process as something that happens after the pitch. Find the candidate, sell them on the company, then run them through the interviews.

The problem with that sequence is that the process itself is already sending a signal and most founders are not paying attention to what it says.

Speed Signals Conviction

A slow pipeline reads as indecision.

A candidate who submits a work sample on Tuesday and hears nothing until the following week has already started drawing conclusions about how the company operates.

The best engineers are almost always interviewing in parallel. They are not waiting.

The founders who close their best candidates move in days, not weeks.

They pre-book interview slots before the candidate is even identified, designate a single decision-maker for each role and send same-day updates at every stage. Not because it is polite, but because speed signals conviction and conviction is what a candidate needs to feel before walking away from a stable job.

Ditch the Generic Job Description

The job description is extremely important. And it’s underplayed.

Most early-stage engineering postings read like a requirements list assembled from other job postings. Generic stack requirements, vague responsibilities, a culture paragraph that could apply to any company in the world.

The job description is the first piece of writing a candidate reads from a founder.

It should read like one.

Name the specific problem the hire will own, describe what success looks like at thirty, sixty and ninety days and be honest about the constraints. Founders who do this attract candidates self-selecting into the actual role, not an idealized version of it.

4. The Equity Pitch Is Broken. Here Is What to Say Instead.

Walk into most seed stage engineering conversations and the equity pitch arrives within the first ten minutes.

Ownership is meaningful in early-stage hiring.

If this works the way it could, this is worth several million dollars. You have a chance to get in early.

Why the Old Math Fails

Engineers have heard this pitch hundreds of times. The ones worth hiring have done the math.

The expected value calculation on early stage equity, once dilution across multiple rounds is accounted for along with the liquidation preference stack that accumulates over time, rarely justifies the compensation sacrifice a candidate makes to earn it.

PitchBook puts the average time to liquidity at eight to twelve years for venture backed companies that do exit. Most never do.

The engineers most worth hiring understand this clearly.

Equity projections dressed in optimistic language do not move them.

Selling Scope, Ownership and Growth

That does not make equity irrelevant. It makes it a supporting detail rather than the main argument.

The candidates worth competing for are moved by scope, ownership and the speed of professional development that only early stage provides.

The specific technical challenges involved, described honestly and in enough detail to signal that the founders actually understand what they are asking for.

The direct access to product and business decisions that no larger company will ever offer.

These are not consolation prizes for founders who cannot afford big salaries.

For a specific type of engineer, the one who will genuinely thrive at this stage, they are more compelling than any equity projection.

The founders closing their best candidates lead with the work, not the financial scenario.

They describe the exact problem the engineer will own, why it is genuinely hard, what the product stakes are and what the person will have built twelve months from now.

That pitch wins where the equity story fails, because it respects the intelligence of the candidate and speaks to what actually motivates someone who chooses early stage over comfort.

5. What You Are Actually Evaluating in 2026

The most common engineering interview process in 2026 was designed for 2019. Algorithmic challenges, abstract whiteboard sessions, problems where the entire signal is whether someone can produce a working function from memory under artificial pressure.

The Death of the Whiteboard Interview

These formats test for skills that matter significantly less than they did five years ago.

And they screen out precisely the kind of engineer who performs best in an AI augmented environment.

Every strong engineer working today uses AI tools heavily in their daily workflow. The ones who do not are, almost definitionally, slower than their peers.

A 90-minute pairing session where AI tool usage is prohibited is not a test of rigor. It is a performance of rigor that produces misleading signal about how the candidate will actually operate on the job.

Testing for AI Augmented Reasoning

What actually matters is how someone thinks.

How they decompose an ambiguous problem.

How they review and interrogate AI generated output rather than accepting it uncritically.

Whether they know when the model is confidently wrong and can reason their way back from first principles.

The distinction between engineers who use AI as a force multiplier and engineers who use it as a crutch is the most important variable in early engineering hiring right now and most standard interview formats have no rubric for measuring it.

The practical replacement is a real pairing session on a problem that resembles actual work, with AI tools present, run as a collaboration rather than an examination.

Watch how the candidate interacts with the model.

Do they accept its first output or probe it?

Do they understand the code they end up with, or are they shipping something they cannot explain?

Do they write better prompts when the first one fails, or do they run the same one again and expect a different result?

Ninety minutes of honest pairing on real work reveals more than five rounds of abstract challenges and evaluates for the capabilities that actually determine performance.

6. Hire One Is a Cultural Template, Not a Headcount Decision

Every team has norms.

Ways of reviewing code, writing documentation, handling disagreement, shipping under pressure, communicating asynchronously across the week.

In a company of fifty, those norms exist because someone built them deliberately or because they calcified from habits nobody ever challenged. In a company of four, they exist almost entirely because of whoever was in the room at the start.

The first engineer does not join a culture.

They co author one. Whatever communication patterns they bring, whatever standard they hold their own work to, whatever relationship they have with ambiguity and ownership, those behaviors propagate forward into every hire that follows.

Early engineers have an outsized influence on what every subsequent hire perceives as normal. And normal is the hardest thing in any organization to change.

Beware the Lone Genius Trap

A founder who brings in a brilliant but isolated engineer, someone who ships fast but documents nothing and collaborates with no one, does not just solve a short term capacity problem.

They install a template.

The clearest version of this mistake is what gets called the lone genius trap.

High output, low process, no knowledge transfer. It looks like a shortcut in month one and functions as a compounding liability for years afterward, producing fragile systems, thin documentation and a team culture where isolation becomes the accepted default.

By hire five, the damage is structural.

Research from organizational psychologist Adam Grant consistently shows that early behavioral norms in small teams are significantly more durable than norms introduced later, because they become the baseline against which all subsequent behavior is measured.

The “Team of Five” Litmus Test

The right question before making the first engineering hire is not “can this person build what we need?” It is “would a team of five people who work exactly like this person be a team worth running?”

If the honest answer is uncertain, the candidate is probably wrong regardless of technical ability.

The founders who get this right treat the first engineering hire with the same deliberateness they applied to their co founder search.

Not because the stakes are identical, but because the logic is the same.

The decision is not just about today’s capacity. It is about what the company will value, how it will operate and what every person who joins afterward will walk into.

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The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

4 months ago · 45 likes · Ruben Dominguez

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That was the ceiling of his musical ability, and it stayed there.

Then look at the credits: Johnny Cash, Jay-Z, Eminem, The Strokes, four decades of records that defined how their eras sounded, all produced by someone who would struggle to play any of them.

What he built instead was subtraction. Hearing the song buried inside the song in front of him, then stripping away everything else until only that remains.

And he runs every project past one question before committing to it: would he still make this if nobody ever heard it?

I watched the full conversation with David Senra so you do not have to. Here are the 10 insights that matter.

1. The One-Tenth Rule (Why Doubling Your Guitars Erases the Guitarist)

Every layer you stack on a project quietly cancels out the layer before it.

“If you’re stacking a lot of things on top of each other, each one of those things becomes less important. So if you have ten things, each one of them is one-tenth as important as one by itself.”

Rubin’s guitar example makes the math audible. A lot of recordings get doubled and tripled into a wall of guitars, and once that wall exists, you hear guitar, not a guitarist. The person disappears into the pile. One player, one take, fingers audible on the strings, is a different animal. “It’s got more personality,” Rubin says. “It’s more human.”

The same math applies past music. Every extra slide dilutes the one that actually mattered, and every extra feature does the same to a product.

Before you ship your next piece of work, cut one input. One instrument, one slide, one clause. Notice what gets louder once it is the only thing left standing.

2. The Ruthless Edit: Cut to 40%, Then Rebuild

Trimming to what you need is a different discipline than cutting past it on purpose.

“Instead of whittling down that 30 to get to the 70, I would say reduce it to 40 percent, let’s say. Force yourself to get to 40 percent. And then add back what’s needed to get to the 70.”

Most people whittle. They start at 100 percent and shave until it feels acceptable, landing close to where they expected to land. Rubin overshoots on purpose instead, and that forces him to learn what the work actually needs instead of what he is afraid to lose.

With the Red Hot Chili Peppers, this means recording 40 or 50 songs for an album and voting on every one: A, B, or C. If everyone picks a song as an A, it makes the album. If the vote splits, it might not. “It helps to get to that point of what are the ones you can’t live without,” Rubin says, “however few it is, and build from there.”

Set a specific number, not a vague “shorter.” Take your next draft, deck, or roadmap and cut it below half its current size before you decide what earns its way back in. Let the group vote if more than one person owns the decision. You learn what the work needs by losing it first, over defending it one line at a time.

If you are cutting your own pitch or product this week, the founders who move first already run the leverage. Start here:

• What top-tier VCs actually look for in 2026

• 50 pitch decks from startups that raised $380M+

• The GTM system most founders build too late

3. The Man in Black Filter

A single invented character did more work than any producer credit on Rubin’s Johnny Cash records.

“What spoke to me about Johnny Cash was this image of the man in black. And the man in black is a mythical character. Yes, Johnny Cash, but it’s not just Johnny Cash. It’s like the mythical Johnny Cash, the Johnny Cash who’s the man in black. And the man Johnny Cash could sing a funny song. The man in black probably wouldn’t sing a funny song.”

Rubin did not just record Johnny Cash. He recorded a mythical version of Cash, filtered through one image, and let that image make calls a producer usually has to make alone. Every song choice ran through a single question: would the man in black sing this. Funny songs were out, over Cash’s ability to sing them well; the character simply would not.

You can borrow this without owning a record label. Pick the persona your project serves, then let that persona reject material that is objectively good but does not fit. The stricter the filter, the more coherent the finished thing feels.

Name the character your current project serves, then ask what that character would never do. Cut accordingly.

4. How Jay-Z Made an Album in 2 Weeks (And Why Eminem Never Could)

2 of the biggest names in hip-hop build songs in opposite ways, and both are right.

“Em is much more, well, he writes down the lyrics, and he’s really studious in the way that he works. And he’ll also record take after take after take after it’s written and try different things. Jay is much more spontaneous. It all happens in his head, and then he’ll get up and say it once or twice, and that’s it.”

Rubin describes watching Jay-Z sit silently in the corner of the studio listening to a beat on repeat, so still it is easy to forget he is even there. Then, without warning, he stands up, walks to the mic, and records an entire verse from memory. He made Magna Carta Holy Grail this way, in 2 weeks. Eminem works the opposite direction: notebooks, take after take, months spent refining a single song.

Neither approach is the mature one, and neither is the amateur one. They are 2 different operating systems producing the same result. Some people build ideas out loud, on paper, in public drafts. Some build ideas silently, then execute in one motion.

Write down the process you actually use when the work is going well, spontaneous or methodical, and stop apologizing for whichever one it is.

5. The 10-Year Diary Rule

Calling your work a magnum opus is the fastest way to freeze up before you finish it.

“I might not do the same thing I would have done 10 years ago, and that’s fine. But I don’t have any regrets about 10 years ago, that’s what I thought. That’s real. Each of those installments are real. So it’s always true. It doesn’t mean that that’s who you are forever. It’s who you are in that moment.”

Rubin’s book describes treating each piece of work like an entry in a diary: honest for the moment it came from, with nothing to be critical about later. 10 years from now you will make it differently. That does not make today’s version wrong. It just makes it today’s version.

This reframe removes the pressure that kills first products and first drafts. You stop building a monument and start logging where you actually are.

Before you publish anything, ask one question only: is this honest for where you are right now, over whether it will define you forever.

6. The 4 Pitfalls Jimmy Iovine Watches For

Overconfidence and self-doubt look like opposites. Rubin says they come from the same place.

“Two different people have the same overnight success. One of them gets really boastful and, ‘I’m the greatest that ever lived.’ And the other one is like, ‘Oh my God, they’re going to find out that I’m not, that I’m really a fake.’ But those are both the same people. It’s the same, they’re two sides of the same coin. The megalomania is a way of hiding the insecurity.”

Jimmy Iovine once broke this down into 4 pitfalls that destroy talented people under stress: drugs, alcohol, women, and megalomania. Rubin’s addition is the insight above. One overnight success gets boastful. Another gets paranoid, certain everyone is about to find out they are a fraud. Overconfidence and insecurity are not opposite reactions to success; they are 2 sides of the same imbalance, and the person wearing either mask rarely knows it.

Sudden success arrives with zero training for how to hold it. If you are scaling fast, watch your team and yourself for either extreme after a win. Both are the same warning light.

If you are watching for these patterns in your own portfolio, the signal shows up in the numbers before it shows up in the room. Start here:

• What top VCs check in due diligence before writing checks

• The onion theory of startup risk: the 5 layers every investor evaluates

• VC horror stories: the week founders stopped pretending

7. The Lazy Workaholic

Rubin’s natural state, by his own account, is doing nothing at all.

“I’m a lazy workaholic... I have to force myself to do it, but I do force myself. My demeanor would be to do nothing... I love the beautiful thing. And it takes a lot of work to get to the beautiful thing. I like to get to the point where it’s like, okay, press the send button and share it with the world.”

He has to force himself into the studio most mornings, over hating the work; showing up is a separate skill from wanting the result. What he is actually addicted to is the finished thing, the moment he gets to share it with the world. Most of his first 25 years were spent in a dark room, 16 hours a day, 7 days a week, in New York City.

Wanting the result and wanting the process are 2 separate motivations. You do not need to enjoy the grind to sustain decades of output. Discipline is the bridge between the two.

Track how many days you show up despite the pull to skip it. That number is your actual work ethic, over your inspiration.

8. Banking Business vs. Church Business

Jimmy Iovine handed Rubin one line that sorts almost any 2 collaborators you will ever work with.

“Jimmy is in the banking business. These are his words. He said, I’m in the banking business and you’re in the church business. And that’s the difference... I’m doing a passion and belief. And he’s doing a bottom line, what’s going to work, what’s going to be good for business.”

Iovine optimizes for what works, bottom line, commercially, every time. Rubin optimizes for belief instead, passion first, business outcome second. When Iovine was producing Tom Petty and heard a song he liked, he told Petty flatly: “That song is going to buy you a house.” Petty had never heard anyone describe music that way.

Neither mentality is superior. Mixing up the two mid-project turns a structural disagreement into something that feels personal.

Ask your co-founder or creative partner which business they think they are in. The answer will explain your last 3 disagreements. The strongest partnerships usually pair one of each.

9. The Professional Listener

Most conversations are just 2 people waiting for their turn to talk. Rubin built a career refusing to do that.

“I think in real life, people like to talk, and they don’t like to listen... I close my eyes. I really pay attention. It’s not wallpaper. It’s like I go into the music... In some ways, I think my relationship with music allowed me to never drink or take drugs, because listening to music for me is totally a psychedelic experience.”

Rubin traces the habit back to how he listens to music: eyes closed, fully inside it, never wallpaper. He points that same attention at people instead of songs, which is part of what makes him effective as a producer and later as a podcast host. He also calls himself a researcher, chasing the best version of anything he likes, coffee included, over settling for whatever is in front of him.

In your next meeting, count how many questions you ask before you offer an opinion. Aim for more than feels natural.

10. The House on the Mountain

Rubin’s clearest answer to the question this whole conversation is built around comes down to one imaginary house.

“You know, I give the example in the book of if you were to move into a house on the top of a mountain that no one could ever come and visit, and you made that the place that you most wanted to spend your time, and you really curated it to your taste. That’s the job. It’s not about, I’m making this to show off to someone else. I’m making this because I want to inhabit this... If you truly love what you do, they couldn’t pay you to stop.”

Picture moving into a house on top of a mountain that no one could ever visit. You curate it entirely to your own taste, because an audience was never the point. That is the job, according to Rubin.

He does not decorate his own home to impress a guest. He decorates it to be the version of a house he wants to live in, and other people’s reactions are a side effect, over the goal. Ask what you are already doing, over what you think you should be doing. Ask what you would keep doing regardless.

Ask yourself the house-on-the-mountain question about your current project: would you still build it if no one but you would ever see it?

The Life’s Work Playbook

Rubin’s whole philosophy collapses into one test:

• Founders: Build the product you would personally want to use, even if it never scales past you. Start there before you chase the pitch deck reaction.

• Investors: Watch for the megalomania-versus-imposter pattern in founders after a raise. It is the same instability wearing 2 different costumes. Ask your portfolio companies which one is showing up this quarter.

• People in tech: Apply the ruthless edit to your own roadmap, not just your product. Cut your task list to 40% of its current size this week, then rebuild it deliberately.

• Other industries: The house-on-the-mountain test works outside of music and outside of tech entirely. Ask whether you would keep doing your current work if no one ever saw the result, then act on the answer honestly.

5 principles to steal:

1. Reduce, don’t produce. Subtraction reveals personality. Addition hides it.

2. Cut past comfortable, then rebuild. You learn what the work needs by losing it first.

3. Pick a character, not just a style. A constraint with a point of view makes sharper decisions than a constraint alone.

4. There is no single correct process. Copy outcomes, over methods.

5. Build the house no one else will visit. If you would still make it for an audience of one, you found the thing.

Everything to build at that speed, in 1 place. Start here:

• The founder operating system: 58 free templates

• 89 best startup essays by top VCs and founders

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Full podcast:

Almost nobody works the money that costs no equity at all.

SBIR grants. The EIC Accelerator. R&D tax credits. Cloud credits worth six figures. The programs exist, the budgets are allocated, and a large share goes unclaimed every year, because finding what you qualify for means reading eligibility criteria across hundreds of programs in three jurisdictions.

Nobody does that. So the money sits there.

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Why Non-Dilutive Wins in 2026

The equity round takes longer than it did and costs more of the company. Non-dilutive capital does three things a round cannot:

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▫️ It stacks. Grants, credits, and equity are not alternatives. Founders working all three raise less and keep more.

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Family offices sit outside all of it. They rarely publish a thesis, almost never announce a check, and the person who decides is not the one on the contact page.

A crowded seed round has forty VCs reading the same deck. The family office two hops from your sector has seen nothing like it this quarter.

When a Family Office Beats a VC

▫️ Generational horizons. A fund with a ten-year clock needs an exit inside it. A family office can sit through a fifteen-year build.

▫️ They fund what the fund model rejects. First-of-a-kind hardware, capital-heavy deeptech, infrastructure, sectors out of fashion. A VC pass is often the model passing, rather than the person.

▫️ No committee. Often one principal and a small team, so the answer comes in a conversation. Faster yes, faster no.

▫️ Operator capital. The wealth came from building something, which means industry relationships and customer introductions.

The tradeoff: harder to find, slower to warm, and every one is different. There is no shared playbook the way there is with institutional VCs, which is why a structured list beats a scattered search.

What Is in It

▫️ Company name and live URL, verified

▫️ Decision-maker name and title, the person who actually allocates

▫️ Direct email and LinkedIn, so you skip the general inbox

▫️ Investment scope, searchable, so you filter to fit in seconds

▫️ Industry, revenue band, headcount, for context before you write

That scope field is what changes the work. Rather than reading 2,000 entries, you filter to the offices with a reason to care and work a list of thirty.

How to Work It

Family offices punish generic outreach harder than funds do, because no associate filters inbound. Your email reaches the principal or nobody.

1. Filter before you write. Thirty relevant beats three hundred sent.

2. Lead with the operator connection. If your industry overlaps with the one the family built its wealth in, that is your opening line, worth more than any metric.

3. Match their horizon. Say it plainly: this is a ten-year build. To a fund that is a warning. To them it is the reason to keep reading.

4. Ask small. One specific question converts better than a request for a call.

5. Keep one source of truth. A contacted flag and a do-not-re-contact list. The investor outreach loop runs this at volume.

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Until now you had to pick one. The VC Corner or The AI Corner. Two subscriptions, two separate libraries, two decisions.

Today I am putting them together.

One bundle. Both newsletters. Full premium access to everything in each. $199 for the year.

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What You Unlock

📂 The 4 VC Corner Libraries

▫️ Investor Lists — 10,000+ verified check-writers, filterable by stage, sector, geography

▫️ Pitch Decks — 300+ decks that raised, plus real investor memos

▫️ Financial Models — every model, calculator, and template

▫️ Resources — every playbook published

🤖 The 5 AI Corner Libraries

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▫️ Prompting & Context Engineering — the prompt packs and context frameworks

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The VC Corner: Most Popular

Investor databases
▫️ 200+ US AI angel investors, all builders, all searchable
▫️ 2,500 SaaS angels
▫️ 590+ US women angels
▫️ The US VC database and the global VC database
▫️ Family offices that cut pre-seed checks
▫️ 80+ non-dilutive funding sources
▫️ The ultimate investor list of lists

Pitch decks and memos
▫️ 26 decks that raised $400M in 2026
▫️ 16 unicorn decks, before the billions
▫️ The leaked Anthropic 2022 deck
▫️ Rippling’s $200M investor memo
▫️ Replit’s seed deck, rejected by YC three times
▫️ Monzo’s original deck

Financial models
▫️ The SaaS financial model
▫️ The 13-week cash flow and runway system
▫️ The SAFE conversion calculator
▫️ The startup valuation tool
▫️ The VC fund economics waterfall model
▫️ The M&A deal structuring toolkit
▫️ The growth accounting and virality model
▫️ The acquisition readiness model

Playbooks
▫️ What top VCs look for in 2026
▫️ What top VCs check in due diligence
▫️ The self-improving fundraising system with Claude
▫️ The McKinsey Pyramid Principle, wired into Claude
▫️ YC W26: the complete company database
▫️ The SpaceX IPO breakdown

The AI Corner: Most Popular

Claude & Anthropic
▫️ The Claude Code chief-of-staff system
▫️ What actually moves the needle with Claude
▫️ The Claude Code system that replaces a 5-person team
▫️ Clone your voice into Claude in a weekend

Prompting & Context Engineering
▫️ Loop engineering for coding agents
▫️ The Claude Code loops library
▫️ The context engineering guide
▫️ Karpathy’s autoresearch method

AI Agents
▫️ Build your first AI agent in a day
▫️ The self-evolving agent stack
▫️ The 5-agent sales team you can build this weekend
▫️ Give your agent its own computer
▫️ AI agent memory and context topology

AI Tools & Models
▫️ You are overpaying for intelligence
▫️ The LLM token cost optimization playbook
▫️ The AI inference engineering playbook
▫️ The AI code review checklist

Business & Investing
▫️ The one-person company operating system
▫️ Your GTM, run by agents
▫️ Build your own stock analyst with Claude, 12 prompts
▫️ The Cerebras Series A deck teardown
▫️ Mistral’s €105M seed memo, in full

The Math

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In early 2026, institutional holders tried to move roughly $600 million of OpenAI stock on the secondary market and could not find buyers.

In the same stretch, Anthropic closed a $5.5 billion tender at a $380 billion valuation with demand running hot.

We’re talking about two of the most valuable private companies on earth, during the same quarter, with opposite results.

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Table of Contents

1. Why Founders End Up Rich on Paper and Broke in Practice

2. The Number Everyone Quotes, and Why It Misleads

3. Eighty Percent of the Money Goes to Twenty Companies

4. Your Discount Was Set the Day You Last Raised

5. What Your Shares Are Actually Worth

6. Four Ways a Founder Secondary Dies Before It Closes

7. The Tax Rule That Costs More Than Any Discount

8. What to Do Before the Window Opens

1. Why Founders End Up Rich on Paper and Broke in Practice

Startup equity has always carried a catch, which is that you cannot spend it.

A founder’s stake only turns into cash at an exit, meaning the company is either acquired or goes public through an IPO. Until one of those happens, the shares are a number on a screen.

That has been a tolerable trade for startup founders in the past, because the wait from founding to exit ran about 5 to 7 years.

But recently, that timeline has roughly doubled. Companies now raise enormous sums privately and stay private for ten to fifteen years.

So a founder can hold $40 million of stock and still be doing sums about rent. Early employees have it worse, because they accepted below-market salaries for equity they cannot touch.

And that’s how the secondary market started growing.

Rather than waiting for the exit, shareholders sell a slice of what they hold to an investor who is willing to wait longer than they are.

2. The Number Everyone Quotes Is Measuring the Wrong Thing

According to a recent source, Carta has measured $61.1 billion of secondary share sales in the 12 months to June 2025, against $58.8 billion raised by every VC-backed IPO over the same period.

The conclusion drawn is that private share sales have overtaken the IPO market as a way for startup shareholders to get paid, which would mean the traditional exit no longer matters much.

But a distinction must be noted here.

Secondary sales put money in shareholders’ pockets. IPO proceeds put money in the company’s bank account. Those are different pots of money going to different people.

Founders and employees usually cannot sell anything at an IPO anyway. A lockup agreement typically bars insiders from selling for around 180 days after listing, so on the day a company goes public its founders receive nothing.

The fair comparison would be secondary sales against cash paid to shareholders in acquisitions, plus insider selling once lockups expire. On that basis, acquisitions alone are still far larger.

Then there is the question of whether anyone can measure this market at all, and the wider liquidity crunch has made it harder.

PitchBook’s Q1 2026 research put direct secondary transactions for the 12 months through March 2026 somewhere between $40 billion and $155.2 billion, midpoint $97.6 billion.

A spread that wide on a single defined quantity is arguably the most honest data point in the market.

Part of the reason is that much of what gets counted is not really a share sale. When a company refuses to approve a transfer, buyers find another way in.

They set up a shell company or setup a “Special Purpose Vehicle” (SPV), which either holds the shares or just holds a contract that tracks their price. Then they sell stakes in the shell instead. Sometimes shells sit inside other shells, two or three layers deep.

Every one of those layers can be counted as trading volume while the underlying shares never move.

Even at the largest estimate, almost none of that money is reachable for most companies.

3. Eighty Percent of the Money Goes to Twenty Companies

Secondary volume does not spread across the venture ecosystem. It clusters hard around a handful of perceived winners.

On Hiive, one of the main platforms where private shares change hands, the top 20 companies accounted for 86.4% of secondary trading value in Q4 2025, with the top five alone at 55.6%.

Caplight found that 75% of special purpose vehicles (SPVs) carrying carried interest were tied to 5 names: SpaceX, Anthropic, OpenAI, xAI, and Anduril.

And the anchor transactions say the same thing. SpaceX ran a $2.6 billion tender in December 2025 at a $1.25 trillion valuation, and OpenAI raised $6.6 billion in October at $852 billion.

That single OpenAI tender represented 6.2% of the entire year’s US secondary volume.

However, there is now real movement at the edges. By Q1 2026 the Hiive top 5 had fallen to 44.6% and the top 20 to 81.1%, which is genuine broadening inside one quarter.

Caplight counted 70 companies seeing their first-ever secondary trade during 2025, totalling $492 million between them. That averages around $7 million per company. Not too shabby.

The on-ramp is widening and it is still narrow. If you run a median seed-stage company, nobody is bidding for your stock next quarter.

For companies that do trade, the price was largely settled long before the founder started thinking about selling.

4. Your Discount Was Set the Day You Last Raised

Private shares usually sell for less than the price of the company’s most recent funding round, and that gap is called the discount.

The most repeated pricing claim in secondary coverage is that discounts have nearly vanished, with the market-wide median falling to low single digits.

That median is close to meaningless, because it averages companies that share nothing except being private.

Caplight’s data, broken out by when each company last raised, tells a different story.

Businesses last priced in 2021 trade at roughly 60% below that round. The 2024 group sits at a 17% discount, 2025 at 1%, and companies priced in 2026 sell at full value.

Your discount tracks when you last raised, not how the market happens to feel.

The reason is that 2021 was the peak of a funding bubble, when interest rates sat near zero and capital was close to free.

Buyers today refuse to pay prices built on conditions that no longer exist, and the 2021 overhang worked its way out of the trading population.

Much of the improvement in the headline median is not prices recovering. It is the 2021 group gradually dropping out of the pool of companies that trade at all.

Even at a fair clearing price, most founders start the conversation anchored to a number that was never really theirs.

5. What Your Shares Are Actually Worth

This is where founders lose the most money, and it comes down to owning a different class of stock from the investors.

Founders and employees hold common stock. Investors hold preferred stock, which comes with a protection called a liquidation preference.

A liquidation preference means investors get their money back first, before common shareholders see anything at all. The headline valuation reported in the press is calculated from the preferred share price, not the common one.

Take a company valued at $500 million after Series C, with 100 million shares and $150 million raised from investors. That is $5.00 a share, so a founder holding 10 million shares looks worth $50 million.

Sell for $200 million and investors take their $150 million back first, leaving $50 million across common. That is $0.83 a share, and the founder’s stake is worth $8.3 million.

At $150 million, common gets nothing.

The $50 million exists at exactly one valuation, which is today’s.

A discount to the preferred price is therefore rational, not a sign of weak demand.

It is also why the formal valuation of common stock, called a 409A, lands far below the last round price, and why how investors price it rarely matches what founders expect.

So, model the waterfall before discussing price. Pricing is the part founders can reason through, and the mechanics are where deals collapse.

6. Four Ways a Founder Secondary Dies Before It Closes

Founders very rarely miss out on liquidity because no buyer existed.

The Clause Nobody Read

Your financing documents almost certainly contain a right of first refusal, or ROFR. It lets the company and its existing investors match any outside offer before your shares can move.

Company-run sales, called tender offers, route around this: one price for all sellers, in a window US rules keep open at least 20 business days.

The 409A Advice That Is Backwards

A 409A is the independent valuation that sets the strike price on employee options, meaning what staff pay to turn options into real shares.

You will read that a tender protects your 409A while a private sale damages it. The reverse is true: a large sale open to everyone is the strongest evidence a valuer will see of what common is worth.

A tender buys control over timing, not protection from the price signal. Schedule option grants for after the number lands, or every hire that week gets a worse deal.

The Employees Who Cannot Take Part

Options must be converted into real shares before they can be sold. The strike price comes out of pocket, and US tax rules can treat the paper gain at conversion as taxable income.

So the longest-serving staff with the biggest stakes face the biggest cash bill, in a year when no cash has arrived. Letting them sell enough inside the same transaction to cover both costs fixes it, and most programmes omit it.

The Signal You Cannot Take Back

A founder selling reads, to the wrong audience, as a founder that is leaving.

So a better way of handling this is selling a small slice attached to a funding round, and applying the same rules as everyone else.

Selling 10% during a round everyone is celebrating signals confidence. Moving 60% behind the alley within a quarter signals what people fear.

7. The Tax Rule That Costs More Than Any Discount

For US founders especially, or those looking to relocate, the date on the share certificate is probably more important than the price negotiated.

There is a tax break called QSBS, short for Qualified Small Business Stock, that can wipe out federal capital gains tax on a sale of startup shares. It is usually the largest single number in the deal.

A law signed on 4 July 2025 rewrote this and shares issued after that date get a sliding scale. The current rule is: 3 years for half the gain tax-free, 4 years for 3 quarters, 5 years for all of it.

Shares issued on or before that date keep the old rules. 5 years or nothing, a $10 million cap, and no way to move existing holdings across.

Most founders selling today still sit under the old rules, where selling at year 4 is worth precisely nothing.

However, the new tiers are thinner than they look. Whatever is not exempt gets taxed at 28% rather than 20%, plus a 3.8% surcharge, so the real rate lands near 15.9% at three years and 7.95% at four.

On a $10 million gain, selling at year four costs roughly $795,000. Waiting twelve more months costs nothing.

If the company buys back its own shares as part of the deal, anti-abuse rules can void the break for shares issued around the same time.

Running the other way, a rollover provision lets you reinvest proceeds into another qualifying startup within 60 days and keep the clock running.

It is also federal only. California among other states ignores it, so a nationally tax-free sale can still produce a state bill.

None of which helps if you learn it during a live window.

8. What to Do Before the Window Opens

At the end of the day, the founders who manage to get liquidity are the ones who did the dull work months earlier. So here’s some advice.

1. Read your own documents. Pull the charter, financing agreements and stock plan, then write down what your ROFR, co-sale rights and transfer restrictions actually allow.

2. Get your tax position in writing. Note the issue date of every tranche, which rules apply, and when each holding period matures.

3. Agree an approach to liquidity with your board in a calm quarter, framed around keeping staff rather than personal need. A year early is the right timing.

4. Then attach the sale to your next funding round, where a fresh valuation and interested buyers are already in place, and a clean cap table makes it faster.

Sales usually cap participation between 10% and 20% of what you hold, so know your dilution across every round before picking a number.

There is a fair question about how long this lasts. Four fifths of trading value sits in companies that could go public soon, and PitchBook expects volume to drop when they list.

The infrastructure survives. Retail investors got access through OpenAI’s latest round, and the London Stock Exchange has launched the first regulated market for private shares.

Liquidity as a scheduled, repeatable process, running years before an exit, is a permanent change in how private companies work. This quarter’s volumes are not.

A founder who knows which tax rules her shares fall under, what her common is worth behind the preference stack, and what her ROFR permits is ready the day a window opens.

From top insights a

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• The Frontier AI Price Wars Continue 💸
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• VC Funds See Valuations Rise, Cash Returns Lag 🏦
  Carta recorded $3.9B raised across 86 new funds in Q1 2026 as fund performance improved on paper with higher median TVPI across most vintages. Actual distributions remain limited, while capital continues concentrating into larger funds with fewer LPs writing bigger anchor checks. [Carta]

  

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• U.S. Venture Partners (USVP) is raising up to $400M for its 14th flagship venture capital fund to back early-stage startups across enterprise, healthcare, and frontier technologies.

• Robinhood Ventures launched Fund II to continue investing in early-stage startups across fintech, AI, infrastructure, and consumer technology.

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

4 months ago · 45 likes · Ruben Dominguez

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Read more

Almost every founder finds out about churn too late. It’s usually the moment they realize that an entire quarter of hard-won new revenue did nothing except backfill the customers who slipped out the side door.

Yes, revenue probably climbed. Yes, the board update went well. But that’s exactly where the problem keeps spreading until it becomes too big to eliminate.

Almost all founders face the real cost of churn. When the existing accounts leak faster than they can be expanded, growth turns into a treadmill and they end up spending all the capital they raised just to stay level.

The reassuring part is that churn is really a stack of smaller problems sitting on top of one another and almost all of them are fixable once you stop treating the cancel button as the place to go and solve them.

Some of these fixes pay off within a week and others take most of a year, so the order you work through them matters as much as the effort itself.

A lot of churn comes down to trust, whether people believe the product actually gets them.

Plaid and The Harris Poll just published a report on that exact question in fintech:

▫️ 55% of people have used AI for money tasks in the last 12 months

▫️ 86% say it helps them understand their money better

▫️ 50% think managing money without AI will feel outdated soon

Worth a read if you’re building anything people need to trust with their money.

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Table of Contents

1. Churn Starts With the Market You Chose

2. Your Blended Churn Number Is Hiding the Truth

3. Activation Is the Lever Almost Nobody Pulls Hard Enough

4. The Lost Customers Who Never Meant to Leave

5. Annual Plans and Exit Flows Buy Time, Not a Cure

6. Negative Churn Is the Only Real Finish Line

1. Churn Starts With the Market You Chose

Before a single tactic matters, the category you built inside has already set the ceiling on how good your retention can ever be.

Why Your Product Category Dictates Your Retention Ceiling

Build a tool a marketer opens every single morning and retention mostly takes care of itself, because the product has become part of how someone does their job.

Build a tool a person reaches for once to make a birthday card and no amount of clever design will save you, since there was never a reason to come back.

Enterprise software holds on because it lives inside a daily workflow, small business churns harder because small businesses themselves come and go and consumer churns hardest of all because so many of the use cases are genuinely one-and-done.

The Compounding Math That Rewrites Your Economics

The compounding is what turns this from a nuisance into an existential problem.

The strongest B2B companies keep monthly churn under 2% and the distance between 2% and 5% a month is the distance between a customer who stays with you for 4 years and one who is gone inside 2, which ultimately rewrites every unit economic you thought you had.

This is why choosing the market is the highest-leverage churn decision a founder ever makes and it gets made long before anyone writes a line of code.

Pick well and everything that follows is optimization, while picking badly leaves you optimizing an exit flow on a product people were only ever going to touch once.

None of this shows up in the single blended number most founders stare at, which is exactly where the next mistake hides.

2. Your Blended Churn Number Is Hiding the Truth

A blended churn rate is an average and averages are very good at hiding the two or three cohorts doing the real damage.

Cohort Segmentation and the Search for Your True Users

You can post a comfortable 10% blended rate while, buried inside it, one cohort churns at 5% and another bleeds out at 20% and it is always that second group dragging the whole business down.

The way out starts with segmentation, which in practice means a short onboarding survey that captures role, company size and use case, then mapping that against who actually stays and who leaves fastest.

Suddenly the averages break apart into people and you can see who your product was truly built for.

Retention Curves and Gross Metrics That Reveal Your Real Floor

The sharper read sits one level deeper, in the shape of your retention curves rather than any headline number.

Plot each monthly cohort over time and watch whether the line flattens into a plateau or keeps sliding toward zero, because a flattening tail is the truest sign of real product-market fit you will ever get.

Even the best consumer apps lose most of their new users in the first week, so a perfectly flat line was never the goal and a loyal base that holds steady is.

One honest check keeps you from fooling yourself here, since net revenue retention can look wonderful while a couple of large accounts do all the expanding.

Reading gross retention alongside it shows the real floor of what you keep before any upsell and if that floor is weak, no amount of expansion will hold the roof up for long.

Once you know which cohort genuinely stays, the fastest way to keep more of them is to get every new user to value before they ever drift away.

3. Activation Is the Lever Almost Nobody Pulls Hard Enough

Activation is the first moment a new user hits the action that makes the product click and it decides more of your churn than anything you do later.

Friction and the Race to First Value

When ten people sign up and only one of them reaches that moment, you have an activation problem wearing a churn costume and the nine who left were lost before they ever gave you a fair shot.

Friction is the thing standing between them and the value, whether it takes the form of a credit card demanded upfront, a cluttered interface, or a long survey where the product should be and every extra step lengthens the time to first value that ends up deciding whether anyone comes back at all.

That single sentence is the whole battle, since a user who feels the payoff fast will forgive a clumsy interface, a missing feature and half a dozen other sins you have not fixed yet.

The founders who crack this almost always do it by hand long before they do it with software, sitting through onboarding calls and watching exactly where a new user stumbles instead of guessing from a chart.

A reliable tell is that generic requests for feedback get ignored, while an offer to personally walk someone to a real result fills a calendar overnight, because you are leading with value rather than asking for it.

The Free Plan as an Activation Engine

The free plan is the most underrated activation instrument in all of this and the best operators treat it as exactly that.

Cursor lets you build until you are locked in and suddenly out of credits, while Duolingo runs almost the opposite play and gates hard to force the habit, yet both work because both are engineered around a single moment of value.

Watching that moment move is the sort of thing a metrics dashboard exists for, since you cannot improve an activation rate you cannot see.

Even a perfectly activated base, though, loses real money through a channel most founders never think to look at.

4. The Lost Customers Who Never Meant to Leave

A surprising share of your churn comes from customers who never chose to leave at all and whose cards simply stopped working.

Involuntary Churn and the Silent Leak of Failed Payments

This is involuntary churn and most founders wave it off as a rounding error right up until they look closely. Across analyses from ProfitWell, Recurly and Paddle, failed payments account for 20 to 40% of all SaaS churn at many companies, which means a meaningful slice of the people you count as gone were loyal customers failed by an expired card or a bank decline they never even saw.

Smart Dunning Systems and the High Return of Technical Fixes

What makes this the highest-return fix on the whole list is that the problem is technical rather than emotional, so you are not trying to argue anyone out of a decision.

A single naive retry recovers only a fraction of those payments, while a proper dunning system of smart retries, card-updater prompts and well-timed emails recovers 70 to 80% of them, according to Chargebee’s work on the subject.

These are customers who wanted to stay, handed straight back to you.

The reason to do this before almost anything else comes down to effort against payoff, since a solid dunning setup is roughly a one-to-two week engineering project that can lift revenue several points with zero new customers acquired.

Most teams skip it because it feels less impressive than a homepage redesign, which is precisely why it stays such reliable free money.

Dunning stops the accidental leak, yet the customers who truly decide to walk need a different kind of friction and a different kind of offer.

5. Annual Plans and Exit Flows Buy Time, Not a Cure

Two tactical levers take a real bite out of churn, as long as you remember they defend the base rather than heal it.

Annual Plans Lock In the Year

An annual plan contractually holds a customer for twelve months, so churn drops mechanically while you collect a full year of revenue upfront and do wonders for cash flow.

The discount you attach doubles as a diagnostic, because an 80% discount to push annual usually signals a company burying a real churn problem, whereas something in the 20 to 40% range tends to be the healthy zone.

The catch is that an annual plan defers churn to the renewal date rather than curing it, so the risk simply reappears twelve months later in one lump.

That means the number worth watching becomes your annual cohort renewal rate, not the comfortable monthly figure that made everything look fine.

Exit Flows Catch the Ones Halfway Out

A good exit flow meets a cancelling customer with a pause instead of a goodbye, offers something like three months for a dollar and asks which missing feature pushed them toward the door so you can go build it.

Anyone who has tried to cancel a streaming subscription knows this move from the inside, because the click meant to end the relationship instead surfaces a discount tempting enough to keep you paying for months you never planned on.

There are tools out there that make these flows easy to run, though it is worth admitting this is the lowest-leverage item on the list, precisely because it steps in after the decision has already hardened.

A save offer wins back a slice, but defense at the final second is a poor substitute for never losing the customer in the first place.

All of these levers protect the base you already have, yet the founders who stop worrying about churn entirely are the ones whose base grows on its own.

6. Negative Churn Is the Only Real Finish Line

The target was never zero churn, which is both impossible and beside the point, because the real prize is a base that expands faster than it leaks.

Net Revenue Expansion and the Reverse Treadmill

Negative churn means the customers you keep add seats, upgrade tiers and buy adjacent products faster than the leavers take revenue away, so your existing accounts grow the topline even if you never sign another logo. This is the treadmill running in reverse, finally working for you instead of against you.

It is worth sitting with one number here, because median small-business software runs net revenue retention near 97%, which means the average SMB SaaS company shrinks inside its own base every year before it wins a single new customer.

At the other end of the spectrum, the best products turn one dollar of existing revenue into far more the following year, the way Slack at its peak grew each customer relationship into $1.40 the next year before any new logo was counted. The distance between those two worlds is the whole game.

Expansion Mechanics and the Gross Retention Caveat

Getting there follows a fairly consistent path, which is to sell into businesses whose teams grow, bake collaboration so deeply into the workflow that the next colleague has to be added to get the job done and expand into adjacent products the way Figma did with FigJam and Figma Make.

How you price is equally important, since usage-based and seat-based models let revenue climb alongside a customer’s own success rather than sitting flat while their usage soars.

One honest caveat keeps this from becoming a vanity metric, because net revenue retention above 100% can still hide painful logo churn when two or three whales are doing all the expanding. Keeping gross retention in view alongside it tells you whether the growth is broad and durable or propped up by a handful of accounts that could all leave in the same quarter.

So you cannot drive churn to zero and chasing it there is the wrong instinct, because a little churn is simply the market being honest about who you fit and who you never did.

Once you stop treating it as a cancel-button emergency and work the stack in order, fixing the market you serve, the first taste of value, the payment leak that never had to happen and a product whose base compounds.

That’s when you finally find the cure.

Nothing about the business changed. Two early contractors had never signed IP assignments. The VC’s legal team caught it in week one, one contractor had moved abroad, and the term sheet expired while they chased a signature. They eventually raised, at a lower valuation, with a worse lead.

Here is the asymmetry nobody tells founders about.

When a private equity firm evaluates a company, they hire McKinsey, Bain, or LEK to run commercial due diligence: 3 to 5 consultants, 6 to 12 weeks, a 100 to 200 page report, at $100K to $500K per engagement. Some firms spend closer to $1M, and if the deal dies, that money is gone.

When a VC evaluates you, that same report gets written. About your company. And nobody sends you a copy.

About 30% of deals fall apart during diligence. Not at the pitch. After the yes.

So the move is obvious: run the diligence on yourself first, using the same structuring discipline the consultants use, and fix what it finds before anyone else looks.

Inside the playbook:

▫️ The MECE data room architecture, the McKinsey structuring rule applied to 8 folders

▫️ Six copy-paste prompts, including the VC-associate pre-mortem that finds your gaps first

▫️ The 9 deal-killers, ranked by frequency and fix time

▫️ The Pyramid Principle applied to diligence responses, and the 24-hour rule behind it

▫️ The three 2026 requests most founders have never been asked

▫️ The staged disclosure tiers and the 10-day build sprint

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1. The Reframe: You Are the Target

Read more

There are moments where a whole market gets rewritten. The best GTM playbooks were built in 2021, when cheap capital and predictable pipelines made scaling simple: add reps, buy leads.

The ICONIQ State of Go-to-Market 2026 report, built on a survey of 150+ B2B GTM executives, shows that broke. Two numbers sit side by side and look like a contradiction:

▫️ Sales cycles fell from 25 weeks to 19 in a single year.
▫️ Sub-one-year contracts climbed from 4% of deals to 13%, while three-year deals slid from 28% to 23%.

Buyers got faster and less committed at the same time. More deals moving at once, and every call now has to earn the next one.

Which is exactly why the seller who remembers what call one asked for is the one who wins call two.

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Walk into call two remembering exactly what call one asked for. That is what buyer confidence is built on

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Table of Contents

1. The Cost of Producing a Signal Collapsed

2. Buyers Are Signing Faster and Committing to Less

3. Pipeline Moved to the Sellers,and Marketing Got Misread

4. The Proof of Concept Became the Whole Sales Process

5. Lean Teams Win Because Handoffs Destroy Conviction

6. Pricing Is Now the GTM Decision That Decides Everything

7. What to Actually Do, Depending on Where You Are

1. The Cost of Producing a Signal Collapsed

Almost every artifact your buyer once read as evidence of effort now costs nothing to produce.

The personalized cold email, the landing page, the case study, the competitive battlecard, the ROI calculator, the tailored demo script.

A competent operator can generate all of it before lunch and so can every one of your competitors.

When a signal becomes free to fake, buyers stop reading it as a signal.

Outbound worked in 2019 because sending ten thousand relevant emails was genuinely hard.

Now it is trivial, so the binding constraint is inbox capacity rather than your creativity and volume plays collapse together instead of decaying gracefully.

Justified Confidence as the Scarce Good

Attention was the scarce resource in 2021 and it is still hard to win.

What your buyer is actually short of in 2026 is justified confidence. They can produce a competent-sounding summary of your entire category in an afternoon. What they cannot produce is proof that your product works on their data, in their stack, under their compliance requirements.

Once you accept conviction as the scarce good, the strange behavior in the buying data starts to make sense.

2. Buyers Are Signing Faster and Committing to Less

The Hesitation is a Hedge

Speed and commitment used to move together and in 2026 they came apart.

ICONIQ recorded a roughly six-week compression in average sales cycles across 2025.

At the same time, sub-one-year contracts more than tripled as a share of new logo deals and multi-year commitments kept sliding.

Read those together and the story is not a budget freeze. Money is pouring into AI. The hesitation is a hedge.

Surviving the Next Model Release

The question in the buying committee is no longer whether they can afford this. It is whether you will still be the right answer in twelve months, or whether their platform vendor ships an adequate version of you for free in the next release.

That fear does not respond to a discount, a mutual action plan, or a better closing sequence.

It responds to a demonstrated reason your value survives the next model release. The workflow you own, the customer-specific data that accumulates in your system, the integration surface, the accountability you carry for the result. The playbook rewriting itself around non-reproducible assets is not a philosophical exercise, it is now a sales objection you have to answer in the room.

If your process does not explicitly answer why this does not get commoditized, procurement is answering it for you in a room you are not in.

Your biggest competitor in 2026 is a sentence and the sentence is ‘Let us wait six months, this space is moving fast.’

That same fear explains why demand looks healthy while revenue does not.

3. Pipeline Moved to the Sellers and Marketing Got Misread

The most quoted number in the report is also the one being misused most aggressively.

Sales Now Owns the Top of the Funnel

Among high-growth companies under $100M ARR, sales generates 62% of new logo pipeline while marketing generates 19%. For their slower-growing peers, those figures are 47% and 34%.

The pattern holds across revenue bands.

The fastest-growing B2B companies are seller-led at the new logo stage, with channel as a secondary lever and marketing as something else entirely.

If your forecast still assumes marketing fills the top of the funnel, the forecast is describing a company that no longer exists.

The Attribution Trap

The trap is reading 19% as a verdict on marketing’s competence.

Buyers now educate themselves through content, peer communities, private Slack groups and language models and then surface inside an outbound reply or a contact form.

Marketing did not stop working. It stopped being attributable.

Gut marketing on the strength of a sourcing statistic and you starve the credibility that makes seller-led outbound land in the first place. You will look efficient for two quarters, then spend the third wondering why nobody replies.

The mandate that actually works is moving marketing from lead volume to buyer conviction.

Public results, hard benchmarks, named references, founder presence, category narrative. If you are rebuilding that engine from a standing start, a GTM system to start from beats improvising channel by channel.

Conviction has one decisive moment and the ICONIQ data is unusually blunt about where it sits.

4. The Proof of Concept Became the Whole Sales Process

Proof Converts Better than Persuasion

One number in the funnel outruns everything else in the report.

Free trial and proof-of-concept motions now convert to paid at roughly 50%, up from about 36% a year earlier. Traditional SQL and demo paths convert at 30% to 40%.

Two forces are producing that gap simultaneously. Proof converts better than persuasion and only serious buyers agree to a real pilot, so the motion selects hard for intent. Both are arguments for treating the pilot as a gate rather than a favor.

Most teams still run POCs informally. No written success criteria, no owner, no clock. That is not a sales stage, it is free consulting that teaches a prospect how to stall.

Designing a Pilot as a Product

A pilot designed as a product looks like this:

• Written success criteria, agreed before anything starts. If the buyer will not put in writing what working means, they are not buying.

• A hard timebox of two to four weeks. Long pilots rarely fail cleanly. They dissolve, which is worse, because nobody learns anything from a dissolution.

• A named owner on the customer side whose internal reputation is attached to the outcome.

• Time to first value, instrumented. Measured from kickoff to the first moment the customer sees something they could not have gotten any other way.

ICONIQ also found that companies scale POC support by contract size, with larger deals receiving dedicated one-to-one help from solutions architects. For AI products this is close to non-negotiable, because a working, well-trained agent running inside the trial window is the entire proof.

Stop qualifying on budget and authority. Qualify on willingness to run a scoped, timeboxed pilot.

A motion that depends on depth rather than volume changes what the team around it should look like.

5. Lean Teams Win Because Handoffs Destroy Conviction

Leaner Teams Also Perform Better

The headcount gap in the ICONIQ data is wide enough that cost discipline cannot be the whole explanation.

At $10M to $25M ARR, AI-forward companies run about 20 GTM full-time employees against 35 for lower-adoption peers, a 43% difference.

The gap holds at every band: roughly 45 versus 65 at $25M to $100M, 125 versus 165 at $100M to $250M and 275 versus 350 at $250M to $500M.

The leaner teams also perform better. Where AI is fully embedded in the GTM process, 67% of ramped account executives hit quota, against 59% where it is not. In SMB the spread is wider still, with high-adoption teams averaging 106% quota attainment against 80%.

Every Handoff is Where Context Leaks

The SDR to AE to SE to CSM assembly line solved an industrial problem, which is too many touches, not enough hands.

It bought throughput by splitting the work into narrow, cheap-to-train roles.

Once the bottleneck moves from touches to depth of conviction, that same division of labor turns hostile, because every handoff is a place where context leaks and context is now the product.

Which is why a smaller number of senior sellers carrying a deal end to end beats a larger relay team. AI absorbs the research, the prep, the notes, the follow-up, the CRM hygiene and the first draft of everything and that is precisely the work the extra headcount used to do.

The failure mode is bolting AI onto a bloated organization. You do not get a lean team out of that. You get a bloated team sending more email.

One honest caveat before anyone screenshots the headcount table: this is a correlation. Good companies adopt AI, run lean and hit quota because they are good companies. Buying the tools will not turn a mediocre org into an elite one.

Efficiency only compounds when the revenue underneath it is durable and that is where the 2026 data gets uncomfortable.

6. Pricing Is Now the GTM Decision That Decides Everything

The compensation shifts in the report are a symptom. Pricing is the disease.

Net New Recurring Revenue as a component of AE compensation rose from 25% of companies in 2025 to 33% in 2026, the largest single-year change ICONIQ tracked and Net Dollar Retention as an AE metric climbed another five points. Median NRR now sits between 108% and 110%, with the top quartile holding above 123%.

The Seat Problem Nobody Says Out Loud

If your product replaces human labor, seat-based expansion is structurally broken. Your ROI story is that the customer will need fewer people. Your revenue model asks them to hire more.

Compressing NRR is the first tremor of that contradiction and no compensation plan repairs a pricing model that punishes you for working.

Outcome Pricing Is the Most Seductive Trap in the Category

ICONIQ’s parallel AI research found 58% of companies still keep a subscription or platform component, with consumption-based structures at 35% and outcome-based at 18% and 37% planning to change their model within twelve months.

Gartner projects that by 2030 at least 40% of enterprise SaaS spend will move toward usage, agent or outcome-based structures.

The catch arrives quietly. When you charge per resolved ticket or per closed deal, you are underwriting the result. Your cost of goods becomes inference and your margin becomes the spread between what you charge and what failure costs you.

That is an insurance business wearing software’s clothes and software multiples get paid for software margins. The tradeoffs across hybrid, usage and outcome-driven models deserve more scrutiny than most founders give them and if you are still setting the number for the first time, get that right before you touch anything else in this article.

One question settles most of it. When this customer succeeds wildly, does my revenue go up? If the honest answer is no, you do not have a go-to-market problem.

Which leaves the only question that matters on Monday morning.

7. What to Actually Do, Depending on Where You Are

ICONIQ surveys well-funded, AI-adjacent, mostly growth-stage companies, so applying these benchmarks uniformly across stages is its own mistake.

Before $5M ARR

Ignore the headcount tables completely. They describe companies ten to a hundred times your size and survivorship bias is doing real work in those numbers.

Your job is a repeatable conviction motion rather than a repeatable lead motion. The founder sells, the founder implements, the founder watches the first ten customers actually use the thing.

Hiring a VP of Sales to discover a motion you have not found yourself was expensive in 2021. In 2026 it is fatal, because the motion itself is still moving.

$5M to $20M ARR

This is where the pilot becomes a gated stage with criteria, an owner and a clock and where you build a loss-reason taxonomy that separates three very different failures: lost to a competitor, lost to no decision, lost to a budget that went somewhere else.

Those are three diseases with three treatments and most revenue leaders prescribe more pipeline for all of them. Rebuilding the funnel math stage by stage against two years ago usually shows the damage sitting late, in the numbers nobody wants to read honestly, not at the top.

Start building the reputation surface here too, because public proof is what makes seller-led outbound land at the next stage.

$20M ARR and Up

Now the benchmarks bite. Audit headcount against the leanest team that could hit the number, not against what your category looked like in 2022.

Audit the comp plan against durability and phase changes in rather than performing mid-year surgery that costs you more in trust than it recovers in behavior.

Then retire the 2021 scorecard. MQLs and marketing-sourced percentages go. Pipeline per rep, pilots started with written success criteria, time to first value, NRR by cohort, gross margin per customer and loss-reason mix take their place and the dashboard that tracks them is worth more than another quarter of activity reporting.

Every finding in the ICONIQ report points at the same place. Demand stopped being the constraint.

Your buyer knows they have the problem, has almost certainly heard of you and can generate a passable analysis of your whole category before the first call ever happens.

What they cannot manufacture for themselves is confidence that choosing you is a safe bet in a market that keeps moving under their feet.

Build the go-to-market around producing that confidence quickly and provably and the numbers in that report start moving in your favor all at once.

So here’s the takeaway: Keep optimizing for volume and you will spend 2026 pouring leads into the top of a funnel that leaks at the exact point where nobody believes you yet.

Before we start: I’ve partnered with someone at the epicenter of this shift.

Rohan Varma is a Product Manager on Codex at OpenAI, their fastest-growing product since GPT. Before that, he was the first PM at Cursor, which scaled past $1B ARR faster than almost any B2B company in history.

He wrote this piece. He also teaches it live inside Product Faculty’s AI Builder Fellowship, alongside PMs and AI leads from OpenAI, Google, and Anthropic.

Become a Fellow (with discount)

So what follows is not theory from the outside. It’s the operating model from someone building it at the frontier:

There’s something that doesn’t add up when you look at how the most successful AI companies actually operate, and I think about this constantly.

• Codex - the fastest growing product of OpenAI after GPT- just runs on two PMs, one designer, and roughly 40 engineers, collectively responsible for 10 to 12 distinct product surfaces. At any traditional company, each of those surfaces would warrant a dedicated squad of 15 to 20 people.

• Cursor operated almost identically (40 engineers, one PM) and scaled to over a billion dollars in ARR faster than almost any B2B company in history.

• Obsidian, a note-taking app used by over a million people and valued at $350 million, runs on 3 engineers and 9 total employees. No VC funding. No org chart. No middle management layer.

And then there’s what Sam Altman said earlier in 2024 that we’ll see the first one-person billion-dollar company within our lifetimes, possibly very soon, powered entirely by AI agents doing what used to require hundreds of people and it REALLY happened…

A company called Medvi, built by Matthew Gallagher, has become one of the strongest examples of AI-enabled leverage.

In 2025, he did $400M+ alone and now in 2026, they’re projected to do $1.8 billion & he has now hired his brother too (imagine just two people).

Not to mention the crazy rise of OpenClaw: how Peter single-handedly built one of the most useful AI personal assistants and scaled it so massively that it became a household name worldwide is insane. Again, just one person with a bunch of agents.

If you showed these numbers to a VP of Engineering at any large enterprise, they’d tell you these companies are running on borrowed time. That you can’t sustain quality, reliability, and growth at this scale without proper resourcing. And for most of the last 30 years of software history, they would have been completely right.

But something changed. And most organisations haven’t fully reckoned with what.

We ran a hackathon with a large enterprise recently: the kind of company with thousands of developers and a mature engineering culture. They scoped a modernisation project at 10 engineers, 12 months.

Our two engineers with Codex completed it in JUST THREE DAYS. Imagine that.

The two engineers who did that in three days think in this operating model by default.

You can learn it directly from the people building it: Rohan, who wrote this piece, teaches it live inside Product Faculty's AI Builder Fellowship, alongside PMs and AI leads from OpenAI, Google, and Anthropic.

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The tools were the same. The intelligence was the same.

The difference was that those two engineers weren’t operating inside a system designed for the old constraint. That result didn’t make headlines the way a new model launch does.

But it’s the evidence that the operating model in the AI-native world itself is up for rethinking from scratch.

My Goal with this Newsletter Deep Dive

This isn’t a list of AI tools to adopt. It isn’t a framework for running better sprints.

It’s the full structural logic of how AI-native companies are organised: the people model, the process model, the tool model, the economic model… and what it looks like to start rebuilding in that direction from wherever you are today.

Because the gap between companies that understand this at a structural level and companies that are layering AI onto their existing operating model is going to be one of the defining competitive fault lines of the next five years.

It starts with the single assumption that every product organisation is currently built on: one that almost nobody has made explicit, because until very recently, nobody needed to.

Section 1: Hardcoded to Code: The Assumption Baked Into Every Product Org

Every decision your product organisation has ever made traces back to the reality so foundational that nobody wrote it down. The belief is this: writing code is hard, and engineers are your scarcest resource. Pull on that thread and watch how much unravels.

The org chart you’re running right now isn’t the result of careful organisational design. It’s the accumulated fossil record of every time your company ran into that constraint and built something to manage it.

People:

• You hire more engineers, then hire layers to coordinate them: EMs, TPMs, scrum masters

• You hire PMs to ruthlessly prioritise because you cannot waste a single engineering cycle

• You hire specialists at every layer (QA, security, platform, data) because the work becomes too complex for generalists

• Then you hire people to manage the specialists

Because everybody knows that 30 to 50 percent of total effort in any traditional team right now is pure coordination overhead. Not building or shipping or creating value.

The builder mentality on that card is exactly what AI-native companies are hiring for right now, and it is teachable.

Product Faculty runs live certifications in AI Product Management, Agentic Engineering, and AI-Native PM with Claude, taught by people doing the work at OpenAI and Google:

Become an AI-native builder

Process:

• Sprint planning exists to make sure engineers build the right thing before they start, because changing course is expensive

• Backlog grooming exists for the same reason

• Code review exists because every human-written line needs another human to verify it before it touches production

• QA, staging, feature flags, phased rollout, on-call incident response: every checkpoint exists to protect expensive engineering time from waste

Tools:

• The IDE is a precision instrument for one engineer writing code character by character

• Jira exists to coordinate humans across a slow-moving pipeline where work takes weeks

• Daily standups exist because when work moves slowly and dependencies are everywhere, people need regular synchronisation just to stay unblocked

Another way to think about it: your org structure is quietly holding you back from what AI now makes possible.

If we were starting from scratch today, with the tools available today, would we design our organizations the same way?

(AI-native companies built in this era might be our best guide for what the answer looks like.)

The answer, almost universally, is no.

That gap (between what you’d build from scratch and what you’re actually running) is precisely the opportunity and the threat hiding inside every product organization in the world right now.

Section 2: Turns Out Code Is No Longer Hard

When coding agents started appearing a few years ago, most engineering leaders filed them under ‘productivity tools’: the same mental category as a better IDE, a smarter autocomplete, a faster CI pipeline.

But the tools didn’t stay there. In just two years, they evolved through three distinct phases: autocomplete assistants like GitHub Copilot and Cursor Tab, to task-level CLI agents like Claude Code, to fully agentic systems like Codex that take complex work, iterate, and ship results on their own.”

Just to put things in perspective: A task that takes an engineer ten hours now takes ten minutes with Codex.

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At Codex and Cursor, entire phases of the traditional SDLC have collapsed or disappeared:

• Code review as a separate stage? When agents verify their own work at the point of generation and humans review outcomes rather than line-by-line diffs, the traditional review process is redundant.

• Elaborate sprint planning? When building the wrong thing costs thirty minutes of agent time instead of two weeks of engineering time, you can afford to build first and evaluate second. Which is exactly what we do.

• The build/decide sequence? Completely inverted. The question is no longer “should we build this?” asked about a spec. It’s “ I had this idea based on XYZ. We built this - should we ship it?” asked in front of a working product that an agent put together overnight.

This one inversion changes the entire epistemology of how product decisions get made, because you’re no longer deciding based on reasoning about what something might be, you’re deciding based on direct experience of what it actually is.

Now, let’s dive into the AI Operating model.

Section 3: Layer 1: People — The AI-Native Talent Model

If you want to understand how an AI-native company thinks about people, forget everything you know about org design and start with a single question: what is the actual work that only a human can (and should ) do right now, and what is everything else?

At Cursor and Codex, the unit of work is almost never more than two or three people: not per squad, but per initiative, including initiatives that at a traditional company would have fifteen to twenty people assigned to them.

That isn’t a resource constraint (they’ve billions in funding) being managed heroically. It’s a structural choice rooted in a simple insight: every person you add to a team doesn’t just add capacity, they add coordination overhead. And that overhead compounds.

Fifteen people can spend more time in coordination than creation without anyone noticing.

But the reframe that matters most here isn’t about cutting headcount.

The leaders who look at this and see a cost reduction opportunity are making a category error. The right frame is leverage.

The reactive ones say: great, I can cut headcount by 80%.

The category-defining ones say: now I can actually do 5x more things.

Output doesn’t shrink. It multiplies. And now you have five compounding bets running in parallel instead of one heavily-staffed bet moving slowly.

In a traditional product organisation, a significant portion of PM work is operational:

• Managing the backlog

• Running sprint ceremonies

• Writing tickets detailed enough for engineers to execute without ambiguity

• Coordinating between design and engineering

• Tracking progress, running retros

All of that exists because someone has to manage the human pipeline. In an AI-native company, almost none of it exists in the same form. What the PM role collapses into (and expands into) is two things:

• Strategic direction & being a builder on a horizon of twelve months or more: making sure the organisation is pointed at the right mountain, funding the right long-horizon bets, staying two degrees ahead of where engineering is naturally going to take the next step and eventually moving away from PRDs to building a working prototype themselves.

• Go-to-market: making sure what gets built actually reaches the people who need it and generates the business outcomes that justify the work.

That second part carries a strange status problem in product culture. GTM sounds less intellectually interesting than product strategy, less creative than design. It has a faint whiff of salesmanship that product people have historically kept at arm’s length.

But in an AI-native company where the build is nearly free and the strategic direction is set, the question that actually determines whether the business succeeds is almost always a GTM question.

• Can you get this to the right people?

• Can you price it in a way that captures the value it creates?

• Can you build the feedback loops that let you iterate toward what the market actually wants?

That’s where the leverage exists.

Jensen Huang said this about top engineers at Nvidia:

“Let’s say you have a software engineer or AI researcher and you pay them $500,000 a year; At the end of the year, I’m going to ask that $500,000 engineer: How much did you spend in tokens? If that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed.”

The best engineers today are running multiple workstreams simultaneously, each delegated to an agent, reviewing outputs rather than writing every line, making architectural and strategic decisions that shape what agents can do rather than personally doing the work.

“If you are ever watching an agent work, you are wasting your time.”

Your job is to be the strategic layer between them all and managing outcomes.

And remember, when an agent fails at a task, the failure is almost never an intelligence problem.

Instead of concluding “agents can’t do this”, ask “what would I need to give every future agent to make sure this doesn’t happen again?”

To close context gaps, you can either build context/soul-style .md files, or set up an LLM-based learning system that continuously pulls context from your apps every ~20 minutes and keeps improving over time (Andrej Karpathy has a great explanation of this).

For most of design history, the designer’s leverage was “in” the making.

The ability to hold Figma in your hands and produce something that an engineer could actually build… that was the skill. The bottleneck was craft, and craft took time.

That bottleneck is dissolving.

Designers at the frontier are shipping PRs directly through Codex because the gap between intent and implementation has collapsed, and with it, a large portion of what the traditional design role was actually doing.

What remains (and what expands) is two things:

1. Taste and creative direction at a higher altitude. When anyone can generate a reasonable interface in minutes, the question shifts from can you make it? to “do you know what it should be?” The best designers are becoming something closer to creative directors: setting the aesthetic and experiential standard that agents and tools execute against, writing the briefs, reviewing the outputs, and catching the ten thousand ways a generated interface can be technically correct and completely wrong. Judgment is the scarce resource in a designer’s arsenal.

2. Systems thinking over screen thinking. When agents are producing interfaces, variations, and flows at volume, the designer’s job isn’t to design each one. It’s to set the standard that makes each one consistent without being touched. That means the design system becomes your primary leverage. Not as a component library that engineers reference, but as a set of constraints precise enough that AI-generated output inherits your taste by default. The designer who builds that foundation shapes everything downstream, including the things no one will ever individually review.

Ramp’s product designer job description makes it operational.

This is what they expect you to do:

1. Start in an LLM. Use Claude to clarify intent, draft short PRDs, surface risks, edge cases, and initial approaches. Align the team before a single frame is made.

2. Validate assumptions with self-serve research. Talk to customers directly. Run quick tests. Treat research as a velocity tool, not a gate.

3. Prototype using AI tools. Use tools like Codex to build and iterate on flows and simple interfaces. Let AI generate the code while you guide structure, behavior, and UX quality. Partner with engineers to decide what moves into the product.

4. Then (and only then) bring it into Figma. Translate validated concepts into full state coverage, system alignment, and production readiness.

Notice the sequence. Figma is last. It’s the finisher, not the starting point. The work happens in LLMs and prototypes and customer conversations first. Figma is where you land once you already know what’s right.

Ramp’s Co-founder: “We only hire builders (and we’re on a hiring spree)! Reply with something you’ve built. I’ll read them personally. We’re interviewing the best ones.”

Curiosity/ “builder mentality” is the single hiring signal that predicts success across every role in an AI-native organisation.

Not seniority or domain expertise or years of experience with a particular stack.

The people winning are the ones who feel productive discomfort every time they do something manually that an agent could have done. Who asks “why didn’t I just run an agent for that?” the same way a good engineer asks “why is this code duplicated?”

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Section 4: Layer 2: Process — The AI-Native Workflow

Most organisations struggling to transform their product processes aren’t struggling because they lack the tools or the talent. They’re struggling because the logic of the old process is so deeply embedded that even when they adopt new tools, they unconsciously reconstruct the old process around them.

The AI-native workflow isn’t a faster version of the traditional SDLC.

It’s a different thing entirely, built on a different answer to the question of what’s scarce and what’s cheap.

In a traditional product process, you build late.

The sequence looks like this: Identify problem → validate hypothesis → write spec → design review → technical scoping → stakeholder alignment → engineering begins

In an AI-native process, the sequence collapses:

Identify direction → agent builds working version in 30-40 minutes

→ team evaluates real software → decide whether to ship

At Codex, we ship roughly two out of every ten things we build. The other eight get thrown away, parked, or inform the next idea.

This also changes what good product judgment actually means.

In the old model, it meant being right before you built: predicting what would work before committing resources.

In the AI-native model, it means recognising signals in a working prototype: knowing what to iterate on versus discard. And because you can test internally before committing, the stakes of being wrong drop dramatically.

You don’t have to be right. You just have to be willing to try.

The skill shifts from predicting to evaluating.

At Codex, we don’t do sprints.

We don’t have a centralised backlog groomed on a two-week cadence. We don’t do sprint planning meetings or retrospectives.

Engineers at Codex are largely self-directing day-to-day, moving toward a direction the product team has set on a twelve-month horizon, making local decisions without a process gating every one of them.

What the PM does instead is think further out and act more externally:

• Are we investing in the infrastructure that will matter in six months?

• Are we building relationships with customers who will tell us what matters in twelve?

• Are we funding the long-horizon bets that engineers won’t naturally prioritise because the immediate next step is always clearer than the strategic direction three steps from now?

That’s where the PM earns their place in an AI-native team.

What Happens to Code Review

When an agent is handling the mechanical checking, code review changes shape. Less line-by-line verification, more outcome evaluation:

• Does this work the way we intended?

• Does it behave well at the edges?

• Does it fit the product strategy we’re executing against?

Those are judgment questions, not technical auditing questions and that’s exactly what code review should be when the machine has already done the rest.

There’s a practical method any team can apply immediately, regardless of where you are in your AI transformation. Take any repeating workflow and decompose it into its atomic steps. Write them out literally. Then sort every step into one of two buckets:

• Human judgment required: decisions that need taste, creativity, relationship, context, or accountability that only a person can carry

• Information processing: steps that follow a pattern, move data from one place to another, apply a rule, or produce a deterministic output given the right inputs

The second bucket is almost always larger than people expect. And it’s the category agents can own right now, with minimal friction, starting this week.

The most important thing to understand about the AI-native process is that it doesn’t feel like efficiency. It feels like creative freedom.

Engineers building things they never would have attempted, PMs exploring ten hypotheses, designers seeing their ideas in production the same day, etc and I believe every leader should give full autonomy to their teams.

Section 5: Layer 3: Tools — The AI-Native Leverage Infrastructure

Two things have changed at the tool layer that matter enormously and each one has implications that go well beyond the engineering team.

What’s replacing the IDE isn’t an upgraded version of it, it’s something categorically different.

I think of it as an ADE: an Agentic Development Environment.

In an ADE like Codex, you’re not just writing code. You’re:

• Briefing agents and running them in parallel

• Connecting them to your tools: Slack, Gong, Notion, Linear, your codebase

• Reviewing what they produce and deciding what to act on

The interface is less about technical precision and more about delegation clarity: can you describe what you want well enough that an agent can go do it? That’s a skill, but it’s one that a PM, a designer, a finance analyst, or an event planner can develop just as readily as an engineer.

Whatever your role is, build more agents to do your task because there will come a point where you won’t be able to compete with anyone or your competitor if you’d just be “using AI” here and there.

No. Agent for every single work and keep refining it until it gives the output as yours or even better.

In the future, you’ll see many roles where the whole job would be to just manage, build, and scale autonomous agents or you can call it, “agentic transformation.”

In a traditional technology business, the dominant cost is human capital: salaries, benefits, equity, recruiting, onboarding, management overhead. Headcount is the primary lever for both capacity and cost. You want to do more, you hire more.

AI-native businesses will increasingly look different but the transition is still playing out in real time.

A year ago, inference costs were low enough that the only thing that mattered was adoption: getting people to actually use the tools. That’s still where most organizations are today. Agents exist, but most employees aren’t using them heavily, so leadership is still focused on the basics: are people using this at all?

The best orgs are already thinking differently. The people burning the most tokens aren’t a cost problem, they’re a signal. They should be put in front of the rest of the organization to show everyone else what’s possible. Token spend, at current levels, is not something to worry about. The value returned is orders of magnitude higher.

A year from now, that calculus may shift. As agent usage scales, inference spend will start showing up meaningfully in budgets, and organizations will need to think more deliberately about where they’re directing that intelligence and why.

But that’s a future problem. Right now, the constraint isn’t cost, it’s imagination.

Section 6: How to Start - The AI Leverage Playbook

The question I get asked most often by the leaders: “okay, I understand the direction, but where do I actually start?”

The honest answer is that there’s no single right entry point. A nine-person startup has different constraints than a forty-thousand-person enterprise. But there are principles that hold across all of them, and there are failure modes that show up so consistently that they’re worth naming before anything else.

The Two Failure Modes to Avoid

1. The first and biggest failure mode (by a significant margin) is optimising the old system instead of rethinking it.

2. The second failure mode is trying to transform everything at once

Here’s how to move forward:

The most reliable way to begin is to find one initiative ( ideally something that doesn’t touch your existing systems, processes, or products) and staff it with two people who have high curiosity and a genuine willingness to operate without the guardrails of the old model.

Give them a real problem, access to the best coding agents available, and explicit permission to ignore how things are normally done. Then get out of the way.

The greenfield constraint matters for a specific reason. When you introduce AI-native ways of working into an existing team on an existing product, the existing process exerts enormous gravitational pull and it gets messy. But in a greenfield space, there’s no existing process to default to: so the team builds from scratch, and what they build almost inevitably looks like an AI-native operating model, because that’s the most natural response to having powerful tools and no legacy constraints.

Cisco - one of the oldest and largest technology companies in the world - they’ve been going wall-to-wall with Codex, starting exactly this way: greenfield pockets of two to three high-curiosity engineers working on modernisation projects that were previously considered too expensive or too slow to attempt, delivering in days what would have taken months.

Once the greenfield pocket has demonstrated what’s possible, the next step is institutionalising a simple cultural reflex across every team: when something hurts to do manually, treat that pain as a signal, not a condition.

“Ask immediately: could an agent have done this?”

99% of the time, the answer is yes, or even maybe, that’s the next thing to work on. Most organisations have an enormous backlog of manual work (in every single department) that nobody has ever seriously thought of automating it with agents. You know exactly what I’m talking about: daily reports, marketing/finance data, analysis, etc.

A useful structure for operationalising this: a monthly workflow decomposition session.

• Thirty minutes, any team, any function. Take one repeating workflow.

• Break it into atomic steps. Sort every step into the two buckets: human judgment required, or information processing.

• Then assign someone to spend an afternoon building an agent to own the information processing steps.

The output is either a working automation that saves time forever, or a learning about what the agent needs that makes the next attempt five percent better. Either outcome is worth the afternoon.

But the more important point is who’s doing this. The best results don’t come from top-down transformation programs: they come from curious individuals using tools like Codex to automate smaller parts of their own job, figuring it out on their own terms. Then sharing what they find. Those people will discover more, faster, than any centrally coordinated initiative will.

Top-down does make sense in specific cases: rolling out AI code review, setting standards, making infrastructure decisions. But the experimentation layer? That belongs to the people closest to the work.

Please, stop making your existing operating model more efficient and start questioning whether it should exist in its current form at all. Think about what you would build if you were starting from scratch today. Genuinely starting over, with the tools available now, knowing what you know about your business, your users, your market.

• What would the team look like?

• How many people, in what roles?

• What would the process look like?

• Would there be sprints? A backlog? A design handoff process?

• What would the tools stack look like?

• How would work get initiated, executed, reviewed, and shipped?

And then start building it & operationalise with clarity and hold everyone accountable.

Know Which Archetype You Are

There are three types of company at this moment, and which one you are should determine the pace and nature of your response.

1. The Legacy Company: pre-cloud, large engineering teams built for a world of expensive, scarce code. Companies like Cisco, traditional financial institutions, industrial conglomerates. They know they’re behind. They’re willing to do the aggressive, disruptive experiments that more comfortable companies won’t attempt. They have to move fast enough to matter while navigating organisational complexity that AI-native companies simply don’t have.

2. The Digital-Native Company: cloud-based, modern stack, built in the last ten to fifteen years, currently doing well by most measures. Airbnb, Databricks, the well-run SaaS businesses that dominate their categories. These companies are, in my view, in the most genuinely precarious position, precisely because they don’t feel precarious. See how companies like Ramp or Airwallex are moving and how Stripe is operating today. You’ll understand exactly what I mean.

3. The AI-Native Company: built in the last two to three years, from scratch, without legacy constraints, with agents baked into the operating model from day one. Codex, Cursor, Anthropic, etc the wave of companies being founded right now by people who’ve only ever known an AI-augmented way of working. The advantage is the clean slate. The challenge is staying AI-native while maintaining FOCUS as you scale: because as teams grow and enterprise customers arrive with their own process expectations, the gravitational pull toward traditional operating models gets stronger.

Whichever archetype you are, the direction of travel is the same. The pace and starting point differ, but the destination doesn’t: an organisation where human judgment and creativity are the protected resources, agents handle the execution layer, and the binding constraint on what you can build is how clearly you can think, not how many people you can hire.

That’s the playbook. And what comes next is bigger than all of it.

Section 7: Where This Goes — Beyond Code, Beyond Product

Coding wasn’t the first frontier because software is special. It was first because the models got good enough at coding first: code is verifiable, has clear success criteria, and there’s an enormous corpus to train on. But the intelligence that got good at code isn’t confined to code.

Look at where it’s already happening:

• Legal: Three years ago, AI summarised contracts. Today, Harvey drafts complex documents and conducts due diligence at a level that would have taken a team of junior associates weeks.

• Finance: Three years ago, AI pulled spreadsheet data. Today, agents build entire financial models and surface insights analysts would have taken days to develop.

• Sales: Three years ago, AI suggested the next email. Today, agents research prospects, personalise outreach at scale, and flag the exact moment for a human to step in.

Every function is on this curve. Most are twelve to eighteen months behind where coding is today.

And it’s already happening inside the companies building these tools. Every employee at OpenAI uses Codex, not just engineers.

Finance teams are building their own automations.

Events teams are spinning up workflows that would have required a developer six months ago. The people closest to the work are figuring out how to use these tools on their own terms, and what they’re building looks nothing like what anyone would have predicted.

That’s the tell. When the finance team starts shipping software, the org chart stops being a useful map.

Every organisational system ever designed… from Frederick Taylor’s scientific management to Bezos’s two-pizza teams… was fundamentally an attempt to extract more from that scarce resource (human execution capacity). That constraint is loosening in a way that has no historical precedent.

When execution is nearly free, strategy becomes everything.

The question shifts from “can we build this?” to “should we build this?”... and that’s harder, not easier, because the set of things you could build expands so dramatically that the choosing becomes the hard part. The organisations that win aren’t the ones with the most people or capital. They’re the ones with the clearest thinking about what’s worth doing: the best taste, the deepest user understanding, the most accurate model of where value actually gets created.

The most valuable resource over the next 5–10 years won’t be talent - it will be compute, and it’s only getting more expensive from here.

Right now is the cheapest window you’ll ever have to experiment, so give your team autonomy (within budget) and push them to go AI-native because the same experiments will cost 10x more later.

I want to be honest about the limits of prediction here. Even at OpenAI, building the models, watching them train… we cannot predict what a model will be capable of once training finishes.

The capabilities are emergent. They surprise us. Regularly.

Which means the most dangerous strategy right now is building for a fixed picture of what AI can do. The leaders positioning themselves best aren’t the ones with the most detailed five-year AI roadmap. They’re the ones building organisations capable of continuously reinventing themselves… running what I think of as the Reinvention Cycle:

• Sense: What new capability exists now that didn’t exist ninety days ago?

• Rethink: Given that capability, what part of how we work is now suboptimal?

• Rebuild: What’s the minimum change to our people, process, or tools that captures the new leverage?

The companies that run this cycle fast compound.

The ones that run it slowly get leapfrogged.

And the metric that predicts which side you’ll be on (more than AI budget, more than AI headcount, more than which tools you’ve adopted) is your Reinvention Cycle Time: how many days between “this capability exists” and “we’ve rebuilt our process around it.”

The Closing Thought

The operating model described here: builders embedded in teams, parallel agents, build-first evaluation, token-level economics, curiosity as the primary hiring signal, etc isn’t some forward-looking theory. It’s a reflection of what’s already working, at scale, inside companies building some of the most valuable products in the world today.

So the real question isn’t where things are headed. It’s whether you choose to move early, while you still have the freedom to shape it on your own terms… or wait until that window closes.

It’s time for you to re-architect your org around AI with AI.

Best of luck!

From top insights a

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  First Round Capital’s Liz Wessel will discuss how investors evaluate founders before traction or product market fit exists. The session explores her founder perspective, early stage decision making, and includes a live audience Q&A. [Luma]

• The AI Layoff Trap: Companies Are Firing Their Own Customers ⚠️
  Automation saves individual firms money, but shrinking consumer spending eventually weakens profits across the entire market. The research argues this incentive trap cannot be fixed by redistribution alone because companies still benefit from replacing one more worker.

  

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• The Mistakes That Cost Founders Their First Deal 🤝
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• Lighthouse or Landgrab: Picking Your AI Sales Strategy 🏃
  Enterprise startups should choose between winning flagship customers first or scaling quickly across proven demand. The right path depends on buyer risk, the value of social proof, and whether reference customers influence future sales. []

• The Best Charity of the Next 50 Years Might Be a Company 💰
  Some businesses create social value through their core economics instead of separating profit from philanthropy. The argument favors companies whose revenue grows alongside their mission while recognizing some challenges still require nonprofits. []

• The Young Founder Experience: Backing Teams Under 20 👶
  Young founders face faster expectations, tougher fundraising standards, and increasing pressure to show traction almost immediately. The advice warns against costly incubator terms and encourages realistic guidance over unrealistic startup success stories. []

  

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Interesting Reports 📊

• The State of Intelligent Finance 📊
  More consumers now rely on AI for budgeting and planning, making intelligent financial guidance an expected feature rather than a bonus. Confidence grows when companies explain how decisions are made, keep humans involved, and stand behind mistakes with clear protections. [PLAID]

  

• Global VC Hits $500B as AI Dominates Funding 💰
  Startups raised more than $500B in the first half of 2026, with AI attracting 77% of all venture investment and driving the largest funding rounds. Germany climbed to fifth globally, Uzbekistan recorded the fastest growth, defense tech reached $40B, and climate tech held steady at $19.5B. [Dealroom.co]

  

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  Private share trading reached a trailing 12 month value of $121.7B, fueled by demand for stakes in OpenAI, Anthropic, and SpaceX ahead of public listings. Stricter transfer approvals, growing SPV fraud concerns, and a key patent dispute are reshaping how the market operates. [Pitchbook]

  

Recently Launched Funds 💸

• Framework Ventures closed its fourth $400M venture capital fund to back companies across crypto, AI, robotics, space, and clean energy.

• Jump Capital raised $350M for its eighth venture capital fund to invest in early-stage companies across AI applications, cybersecurity, and AI infrastructure.

• Align Ventures closed its second $125M venture capital fund to back early-stage consumer brands across beauty, health, wellness, pet, home, and personal care.

• Italian Founders Fund closed its second fund at over €100M to invest in Italian-founded startups from pre-seed through Series A.

• Psalion launched its third $50M venture capital fund to invest in pre-seed and seed blockchain startups across infrastructure, RWA, stablecoins, DeFi, and Web3

The AI Corner

The Claude Code system that replaces a 5-person team

Most Claude Code articles list a few hacks and stop there…

Read more

4 months ago · 45 likes · Ruben Dominguez

VC Jobs 💼

• Revent (London, England): Senior VC Associate apply here

• 10x Founders (Munich, Germany): VC Associate apply here

• Eka Ventures (London, England): VC Associate apply here

• Wavemaker Ventures (Remote): VC Principal apply here

• Kapor Capital (Oakland, CA): VC Partner apply here

• FinTech Collective (New York City, NY): VC Internship apply here

• Pear (Remote): VC Fellowship apply here

• Forum Ventures (Toronto, Canada): VC Analyst apply here

• Redesign Health (Remote): VC Associate apply here

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Hottest Deals 💥

• Commonwealth Fusion Systems raised $1B in additional equity financing to accelerate the development and commercialization of fusion energy. (read more)

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• Antora Energy raised $550M in Series C funding to scale thermal energy storage for industrial, grid, and data-center applications. (read more)

• K2 Space raised $500M in Series D funding at a $6.8B valuation to accelerate production of large, high-power satellites. (read more)

• ThreatLocker raised $190M in Series F funding to expand its Zero Trust cybersecurity platform and accelerate growth. (read more)

• CAIS raised $170M in Series D funding at a valuation above $2B to expand its alternative-investment platform for financial advisors. (read more)

• Groundcover raised $100M in Series C funding to scale its cloud-native observability platform. (read more)

• Enigma raised $71M in seed funding led by Index Ventures and Ribbit Capital to develop physical AI technology for robotics. (read more)

• ChipAgents raised $60M in Series A2 funding to advance its agentic AI platform for semiconductor design. (read more)

• Claros Technologies raised $55M in Series B funding to scale technology that destroys PFAS and other persistent chemicals. (read more)

• Fish Audio raised $52M in seed funding to expand its AI voice platform for real-time text-to-speech, voice cloning, and voice agents. (read more)

• inforcer raised $50M in Series C funding to scale its Microsoft security and AI management platform. (read more)

• Agon raised $30M across pre-seed and seed funding to develop virtual battlefields and drone-defense technology. (read more)

• Hush Security raised $30M in Series A funding to expand its non-human identity and AI-agent governance platform. (read more)

• Flourish Health raised $26M in Series A funding to expand its psychiatrist-led pediatric mental-health platform. (read more)

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We rebuilt the entire layer as 375 prompts across eight files. Paste one into Claude, fill in the brackets, and the analyst’s two weeks happen in an afternoon.

Which is exactly why fundraising is about to get harder, not easier.

When the research layer was expensive, having it was the advantage. Now every founder in your batch generates the same list, the same fit analysis, the same clean cold email, for the price of a prompt. The analysis just commoditized. What did not commoditize is the part the prompts cannot touch.

A prompt builds the target list. It cannot tell you which partner told another founder, over coffee last week, that they are full this quarter.

A prompt ranks funds by thesis fit. It cannot tell you their last two bets in your space went to zero and the partner is gun-shy.

A prompt writes a flawless cold email. It cannot tell you one warm intro from the right founder beats two hundred of them.

Every one of those is judgment, timing, or a relationship. None of it is in the book. All of it decides whether you close.

The VC Corner

The VC Corner is where founders stay ahead: sharp news, fundraising strategies, battle-tested playbooks, and the tools to grow.

By Ruben Dominguez

So here is the deal. You get the whole book, all 375 prompts. And we are honest about the 20% it does not solve, because that 20% is what closes the round.

375-prompt-book/                  replaces the analyst's two weeks
│
├── diagnostic-skills/            decide whether, when, and who to raise from
│   ├── raise-readiness.md            40 prompts · fundable now, or fix this first
│   ├── investor-mapping.md           50 prompts · build, tier, and right-size the list
│   ├── thesis-fit.md                 45 prompts · score each fund's real thesis fit
│   └── warm-path-mapping.md          35 prompts · find and rank the warm path in
│
└── execution-skills/             turn the list into booked calls and a closed round
    ├── outreach-sequencing.md        45 prompts · the multi-touch outreach cadence
    ├── personalized-hook.md          50 prompts · first lines that earn the reply
    ├── materials-prep.md             40 prompts · deck, memo, and data room
    └── pipeline-and-objection.md     70 prompts · track, handle objections, close

What You Get With Premium

The 375 prompts are the thinking layer. Your subscription unlocks the verified data and playbooks underneath, the exact 20% that turns the prompts into a closed round:

📂 Investor Lists
10,000+ real check-writers, filterable by stage, sector, and geography. The verified list every prompt in the book assumes you already have.

📊 Financial Models
The runway, valuation, and SAFE math the diagnostic folder points at.

📈 Pitch Decks
The deck and memo bar the materials folder prepares you to hit.

🧰 Resources
Every playbook and template, one library.

→ Start your free 7-day trial

Cancel anytime. First subscribers keep 50% off for life.

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The 375 Prompt Book for Fundraising

What’s Inside

1. The 375 Prompt Book

2. The 80% is the book. The 20% is what closes.

3. How to run the whole thing in a week

4. The one thing 375 prompts cannot do

Read more

On the Relentless podcast he said it flatly: “We are now, like, in the singularity. This is the moment.” Not future tense. Present tense. And present tense changes what counts as a reasonable bet.

That one reframe explains the rest. Why OpenAI survived a fight it should have lost. Why he flew to 28 countries in 35 days. Why a ten-week-old startup can now out-build a company that took a decade to reach the same point.

The proof is in the shipping cadence. Anthropic went from 444 days between frontier models to gaps you now count in weeks, shipping Opus 4.6, 4.8, Fable 5, and Opus 5 inside a single year. That is what an exponential looks like from the inside.

I watched the full interview so you do not have to. Here are the ten that matter.

1. The Day ChatGPT Crossed a Million Users (And What He Told His Partner That Night)

Growth curves lie, right up until the day they don’t.

Four days of watching ChatGPT usage spike, dip, and spike higher. His own researchers called it a fluke each time. He did not believe them. Years of running Y Combinator office hours had taught him what organic, compounding growth looks like on sight.

On day five, ChatGPT crossed a million users. He went home and told his partner the opposite of good news: their quiet life was ending.

▫️ Track the shape of growth, not just the size of it.
▫️ Discount groupthink from the people closest to the product.
▫️ Treat organic compounding as a signal, not a coincidence.
▫️ Prepare for the personal cost before the metric forces it on you.

Why it matters: most founders wait for a press cycle to confirm something is real. By then the shape has been visible for days. Learn to read the curve yourself.

2. Plan for the Curve to Keep Climbing

What used to be a joke at lunch is now the news.

The idea Altman used to joke about at the lunch table is now the actual state of the world. A decade ago transformative AI felt improbable. Today it is operational.

He is clear-eyed about the risk in that same sentence. His worry is not a rogue machine. It is a small number of companies or people deciding they should control the outcome for everyone else.

Why it matters: if you are building anything that assumes AI capability plateaus, you are planning for a world that already ended. Plan for the curve to keep climbing.

3. Ask for the Hard Thing Before Assuming No

Everyone told them not to bother, so they asked the team anyway.

OpenAI was badly behind on coding tools. The standard advice: once a competitor owns a category, move on. They asked anyway, and framed it as a mission that mattered. Codex became the product many of the best engineers he knows now use daily.

The lesson is narrower than “always fight the incumbent”:

▫️ Ask for the hard thing before assuming the answer is no.
▫️ Frame the mission as important, not just possible.
▫️ This only works when the team believes the stakes are real.

Why it matters: founders self-select out of categories that look occupied. Occupied is not closed, not when the technology still moves fast enough to reshuffle who is actually ahead.

4. Watch Power Concentration, Not Rogue Machines

The scary version of AI is not a machine waking up. It is a small group deciding who gets access.

His top concern, stated plainly: a handful of firms deciding they should run this technology for everyone else. His counter is abundance. Make it cheap, powerful, and widely distributed instead of scarce and permissioned.

▫️ Decentralized access matters more than centralized safety theater.
▫️ Power concentration is the risk to watch, not just capability jumps.
▫️ Every historical trade of liberty for safety has been a long-term loss.

Why it matters: if you build on foundation models, the distribution strategy of the labs you depend on is a business risk to track, not a philosophical debate to ignore.

5. Your Startup Is a Structure, Not Just a Product

Altman says the real breakthrough behind modern prosperity was not a machine. It was a piece of paper.

As a kid he debated his parents over which Industrial Revolution technology mattered most. His answer today is not a technology at all. It is the joint stock corporation. Before it existed, business ran on personal trust between people who knew each other. No shareholders, no legal shell able to raise capital from strangers.

Once it existed, capital could pool, risk could be shared, and speculative technologies had a vehicle to fund them. He ties this directly to the collapse of extreme poverty over the last century.

▫️ Liability protection let people take real risks.
▫️ Capital pooling let strangers fund unproven ideas together.
▫️ Specialization let separate companies interlock instead of duplicating effort.

Why it matters: your startup is an incentive-alignment machine. That structure, more than any single technology, is what actually compounds.

6. The Opposite of a Bad Day Is No Day at All

Most people are optimizing for the wrong opposite.

You can spot new founders in office hours by how they react when something goes badly wrong. Experienced founders have already survived nine company-threatening moments. The tenth does not hit the same way.

Most people treat a good day as the opposite of a bad one. Altman argues the real opposite is no day at all. He credits Naval Ravikant with the version that stuck: if your life had a fast-forward button, your life would be over. The boring stretches and the painful ones still beat skipping to the end.

Why it matters: this is an operating principle for founders about to have a very bad week. Gratitude for the bad day is trainable. It is not a personality trait.

7. When Your Product Outruns Understanding, Show Up in Person

When world leaders started asking whether to shut AI down, he got on a plane instead.

After GPT-4 launched, public reaction turned chaotic almost overnight. Leaders were openly asking whether someone needed to halt the technology. OpenAI was still small, and Altman decided showing up in person was the only way to keep things from escalating. Brian Chesky, who had done the same for Airbnb at smaller scale, told him to go big.

Altman took on 28 countries in 35 days. He hated it. No jet lag cure works, and by the end he was having half-asleep visions of his childhood bedroom, which he read as his brain telling him to stop.

Why it matters: when your product outruns public understanding of it, distance from the people affected becomes a liability. Sometimes the fastest way to de-escalate is to physically show up.

8. Betting Against Incumbent Sclerosis Is Not Reckless

By his own logic, Google should have crushed this company by 2019.

Altman is blunt about how improbable OpenAI’s survival was. By 2019 a company with Google’s resources should have pushed it out of relevance entirely. It did not happen. He borrows Jeff Bezos’s description of AWS’s seven uncontested years as a business miracle, and applies the same label to his own company.

His explanation: groupthink at large incumbents, the rarity of true exponential windows, and a level of internal conviction he admits worried him at the time as possible self-deception. In hindsight, the conviction was simply correct.

Why it matters: big companies get set in their ways for structural reasons, not because their people lack intelligence. That sclerosis is a real, repeatable opening. Betting against it is not reckless if your conviction is earned.

9. AI Is a Genie. Math Is the Wish It Grants Too Fast.

The upside of AI is obvious. The unsettling part is what happens right after it says yes.

Altman frames the industry’s real challenge in stages: AI is close to granting enormous requests, the first ones need to broadly benefit humanity, and the space of possible requests is vast. Then a fourth stage nobody talks about enough. You make the wish, it is granted, and you are left unsure what you just unlocked.

He points to mathematics as the clearest live example. A model cracked a decades-old open problem in days. He expects most jobs to adapt better than people fear. Math is the exception, because the capability arrived faster than the field could absorb it.

Why it matters: track which domains start resembling math instead of resembling coding. That is the signal for where adaptation stops keeping pace with capability, and where a two-week-old startup can rewrite a field before incumbents notice.

10. Corporate Conditioning Leaves Ambition Catastrophically Low

The average new founder is not short on talent. They are short on permission.

Altman describes his first office hours with founders who had spent years in corporate America. The ambition and self-belief he saw: catastrophically low. Not an insult, a diagnosis. Most had never gone a stretch of life without a boss, teacher, or parent shaping what they were allowed to want, and most cultures carry a social penalty for wanting too much out loud.

Untraining that takes real time. The fix is not a speech.

▫️ Give people a task slightly beyond what they think they can do.
▫️ Let the win, however small, speak louder than encouragement.
▫️ Repeat until the belief updates on its own.

Why it matters: if you hire out of big companies, budget real time for unwinding this. It is not a character flaw. Years of incentive training built it, and years is what it takes to unbuild it. The founders who move fast in this market already untrained it in themselves.

The Singularity Playbook: 5 Things to Steal

Altman’s thesis across all ten moments is consistent. The exponential is real, it compounds faster than institutions adapt, and the advantage goes to whoever builds forward from today’s state instead of waiting for permission or certainty.

• For founders. Stop benchmarking your team against what a startup looked like ten years ago. A ten-week-old company today can outbuild a year-old company from 2015. Build for the models you will have in six months, not the ones you have now, and run it on a one-person operating system if that is your headcount. Start this week, not next quarter.

• For investors. The market still has not priced in that model progress compounds the same way founder growth does. Altman calls this the single most important thing he would want a founder to internalize. Underwrite for the exponential, not the current snapshot, before your comps go stale. The frameworks in what top VCs look for in 2026 already assume it, and the investor lists are where the founders who get it are raising.

• For people in tech. The sclerosis Altman describes at Google circa 2019 is structural, not a personality flaw of any one leader. If you are inside a company that size, that same blind spot is probably sitting in your own roadmap right now. Go find it before a two-week-old startup does.

• For other industries. The same logic applies to any field where cost and cycle time are compressing, not just software. Watch for the signature Altman describes: organic, compounding usage that experts keep calling a fluke. Take it seriously the first time you see it, not the fifth.

The five to steal:

1. Read the curve yourself. Do not wait for consensus to confirm what the data already shows.

2. Ask for the hard thing. Momentum belongs to whoever asks, not whoever waits for an opening.

3. Distribute power, do not concentrate it. Abundance beats gatekeeping over any long horizon.

4. Treat pain as data, not failure. The opposite of a bad experience is no experience at all.

5. Plan forward from a few convictions. Do not plan backward from a rigid twenty-year picture.

The ground is shifting faster than most people are willing to admit.

Build for where the curve is going, not where it sits.

The founders who win this decade will be the ones who asked anyway.

Everything to build at that speed, in one place:

▫️ Build your first AI agent · loop engineering for coding agents · Claude Code loops library
▫️ The one-person company operating system
▫️ AI Skills: the complete playbook
▫️ The Claude Opus 4.8 founder playbook
▫️ What top VCs look for in 2026 · the VC database · the investor lists
▫️ The full AI Corner library and the VC Corner resources

If this breakdown saved you an hour, share it with one founder or investor who needs it.