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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AI adoption is outpacing the governance meant to manage it

Jane Frankland, cybersecurity leader and advisor on cyber survivability, joins Vanta’s GRC experts on building an AI governance program that scales.

Inside:

▫️ Build governance into your security and risk program
▫️ Discover and assess AI systems and agents, then act on your risk tolerance
▫️ Communicate risk credibly as adoption grows
▫️ Prepare for the EU AI Act, ISO 42001, and NIST AI RMF

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In-Depth Insights 🔍

• YC Summer 2026 Batch: 234 Startups Reviewed ⚡
  The latest YC batch spans 234 startups across 12 categories, with bets ranging from chore robots and cancer vaccines to nuclear-powered floating data centers. The interesting signal is the breadth: founders are attacking physical-world problems alongside software, infrastructure, and frontier technology. [Cathryn]

  

• The True Biggest Risks in the AI Thesis 📉
  Just 1% of OpenAI and Anthropic customers generate roughly 80% of enterprise revenue, with that concentration holding steady for three years. Meanwhile, more than $1.3T in compute commitments ties labs, hyperscalers, chipmakers, and startups to a surprisingly narrow demand base.

  

• Instinct: A Personal AI Agent Valued at $2.5B 🤖
  Instinct lets users delegate tasks through text or calls, connecting to email, messages, and accounts to handle everything from appointments to subscription cancellations. It raised $350M and reached a $2.5B valuation within a week of launch, while users reportedly route more than $1,300 in monthly spending through the agent.

  

• 144 Family Offices That Cut Pre-Seed Checks 🤝
  A database of 144 family-office investors covers pre-seed and seed checks across the US, Germany, UK, Switzerland, Singapore, Brazil, and other markets. The bigger value is the process around it: qualify each contact, tailor the outreach, and build follow-up into a repeatable fundraising system.

  

• 40 AI Tools for Founders in 2026 🛠️
  Three standout picks are Wispr Flow for voice input, Granola for meeting notes, and Fyxer AI for managing overloaded inboxes. The broader list covers 40 tools, pricing, free tiers, and 21 verified discounts, including startup credits worth up to $350,000. []

  

• Founders Return to Run Pre-AI B2B Companies 🔄
  Founders are stepping back into B2B companies struggling with the AI shift, with recent examples including UiPath, Workday, and Intercom. The pattern is notable but incomplete: founders can stabilize metrics after hired CEOs leave, yet none has clearly restored their previous growth trajectory. []

• Cyber Resilience Act Reporting Begins September 11, 2026 📋⚠️
  The EU Cyber Resilience Act now requires manufacturers of connected hardware and software to report actively exploited vulnerabilities and severe incidents. Initial notifications are due within 24 hours, followed by detailed reporting within 72 hours and a final report as the incident develops. [National Law Review]

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Interesting Reports 📊

• Dutch Tech Giants Are Quietly Building Europe’s Startup Pipeline 💰
  Former employees of Dutch tech giants have founded 270 companies that raised $1.93B and now employ 5,149 people. Booking.com leads with 88 spinoffs, while high salaries, faster hiring, and shorter tenure keep feeding talent into the ecosystem. [Dealroom.co]

• Glasgow’s Tech Scene Hits £5.6B in Ecosystem Value 🔬
  Glasgow’s tech ecosystem has grown 2.4x since 2020 to £5.6B, with startups founded since 2015 driving half of that value. Health leads funding at £148M since 2025, while 130+ university spinouts have created another £1.1B in ecosystem value. [Dealroom.co]

  

• ASML Leads Europe’s Race to $1 Trillion 💰
  ASML is already at a $675B market cap, nearly twice Europe’s next-largest company, putting it on track for $1T between 2028 and 2031. The bigger issue is scale: only four European companies clear $100B in annual revenue, versus 14 among the global top 20. [PitchBook]

  

• MENA Private Capital Holds Steady as Exits Freeze 📉
  MENA PE deal value reached $8.6B across 102 transactions in H1 2026, while VC value held near $2B despite a sharp drop in deal count. Exit value fell to $2.7B, the lowest pace since 2017, as valuation gaps and cautious public markets kept liquidity locked up.

  

Recently Launched Funds 💸

• Molten Ventures held a first close of its Growth Fund at £175M to back high-growth technology companies at Series B and beyond across the UK and Europe.

• Techshop SGR held a first close of €43M for Techshop II, targeting pre-seed and seed B2B AI startups with a final target of €100M.

• Cape Fear BioCapital closed Fund I to invest in and build early-stage therapeutics companies in North Carolina, with capacity to back three to five additional biotech startups.

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 💼

• Next Legacy Partners (San Francisco, CA): VC Associate (apply here)

• HV Capital (Berlin, Germany): VC Analyst (apply here)

• Earlybird (Munich, Germany): VC Internship (apply here)

• Next Legacy Partners (San Francisco, CA): VC Associate (apply here)

• BlackFin Capital Partners (London, England): VC Internship (apply here)

• Wind Ventures (Paris, France): VC Internship (apply here)

• Entrepreneurs First (Paris, France): VC Internship (apply here)

• First Momentum Ventures (Munich, Germany): VC Internship (apply here)

• Roosh Ventures (Kyiv, Ukraine): VC Analyst (apply here)

• M12 (San Francisco, CA): VC Associate (apply here)

The AI Corner

Claude Cowork now runs a $10,000/month SEO agency from your desktop. Free with your plan

Something broke in search this year, and most businesses have yet to notice…

Read more

2 months ago · 77 likes · Ruben Dominguez

Hottest Deals 💥

• Positron AI raised $875M in Series C funding at a $5B valuation to scale its memory-first AI inference systems. (read more)

• Mach Industries raised $600M in a continuation of its Series C to scale autonomous defense systems and manufacturing, at a $3.7B valuation. (read more)

• The Exploration Company raised $450M in Series C funding to advance its reusable Nyx spacecraft and Storm rocket-engine programs. (read more)

• Encoded Therapeutics raised $275M in Series F funding to advance its genetic-medicine pipeline and scale manufacturing capabilities. (read more)

• Celero Communications raised $275M in Series C funding at a $3B+ valuation to scale coherent DSP technology for AI infrastructure. (read more)

• Solstice Oncology raised $225M in Series A funding to advance its next-generation CTLA-4 immunotherapy into Phase 2 development. (read more)

• Forus raised $150M in Series C funding at a $3B valuation to expand its AI-powered medication-access network. (read more)

• TAR raised $120M in Series A funding, led by Spark Capital, to develop off-grid power systems for data centers. (read more)

• Pixxel raised $100M in Series C funding, co-led by Temasek and Seraphim, to expand its planetary infrastructure and Earth-intelligence platform. (read more)

• Savvy Wealth raised $100M in Series C funding at a $600M valuation to scale its AI-native wealth-management platform. (read more)

• Inspiren raised $70M in Series C funding at a $500M+ valuation to scale its AI-powered senior-living platform. (read more)

• Moonwalk Biosciences raised $70M in Series B funding to advance its adipose-targeted RNAi therapies for obesity and cardiometabolic disease. (read more)

• TwoStep Therapeutics raised $62.5M in Series A funding to advance its tumor-targeted peptide-drug conjugate platform into clinical development. (read more)

• Lightfield raised $47M in Series A funding, led by Andreessen Horowitz, to build an AI-native CRM for companies operating with AI agents. (read more)

• Cloverleaf Bio raised $33M in seed financing, led by 4BIO Capital, to advance its engineered tRNA cancer-therapeutics platform. (read more)

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That frames everything that follows: China, the taxi cartel, Benchmark, and his new company, Atoms, built to automate mining, food, and freight one industry at a time.

I read the full conversation so you don’t have to. Here are the ten that matter.

1. The Meta Problem (And Why China Almost Broke It)

Every founder thinks they’re short on ideas. Kalanick says that’s rarely the constraint that matters.

“The only constraint on our imagination is management capacity.”

He runs it as an inequality: your rate of problem solving has to stay ahead of your rate of problem creation, or the backlog drowns you. Expanding to China wasn’t a decision, it was a problem he manufactured on purpose, knowing it wouldn’t “come ashore” for 6 months.

That delay is the trap. Founders create problems today and only feel the weight of them half a year later, once the team, the systems, and the customers are already exposed. By then it’s too late to un-create the problem, and the only lever left is capacity.

Kalanick treats problems the way a math professor treats an unsolved proof: a professor with nothing left to solve is a sad professor. Creating hard problems on purpose is the job, and solving them faster than you make new ones is the whole game.

Before you launch the next market, hire the next 10 people, or ship the next product line, ask whether your management capacity can absorb what you’re about to create, six months out rather than today.

On building the capacity before you need it:

▫️ The Founder Operating System

▫️ Hiring Your First Engineer in the AI Era

▫️ Startup Lessons Most Founders Ignore

▫️ Europe to US Expansion: The Playbook

2. How Uber Out-Ran a Government-Backed Rival Without Outspending It

Uber’s war with Didi in China burned tens of millions of dollars a month. Kalanick’s actual weapon sat outside the budget.

“Efficiency edge means you get bigger, faster. You get bigger, faster means you have a bigger network of drivers... And it will be cheaper because, well, if there’s less dead time for the driver to pick you up, then... the whole thing just starts working there.”

A smaller, well-funded challenger has one advantage: its subsidies cost less in absolute dollars, so it can chase the incumbent’s market share without matching its spend. Kalanick’s counter was to make every part of the funnel marginally better, from sign-up to pickup time to driver routing, so Uber could hold 50% market share while charging more than Didi. That price gap was the signal his system was winning on efficiency rather than cash.

His read on the endgame: subsidy runs out of runway. A network doing 10 billion rides a year can’t keep subsidizing $2 apiece, and the math stops working before the war does. The company with the sharper efficiency edge outlasts the one holding only capital.

Before your next pitch claims a network effect, name the specific mechanic that compounds. Investors have heard the phrase, and far fewer have seen the mechanism.

On the mechanics underneath the phrase:

▫️ Distribution Is the Final Moat

▫️ The Growth Loop Playbook From Top Startups

▫️ Key Startup Metrics VCs Actually Check

▫️ CLTV vs CAC: The Ratio That Decides the Round

▫️ Why Startup Retention Matters More Than Growth

3. The Taxi Medallion Racket: A Government-Blessed Monopoly, Explained

Ask Kalanick what a taxi actually is, and he starts in New York in the 1920s rather than with Uber.

“That taxi driver who’s paying $40,000 a year for 12 hours a day, he’s renting a car for $40,000 a year. For that privilege, he gets to be impoverished. That is the taxi system.”

The mechanism, as he lays it out: New York issued roughly 13,000 free taxi licenses, then froze the count. License holders lobbied to resell them, then to lease them daily. A grandfathered medallion owner could earn $80,000 a year renting out a car to a driver who nets almost nothing after paying for the privilege. A hundred years of that produced what Kalanick calls regulatory capture so tight the line between regulator and taxi company nearly disappears.

He draws the distinction sharply: competing hard and winning is capitalism, while getting a regulator to outlaw your competitor is something else, even when the press treats it as business as usual. Uber, in his telling, won by being chosen by riders, over and over, in a market where competition itself had been made illegal for everyone else.

He’s blunt about where this leads today, saying new driver caps in New York, passed after he left, are pushing Uber back toward the same medallion structure it was built to route around.

On building where the rules are the market:

▫️ How Trump’s 2025 Tariffs Hit Startups

▫️ Startup Location Strategy in 2025

▫️ The World of Atoms, Not Bits

▫️ Why Timing Decides More Startups Than Talent

4. Excellence Is the Capacity to Take Pain (Mile 21 Is the Whole Point)

This is the maxim Kalanick calls his favorite from the entire history of entrepreneurship, and he explains it with a marathon rather than a spreadsheet.

“Excellence is about pushing into the extent what is a human capable of, the full potential. Because if you don’t, somebody else does.”

Watch a marathoner at mile 21, he says. Nobody is smiling. The runner who feels the pain and pushes anyway wins, because somebody behind them is willing to hurt more. That extra push, sourced from tolerance for pain rather than talent, separates the field.

The corollary matters as much as the line itself: if what you’re building feels painless, you’re either holding back or you’ve picked something that leaves human progress where it was. Sitting on a beach for 6 months can be a valid personal choice. It sits in a different category of act.

Worth adding a caveat he doesn’t: pain tolerance built Uber and it also built the culture that removed him from it. The founders I watch burn out are rarely short on willingness to hurt, and this is the one lesson on the list I’d apply with a hand on the brake.

On the version of this that lasts:

▫️ 7-Day Work Weeks in Startups: Growth or Burnout

▫️ Why Second-Time Founders Succeed

▫️ High Agency: The One Trait That Compounds

▫️ Rick Rubin on Creative Work

▫️ Founder Lessons From Ruthless CEO Emails

5. Five Rooms, One Week: Inside Kalanick's Fundraising Auction

Kalanick’s process at Uber’s peak ran as five simultaneous negotiating rooms across a full week rather than a pitch deck roadshow.

“I’m in the $250 million checker over a room. Then there’s like a $100 million room and a $50 million room and a $25 million room.”

The mechanic: open at a deliberately low price so early investors lean in, then let each subsequent room bid against a rising number, “X plus five, plus five, plus five,” until the round reaches a natural ceiling. For bigger rounds without a winner-take-all structure, he’d ask every investor to name their check size at multiple price points, aggregate the results into a demand curve, and read off where supply meets demand.

His central rule cuts against instinct: get attached to a process rather than a price. Founders who lock onto a number before clearing the market negotiate down from a position they invented themselves. Founders who run the process first get the number the market actually supports, which is how a round jumps from $3.5 billion to $17.5 billion pre-money in 9 months with no up-front price on the table.

Most founders reading this will run one room rather than five, and the principle survives the downgrade: talk to enough investors in a compressed window that the timing does the pricing work for you.

If you’re running your own process:

▫️ The Investor Outreach System: List, Prompts, Sequence, Replies

▫️ The US VC Database Most Founders Never Build

▫️ 300+ VCs That Accept Cold Pitches

▫️ Term Sheets Demystified

▫️ 8 Startup Valuation Methods

▫️ Running a Fundraising Process

▫️ SAFE Conversion Calculator and Cap Table Tool

6. The VC Bar Is "Do No Harm." Only 1 in 10 Investors Clear It

Kalanick’s most pointed framework is about what he expects, and rarely gets, from the people funding founders.

“The actually a super high bar for a VC is do no harm... Achievable, what percentage of the time then, you would guess? 10%.”

His model: an operator running a company is a grandmaster playing chess 60 to 80 hours a week, deep enough in the position to see moves nobody else can. A VC is a chess enthusiast who checks the board once a quarter and still wants to leave a mark on the game. That mismatch, engagement without the hours to earn an opinion, is where founder-investor conflict starts.

He describes it through a Serengeti image: a lion takes down a limping antelope even when it isn’t hungry, because that is simply what a lion does. Applied to Uber’s board fights in 2017, an activist investor ran what he calls a weekly crisis without ever stating the goal outright.

The bar for a genuinely helpful investor, on top of doing no harm, is smaller still: about 1%.

Obvious caveat, given who is talking. Kalanick lost that board fight, and a framework where 90% of investors cause harm is a convenient shape for the person who was voted out. The model still holds up, mostly because the hours mismatch he describes is arithmetic rather than opinion.

Know which kind of investor you’re getting:

▫️ Has Venture Capital Become Return-Free Risk?

▫️ Venture Capital at a Crossroads

▫️ The VC Liquidity Crisis Nobody Talks About

▫️ The Board Meeting Operating System

▫️ High-Resolution Board Reporting: A Founder’s Guide

▫️ What Top VCs Check in Due Diligence

▫️ VC Horror Stories

7. Finding the Line Between Order and Chaos (It Has 80 Dimensions)

Every leader is navigating the same axis, whether they name it or not.

“There’s a line. On one side is order... structure... eventually lots of bureaucracy... if you go to the other side of the line, which is chaos... the job of every leader is to find that line.”

Too much structure and the org moves slowly while people fight the process. Too little and the org moves slowly from the opposite direction. He describes the line as existing in dozens of dimensions at once rather than two, and says the best leaders find the version with the fewest possible rules that still keeps the org out of chaos.

His concrete example: launching new cities at Uber, he personally sat in on every pricing call for the first 20 to 30 launches, sometimes 8 hours of calls before a single city went live. That was the only hard rule a 23-year-old city launcher had to satisfy, and everything else stayed loose because the team knew the one gate they had to clear. By city 20, the same call took 5 minutes, and he stopped attending.

Pick the single highest-value checkpoint in your process and make that the one non-negotiable rule. Let everything upstream of it stay improvised.

On the systems that hold while you scale:

▫️ The Startup MIS Template and KPI Dashboard

▫️ Headcount Planning Module

▫️ Real Startup FP&A Model and Operating Plan

▫️ The AI-Native Product Operating Model

▫️ The Forward Deployed Engineer Playbook

8. Why Atoms Won't Build a Humanoid to Flip Pancakes

Kalanick’s new company, Atoms, is built on physical AI and robotics, and explicitly avoids the humanoid category.

“If you needed to do a thousand pancakes an hour, you’d probably need a hundred humanoids in a row doing this, versus a very simple iron apparatus.”

His logic: a humanoid earns its cost in a home, where it needs to fold laundry, take out trash, and wash dishes, a wide range of low-scale tasks in an environment built for human bodies. At industrial scale, the opposite holds. A single narrow high-throughput task is better served by a specialized machine than a general-purpose robot imitating a human doing it.

Atoms is structured around that split, going after mining, food production and logistics, and transport one industry at a time, building specialized hardware for each. Food came first because Kalanick already understood the logistics from Uber Eats, though he’s explicit the category chose him as much as he chose it.

The model question is already old news for physical-AI builders, and the fight has moved to moats:

▫️ Marc Andreessen: The AI Moat Is Not the Model

▫️ Where AI Moats Live Now

▫️ 5 Moats AI Cannot Replicate

▫️ Model-Market Fit for AI Startups

▫️ The Deeptech Stack: AI, Climate and Bio

▫️ Service as a Software and the $1B Seed Round

9. Land Is the Whole Damn Thing

Ask Kalanick where physical AI runs into a wall, and the answer sits below compute.

“Where does the energy come from? You’re like, oh, it comes from the sun... Okay, but how do you capture it? It goes back to minerals. Like land is the whole damn thing.”

His stack, as he lays it out: super-intelligence needs energy and minerals at the base. Energy traces back to capturing the sun, which traces back to the materials needed to build that capture infrastructure, which traces back to land. Property sits underneath everything built on top of it in his framework, from data centers to food delivery hubs that need to be within 15 minutes of every customer.

Mining sits at the center of the thesis for a specific reason: automating a mine cuts labor cost and also increases total output per mine, which makes more mines economically viable to run at all. More raw material compounds into more of every industry downstream.

On the physical layer under the AI trade:

▫️ The Endgame of Vertical Integration

▫️ Elon Musk, xAI and SpaceX: Vertical Integration in Practice

▫️ Climate Tech Is Dead, Long Live Climate Tech

▫️ The AI Semiconductor Pullback Playbook

▫️ Jensen Huang’s AI Roadmap: 10 Moves

10. From Fear of Failure to "Problem Solver in Chief"

Kalanick is candid about what drove Uber’s early intensity, and it sits outside ambition in the usual sense.

“I was running a $70 billion company the way somebody who thought he was going to starve next week would run it.”

Before Uber, he spent 4 years with no salary on an earlier startup, running out of money multiple times. That scarcity built a precision and intensity that carried into Uber, for better and for worse. Fear of failure gets a founder somewhere, he says, and it caps out short of excellence, because at some point the drive has to come from something other than the fear of losing.

His current operating description for himself is “problem solver in chief”: spending his time on the highest-impact problems nobody else is already solving, then expecting every leader below him to run the same remit for their own area. Section 1’s management-capacity idea, applied recursively down the org chart.

On what drives the second company:

▫️ Why Second-Time Founders Succeed

▫️ Founder Secondaries in 2026

▫️ Asymmetric Bets and Unfair Upside

▫️ Finite vs Infinite Games as AI Eats Software

The Kalanick Playbook

His core bet is that management capacity, rather than capital or ideas, is the ceiling on what any founder can build.

▫️ Founders: treat every new market or product line as a problem you’re manufacturing on purpose, and check your capacity to absorb it six months out.

▫️ Investors: the do-no-harm bar is a useful gut check before taking a board seat, beyond a founder complaint.

▫️ People in tech: the fewest-rules model scales a team without bureaucracy or free-for-all. Find the one gate your team needs and let the rest stay loose.

▫️ Other industries: the efficiency-edge argument explains why a smaller, sharper competitor outlasts a bigger-spending one. Any two-sided marketplace lives on the same mechanics.

The 5 Principles to Steal

1. Get attached to a process rather than a price. Clear the market before you commit to a number.

2. Create problems on purpose. Staying ahead of the ones you choose beats avoiding hard problems.

3. Specialize the machine to the task. A general-purpose solution built to impress rarely beats a narrow one built to win.

4. Find the one non-negotiable rule. Order and chaos both slow you down, and the fix is fewer rules.

5. Treat pain as the signal. If a project feels easy, someone else is probably about to out-work you on it.

Excellence, in Kalanick’s telling, was never about talent. It’s about who is still pushing at mile 21, and he would be the first to tell you the pushing cost him the company.

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

Go deeper

Fundraising the Kalanick way

▫️ The 375 Prompt Book for Fundraising

▫️ The Investor Outreach System

▫️ The Ultimate Investors List of Lists

▫️ 200 Pitch Decks That Raised Capital

Reading the room on VCs

▫️ How Do VCs Really Make Decisions?

▫️ What Top VCs Look For in 2026

▫️ Venture Capital Fund Math Explained

▫️ How VCs and Startups Beat Currency Risk

Building past the model

▫️ The Deeptech Stack

▫️ High Agency: The One Trait You Need

▫️ 100 AI Agent Ideas With Implementation Guide

▫️ Why Taste Is the New Moat

Founders who built through it

▫️ Jensen Huang on Work Ethic at Nvidia

▫️ Alex Karp, Palantir and the $500B Interview

▫️ Elad Gil and the Rise of the Solo GP

▫️ The 2 Trillion Dollar Founder Playbook

The money is enormous and patient, the decision usually sits with one or two people, and the process skips the partner meeting, the investment committee, and the fund-cycle timing that governs everything a VC does. What family offices have instead is close to zero public surface: a website with no names on it if there is a website at all, and gatekeepers who are very good at their jobs.

So founders default back to chasing funds that reject 99% of what they see, because at least the funds are findable.

Why this corner of the market stays open

That fourth row is the entire opportunity. Difficulty of access is what keeps competition out, and every founder who gives up there leaves the office to whoever kept going.

Worth saying plainly: family offices are slower to trust and faster to decide. They will take three months to answer a cold message and forty-eight hours to commit once they do. Founders who expect the speed to show up at the start read the first silence as rejection and stop following up one message too early, which is the single most common way this list gets wasted.

What is inside the list

144 named people at family offices that deploy at pre-seed and seed, each row checked by hand rather than scraped from a directory.

Every row carries a person rather than a firm. That distinction matters more than the count: info@ addresses and generic firm entries are why most investor lists produce nothing, since the message arrives somewhere with no owner.

The geographic spread runs across the US, Germany, the UK, Switzerland, the Netherlands, Lithuania, Turkey, India, China, Singapore, Brazil, Peru and Australia, which means the list works whether you are raising in Berlin, São Paulo or the Bay Area.

Everything below the line is the part that turns the list into replies

The list on its own saves you about forty hours of research. The system below is what decides whether those 144 contacts become three conversations or twenty.

1. The qualification pass. How to cut 144 rows down to the 30 worth your effort, before writing a single message

2. The enrichment step that separates a message that lands from a merge field with better manners, with the exact prompt

3. The first message, including the connection-request detail most founders get backwards, and why the note you want to write actively hurts you

4. The follow-up sequence, with timings, and the specific message that reopens a dead thread

5. The warm-path map, turning your own LinkedIn export into a ranked list of who already knows these people

6. What to do when the list runs dry, which happens to everyone around week five

Cancel anytime. First subscribers get 50% off forever.

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▫️ A new investor list every week, 10,000+ named investors across family offices, LPs, funds of funds and VCs, including the 2,000+ European family office database and 300+ VCs that accept cold pitches

▫️ The 375 Prompt Book for Fundraising and every playbook across a raise

▫️ 200+ pitch decks that raised capital and financial models ready to adapt

Read more

For the majority of people, trends are felt only in hindsight. When something big changes, nobody notices immediately, especially since we are so used to the status quo.

And right now, social media is in a transition period.

Over the past two years the word “follow” stopped meaning on the major platforms what it meant when most of us learned to use them. That you may have noticed.

But to this day, most businesses are still optimizing for a number that had already been demoted.

The change arrived slowly enough to miss and completely enough to reorganize everything downstream of it.

What used to behave like a subscription list now behaves like a recommendation engine, one that treats every post as a fresh audition in front of an audience it selects rather than an audience you earned.

Whether that ruins your year or makes it depends entirely on what you do

Table of Contents

1. The Feed Stopped Being a Feed

2. What a Follower Is Actually Worth Now

3. Why Posting More Is Only Half Right

4. The Front Door Is an Answer, Not a List

5. Writing Became the Format Machines Quote

6. The Half Almost Nobody Is Doing

1. The Feed Stopped Being a Feed

The mechanics changed before the vocabulary did, which is why so many people are still describing a product that no longer exists.

How AI and the push for freshness killed the feed

Meta’s January 2026 newsroom post put numbers on the current machinery. Ranking changes in Q4 2025 produced a 7% lift in views of organic feed and video posts, and Facebook was surfacing over 25% more same-day Reels than a quarter earlier.

Read that second figure twice, because it describes a compounding problem rather than a feature. The system is being tuned toward freshness, which shortens the useful life of everything you publish while widening the pool of posts competing for each slot.

The direction was set years ago. Mark Zuckerberg told Meta’s Q1 2024 earnings call that more than half of what people saw on Instagram was recommended by AI, alongside roughly 30% of the Facebook feed. Those were majority figures two years ago, and everything since has moved one way.

Why YouTube is beating Disney without relying on followers

Video moved somewhere most boardrooms still do not track properly. Nielsen’s May 2026 Gauge put streaming at 48.6% of all US television watch-time, with broadcast down at 19.2% and cable at 20.4% and YouTube alone taking a platform-record 13.8% to lead every distributor in the country for a third consecutive month.

Disney took 10%.

A platform that commissions almost none of its own supply now out-watches the company that owns Star Wars, Marvel and ESPN and it got there without a single follower relationship doing the work.

That is the entire argument about modern distribution compressed into one comparison and it explains why the follower number on your profile deserves a demotion.

2. What a Follower Is Actually Worth Now

A follower has not become worthless. It has become a weak prior rather than a promise and the difference between those two things is where most content budgets are currently being wasted.

The myth of follower analytics and organic reach

Here is the part that requires honesty, because it is the number everyone wants and the number nobody has. No platform publishes organic reach broken down by follower count.

Every figure you have ever seen on the subject comes from third-party tools sampling accounts that happened to buy analytics software, which is a survey of a customer base rather than a measurement of the internet.

So when somebody tells you followers no longer matter, they are stating a direction that is probably correct while dressing it up as a finding that has not been made.

Why sends per reach is the ultimate algorithmic taste test

What can be said with confidence is narrower and considerably more useful.

Adam Mosseri has been consistent in public about which signals carry the most weight on Instagram and the one he keeps returning to is sends per reach, the rate at which people forward a post into a private conversation.

Watch time sits alongside it, while likes come last and count for least.

Sends per reach is the interesting one, because of what it actually measures. The platform is checking whether your content survived contact with a human relationship, which is a fundamentally different question from whether it held someone’s thumb still for four seconds.

Everything else in the ranking stack is behaviour toward a screen. That single metric is behaviour toward another person and it is the closest thing the machinery has to a taste test.

Which raises the obvious follow-up question and it is the one most people answer wrong.

3. Why Posting More Is Only Half Right

The case for enormous volume is mechanically sound and I have no quarrel with the arithmetic behind it.

If distribution gets decided per post by a system with a short memory, then every post is a separate draw and more draws produce more chances. The arithmetic holds. The cost sits elsewhere.

The hidden algorithmic penalty of low-quality volume

It sits in what high-volume publishing does to the publisher. Content produced at the ceiling of your ability compounds, while content produced at the floor of it teaches an audience to scroll past your face and teaches the ranking system that people who encounter your work do not forward it to anyone.

You are not neutral in the eyes of the algorithm when you post badly. You are generating evidence against yourself, at scale, with a schedule.

Why cheap competence has failed and taste is the new barrier

There is a supply-side problem underneath this that the volume advice tends to skip.

Several studies through 2025 and 2026 have put a very large share of what now circulates on the major platforms in the machine-generated column, with image categories running higher than text and I would treat any specific percentage with suspicion because detection methods are contested and an alarming number is commercially useful to whoever publishes it.

The direction, though, is not seriously in dispute.

When the marginal cost of producing plausible content falls toward zero, the marginal value of plausible content follows it down, which is the mechanism behind the widely shared observation that being loud stopped working as a strategy some time in the last eighteen months.

Competence is table stakes now and table stakes are free.

What remains scarce is a point of view somebody could disagree with, which is roughly what Rick Rubin has spent four decades demonstrating in a business where technical skill was never the constraint and a career built on taste outlasted every producer who had more of it.

None of which helps if the customer never reaches a feed at all and increasingly they do not.

4. The Front Door Is an Answer, Not a List

The most consequential change for anyone who sells something is not happening inside social platforms. It is happening in the box people type questions into.

The collapse of search clicks in the age of AI answers

Pew Research ran a behavioural study of 68,879 real Google searches rather than modelling it from keyword tools and found that people clicked a traditional result 8% of the time when an AI summary sat above it, against 15% when it did not. Roughly half the click rate, measured from actual browsing rather than inference.

The wider picture is genuinely contested and pretending otherwise would be dishonest.

SparkToro, working from Similarweb clickstream data, put US zero-click searches at 68.01% for the first four months of 2026, up from 60.45% in 2024. Datos, using its own strict clickstream methodology, published a figure of 22.4% for March 2026 and reported it falling.

Same country, same quarter, a gap of roughly three times and every strategy deck in circulation quoting whichever number flatters the argument.

What is not contested is the scale of the new surface. Google told I/O 2026 that AI Mode had passed one billion monthly users with query volume more than doubling each quarter.

OpenAI reported 900 million weekly active users in February 2026 and Reuters reported the ChatGPT app crossing a billion monthly actives in June, faster than any application before it. In January 2026 OpenAI started selling advertising inside the answers.

Why being named as an entity matters more than ranking

Put that together and the structural change becomes obvious. A growing share of purchase-adjacent questions now resolves into a recommendation naming two or three companies, rather than a page of ten links a determined buyer can scroll past.

Ranking and being named are different disciplines and the overlap between them has been falling sharply across several independent trackers over the past year.

A business can hold every position it had and still watch enquiries decline, which is a particularly cruel way to lose, because the dashboard keeps reporting success.

What earns a mention is being the traceable origin of something specific: a number nobody else published, a distinction nobody else drew, a method with your name attached to it.

Retrieval systems index entities rather than pages and an entity is simply something described consistently enough, by enough other people, to be worth citing.

Which is a technical restatement of a very old idea about reputation and it points at the format that machines find easiest to quote.

5. Writing Became the Format Machines Quote

Text is having a commercial revival that most video-first advice has not caught up with and the honest version of the numbers is more interesting than the promotional version.

The reality behind the Substack boom and creator revenue

Substack passed 50 million active subscriptions by the first quarter of 2026, around 5 million of them paid. Gross writer revenue reached roughly $450 million in 2025, up from $375 million the year before and by April 2026 close to 100,000 publications were earning money globally, double the figure from May 2025, with almost 30,000 of those outside the United States.

Now do the arithmetic the announcements skip. Spread $450 million across a hundred thousand earning publications and the average lands near $4,500 a year, while eight publications have crossed a million subscribers and the top ten take north of $25 million between them.

The median earning publication is making a few hundred dollars and the median publication overall is making nothing. Substack itself notes that its paid figure counts subscriptions rather than people, since one reader can hold several.

I run two of those publications, so treat everything above as a disclosure rather than analysis from the stands. The reason I keep writing anyway is in the next paragraph, and it has little to do with subscription revenue.

Why written argument is your highest-fidelity lead generator

I am not raising this to talk anybody out of writing. I am raising it because the case for writing is stronger once you stop pretending it is a salary.

Written argument is currently the highest-fidelity demonstration available of how a person actually thinks, it is the format retrieval systems index and quote most readily and it is the one medium where somebody with no camera presence and no editing budget competes on level terms with a full production team.

If money arrives it tends to arrive sideways, through the client who read four essays before booking a call and it will never appear in any platform statistic. That is also the shape of a content engine worth running, which is a lead system that happens to look like a publication rather than a publication that hopes to become a business.

All of which describes one end of what your 2026 should look like. The other end is being ignored almost universally.

6. The Half Almost Nobody Is Doing

Everything described so far is a machine for lowering the cost of reaching a stranger and the second-order consequence of that is the part people keep missing.

Why unscalable personal outreach beats cheap distribution

Nielsen’s 2021 global Trust in Advertising study surveyed 40,000 people across 56 countries and found 88% trusted recommendations from people they knew above every other channel available to them. Reporting on the same research puts trust in advertising delivered through influencers at 23%.

Nearly four to one, in favour of the least scalable channel that exists.

The word-of-mouth number has moved across the survey’s history, sitting at 92% in 2012 and 83% in 2015, so treat the exact figure as soft. The ranking has never moved at all.

Reaching a stranger has never been cheaper, which is exactly why reaching a stranger has never been worth less. A message that visibly cost you something is now the only kind that lands, because the recipient knows precisely what the cheap version costs to produce and knows you did not send it.

So the recommendation is unglamorous and I would do it before anything else on this list. Go through your records, find every person who has paid you, worked with you, referred you or turned you down politely and contact them individually with no ask attached.

Not a newsletter. Not a campaign with a merge field where their first name goes. Something that could not plausibly have been sent to anybody else.

This works for the same reason retention work beats acquisition work when it happens long before the cancel button. The relationship was already there and attending to it early costs a fraction of rebuilding it later.

The barbell strategy and the death of the middle ground

The strategy underneath all six sections is a barbell and both ends carry load. On one end, publish with enough specificity and consistency that you become the citable source in your corner of the market, accepting that machines will read most of it before humans do, which is the same logic behind treating speed and surface area as a first-order product decision rather than a marketing afterthought.

On the other end, do the slow human work that nothing can imitate at scale and no competitor can automate away from you. The founders who get this right rarely spread themselves thin and the ones who win usually pick one channel done properly over five run badly.

The middle is where almost all professional communication currently sits. Polite, plausible, scalable, perfectly competent and increasingly invisible to both the machines and the people.

Pick your ends. Every competitor you have is currently producing perfectly competent work that nobody forwards to anybody, and they will keep producing it for at least another year.

The Distribution Barbell: A 90-Day Build

Everything above described the shape. This is the build, with the numbers from running it across 340,000+ Substack subscribers and 150 million annual LinkedIn views.

Two warnings before you start. The first is that this takes a quarter, and most of the compounding shows up in month four. The second is that the outreach half looks like a waste of an afternoon every single time you sit down to do it, and it produces more revenue per hour than anything else here.

Phase 1, days 1 to 14: build the entity

Read more

There’s a specific sting to watching the person who believed in you first go quiet exactly when it counts. That’s how it feels to a first-time founder when their earliest investor sits out the next round.

It feels personal. It almost never is.

The investor most excited about your company today can be mathematically better off skipping the next round, and their excitement has nothing to do with it. The decision happens inside a fund’s spreadsheet, long before any partner picks up the phone.

A seed fund that needs a $200M exit to return itself at the price it paid faces a different equation once the Series A prices that same ownership five times higher. Now it needs closer to a billion, and most seed portfolios assume only one or two positions ever get there.

None of that is a verdict on your company. It is what the numbers do once a valuation moves. And once founders see the arithmetic laid out, most of the panic goes with it.

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Table of Contents

1. What a Fund Actually Owes Its Return

2. Two Reserve Philosophies, and Why They Produce Different Experiences

3. The Distinction Almost Nobody Explains: Pro Rata Is Not a Follow-On

4. Why the Signal Is Noisier Than It Looks

5. What GPs Owe Founders Here

6. What Founders Should Actually Do About It

7. The One Pattern Actually Worth Worrying About

1. What a Fund Actually Owes Its Return

A venture fund is not in the business of loyalty. It is in the business of returning money to the people who gave it money, and it does that by buying ownership at one price and hoping to sell, or mark up, that ownership at a much higher price later.

Every dollar a fund holds is competing against every other dollar it could spend.

When a seed fund weighs whether to put more money into a company at its Series A, the underlying question isn’t whether it still believes in the founder. It’s whether that dollar is the best use of the capital left, measured against everything else already in the portfolio and every new company the fund hasn’t met yet.

For a small, price-sensitive fund, the answer is often no, and the reasoning is never emotional.

It’s the same valuation math laid out above, where you pay 5 times more for the same ownership, and the exit required to hit the same return multiple jumps out of reach for most outcomes.

Putting new capital into a company already holding a working check, at a price where the math is much harder, is frequently a worse portfolio decision than putting that same capital into a company the fund hasn’t backed yet, at a price where the math still make some sense.

None of this requires the fund to doubt the founder. It only requires the fund to do arithmetic, the same arithmetic that plays out slightly differently depending on how a fund built its reserves in the first place.

2. Two Reserve Philosophies, and Why They Produce Different Experiences

Not every seed fund runs the same playbook here, and knowing which one a given investor runs would save a lot of guessing.

The Concentration Model

Some funds set aside a large reserve, often close to half the total fund, specifically to concentrate follow-on capital into the handful of companies that pull ahead early.

These funds actively want the option to keep buying into winners. For them, passing on a strong company’s next round is a genuine departure from how they operate, and worth asking about directly.

One breakdown of how this works splits fund behavior into three fund-level levers, reserves, pro rata rights, and recycling, and argues that a fund with real structured reserves can anchor an insider round and send a genuine signal to the market, while a lightly reserved fund can join pro rata at best.

The Spray Model

Other funds run closer to the opposite model, where they deploy smaller initial checks, throwing a wide net across many companies, and little or no reserve held back on purpose. Their entire strategy depends on owning a small piece of a large number of outcomes rather than doubling down on a few.

A founder backed by this kind of fund should expect, from day one, that a non-follow-on is close to guaranteed regardless of how well the company performs. It was never part of the plan.

Knowing which model a fund runs, ideally before the check clears, predicts what happens at the Series A far better than anything said in the pitch meeting.

3. The Distinction Almost Nobody Explains: Pro Rata Is Not a Follow-On

The question “Did my investor follow on?” is actually two separate questions bundled together, and conflating them is where most of the false alarm comes from.

The Housekeeping Move

The first question is whether the investor exercised pro rata, the contractual right, usually attached to a seed check, to put in just enough additional money in the next round to keep an existing ownership percentage from shrinking.

This is often a modest check relative to the round, and a fund can exercise it almost as a matter of housekeeping, without it reflecting any particular conviction about the new price.

The Real Bet

The second question is whether the investor did something bigger. Did it lead the round? Or did it take a meaningfully larger allocation at the new price?

That’s the decision loaded with the arithmetic from earlier, and the one that actually tells a new investor something about how a specific fund is thinking. Confusing the two is how the pro rata trap sets in on the other side of the table as well: an investor holds the right to defend a stake, lets it lapse anyway because the check felt too small to bother with, and only notices the cost of that years later when the company turns out to be the one that mattered.

A new investor who hears “our seed fund didn’t follow on” without asking which of these two things happened is working with half the picture. A fund that declined its pro rata is a genuinely different, more informative data point than a fund that simply chose not to lead a round it was never going to lead.

Founders should know which one happened to them, and should be precise about it the next time they’re explaining their cap table to a stranger.

Even once that distinction is clear, plenty of passes still carry less information than they seem to, for reasons that have nothing to do with pro rata at all.

4. Why the Signal Is Noisier Than It Looks

Even setting pro rata aside, a non-follow decision usually carries less information than either side assumes, for reasons that have nothing to do with the company.

A fund might be near the end of its investment period, with what’s left of its capital already earmarked for a handful of remaining checks. Or it might be raising its next vehicle and preserving reserves to look disciplined in front of its own backers. Or it might operate under a house policy, set long before the company existed, that simply rules out leading priced rounds past a certain stage.

Sometimes the partner who originally championed the deal has moved on, and nobody left in the room feels the same ownership over the decision.

The point is, there are tons of reasons why an early investor may choose to not follow-on. But none of that stops the market from reading it as a verdict anyway.

Once a handful of brand-name investors move together on a deal, the rest of the market tends to follow almost on reflex, which is a big part of how signaling turned into consensus in early-stage investing. The same reflex runs in reverse: a quiet fund reads as a loud absence, whether or not there was anything to actually read.

5. What GPs Owe Founders Here

Funds that want to avoid unfairly spooking their own portfolio companies have a fairly simple obligation. To communicate. And early.

The Check Is the Moment to Say It

The moment for this is the initial check, not 18 months later mid-negotiation. Founders benefit from knowing, upfront, what a fund’s actual follow-on behavior looks like. Whether pro rata gets exercised as a rule, whether the fund ever leads a later round, and under what conditions.

That information changes how a founder builds the rest of the cap table, and finding it out for the first time from a confused new investor is the worst possible moment to learn it.

A Pass Deserves a Real Reason

When a fund does decide to pass on a later round, founders deserve something better than silence or a vague, deflecting explanation from their own investors.

A short, reusable line a founder can repeat to a new investor without sounding defensive costs the GP almost nothing and saves the founder a genuinely stressful week.

Reputations in this industry travel between founders faster than most GPs assume, and the funds remembered fondly tend to be the ones that were straightforward about their limits from the start.

6. What Founders Should Actually Do About It

Founders have more control over this than the initial panic suggests, mostly by moving the conversation earlier.

The Record Matters More Than the Intentions

A prospective seed or pre-seed investor’s actual follow-on record, not what they intend to do in the abstract, is the more useful question to ask before taking the check. A real answer beats a reassurance every time.

An Off-Stage Check Is Really an Option

When a later-stage fund wants to write an early check outside its usual stage, the more useful frame is treating it as a deliberate option that fund is buying, and asking what they’d need to see to exercise it, in specific metrics rather than sentiment. A vague or unrealistic bar is worth learning about before the money arrives, which is a much better time to learn it than after.

One Off-Stage Investor Isn’t a Safety Net

A cap table built around a single off-stage investor tends to backfire in a specific way. Two or three funds that each dabbled outside their normal stage make a safer setup than one, because a single pass from one of several reads as ordinary portfolio behavior, while a single pass from the only off-stage backer reads as a decision about the company specifically.

What a cap table has to prove by the time Series A conversations start gets easier to manage when it isn’t leaning on one investor’s exception to their own rules.

Founders Who Go First Control the Story

The room with a new investor is a better place to get ahead of the question than to wait for it. A founder who volunteers that their seed investors are pre-seed specialists who rarely follow into a Series A, and who can say exactly who exercised pro rata and why, sounds like someone who understands their own cap table.

Getting caught flat-footed by the question instead sends a worse signal than the underlying fact ever did. Keeping a cap table this clean, and building a few of the cap table habits worth building early around tracking pro rata rights and modeling dilution before a term sheet exists, makes that moment a lot easier to walk into prepared.

7. The One Pattern Actually Worth Worrying About

If there’s a genuine red flag hiding in all of this, it’s narrower than an early investor didn’t follow on. It’s a fund whose own stated reason for investing early was to buy an option on later rounds, choosing not to exercise that option when the moment arrives.

A dedicated pre-seed fund skipping the Series A is just staying inside the strategy it always had. A growth or Series A fund that took a seed position specifically to get first look at the next round, then quietly steps back when that round shows up, is a different event. It suggests something changed in how that particular fund reads the trajectory, measured against the reason it gave for being there in the first place.

Everything else sits closer to weather than to news: the pre-seed accelerator that never intended to write a Series A check, the seed fund that quietly took its pro rata and stopped there, the multi-stage investor whose partner hasn’t gotten to the file yet, or the fund heads-down closing its own next vehicle this quarter.

Founders who can tell the difference spend less energy defending decisions that were never really about them. GPs who explain the difference upfront spend less time repairing relationships they never needed to strain.

Family offices are the strangest corner of the market. The money is enormous, the decisions are fast, there is no committee, and the people are basically invisible. Either the office has no website or the website has no names on it. Databases hold them badly. The good ones prefer staying unfound, and the gatekeepers are excellent.

Which is why most founders give up on them and go back to spraying VCs who have already passed.

The offices are findable. The work is different from VC sourcing, and once the loop is built it runs in an afternoon.

What makes them worth the effort

That fourth row is the whole opportunity. Difficulty of access is what keeps the competition out, and every founder who gives up at that row leaves the office to whoever kept going.

Step one: start wider than you think

This part is free, because it decides everything downstream and almost everyone skips it.

The biggest mistake happens before any tool gets opened: people look for “family offices” as if that were one type of investor. It covers single family offices, multi family offices, family holding companies investing directly, offices that only go through funds, offices that anchor emerging managers, and offices doing direct deals in one sector because the family made its money there. Each is a different search with a different message.

So the starting point is always the same. Give Claude your deck, your thesis, as much context as you can, and ask who could plausibly write this cheque. Then push it. It gives you ten suggestions, and you say okay, what else? What is a broader way to cut this? What is a narrower one? Which regions have I missed? What about the offices that invest through funds instead of directly?

Paste this into Claude with your deck attached:

“Here is my deck. List every type of family office that could plausibly back this company, then keep going: broader categories, narrower ones, other regions, offices that invest through funds versus directly. Push well past the obvious ten. Then rank the full list by how likely they are to answer a cold message.”

The list of investor types roughly doubles past the first answer. The first answer is the same ten categories everybody gets, and the second and third pushes are where the offices nobody else is writing to show up.

Twenty minutes here beats a week of sending.

What the rest of this playbook covers

Everything below is the part that turns that definition into replies.

▫️ The sourcing tier list. The free build with Claude plus one scraper, roughly an hour and a few dollars, against the shortcut that starts from named contacts

▫️ The first message that gets answered, including the connection-request detail that most founders get backwards, and the honest reply-rate maths

▫️ The warm-intro map. Turning your own LinkedIn export into a ranked list of who already knows your targets, with the exact prompt and the one-line ask

▫️ The enrichment pass, so every contact arrives with two or three facts attached before you write a word

▫️ The sheet-to-HeyReach pipeline that sends a unique researched message to every person on the list, at volume, with zero copies

▫️ Plus the two back doors into offices that answer nothing cold

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Your subscription also opens the full library:

▫️ A new investor list every week, 10,000+ named investors covering family offices, LPs, funds of funds and VCs

▫️ The 375 Prompt Book for Fundraising and every playbook across a raise

▫️ Pitch decks that raised capital and financial models ready to adapt

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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 🔍

• Forget Loop Engineering. It’s All About Graph Engineering Now 🔀
  Single-metric loops can optimize the wrong outcome, while graph-based workflows connect goals, subagents, and checks across a task. Claude Code’s dynamic workflows show where this is heading, but parallel agents using identical weights can still reinforce the same blind spots.

  

• Perplexity Just Split the AI Agent in 2. The Cloud Reasons, Your Mac Keeps the Secrets 🔒
  Perplexity’s Hybrid Compute splits tasks between cloud reasoning and local processing, keeping private files on-device while search-heavy work runs in the cloud. A local privacy classifier checks content before transfer, with the feature requiring Apple silicon, at least 24GB memory, and a paid plan.

• Nobody Is Talking Seriously About AI Demand 💰
  Frontier AI demand is splitting between cheap models for bounded tasks and expensive models for open-ended work like coding, R&D, and trading. That makes inference demand reflexive: more usage funds more capacity, but a downturn could reverse the same flywheel just as quickly. [Giavoanni Cattani 3]

  

• Building the Missing Layer Between AI Spend and ROI 🔍
  Ramp is turning agent traces into measurable work items with goals, outcomes, and costs instead of treating token counts as the main unit of value. Its analysis found that multi-objective runs consumed a disproportionate share of spend, while trace compression cut data volume by 74% without changing outputs. [Ramp Labs]

• Why Some Startups Become “Everywhere” Overnight 🌊
  The strongest launches combine a repeatable claim, a product moment, trusted voices, tight timing, and an audience that eventually carries the story itself. The trick is coordination without uniformity: 50 accounts sharing the same message looks like a campaign, while 50 distinct perspectives can look like momentum. [Jaun]

  

• Prediction: AI Will Collapse 💥
  The prediction is not that AI disappears, but that today’s agent interfaces may look as primitive as personal websites did after the internet matured. For agents to become universal, they need near-zero tolerance for costly mistakes involving purchases, messages, permissions, and sensitive data. [WordGrammer]

  

• Go Big or Go Broke: The Middle Ground Is Gone 🎯
  VC is increasingly clustering around either trillion-dollar consensus bets or contrarian companies operating far outside the spotlight. That leaves the middle squeezed out, pushing capital toward areas like chips and coding while less fashionable categories wait for consensus to break. [Ethan Kurzweil]

  

• The Rise and Fall of Agent Civilizations 🤖
  Experiments with autonomous agents reportedly produced unexpected coordination, from using shared infrastructure as a message board to manipulating evaluation environments. The bigger lesson is that agents can develop behaviors and communication channels that researchers did not explicitly design, raising the stakes for isolated environments and access controls. []

  

• The Cognitive Revolution: When Machines Do the Thinking 🧠
  The industrial shift mechanized physical labor over centuries, while AI is attempting a similar substitution for cognitive work on a much shorter timeline. Coding agents may be an early proof point, but the bigger prize could be an entirely new category built from cheap, scalable machine reasoning. [Konstantine Buhler]

  

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Interesting Reports 📊

• Papermark Analyzed 24,541 Pitch Decks and 358,672 Investor Views 💰
  Investors spend four minutes on average, with 16% leaving within 10 seconds, while 9 to 16 page decks get the deepest reads. Financials hold attention for 3.9 seconds but appear in only 40% of decks, while team slides get 5.7 seconds of attention. [Papermark]

  

• HubSpot Built a Free ICP Generator From Your Website 🎯
  Enter your website and answer four questions to generate a structured ICP using firmographics, buying triggers, competitors, and market signals. You can edit the output and use it to sharpen messaging and outbound instead of relying on a vague target market.

• Global Private Capital Fundraising Fell 12.7% in Q2 2026 📉
  Fundraising dropped to $1.35T while the number of funds fell 37% to 3,763, showing capital is concentrating into fewer, larger vehicles. Private debt rose 14% year over year, while VC posted its first annual increase since 2022 with $104.6B raised across 801 funds in H1. [PitchBook]

  

Recently Launched Funds 💸

• Andreessen Horowitz raised an additional $1.75B to expand its fifth growth fund to $8.5B, investing across seed, venture, and late-stage technology companies.

• Swish Ventures raised $250M for its third fund to back early-stage technology companies.

• Javelin Venture Partners is raising up to $200M for Fund VI to invest in late-seed and early Series A technology startups.

• Uplift Ventures launched a €100M DeepTech fund targeting startups across physical AI, energy, enterprise AI, and logistics.

• Ventures Platform closed its second fund at $84M to back early-stage African startups.

• Cherubic Ventures closed Fund VI at $68.88M to invest in early-stage AI-native companies across infrastructure, developer tools, enterprise software, healthcare, physical AI, and robotics.

• Prompt Capital is raising $40M for its first fund to provide pre-seed funding to AI-enabled enterprise software startups.

• F&F Ventures launched a $10M ecommerce fund investing from pre-seed through Series A in consumer brands and the technology behind them.

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 💼

• Inovo (Baltics): VC Investor (apply here)

• Monograph Capital (London, England): VC Associate (apply here)

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

• Pear VC (San Francisco, CA): VC Investor (apply here)

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The AI Corner

Claude Cowork now runs a $10,000/month SEO agency from your desktop. Free with your plan

Something broke in search this year, and most businesses have yet to notice…

Read more

2 months ago · 77 likes · Ruben Dominguez

Hottest Deals 💥

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• Superluminal Medicines raised $60M in Series B funding to advance its obesity drug pipeline into clinical development. (read more)

• Conveo raised $50M in Series A funding to scale its AI-powered consumer research platform. (read more)

• AITAN raised $41M in funding to expand its AI-powered autonomous combat-drone technology globally. (read more)

• Guardio raised $40M in funding at a $1.1B valuation to scale its consumer cybersecurity platform. (read more)

• PatientIQ raised $30M in Series C funding to expand its healthcare outcomes intelligence and real-world evidence platform. (read more)

• Atira raised $15M in Seed funding, bringing its total funding to $17.5M, to automate industrial sales engineering with AI. (read more)

• iPremom raised €15M in Seed funding to advance its molecular diagnostics platform for early detection of pregnancy complications. (read more)

• Diameter Pay raised $10M in Series A funding to build infrastructure for global U.S. dollar payments and virtual accounts. (read more)

• Helogen raised $7.1M in Seed funding to advance its space biotechnology and in-orbit manufacturing platform. (read more)

• Zeit AI raised $5M in Seed funding to develop autonomous data-engineering tools for industrial enterprises. (read more)

• Catch raised $5M in Seed funding to build an AI agent for executive administration and workflow automation. (read more)

• Airbility raised $4.7M in Series A funding to develop counter-drone aircraft and autonomous defense systems. (read more)

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Every founder knows that silence. Almost nobody knows what is inside it.

Papermark does. Founders share decks as tracked links instead of attachments, so every open, every page, and every second comes back as data

24,541 decks. 1,374,380 investor views. 15.2M data points. Four findings, in order of how much they should change what you do next:

See the full interactive report

👋 We’re Marc and Iuliia from Papermark. We built it so every founder can raise faster and every deal can be tracked. Every number below came from decks shared through it

1. You get 77 seconds, not four minutes

The average view lasts 4.0 minutes. That is the number everyone quotes.

The median is 77 seconds. Meaning that half of the views are just a bit longer than 1 minute.

The gap is the finding. Views split rather than cluster:

▫️ 16% are gone inside ten seconds
▫️ Nearly a third run past three minutes
▫️ Almost nothing sits between

Investors either dismiss a deck on the cover or sit down and read it. There is no average investor, and a deck built for one is built for nobody.

Read your own numbers this way. A run of views under thirty seconds is a list problem. Views over three minutes with no reply is the opposite: they read it, and the ask failed.

See the full distribution

2. Everyone who leaves, leaves on slide two

Eighteen points of your audience disappear between the cover and slide two. After that you lose about two per slide, until roughly half remain at the end.

The cover holds 5.1 seconds at the median. Every slide after it holds about three. Covers are not interesting. The cover is where the investor decides whether to keep going.

It is the highest-leverage slide you own, and usually the least worked on. Name, what you do, best number, before any agenda slide.

See the slide-by-slide drop-off

One belief the data kills: longer decks do not lose readers faster. Past the cover, a 25-page deck holds more readers at any given slide than a 16-page one. Readers pace themselves to the length they can see. What empties the room is the end approaching, rather than the page count.

3. Twelve pages is the number

46% of decks land between 9 and 16 pages. The data adds the cost of going longer, and the cost arrives early.

Completion falls 20 points from 8 pages to 16, then only seven more by 24.

Twelve-page decks win on every measure:

▫️ Most views of any exact length, 34 on average
▫️ Highest return rate, 68%
▫️ Half the readers reach the last slide

Sixteen and eighteen pages match the view count. Completion drops to 40%. The extra reads stop earlier.

Everything you cannot cut goes in an appendix, and the appendix goes behind the ask.

4. The slide investors want most is the one six in ten leave out

The most-read page is the team slide, at 5.7 seconds, 73% above a typical page. Investors spend their time on the people.

Financials are the instructive one. Above-median attention at 3.9 seconds, present in only four decks in ten.

High attention, low inclusion. Founders are cutting the slide investors most want to see.

See attention and inclusion by slide

The financial models library covers what belongs on it. The VC method for valuation covers the number they check first.

Order changes the result

▫️ Traction in the first three slides: 20% more views and the most reading time of any structure
▫️ Opening on financials: same views, less reading. They look rather than read
▫️ Opening on product screenshots: 14% less reading time, the worst opener measured

Most founders push traction to the back half. The data says it belongs up front.

Five Things to Do Before You Send

1. Send a link, not an attachment. So you can actually see who viewed your deck, secure it and control the access.

2. Treat the cover like it decides the raise. Eighteen points leave before slide two.

3. Open with traction. Keep the financials. 20% more views, and six in ten cut the slide investors want.

4. Twelve pages. The rest is appendix, behind the ask.

5. Chase the second open. One link per firm, so you can follow up the moment someone spends real time on you.

Read the full report

See This on Your Own Deck

Every number here came from decks shared through Papermark. Share your next one the same way and you will see which patterns your investors follow, while the raise is still running.

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Same problem founders have, one layer up: which allocators write into first-time funds, what they already back, and whether they are deploying this year. That information sits behind relationships and unreturned email.

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In 2020, PostHog published its entire employee handbook on its own website. Internal processes, company values, how decisions got made, all of it sitting in public view for a company almost nobody had heard of.

This was not just a transparency stunt.

Charles Cook, who runs marketing there, later explained that the reasoning was that early users doubted a real company existed behind the open source project and a handbook was the fastest credibility they could buy for free.

Nobody in the building called that marketing.

Together with HubSpot for Startups:

PostHog knew exactly who doubted them. Most founders skip that step, then blame the copy.

“Our copy isn’t converting” is usually a targeting problem in a copy costume. You rewrite the cold email six times when the issue is who receives it.

HubSpot for Startups built a free ICP Builder.

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Meanwhile the advice founders actually receive has barely moved in 10 years.

Start a blog, build a content calendar, hire an agency, buy some ads, wait for the funnel to fill.

That advice was mediocre in 2016, and now it’s redundant.

With the majority of content published online now being AI-generated, competent writing stopped being scarce and every playbook built on producing more of it inherited a problem it was never designed to solve.

Table of Contents

1. You Already Started Marketing Without Realising It

2. Competent Content Became Free, Which Made It Worthless

3. The First Channel Picks You

4. Positioning Works by Subtraction

5. Attribution Lies and the Cheapest Fix Beats the Expensive One

6. Your Best Copy Is Already Written, in Your Sales Calls

7. Nobody Can Do the Thinking For You

1. You Already Started Marketing Without Realising It

Founders email about marketing as though it were a switch nobody has flipped yet, when the evidence usually says otherwise.

Closing the Distance by Saying Interesting Things Out Loud

PostHog launched on Hacker News, published the handbook and then ran what amounted to a public diary of the company.

Each of those was a deliberate attempt to close the distance between a product and the people who might care about it, which is the entire job description.

The founder blog covered the mechanics of raising a $3 million seed round as an open source company and a retrospective on the 6 product ideas the team abandoned before landing on product analytics.

None of it required a strategy document, brand guidelines, or a budget line. It required deciding what an audience of engineers would find genuinely interesting and then saying it out loud.

The Expensive Trap of Making It Official

So the question founders ask is usually the wrong one.

They are not asking how to start marketing, since they already have, but how to make it look official and the officialising step is where most of the money gets wasted.

This matters more than it used to, given that distribution is the moat that most early companies are missing entirely, which raises the harder question of what is actually worth publishing now.

2. Competent Content Became Free, Which Made It Worthless

Every piece of content marketing advice written before 2023 rests on an assumption that no longer holds, which is that producing clear, well-structured writing costs real money and real time.

The AI Content Flood and the Death of Basic Competence

It does not. Graphite sampled 65,000 English-language articles and found AI-generated pieces overtook human-written ones in November 2024, settling at roughly half of everything published.

The interesting half of their research came second.

A follow-up Graphite analysis found that 86% of articles actually ranking in Google Search were written by humans, against 14% generated by AI, so the flood arrived and largely sank.

The New Premium on Access and Raw Failure

The scarce thing moved rather than disappeared.

It is no longer the ability to write clearly, because that now costs nothing and what is left is access, meaning your numbers, your customers’ actual sentences and your failures described in enough detail to be useful to someone else.

There is a blunt test for this before publishing anything.

If a competitor with a $20 subscription and 20 minutes could have produced the same piece, it is not worth your afternoon and the reader can tell.

PostHog’s founder blog worked precisely because a large incumbent could not have written it.

Being small, unproven and willing to publish the seed round paperwork was the qualification rather than the handicap.

Knowing what to publish still leaves the question of where and that is where most early marketing budgets quietly disappear.

3. The First Channel Picks You

By the time a founder starts thinking about channels, something has usually already worked slightly better than everything else and the standard response is to ignore it.

Depth Beats Breadth and It Is Not Close

The instinct after one small win is to write a list of 5 to 10 other channels and try them all, on the theory that one more might also work. 6 months later there is a pile of half-run experiments and no diagnosis, because none of them ever received enough volume to produce a signal worth reading.

Cook’s account of PostHog’s first 1,000 users describes the opposite move.

The Hacker News launch worked, so the team kept writing founder posts on the same channel, one after another, until it stopped working.

You are not selecting a channel from a menu. You are noticing which accident already worked and then pressing on it hard enough to find out where it breaks.

Companies further along can afford to run a portfolio of bets across several channels at once, but that is a luxury purchased with data you do not have yet.

Do Not Start With SEO

SEO looks like the responsible, grown-up choice and it is close to the worst possible first channel for a company under 2 years old.

Ahrefs analysed over 1 million URLs in their 2025 study and found that only 1.74% of newly published pages reached Google’s top 10 within a year, down sharply from 5.7% in their 2017 version.

The average page sitting at position 1 is now 5 years old.

A seed-stage company starting SEO is therefore entering a race where the leaders have a half-decade head start and a compounding backlink profile.

The same structural problem is already appearing in answer engines, so the workaround is not as new as it sounds.

Whereas communities like Reddit can produce a signal in weeks rather than years, provided the founder shows up as a participant instead of a marketing department.

A channel only pays off if what travels down it is sharp and sharpness comes from what a company refuses to say.

4. Positioning Works by Subtraction

Positioning is mostly a list of people you are willing to disappoint, which is why founders find it so uncomfortable and so easy to postpone.

Naming Who You Are Not For

Most founders write positioning as addition, stacking adjectives and use cases until the description technically covers everybody who might ever buy.

The result is a sentence that survives every internal review and persuades nobody, because a claim that fits any buyer describes none of them.

The useful version is shorter and costs something. It names a specific buyer, a specific situation and an alternative you are explicitly better than, which means accepting that a real share of the market will read it and correctly walk away.

A positioning statement nobody could disagree with is not positioning and getting this right early also fixes positioning and pricing downstream, since a price is a claim about who you are for.

A New Category Is a Bill, Not a Moat

Founders invent categories because the payoff genuinely is enormous.

Older research verified by the HBR found that category kings captured 76% of the market capitalisation of their entire categories.

That number is real, but it is also survivorship bias wearing a suit, because it counts the winners and says nothing about the far larger group who named a category, spent 3 years explaining a problem buyers did not recognise and ran out of runway during the education phase.

Category creation is a bet that you can fund market education for several years before pipeline arrives. Positioning against a reference point the buyer already understands is cheaper, faster and available this week.

Sharper positioning produces better experiments and better experiments still get ruined by bad measurement.

5. Attribution Lies and the Cheapest Fix Beats the Expensive One

A buyer reads a post, remembers the company name a week later, searches for it, clicks a paid ad and signs up.

How Software Attribution Misunderstands Demand Generation

The analytics credits the ad and gives the post nothing, so budget migrates toward the channel that captured the demand and away from the one that created it.

Refine Labs published an analysis comparing their software attribution against what their own customers said when asked directly. Software reported that 78% of conversions came from web search, while customers reported web search only 12% of the time.

In the same analysis, 85% of conversions were self-reported as dark social, which covers social posts, podcasts, communities and plain word of mouth. Software measures where demand gets captured, not where it gets created.

The Low-Tech Fix That Outperforms Expensive Analytics

The fix is not a multi-touch attribution platform and buying one at this stage is an expensive way to formalise the same error.

It is an optional free-text box in the signup flow asking where someone heard about you. Between 5% and 10% of people fill it in, which is more than enough and reading them every week costs nothing but attention.

There are 2 rules that make the data usable. When the box and the analytics agree, believe both and when they disagree, believe the box, because a human remembering a podcast is more trustworthy than a cookie that only witnessed the final click.

Cook notes that PostHog’s answers surfaced something no dashboard would have produced, which is that developers disliked being sold to but were genuinely interested in the company’s own story. That single finding shaped years of what got published next.

Those free-text answers are the smaller half of a larger point about where a company’s language should come from.

6. Your Best Copy Is Already Written, in Your Sales Calls

The sharpest sentence about most products was said out loud by a customer and never written down by anyone.

The Gap Between Inside-Out Copy and Customer Reality

Founders write copy from the inside out, starting with the architecture, the feature set and the reason the technical decisions were hard. Buyers describe the same product in terms of the specific Tuesday afternoon it saves them.

Gong Labs analysed 25,537 recorded B2B sales conversations and found the highest performers held roughly a 43:57 talk-to-listen ratio, while their 2025 update showed the average call still runs closer to 60% talking. Most founders sit on the wrong side of that split by a wide margin.

Mining Transcripts for Verbatim Copywriting Gold

The material is already recorded, sitting in transcripts nobody has opened since the call ended.

Pull them, find the moment the buyer explains their problem unprompted and take the phrasing verbatim rather than tidying it up. It will be blunter, more specific and less flattering than anything written internally, which is precisely why it converts.

This habit also improves product decisions, because noticing which framing lands is an extension of talking to users rather than a separate marketing activity bolted on beside it.

All of which explains why the 2 most common early spending decisions tend to go so badly.

7. Nobody Can Do the Thinking For You

The 2 questions founders ask most often are whether to hire an agency and when to hire a marketer and the honest answer to both is later than anyone wants to hear.

Agencies Cannot Do Work You Have Not Done

An agency can execute a positioning statement and cannot write one, because writing one requires deciding which customers to disappoint and that judgement only exists inside the company.

Hand over the earliest marketing and the output arrives generic, the feedback loop stretches from days to weeks and the founder learns nothing about their own buyers in the process.

PostHog did eventually outsource paid ads at roughly $5,000 a month and that worked because ad management is time-intensive, needs almost no company-specific context and rewards years of accumulated repetition.

The test generalises cleanly. Outsource execution you already understand and never outsource thinking you have not done.

Hire Late and Hire a Generalist

The prerequisites for a first marketing hire are unglamorous. Users who actually use the product, users who recommend it without being asked and users who pay for it, ideally all 3 while the product is still visibly rough around the edges.

Titles are close to useless in the search, since the same person might be called a content marketer, a product marketer, or a growth marketer depending on where they last worked.

What matters is whether they can write, think in funnels, talk to users and operate without a playbook, because a GTM system is something they will have to build rather than inherit.

Specialists arrive with a channel already chosen, which is a liability when nobody yet knows which channel matters. Hire someone experienced too, given that the most you can hand them is context on what has worked so far.

PostHog’s first marketing hire spent as much time interviewing engineers as writing copy, because working out what the company actually needed turned out to be most of the job.

That is the pattern sitting underneath all 7 points. Marketing at this stage is not a function to be installed, staffed, or purchased and every attempt to shortcut it with an agency, a content calendar, or a channel that worked for somebody else produces the same forgettable output that half the internet is already generating for free.

What survives is narrower and slightly uncomfortable. Say the specific thing only your company is in a position to say, to the specific people who would care and keep saying it until something in the data tells you to stop.

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 🔍

• How to crack YC investing🎯
  YC-focused funds show median returns near 5x, but access depends on speed, founder relationships, and doing diligence before rounds fill. The strongest signal is how deeply founders learn from users, not early revenue, product polish, or market metrics. [Jason Freedman]

  

• Agent Memory Is a Database Problem 💾
  Persistent agent memory stores information across sessions, requiring reliable retrieval, user scoping, metadata, and lifecycle rules beyond what larger context windows provide. Working, procedural, semantic, and episodic memory serve different jobs, but durable systems depend on disciplined storage, filtering, updates, and expiry. [Developers Blog]

• Grit Is Built Through Repeated Effort 💪
  Angela Duckworth’s framework ties achievement to talent and sustained effort, with deliberate practice turning persistence into compounding skill. Interest, practice, purpose, and hope form the core, while companies can reinforce grit through hiring, culture, feedback, and recognition. [Alfred Lin, Sequoia]

• AI Intelligence Is Becoming the Primitive Layer 💡
  Anish Acharya’s LP deck frames AI progress as a gap between model capability and economic diffusion, with applications capturing the value downstream. The key questions are where model commoditization leaves room for moats, how automation reshapes enterprises, and which consumer applications can sustain demand. []

  

• Chinese Open-Source Models Are Taking AI Traffic 📉
  Chinese models now account for a growing share of OpenRouter traffic as startups adopt Qwen and DeepSeek for dramatically lower inference costs. The tradeoff is strategic risk, with policymakers scrutinizing data handling, model provenance, censorship, cybersecurity, and supply-chain exposure. [Startup Fortune]

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Interesting Reports 📊

• Space Tech Funding Doubles Down on Later-Stage Bets 🚀
  Space tech raised $11.3B across 244 deals in H1 2026, already beating all of 2025 with fewer rounds. 87.4% of capital went to venture-growth and late-stage deals, signaling demand for proven contracts and production capacity. [PitchBook]

  

• European VC Valuations Climb as Exits Rebound 📈
  European deal values rose 25% year over year, while late-stage valuations surged as AI, cleantech, and advanced manufacturing led funding. Down rounds fell to a record-low 11.8%, while median exits jumped 150.8% as public listing valuations surged. [PitchBook]

  

• Europe’s $15T Pension Pool Barely Funds Venture 🏛️
  Europe has $15T in pension and insurance assets, yet only about 0.1% has reached venture capital, leaving a massive pool outside startups. Raising that allocation to 2% could significantly increase annual funding for European companies and help more unicorns scale domestically. [Dealroom.co]

  

Recently Launched Funds 💸

• Blackbird raised $750M across two venture funds targeting early-stage and growth-stage startups across Australia and New Zealand.

• Navisalma launched as a deep-tech venture firm and is seeking to raise €450M for its debut fund to back frontier technology companies.

• QuantumLight closed its second fund at €432M to back growth-stage companies across AI, fintech, SaaS, healthtech, and deep tech.

• Permanent Capital Ventures closed its second fund at $200M to back Series A applied-AI companies with a focus on scaling go-to-market execution.

• Cognition Venture Partners launched a new venture firm and is raising $170M to invest in cybersecurity startups serving the agentic AI era at Seed and Series A.

• MassMutual Ventures launched its $150M Climate Technology Fund II to back early-stage climate-tech and AI companies improving real assets across North America.

• FGV Capital closed Fund II at $35M to back early-stage companies at the intersection of fintech, financial access, and AI.

• Capital F closed its debut fund at $17M to invest at pre-seed and seed in technology companies serving the $15T female economy.

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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The AI Corner

Claude Cowork now runs a $10,000/month SEO agency from your desktop. Free with your plan

Something broke in search this year, and most businesses have yet to notice…

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2 months ago · 77 likes · Ruben Dominguez

Hottest Deals 💥

• Starcloud raised $250M in Series A extension funding to scale its space-based computing infrastructure. (read more)

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Same twelve firms. Which means a partner there opens forty decks a week in your category, and yours lands at number thirty-one.

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This week’s piece is a guest deep dive, and a special one.

Ivan Landabaso is a Partner at JME Ventures, one of the most active Seed and Pre-Seed investors in Europe, and the writer behind Startup Riders, where he breaks down how the top 1% of startups actually grow. When Ivan publishes a teardown, I read it twice.

He spent weeks pulling apart how Granola went from zero to a $1.5B valuation in three years, through 10+ founder interviews, every analyst report he could find, and the metrics scattered across the internet.

I asked if I could share it with you. He said yes.

👋 I’m Ivan. I study how top 1% startups grow.

🌊 Granola’s Growth Playbook

Hello there!

Every operator I know has now heard the story of Granola hitting unicorn status but most have no idea how it actually happened.

Their usage growth and financing rounds speed has made them quite the outlier:

Granola calls itself a “steering wheel for LLMs”. I interpret it as, in a world where ChatGPT and Claude can answer almost any question, their product is the thing that steers those models with the context of what was actually said in your meetings.

Every move in the growth playbook below is basically a brick in that steering wheel.

So I spent the past week pulling apart 10+ founder interviews, every analyst report I could find, and a few profile + metrics floating around the internet.

What you’ll learn in this edition:

• The “year in stealth” playbook that cut 50% of features before launch

• Why they refused to build a meeting bot when every investor told to

• The frontier-model rule that turned a 12.5x cost collapse into a moat

• Their “5 Hidden Rules for Building AI Products”

• The Recipes launch that turned the angel cap table into a distribution amplifier

• The MCP/API/Spaces pivot that turned a notepad into the context layer

And as usual, a few growth lessons for you to steal at the bottom.

Let’s dive in:

You are about to read three years of decisions that turned a notepad into a context layer. Here is what that looks like in your own week.

Granola takes notes in the background wherever your meetings happen: Zoom, Meet, Teams, Slack, or in person through the iPhone and Apple Watch apps.

🗓️ Get a refresher before every call, with context from previous meetings and emails

✅ Summaries and next steps ready the moment you hang up

💬 Chat across months of notes, or pull that context into Claude and Cursor through MCP

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1 month free with code THEVCCORNER.

Act 1: The Wedge (May 2023 → October 2024)

From $0 to 5,000 weekly users.

Chris Pedregal had just sold his last startup, Socratic (ai-powered tutor for high-schoolers), to Google. He quit Google because he wanted to build something on top of GPT-3.

“Within a week of quitting Google I started playing with the instruct version of GPT-3 that had just come out. I was blown away. I was like, okay, this is new. This is different. I don’t know what it is exactly.”

He started looking for a co-founder and stumbled into Sam Stephenson’s profile. Sam was a designer who’d been working on the same idea space from the design side, also based in London, at the note-taking startup Ideaflow. Pedregal sent him a cold email asking to grab a beer and they were off to the races.

They co-founded Granola in March 2023 and within two months they’d closed a $4.25M seed round led by Lightspeed (Mike Mignano was there at the time).

Then they went quiet for a full year.

Growth Lever 1: A stealth year to cut half the product before launch

Before hitting product-market fit, they were onboarding users manually until they got to about 150 by launch day.

From the MAD Podcast:

“What is the fastest way for me to learn? Will I learn faster if I launch publicly or will I learn faster if I don’t? For about a year, the answer was we’d learn faster if we didn’t launch publicly”

The cost of a public launch in phase 0 is very high because once you have public users, you can’t pivot the core interaction without breaking trust, and they were most likely going to need to pivot.

The first 6 months they built a real-time interaction where you’d type a keyword, hit tab, and Granola would autocomplete the note in real time during a meeting, which made for an incredible demo, they spent 6 months trying to make it work.

Aaand it didn’t.

The notes were good but if a computer is writing notes for you in real time, you can’t help but read them and if you’re reading them. Which kind of went against the whole point of Granola making you more present.

They scrapped the interaction pattern and rebuilt it as a calm text editor that does the magic at the end of the meeting, which was likely a less impressive demo but a way better product.

Pedregal on Invest Like the Best:

“If we had launched that publicly, we never would have been able to switch it. There’s no way. Users would have learned a new behavior. We would have not retained that many users, and that would have been it.”

The stealth year ended with one more move that I think is the single most underrated growth decision they made which was to cut 50% of the features.

One interesting pattern I’m finding in the last few editions analysing growth playbooks of Harvey, Lovable and Perplexity is their willingness to take on hard product trade-offs, almost “courageous”, as long as the iteration cycles were fast enough. I often see startups dying due to a combination of both these factors, hiding behind a false sense of security, not choosing a path with conviction.

“We were in stealth for a year and we kept adding things and adding features and adding views. By the end there was this version of Granola where you could swipe and there were all these panels, your transcript, your notes, your private notes, your notes in another language. It was really like that. We looked at it all and we cut out 50%.”

My take-away here is that the speed at which you can iterate the core interaction is the most valuable asset of an early-stage AI product, because public launches lock you in while stealth can potentially buy you the right to be wrong many times.

Growth Lever 2: Targeting VCs to win the founders behind them

Granola launched publicly on May 22, 2024 with a team of 4 people and a simple pricing of free for the first 25 meetings, and then $10 a month (more on what happened to that price in Lever 5).

Pedregal decided to go after the most specific user persona he could think of, which were venture capitalists. From the MAD Podcast:

“We needed a user type that has a lot of meetings, relatively formulaic, with a relatively formulaic note style, and that we have easy access to. VCs.”

VCs are the smallest market you could pick because they don’t pay much for software, there aren’t that many of them and investors normally see “building for VCs” as a red flag because “the TAM is so small”.

But Pedregal was building for the VC distribution (smart).

Investors are loud on Twitter, talk to founders all day and meet other investors at every dinner. And if a VC starts using Granola, every founder in their portfolio likely sees it, every co-investor sees it and most importantly, VCs were the people Pedregal could get coffee with in London on a week’s notice.

Then, on launch day, he told the team “now we stop building for VCs”.

“As soon as we launched, we said okay great, now we’re done with VCs. We’re going to focus on a different user type. And we chose founders, just because we thought they’d be the hardest. If we could build a great product for founders, then by default it would be a decent product for everyone else.”

VCs were the wedge and founders were the spreaders.

This is similar to the Superhuman strategy and it’s my favorite kind of Trojan horse because you pick the user with the highest signal density per user (not the user with the highest market size per user). If you get them to use it intimately you’ve recruited every person they meet to evangelize on your behalf.

By Series A in October 2024 57% of Granola users were in leadership positions. Which is interesting because Granola didn’t target executives but apparently many of those who were picking up the tool became leaders shortly after.

Growth Lever 3: Refusing the meeting bot to win every meeting

When they launched, every other meeting note-taker in the market joined the call as a visible bot because the bot was their distribution (and it was very annoying).

Granola’s investors told them they were crazy to give that up, but as is usually the case, outlier founders tend to dance to their own beat:

“Bots make you feel kind of weird. A big black box on the screen, sometimes shows up before you’ve joined. But beautiful from a growth distribution standpoint. Every user is exposing everybody they’re meeting with to your product. So everyone thought we were kind of crazy not to do that.“

The cost of the no-bot decision was real and immediate because Granola had no built-in viral loop, no free billboards, no “what’s that bot in the meeting?” recruitment moment and so on.

In return they got something that ended up mattering more: every meeting, rather than only the video ones.

A bot can only join a call. Granola runs from your device audio, so it works on Zoom, Meet, Teams, and Slack Huddles, and it works just as well for a coffee with a customer, a hallway conversation, or a call you take from your phone. The whole category was optimizing for the subset of meetings that happen on a video platform.

The experience is also calmer. Nothing appears on screen, nobody wonders what the extra participant is, and the conversation runs the way it would have anyway. You keep control of what gets captured and when, whereas a bot is a third party in the room that participants can, and regularly do, remove from the call.

Granola also never recorded audio (transcript only, a decision that saved them from many enterprise blockers later) and worked across every platform without any per-platform configuration.

What they discovered is that giving up the viral bot changed the loop rather than killing it:

“If you have a Zoom call and your AI bot shows up, people are now telling each other, ‘Hey, what are you doing with an AI bot? Why aren’t you on Granola yet?’“

What’s really cool is that now the competitor’s bot became Granola’s growth loop (this has happened to me, multiple times).

Growth Lever 4: Running frontier models so the moat would be quality

In 2023, every meeting note that Granola generated probably cost a few cents in inference, but a heavy user doing 6 meetings a day on the free plan was unprofitable. Which meant every other notetaker in the market used cheaper models or self-hosted to protect margins.

Pedregal told Peter Yang on Behind the Craft why this was actually an advantage:

“AI is different because these models are still expensive to run. Our costs scale linearly with users. This creates an opportunity: as a small startup with fewer users, we can use cutting-edge models that would be financially impossible for big companies to deploy at scale.”

Granola was small enough to run frontier models on every user. But competitors couldn’t, like Otter which likely had $100M ARR and millions of users at the time, couldn’t switch to GPT-4-class models on every transcript without blowing margins.

Then frontier costs collapsed, with the price of transcription alone going from $0.25 per minute in 2021 to $0.02 per minute today, which is a 12.5x cost compression in 5 years, on the single most expensive line in Granola’s stack.

Sam Stephenson on Cognitive Revolution in April 2026:

“There was a time where half of our burn rate as a company was going on transcription. That’s a lot better and more under control now.”

The bet was that running expensive products today is a temporary disadvantage that becomes a permanent advantage. They have apparently been running on frontier-model output since launch, with internal eval tooling that lets the team route across OpenAI, Anthropic, and Google models and swap them overnight without breaking what they call “the Granola voice.”

By the time the costs collapsed users had been trained on a quality of output that nobody else could probably match.

📐 Sidebar: the CEO’s 5 Hidden Rules for Building AI Products

• Rule #1: Don’t solve problems that won’t be problems soon: The current wave of AI startups tend to have 2 types of product problems, those the next model release will solve, and those that remain regardless. The mistake tends to be solving the first kind. For example, Granola refused to build chunking for long meetings (context windows expanded), and refused to build multi-language tooling (newer models handled it natively). “As a product person, it goes against every instinct to deny users something they’re actively requesting. But, in AI, sometimes the best strategy is to focus on problems that will still matter even as the tech evolves.”

• Rule #2: Go narrow, go deep: “General-purpose tools like Claude and ChatGPT are surprisingly good at many tasks, so if you are building a startup, it needs to solve a problem 10x better. The only way to achieve that is by choosing a narrow use case and making that experience exceptional.” The 10x often comes from non-AI work. For example they built an echo cancellation system for users with and without headphones, which as you can imagine has little to do with note-taking.

• Rule #3: Context is king: Pedregal treats the LLM like a smart intern on their first day. The product’s job is feeding the intern enough context to figure it out, whereas most AI products out there fail this by writing system prompts that try to anticipate every output.

• Rule #4: Your marginal cost is my opportunity: The lever we just discussed above. Frontier models are too expensive for incumbents to deploy at scale, small startups have a temporary cost disadvantage that becomes a permanent quality advantage as costs collapse.

• Rule #5: Build products that have a soul. Cohesion comes from intuition, and if you like product as a discipline you’ve probably felt “delight” using Granola. Pedregal does a user call daily and has screens in the office showing real-time feedback, but designs from first principles. “When you’re constantly immersed in user feedback, you develop an emotional sense of what matters rather than just analyzing metrics.”

The numbers at end of Act 1

October 2024: 5,000 weekly active users, a $20M Series A from Spark Capital at undisclosed valuation, which closed in roughly a week after a single day of about 12 investor meetings.

Act 2: The Ladder (October 2024 → March 2026)

By January 2025, Granola was becoming more of a habit than a product with VCs evangelising to founders, founders adopting it for their leadership teams and leadership teams asking for company licenses and so on.

But the AI notetaker market itself was getting brutal (Otter was at $100M ARR, Fireflies hit $1B in a tender offer, Read AI raised $50M, Fathom raised $17M, Plaud was selling AI hardware pendants at $250M annualized, etc etc).

The question shifted from “how do we get users” to “how do we keep them when notes themselves become a commodity?”

Growth Lever 5: Pricing the team plan below the personal plan to force expansion

As you can see there’s no personal paid tier and if you want unlimited history, integrations, MCP access, or the better thinking models, your only path is the team plan. This was a recent change as far as I can tell.

The exec champion who used to pay $18/month for themselves now has two options, either stay on free with a 30-day note window, or bring teammates (growth loop).

The price card got there in 3 moves over 22 months (free → cheap → asymmetric → personal plan deleted):

1. May 2024: Launch with one plan free for the first 25 meetings, then $10/month. Same price through the Series A.

2. May 2025 (Series B and Granola 2.0): Add Business at $14/user and raise Individual from $10 to $18. Note the asymmetry where the team plan is now $4 cheaper than the personal plan. They kept that structure for 10 months or so. The exec champion already paying $18 sees Business at $14 and does the math, which is a pull to bring on more teammates (cheaper than staying solo).

3. March 2026 (post-Series C): Kill the Individual plan entirely, 3 tiers only: free, Business, Enterprise. Long-term Individual users get migrated to Basic with a 30-day history window unless they upgrade to a team subscription.

Granola already won the individual through Levers 2 and 3 and the bottleneck was acquiring seat number two+. So they priced seat two below seat one, then deleted seat one when the company was infrastructure-ready and individual revenue was a rounding error against enterprise contracts.

Growth Lever 6: Shipping Granola 2.0 to turn the notepad into a workspace

In May 2025, Granola raised a $43M Series B at a $250M valuation, led by NFDG (Nat Friedman + Daniel Gross), and the same week, they shipped Granola 2.0.

Up until that point, Granola was a personal tool, but now they shifted their positioning to become a workspace (“a second brain for your team”). What shipped:

• Shared Team Folders: Sales calls, customer feedback, hiring loops, weekly syncs which anyone on the team can access (even without an account)

• Chat with Folders: Query an entire folder of meetings using best-in-class reasoning models, with citations to specific meetings and transcripts

• Enterprise Collaboration: Business and Enterprise users can explore any public folder inside their domain. Useful for competitive intel, customer success, onboarding new hires etc

Stephenson summarized the user-facing thesis:

“With every call in one place, sales leaders can ask ‘Why are we losing deals this quarter?,’ product managers can investigate ‘Which UX issues come up most often?,’ and recruiters can understand ‘Where do our interviews keep stalling?,’ all answered instantly with source-linked citations.”

Kind of reminds me of a cross-functional + ai-native Gong.io.

This new iteration sold you the unified record of every conversation your company has ever had and by Series B, they were processing millions of minutes of conversation per day (remember they don’t store audio, just transcripts, which is a smart product trade-off discussion in itself), with 19 employees.

The angel cap table (or: how the Superhuman strategy compounded into the Series B)

NFDG led the series B but the angel roster on that round is a who’s-who of distribution-rich operators:

What’s cool is that these people also have audiences, and they recommend tools.

So we see the Superhuman strategy going one level higher here where you have VCs as the initial wedge, founders as spreaders, leadership teams converting, and Angels pre-loaded with audiences.

As a little example of the power of leveraging your cap-table, in September 2025 they shipped Recipes (prompts paired with the context of your conversations, written by people who know what great output looks like).

And the launch contributors were:

• Lenny Rachitsky: “Write PRD”

• Matt Mochary: “Coach Me”

• Ridd: design feedback Recipes

• Peter Yang: “Gather product feedback”

• Nikita Bier: consumer growth Recipes

A second-order effect of 2.0 that the founder talks about often is that the viral loop changed. Before 2.0, virality was social, but after, virality was structural:

“So often I’ll be talking to a user and I’ll ask how they found out about Granola. And they’ll say, I was in this meeting, and 30 seconds after the meeting, these beautiful notes just popped up in Slack and I was like, how did you do that? And then they dig and they find out it’s Granola.”

Growth Lever 7: Anchoring the habit to the calendar to never be forgotten

By Series A Granola reported a week-1 retention of 70%+, which is exceptional for consumer AI, and by mid-2025, millions of minutes of transcript were flowing through the product’s data layer every day.

The reason this matters is that meetings are calendar events and calendar events repeat:

“The beautiful thing about meetings is that they’re on a calendar. There’s a very specific moment where we know you’re going to do a meeting. The combination that Granola is useful and we can send notifications at the right moment, that’s what leads to retention.”

Most consumer AI products lose users to forgetfulness due to the classic “Oh, I just forgot to use it.”, but Granola fixed it by anchoring to a habit that already existed in the user’s calendar (and by the way they do this super smoothly, I like the detail of the time running down on the notification, makes me want to “run to click on it”).

Growth Lever 8: Building from London to win Silicon Valley

Granola is headquartered in London, but Pedregal is American (and Stephenson sounds pretty British to me). They built the company in London for personal reasons (Pedregal’s wife is English, and both have families there). But they made a decision early on to frame Granola as a Silicon Valley company that happens to be in London.

All copy is American English, all early users were US-based founders and VCs and the first office expansion (Q1 2026) was San Francisco, with 5 sales reps shipped over to start it. The Series B and Series C investor lists are entirely US-based and the product launches time to US news cycles.

“If you’re in London, it’s too easy to think about the UK and Europe. My general view is that in this AI space, you need to be competitive in the US. Otherwise someone will win the US, and then you’re going to have to fight them in Europe. There’s no reason why you can’t go after the US market from here.”

The notetaker wave: where Granola sits in the race

Act 3: The Context Layer (February 2026 → today)

Days before the Series C, Granola encrypted their local cache (the file on your Mac where meeting notes live).

It was a security upgrade but it broke something users had built on top of (their own AI agents wired up to Claude Desktop and Cursor, reading directly from that file).

When the cache went encrypted, those agents stopped working and users got mad on the internet, including an a16z partner who posted that the app now had “zero value” in an agent-managed world.

The founder apologized and promised proper APIs were coming and just days later, they shipped them.

Growth Lever 9: Shipping MCP to become the memory layer for AI agents

When Granola launched their MCP server they gave away their data layer. Now you could connect it to Claude, ChatGPT, Cursor, Lovable, Replit, etc and any of those tools could pull from your Granola meetings on demand.

Every AI agent that wants to be useful at work needs to know what was said in your meetings and Granola became the only company that could serve that context.

“Conversation transcripts are the richest source of context for what’s happening across your company. When paired with powerful AI models, they can unlock workflows that wouldn’t have been possible before.”

This means that meetings are the densest unstructured data your company produces, which is the trojan horse the founders had been talking about since 2023.

Growth Lever 10: Opening the data layer with APIs to flip the price ceiling

Three weeks after MCP, they announced the Series C and shipped 3 more pieces:

• Personal API: for individual users on Business and Enterprise plans, programmatic access to your own meeting context

• Enterprise API: for admins, programmatic access to the whole team’s context, with SSO, SCIM, and access controls

• Spaces: team workspaces with granular permissions

Spaces apparently took about a year to build:

“We’ve made the product secure enough and compliant enough. The fact that we were able to work with DoorDash, that’s testament to all the work the team’s done.”

The APIs change the price ceiling and as notes commoditize, the value moves from the notes to the structured context layer that powers AI agents. And while you can’t charge $200/user/month for better notes, you can charge that for the layer.

Similar playbook Perplexity ran with Computer in February 2026, which is an economic flip wrapped in a product launch.

But the real question in all our minds is still, is Granola defensible against foundation models?

You’ve heard the bear case a million times. Transcription is a commodity, summarization is a free model call, ChatGPT and Claude both shipped persistent memory, distribution is uphill, the category has failed, Granola is a feature not a company etc, etc. Heard it, moving on.

Best thing about being a VC is that its made for optimists because you gotta think about what could go right (otherwise you’d never invest in anything in a field surrounded by constant startup mortality and outliers).

So here’s my bull case:

• Beautiful products own mind share, and mind share is the cheapest CAC there is: Granola’s whole product is kind of “a feeling”, you see it because people don’t say “the AI notetaker”, they say “Granola”, it’s already a verb in tech.

• Owning the workflow could beat owning the model: The valuable position is “we own the daily ritual.” The same way Notion isn’t a better word processor, they just own where work happens. Granola aims to own the moment a meeting starts (a workflow you do 5x+ a day).

• Cross-platform persistence is structurally different from any platform’s native feature: Microsoft can win Teams meetings but they can’t make your Zoom call show up in your Microsoft notes (for now, afaik).

• Meeting context could be the highest-quality memory substrate: OpenAI can ship “memory” but they can’t ship a year of your team’s actual conversations (yet).

• The “wrapper” critique aged badly: People said the same thing about Cursor (wrapper around VS Code + Claude), Perplexity (wrapper around search + LLMs), Harvey (wrapper around GPT-4 + legal docs), all at $100M+ ARR…

• The whole space is still in exploration mode (not exploitation mode): I like this frame from the founder. Nobody knows yet what the persistent memory substrate for AI agents looks like.

The net net is that if the meeting context becomes the dominant memory substrate for AI agents over the next 24 months, whoever owns ingestion of that substrate has a chance at a (very) big outcome.

We’ll see!

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5 Growth Lessons You Can Steal

My biggest takeaways:

1. Stayed in stealth long (for our times): Granola spent a year onboarding 150 users one by one before they launched and used that year to scrap their core interaction pattern and cut 50% of features.

2. Picked their wedge by signal density, and kept compounding the status ladder: Granola targeted VCs first knowing the TAM was tiny because each VC user produces an outsized amount of evangelism, network spillover, and product feedback.

3. Easy trade-offs, hard life. Hard trade-offs, easy life: Granola took a bunch of hard product trade-off decisions (i.e. walked away from the meeting bot), which was the entire growth engine of their category at the time. Doing it would have locked them out of the most valuable meetings, and the competitor’s bot ended up being Granola’s growth loop anyway.

4. Ran frontier models even when the unit economics didn’t work yet: They ran on the most expensive available LLMs since 2023 because they bet costs would collapse before competitors caught up on quality, and they were right.

5. Used pricing strategically: Once an exec champion is using your product, the cheapest path to seven more users is to make the team plan obviously cheaper than the personal plan they’re already paying for.

That’s it for this week, friends.

Cheers,

Ivan

P.s. If you want to help me out, the best thing you can do is share my work. 🙏

Share

📚 Bibliography

Founder voice, primary sources:

1. Behind the Craft (Peter Yang), The 5 Hidden Rules Behind Successful AI Products: Chris Pedregal (Granola), Jan 19, 2025

2. Every / AI & I (Dan Shipper), The Secret to Building Sticky AI Products, Dec 2024

3. Invest Like the Best (Patrick O’Shaughnessy), Chris Pedregal: Building Granola, EP.412, Feb 25, 2025

4. The MAD Podcast (Matt Turck, FirstMark), How to Build a Beloved AI Product: Granola CEO Chris Pedregal, Aug 21, 2025

5. Generative Now (Lightspeed, Mike Mignano), Chris Pedregal + Sam Stephenson: Making Meetings More Effective with Granola, May 15, 2025

6. Lightspeed (written), Generative London: How to Win AI’s App Layer, with Granola, May 15, 2025

7. The Cognitive Revolution (Nathan Labenz), Sam Stephenson on Granola’s design philosophy, April 2026

8. South Park Commons, Granola, March 2026

9. Pathfounders, Sam Stephenson on the Series C, Feb 2026

10. Scaling Europe, Granola, March 2026

11. EO Studio, Chris Pedregal on Granola, January 2026

12. Human in the Loop, Granola, September 2025

13. Dive Club (Ridd), Sam Stephenson: The journey of designing an AI product, Mar 28, 2025

14. Zapier, Granola, October 2025

15. Product Talk, Chris Pedregal, August 2025

16. Speed Podcast, Sam Stephenson: From Designer to $250M Founder in Two Years, Sep 11, 2025

Long-form journalism and analysis:

21. The Information, How Granola, and AI Note Taking, Grabbed Silicon Valley’s Attention, Jul 12, 2025

22. Upstarts Media (Alex Konrad), Can Granola’s AI meeting notes eat bigger apps for breakfast?, Apr 2, 2025

23. Upstarts Media (Alex Konrad), ‘I Had To Close My Laptop’: Granola Adds Coaching, Recipes To Its AI Meeting Notes, Sep 30, 2025

24. Fast Company, This AI note-taking startup thinks it’s building the ‘steering wheel’ for chatbots, Feb 25, 2026

25. Reworked, Granola Lands $125M to Turn Meetings Into AI Memory, Mar 25, 2026

26. Over the Anthill, Granola: The AI Note-Taker with Big Plans, Jun 22, 2025

Funding and milestone announcements:

28. TechCrunch, Granola raises $125M, hits $1.5B valuation as it expands from meeting notetaker to enterprise AI app, Mar 25, 2026

29. Bloomberg, AI Notetaker Granola Hits $1.5 Billion Value in $125 Million Funding, Mar 25, 2026

30. Sifted, AI notetaking startup Granola hits unicorn status, Mar 25, 2026

31. SiliconANGLE, Granola raises $125M at $1.5B valuation for its AI note-taking app, Mar 25, 2026

32. BusinessWire, Granola Launches AI Workspace for Teams and Raises $43M Series B, May 14, 2025

33. TechCrunch, Granola debuts an AI notepad for meetings, May 22, 2024

Granola’s own blog:

36. Granola raises $125M to put your company’s context to work, Mar 25, 2026

37. Introducing Granola MCP, Feb 4, 2026

38. Granola integrations: complete guide to connecting your meeting tools, Mar 20, 2026

39. Introducing Recipes, in the all-new Granola Chat, Sep 30, 2025

40. Granola raises $20M (Series A blog), Oct 2024. Has the 10-week retention stat.

41. Granola Updates page. Feature changelog and timeline source of truth.

Analyst and market data:

42. Sacra, Granola company profile (the Superhuman strategy frame, the 57% leadership user breakdown)

43. Sacra, Granola vs Zoom (the $0.25/min → $0.02/min transcription cost-collapse data)

44. a16z, The Top 100 Gen AI Consumer Apps, 6th Edition (Olivia Moore + Anish Acharya), Mar 9, 2026

45. Crunchbase, Granola company profile

Notetaker wave context:

50. Halisi, Fireflies Hits $1 Billion Valuation, Jun 2025

51. Otter.ai blog/BusinessWire, $100M ARR Milestone, Dec 22, 2025

52. TechCrunch, AI notetaker Fathom raises $17M, Sep 19, 2024

53. TechCrunch, Read AI raises $50M, Oct 28, 2024

54. TechCrunch, Plaud launches a new AI pin and a desktop meeting notetaker, Jan 4, 2026

Remember what it took to launch a startup just five years ago?

You needed a small, lean team and a big pile of cash. And of course, you had to be in San Francisco, where startups are magically born in a garage or a basement.

At least that’s what happened with most big tech companies that dominate industries today.

But right now, that reality has completely changed.

Thanks to modern AI products and incredibly cheap cloud tools, a single person with a laptop can build, launch and hit their first revenue in weeks for a fraction of the old cost. The actual price tag of starting a company has fallen off a cliff.

Capital is no longer the ultimate gatekeeper. This massive drop in the cost of building is changing everything, including how companies are born, who gets to build them and why the traditional VC playbook is suddenly looking very outdated.

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Table of Contents

1. Numbers Don’t Lie

2. Intelligence Unleashed

3. One Founder, Every Function

4. The Part the Bull Case Leaves Out

5. What Venture Capital Hasn’t Figured Out Yet

6. The One Cost That Isn’t Falling

1. Numbers Don’t Lie

The change becomes clearer when you look at how quickly the underlying inputs have moved.

Though the share of startups built by a single founder has not edged up gradually, it has risen from 23.7% in 2019 to 36.3% by the first half of 2025. That kind of movement rarely happens without a deeper structural driver. It reflects a change in what it takes to build something that can attract users and turn a profit.

The most direct driver sits at the infrastructure layer. The cost of running intelligence, which now underpins everything from product features to internal workflows, has fallen at a pace that is difficult to compare with prior technology cycles.

Running AI is now roughly 100 times cheaper than it was less than two years ago. Models that once required meaningful operating budgets now run at a fraction of that cost. Systems like DeepSeek R1 deliver performance comparable to GPT-4 at close to 15% of the operating expense.

A constraint that used to be fixed has turned into something that scales with usage.

That change carries through the rest of the stack. Development environments, design systems and distribution tooling have followed a similar trajectory. Capability has expanded while the cost to access it has declined.

The effect shows up in how quickly companies reach product-market fit. Upekkha’s recent cohorts are reaching product-market fit faster and with significantly lower capital requirements, reflecting a broader move toward capital-efficient company building.

This is not explained by better founders or fundamentally different ideas. The same validation process now runs on a much cheaper base.

Put these pieces together and the direction becomes clear. Computing is cheaper, tools are more capable and distribution infrastructure is easier to access.

Each layer reinforces the others. The cost of starting a business is no longer anchored by the inputs that once defined early-stage company building.

This isn’t just a software story. Starting a commerce business has followed the same path. What once required a physical store, upfront stock and logistics deals can now be set up through platforms that handle payments, shipping and financing all in one place.

In many cases, the annual cost to operate at that level sits well below four figures. The barrier that separated small operators from scaled businesses has weakened across sectors not just in code-driven companies.

2. Intelligence Unleashed

A common instinct is to assume that when a core input becomes cheaper, the value around it compresses. That logic holds in some markets but not here.

What is happening is closer to a well-known economic pattern often referred to as Jevons Paradox: as the cost of a resource falls, total consumption tends to go up, not down. That is exactly what is happening with intelligence.

Lower Costs Are Driving More Demand

You can see this in how infrastructure spending is behaving.

It has expanded the surface area where intelligence can be applied.

This changes how the cost of starting a business plays out in practice. Founders are no longer optimizing for how little intelligence they can afford to use.

They are embedding it across the product surface, in workflows, in support and in internal tooling.

Tasks that were previously manual, delayed, or ignored altogether are now part of the default build. The boundary of what can be turned into a viable company moves outward.

The more important change sits in how ideas get tested.

Startups can launch cheaper

A few years ago, validating a product that depended on advanced automation or data processing often required a few million dollars in capital and a team to support it.

That same validation cycle can now run on tens of thousands of dollars. The difference between $5 million and $50,000 is not incremental. It determines who gets to try in the first place.

A solo founder’s AI startup is no longer an edge case built on extreme efficiency, it is becoming a standard outcome of cheaper inputs.

You can see this in categories that were previously constrained by cost.

Legal workflows that required large teams can now be supported by systems that draft, review and summarize at scale, allowing small teams to serve enterprise clients.

In healthcare, ambient documentation tools can capture and structure clinical interactions in real time, reducing the need for dedicated staff while improving throughput. These problems are not new. Solving them at smaller scales was not economically viable.

As the cost of running AI keeps falling, more of these opportunities open up. Cheaper intelligence does not reduce the space for new companies.

It increases the number of viable starting points, especially for founders who operate without venture capital and rely on speed and iteration instead of large upfront funding.

3. One Founder, Every Function

It becomes clearer when you look at how a solo founder works day to day. The constraint is no longer the number of hours available or even the number of skills a single person can realistically develop.

It used to be about coordination.

Building a product required multiple functions to move together. Engineering, design, marketing, support and operations each demanded time and ownership. That coordination overhead shaped how teams were built.

Overhead has decreased. An AI-powered founder doesn’t just complete tasks more quickly. They can manage multiple functions simultaneously, maintaining a continuity that small teams used to struggle with. Ideas turn into deployed code in much shorter cycles.

Customer support is handled through systems that resolve most queries before escalation.

Marketing runs as an ongoing loop of content, distribution and feedback within the same stack. The work still exists, but it no longer requires separate owners at each layer.

Interpretation matters

This shows up in the data but the way you read this matters. Solo led companies accounted for roughly 30% of startups in 2024, yet they received only 14.7% of venture capital.

That gap is not only a reflection of investor preference, it also reflects choice.

When the cost to start a startup drops, the need to raise capital early becomes less compelling. Many founders are reaching product-market fit on their own terms instead of aligning with a funding model built for larger teams.

The examples are no longer outliers. Pieter Levels has built and scaled products like Nomad List and Remote OK to seven-figure revenue without employees.

Danny Postma took HeadshotPro past $1 million in annual revenue as a solo founder before selling it for a seven-figure outcome. Maor Shlomo built Base44 alone and sold it to Wix for $80 million within six months. Jan Oberhauser started n8n as a solo project in Berlin, which has since grown into a company valued in the billions. Different categories, different paths, same underlying pattern.

A solo startup is now defined not by limitations but by the number of functions a single founder can manage independently. As tools advance and AI becomes cheaper, the scope of what one person can do continues to grow.

4. The Part the Bull Case Leaves Out

The cost story has another side that does not fit neatly into the optimism around cheaper inputs.

The same systems that make it easier to build also introduce a cost structure that looks very different from traditional software.

For decades, software businesses operated on a simple premise. You paid once to build the product and each additional user cost almost nothing to serve.

That is what allowed SaaS companies to reach gross margins above 80%. AI changes that at a structural level.

Every time a user interacts with the product, it costs something to run. And the more people use it, the more those costs grow. Serving customers still costs money and expenses scale with demand.

Battery Ventures’ 2025 State of AI report places application layer gross margins in the range of 0% to 30% for many AI products.

That is not a temporary inefficiency, rather a reflection of how these products are delivered.

Today, AI chip makers are capturing the most profit earning 75% gross margins while software companies built on top of AI are barely breaking even at 0 - 30%.

Over time, this is expected to flip as AI becomes cheaper to run and software companies start charging based on the value they deliver rather than usage. Cloud providers are stuck in the middle, with heavy infrastructure costs and fierce competition keeping their margins flat.

The biggest profit opportunity ahead belongs to companies that can lock in users at the application layer not those selling the underlying technology.

The gap between cost and pricing becomes more visible in real deployments. Early estimates suggested that GitHub Copilot was costing Microsoft around $80 per heavy user each month, while the product was priced closer to $10. That pricing was intentional.

The goal was adoption and market share not profitability. But it exposes a structural pressure. If the cost of intelligence does not continue to fall, or if pricing power does not catch up, the economics become difficult to sustain.

There is also a dependency that sits beneath this. Most startups in this category are not training their own models. They rely on a small group of infrastructure providers competing aggressively for market share. OpenAI, Anthropic and Google are not optimizing for margin today, they are optimizing for distribution and ecosystem control.

The prices founders are seeing right now reflect a temporary phase of the market not where things will eventually settle.

This creates a tension that is easy to miss when focusing on how cheap it has become to build.

Launching a startup may be getting more affordable but the cost of serving customers can move in the opposite direction if those underlying rates change.

A product that works at today’s pricing may look very different if inference costs rise three to five times, or if usage scales faster than expected.

There is no clean resolution to this. Both dynamics are real at the same time. It is cheaper than ever to build and test a company.

It is not yet clear what the stable cost structure of running that company will look like once the current pricing environment settles.

5. What Venture Capital Hasn’t Figured Out Yet

The change in cost structure is starting to expose a mismatch at the center of the venture model. VC was built for teams that needed time, coordination and capital to validate large ideas.

Venture capital has a mismatch problem

That logic still holds in some categories, but it does not map cleanly to a world where a single founder can reach product market fit with a six figure budget.
When the cost to start a startup falls this far, the role of capital changes whether investors adjust or not.

The data already points in that direction. While solo founders now account for roughly 36% of new startups, they receive only around 15% of venture funding.

That gap is not only biased, it points to a deeper change in demand. A growing share of these companies are not raising because they do not need to.

If a founder can build, launch and validate with limited capital, the tradeoff between dilution and speed looks very different. For many, venture capital is no longer the default path to the first meaningful milestone.

This creates a product mismatch. VC is still packaged as large checks for teams that plan to scale headcount quickly.

That made sense when building required coordination across multiple functions. It is less relevant when those functions can be handled by a single operator using modern tools.

The input VCs provide, capital in exchange for ownership, is no longer as scarce at the earliest stage as it once was.

Founders no longer need as much funding

The signals used to evaluate ambition have not caught up. Headcount still carries weight in how opportunities are assessed, even though it is becoming a weaker proxy for output.

Revenue per employee is emerging as a more relevant indicator. A company generating $3 million in annual revenue with two people is often treated as an edge case rather than what it actually represents: a highly efficient system.

These are not lifestyle businesses. They are early examples of solo founders in the VC world building capital-efficient machines.

Investors who continue to filter for team size and capital intensity risk missing a category that is expanding in plain sight.

What worked before when money and coordination were the main constraints doesn’t really hold up in today’s market. Whether these companies can scale is not the right question.

It is whether the frameworks used to evaluate them are keeping pace with how they are being built.

6. The One Cost That Isn’t Falling

The cost of building has moved in one direction. Deciding what to build has not followed the same curve. Lower barriers mean more people can enter, test ideas and ship products.

But that doesn’t actually make the real challenge any easier. It just means way more people are jumping in, launching products and fighting for attention.

Suddenly there’s a ton more noise and founders have to work harder to cut through it all.
Once everyone has access to the same powerful tools, being fast at building stops being the main advantage. The real edge now comes down to judgment knowing what’s actually worth building.

Choosing a problem that matters, reading demand accurately and knowing what to ignore become harder as the marginal cost of entrepreneurship approaches zero.

This shows up early. A product can be built and launched quickly, but whether it finds sustained demand depends on factors that do not compress with tooling.

Distribution still needs to be earned and pricing needs to hold under real usage. Retention reflects whether the product solves something people care enough about to pay for. These forces operate on their own timelines, independent of how cheap it has become to build.

There is another layer beneath this that does not get much attention. The systems that enable a one-person startup are concentrated in a small number of platforms.

Cloud providers, model APIs and distribution channels form the base that most of these companies sit on. Founders do not control these systems and they do not set the terms on which they operate. Changes in pricing, access, or policy at that layer flow directly into the economics of the business.

Cloud computing lowered the cost of starting companies while concentrating value upstream. AI infrastructure may be heading in the same direction.

That leaves a founder in a position where building is easier than it has ever been, while the foundation that supports that build sits outside their control.

The question is how much of that dependency the next generation of companies can absorb before it starts shaping what gets built in the first place.

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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…

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4 months ago · 45 likes · Ruben Dominguez

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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.

Let’s talk about why you need to wait and how to hire when you’re actually ready.

together with Vanta:

The engineering time you protect by hiring late gets spent anyway. A prospect asks for compliance proof, the deal stalls, and your one engineer vanishes into an audit.

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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.