Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - SEMRUSH ONE

Your customers are searching on Google AND ChatGPT. Can they find you on both?

Search has changed. Ranking on Google still matters, but buyers are also asking AI tools which products to use, which companies to trust, and how different options compare.

Semrush One brings both sides into one place. You can track your Google rankings and AI mentions, see the prompts driving demand, compare your visibility against competitors, and find gaps you can actually act on.

It monitors 239M+ relevant LLM prompts globally, alongside the SEO data Semrush is already known for.

See where your brand shows up across Google + AI →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How fast should your AI product show value? Inside the onboarding of 30 AI products

Recently, we pulled apart the homepages of 34 funded AI startups to see what gets a visitor to click. A lot of you wrote back with the same follow-up question: fine, they clicked, now what? Because the signup is where most founders stop measuring, and it’s where most of the loss actually happens.

Here’s the shape of it. Someone lands on your page, likes it, and clicks “Start for free.” Then they hit an email verification screen, four questions about their role and company, a pricing table, and finally a blank workspace with a button that says “New project.” You count them as a signup. They never come back.

RevenueCat’s 2026 data says 55% of three-day trial cancellations happen on day zero, the same day the person signed up. And in their data, AI apps churn 30% faster than everything else, even though they earn 41% more per paying user. The product isn’t the problem. The first ten minutes are.

So I did for onboarding what I did for landing pages. I went through the documented signup flows of 30 self-serve AI products, from Perplexity and Lovable to Clay, Synthesia and Cursor, and logged what stands between a new user and their first real output: whether you can try it before creating an account, how many questions it asks, whether a card is required, whether pricing shows up before you’ve seen anything, and roughly how many minutes it takes to get to a result. Then I did the same for the emails that follow, across 11 documented sequences.

The numbers split the 30 products into two clear groups, and the gap between them is bigger than most founders think.

Here’s what we’re covering:

• How long does it actually take to get a first result at 30 AI products, and where is the line that separates the fast ones?

• What do the fast products do differently, and what do the slow ones put in the way?

• How many questions are too many before the product? (The corpus has a clear answer.)

• Has “free forever” beaten the free trial, and what does the RevenueCat data say about the trade-off?

• What does the first screen look like at the products that activate well?

• When do the onboarding emails actually arrive, and where’s the gap that loses people?

• The first-session template: six steps, from try-before-signup to the first upgrade prompt

• The seven-day email sequence: every email, the day it sends, and what it does

• The full corpus: all 30 products, with what each one asks and gives

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - FRAMER

Framer’s AI agents can now help build, edit, and manage your website

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

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

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

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

Trusted by teams at Miro and Perplexity.

See what Framer’s AI agents can do →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

What does an AI startup landing page that converts look like?

Whether you’re putting up your first homepage or you’ve already rewritten yours three times, you’ve probably had the same thought: what are the AI startups that are actually growing doing on their websites that I’m not?

If you already have a site, you know how this goes. Each rewrite feels better, and nothing changes. Signups stay flat, demo requests come in at the same trickle, and eventually someone on the team says, “We should look at what Linear does” (the project management startup famous for its design), and the whole thing starts again. If you’re building from scratch, it’s the same problem from the other side: a blank page and a folder of screenshots from companies you admire.

Either way, you end up copying taste, not structure. Linear’s page works for Linear. What you actually need to know is what the pages of companies like yours, selling the way you sell, have in common. Nobody had written that down for AI startups, so I did.

In the last week of October, I went through the homepages of 34 funded AI companies, from early-stage startups to Harvey and Sierra, and logged what a visitor sees before they scroll: the headline, the sentence under it, the buttons, the proof sitting nearby, what’s next to the headline, and whether pricing is in the nav.

Then I sorted them by how each company sells, because that turned out to shape almost everything else on the page.

What came out isn’t a design opinion. It’s a count. And the patterns are clear enough that you can rebuild the top of your homepage, or write it for the first time, without guessing.

Here’s what we’re covering:

• What do 34 funded AI startups actually put above the fold, counted?

• Why does how you sell matter more than what you sell when it comes to the page?

• What kind of headline do most of them use, and why is that the wrong one to copy?

• What proof do they show, and what proof do they skip? (The answer surprised me.)

• What sits next to the headline: screenshot, video, demo, or nothing?

• The five patterns that repeat, and the five mistakes early-stage pages make

• The full corpus: every headline, CTA and proof block, sorted by business model

• The template: the block-by-block landing page structure, with the copy patterns to fill in

What 34 funded AI startups put above the fold

Let’s start with proof, because it’s where the biggest surprise was.

Three in four pages (76%) have customer or partner logos in or directly under the hero. Half have a number: “3,500+ legal organisations” (Harvey), “500,000 GTM teams” (Clay), “323M+ patient interactions” (Hippocratic AI), “3M+ developers” (Tavily). Fewer than one in five have a rating or award badge. And only 12% have a customer quote anywhere near the top.

That last number matters, because the customer quote is the first thing most founders add when they want “social proof.” The companies that have actually converted at scale don’t lead with quotes. They lead with logos and a count, and push quotes down the page where someone who’s already interested will read them.

Four pages have no proof at all above the fold: Lovable, Cursor, Gamma and Rox. Three of those are consumer-scale brands with tens of millions of users. If you aren’t one of them, you don’t get to skip this block.

How you sell decides what your page looks like

I expected pages to cluster by category: legal AI pages would look alike, voice AI pages would look alike. They don’t. They cluster by business model. Sort the 34 into three groups: companies that sell through a sales team, companies that sell with a credit card, and companies that sell to developers through an API, and the pages separate cleanly.

Pricing in the nav is the clearest split. Every one of the 10 self-serve pages and every one of the 9 developer pages has a Pricing link. Only 4 of the 15 enterprise pages do. Demo-only CTAs are the mirror image: 11 of 15 enterprise pages have “Book a demo” or “Request a demo” as the only way in, and zero self-serve or developer pages do. Every self-serve and developer page offers a free path, usually paired with a sales option.

Logos are nearly universal for developer products (9 of 9) and enterprise (11 of 15), and a bit less common for self-serve (6 of 10), where the brand itself often carries the page.

So before you touch a word of copy, decide which of the three you are. If you sell to a procurement team, your page is a demo page, and pricing in the nav will mostly generate tyre-kickers. If someone can sign up with a card, hiding pricing puts you at odds with every competitor in your motion.

Most headlines promise a feeling. That’s the wrong one to copy.

Here’s where it gets interesting, and where I think most early-stage founders go wrong.

Aspiration headlines are the most common type, and the ones most founders copy. But look at who actually writes them, and you’ll see why they backfire for almost everyone else. Seven of the eleven come from the same kind of company, and that’s the whole problem.

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - SEMRUSH ONE

Your customers are searching on Google AND ChatGPT. Can they find you on both?

Search has changed. Ranking on Google still matters, but buyers are also asking AI tools which products to use, which companies to trust, and how different options compare.

Semrush One brings both sides into one place. You can track your Google rankings and AI mentions, see the prompts driving demand, compare your visibility against competitors, and find gaps you can actually act on.

It monitors 239M+ relevant LLM prompts globally, alongside the SEO data Semrush is already known for.

See where your brand shows up across Google + AI →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

What does a Y-Combinator seed deck that actually raises money look like?

You’ve probably sent your deck to a dozen investors by now. Maybe more. And if you’re being honest, you built it by copying whatever structure felt right: a template from Google, a friend’s old deck, some slides from an accelerator you didn’t even attend.

Here’s the thing nobody tells you:

The founders who raise fast aren’t writing better copy or building slicker slides. They’re using a specific structure - the same nine-slide skeleton, in the same order, almost every time and most founders have never actually seen it laid out.

We pulled 50+ Y Combinator decks - startups that went on to raise a combined $450M+ and reverse-engineered what’s actually on the slides. Not YC’s public advice about “keep it simple.” The real decks. What order the sections come in, what gets a full slide versus a single line, where the traction numbers sit relative to the ask.

There’s a reason this structure keeps showing up across decks with nothing else in common - different industries, different founders, different check sizes. It’s not a style choice. It’s what investors are scanning for, in the order they’re scanning for it, and almost nobody hands it to them that way.

Below: the exact nine-slide structure, what goes on each one and why, the three design rules YC enforces that most founders skip, and the full set of decks - link included - so you can see it for yourself.

Here’s the structure - slide by slide.

(Also scroll down to find the linkd to 50+ Y Combinator decks that raised $450M+ in funding)

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - BEZEL

💰 AI labs could pay your company $100K to $3M for data you already have…

Bezel (YC-backed) helps you partner with labs that buy anonymised operational data from companies like yours.

Your operational workflows, documentation, processes, and internal knowledge like Slack are extremely valuable. Now you can start a new recurring income stream for your business in less than a month while maintaining privacy.

We’re looking to partner with companies that have more than 20 employees and have existed for more than a year. Our partners have already paid out over $200 million for this data.

See if your company qualifies →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Before VCs invest, they’ll call your customers. But what do they really ask?

A while back, we went through what happens when a VC reference-checks a founder - the backchannel, the people they call who aren’t on your list, what they’re actually testing for.

Founders read that and prepare for it. Then they get blindsided by the second set of calls, which almost nobody prepares for and which carries a cost the first one doesn’t.

Your investors are going to call your customers.

And here’s what makes it different. When a VC calls your former colleague, the worst-case scenario is that you don’t get the money. When a VC calls your biggest customer, the worst case is you don’t get the money, and your biggest customer now knows you’re running out of it.

That’s the part founders don’t price in.

What you’re actually spending

I’ve talked this through with a few investors, and with people who’ve run diligence from inside firms. The framing that stuck: the damage isn’t what your customers say. It’s what the call puts in their head.

And if that investor passes, it quietly costs you something. Do it across five funds with four passes, and your customers form a private view that you’re struggling. Nobody says it out loud. It shows up later, in a renewal conversation that’s harder than it should be.

You’re not spending your customers’ time. You’re spending their confidence, and it doesn’t refill.

This is also the only part of diligence where you’re removed completely - no chance to read the tone, clarify a bad answer, or steer. Your customer has probably never done one before. The investor will push, will spend real time on the negatives, and isn’t listening for one bad comment. They’re assembling a pattern.

The mistake almost everyone makes

An investor asks for customer intros. The founder, wanting to look cooperative, sends them.

That’s it. That’s the mistake.

You don’t have to, and the investors I spoke to were clear about it. You can hold customer references for funds in the final stage of diligence. There’s no reason to hand them over after a first or second call, before you’ve even spoken to someone who can write the cheque.

Almost no founder knows this. So a twenty-fund process turns into the same three customers taking six investor calls, five of them pass, and by the time the round closes, your customers have drawn their own conclusions.

The round closed. The relationship got worse. Nobody ever connects the two.

What’s inside today’s deep dive

• When should you actually agree to a customer intro? The three rules for deciding which funds get access, and the sentence that lets you say “not yet” without sounding defensive.

• What do they actually ask your customer? The full question set, grouped - plus the three questions that decide the call.

• Which customers should you never put forward? Including one your instinct says is your strongest.

• How do you prepare a customer without coaching them? The line founders cross without noticing, and the prep note you can send as is.

• What protects the relationship if the fund passes? One sentence, said before the first call, that changes how a pass looks to your customer.

• What does a reference that kills a deal actually sound like? Versus a lukewarm one, which is usually fine.

Plus the debrief loop that makes every call better than the last, and the prep script to send your customer.

When should you actually agree to a customer intro?

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - UNITARD

The 2021 hangover has a leaderboard now….

Some people YOLO’d into crypto in 2021. Tiger Global, SoftBank and BlackRock did it with unicorns: billion-dollar valuations, done over a weekend, priced as the party would never end. It ended.

440+ startups once worth $1 billion and more have fallen below the line, and about $1.46 trillion of paper value went with them.

Meet the Unitard Index, where every fallen unicorn is tracked: the peak, the drop, and who wrote the check. Check your fund before your LPs do.

Find every fallen unicorn and who backed it →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Can you raise on a vibe-coded product?

Somewhere in the last eighteen months, “I built it myself” stopped meaning what it used to. A product that would have taken a technical cofounder and three months now takes a weekend and a Lovable subscription. Roughly four in five Lovable users are non-technical. A quarter of one recent YC batch had codebases that were 95% written by AI, and those founders were all engineers.

The building part is solved. The getting-paid part isn’t: in Lovable’s own survey of 14,000 users, only about one in ten reported direct revenue from what they’d built. Raising sits somewhere between those two numbers, and that’s the gap this piece is about.

So the question I get more than any other right now, from founders who’ve built something real without writing much code, is some version of this: will an investor fund it, or will they laugh me out of the room the moment they ask who wrote the backend?

The honest answer is that both happen. If you haven’t read the piece on what investors check in your financial model, this is the same idea applied to your code: there is a hidden check, and you can pass it if you know it exists, and the difference isn’t luck. I went looking for every company I could find that built its first product with a vibe-coding tool and then raised money, and I read what the investors who backed them said about why.

I also put the question to a few investors I know who write seed cheques, and their answers surprised me. Not one of them said AI-written code was a problem. Every one of them described a specific thing they check that most founders fail.

I’ll give you the data, the companies, what the audits find, and the eight questions that decide it.

Here’s what we’re covering:

• Who has actually raised on a vibe-coded product, and how much?

• Why don’t investors care that the code was AI-written, and what do they care about instead?

• What does a technical audit find in a vibe-coded app? (The numbers are worse than you’d guess.)

• At what stage does a vibe-coded product stop being enough: pre-seed, seed, Series A, or enterprise?

• The eight questions investors ask, with what a pass and a fail look like for each

• When should you rebuild, and what does it cost if you wait?

• How do you position a vibe-coded product in the deck and the first meeting?

• The pre-raise checklist: the fixes that take a weekend, the ones that take a month, and the ones that need an engineer

Vibe-coded companies are raising, and from serious funds

Let’s start with what’s actually happened, because most of the discourse on this is opinion.

• Roadrunner is building AI-native configure-price-quote software. Its CEO, Joubin Mirzadegan, is a Kleiner Perkins partner. Mamoon Hamid, also at Kleiner, told Fortune that Mirzadegan walked into a one-on-one and said he’d “vibe-coded this over the weekend.” Kleiner led the seed. Founders Fund led the Series A. Total raised: $27M, and the first seven-figure customer deal closed within three months of the prototype.

• Peec AI monitors how brands show up in ChatGPT, Perplexity and Gemini. The team built the first prototype in Vercel’s v0 in about a day and a half. It raised an $8M seed led by 20VC and then a $21M Series A led by Singular, and crossed $10M ARR about 16 months after launch.

• Brickwise is an AI property manager that takes tenant calls. Built on Lovable, it got into YC’s Fall 2025 batch and has raised more than $3M.

• Anything, a vibe-coding tool itself, hit $2M ARR two weeks after launch and raised $11M at a $100M valuation from Footwork, Uncork and Bessemer.

• Plinq, a Brazilian safety app for women, built its first web version in Lovable in 45 days and raised R$1.3M, with Lovable’s own CEO Anton Osika among the angels.

• Lumoo, a fashion imagery tool, started as a Lovable experiment and raised SEK 6M from angels.

That’s five companies spanning pre-seed to Series A, with Kleiner, Founders Fund, Singular, Bessemer and YC on the cap tables. The idea that vibe-coded means unfundable is dead.

But look closer at the list, and a pattern shows up.

• Roadrunner’s prototype was a demo for CROs, not the product.

• Peec’s technical team rebuilt the real product in about six weeks after the v0 prototype proved demand.

• Anything built its own backend infrastructure in-house rather than relying on Supabase like its competitors.

• Lumoo added an engineer to harden the backend.

In every case that got past seed, the vibe-coded version was the beginning of the story, not the thing that got funded at Series A.

Investors don’t care that AI wrote it. They care about something else.

When Business Insider asked seed and growth investors about vibe coding last year, the answers were consistent.

• Bob Thomas at Oxx said investors are “shifting away from backing purely technical founders” and that AI fluency at seed is a plus.

• Zoe Qin at Dawn Capital said she doesn’t grill CTOs on AI use, because the skill “should be evident in the depth and breadth of the product they’ve built.”

• Yashwanth Hemaraj at BGV said the thing he looks for is an “architect mindset,” meaning systems-level thinking, not typing speed.

• YC’s partners said something sharper when they disclosed the 95% figure.

  • Diana Hu: “You have to have the taste and enough training to know that an LLM is spitting bad stuff or good stuff.”

  • Garry Tan: the first versions of reasoning models “are not good at debugging,” so founders must understand what’s happening in the product in depth. Tan has since said YC treats a founder’s public repo as the audit trail for agency.

The investors I spoke with for this piece said the same thing in plainer terms. One put it like this: the question isn’t whether AI wrote the code, it’s whether anyone on the team can explain it. If the founder can walk through the auth flow and say what happens when two users are logged in at once, the AI question goes away. If the answer is “Lovable set that up,” the meeting is effectively over, whatever the demo looked like.

That’s the whole game at the pitch stage. But the pitch isn’t where vibe-coded products actually fail. They fail later, when someone opens the code.

And when someone does open it, the results are bad. In one audit of 26 AI-built apps this summer, 22 failed outright, and none passed cleanly. Across more than 5,600 deployed vibe-coded apps, researchers found over 2,000 serious vulnerabilities. The good news is that the failures cluster in the same few places, every time, which means you can find them before an investor does.

Below the line:

• What an investor’s technical audit actually finds in a vibe-coded app, with the numbers from four separate studies

• Where vibe-coded gets you and where it stops: pre-seed, seed, Series A and enterprise, with the companies at each rung

• The eight questions investors ask about a vibe-coded product, with what a pass and a fail look like

• When to rebuild, what it costs, and why month four is the cheapest point

• How to position it in the deck and the first meeting, including the one line that turns it from a weakness into a signal

• The pre-raise checklist, split into weekend fixes, month fixes and hire-an-engineer fixes

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Would your financial model survive the first ten minutes with an investor?

You spent three weeks on that model. Rebuilt the revenue tab twice. Argued with your co-founder over whether month-18 growth should be 15% or 20%, landed on 18% because it “felt defensible.” Sent it over as a follow-up after a good first call.

Here’s what actually happened to it.

We’ve looked at hundreds of these over the years, and the pattern never changes: founders almost never find out their model was the problem. They chalk up the pass to market size, or timing, or “not a fit right now.” What actually happened is smaller and way more fixable - an associate opened the file, ran through a ten-minute mental checklist, and moved on. Not laziness. They’ve seen 400 of these this year. The checklist is just how they survive that volume.

Your model isn’t being read. It’s being pattern-matched against every other one that associate has opened this quarter, and sorted into a pile before anyone gets to the story you’re telling.

And here’s the part that actually costs you the round: get this wrong, and it doesn’t cost you one line item. It rewrites how the investor reads everything else in your deck. A model that doesn’t hang together tells them you don’t fully get your own business, and once they’ve decided that, no traction slide fixes it. We’ve watched founders with genuinely strong numbers get quietly passed on for exactly this reason. Not a weak business. A model that made them look like they hadn’t done the work.

Most founders think the model’s job is to prove the business works. It’s not. Nobody at pre-seed or seed believes your five-year revenue line - the associate reading it doesn’t either, and they know you know that.

The model’s real job is to prove you understand your own business well enough that the assumptions hang together. It’s a coherence test wearing a spreadsheet costume.

The problem: that coherence gets checked through a handful of specific ratios and patterns, and almost nobody tells you which ones or what failing looks like. You usually find out when the partner goes quiet on a call, or the follow-up just never comes.

The room you’re not in

Seed-to-Series A conversion has fallen from roughly 50% to about 38% over the past few years, and rounds are pricing higher on less proof than they used to. That combination means the screening pass on your model has gotten more consequential, not less - there’s more capital chasing fewer credible next rounds, so the bar for “credible” has tightened even as check sizes have gone up.

What actually happens in that ten-minute pass:

• The first ratio an associate pulls out of a model, before they read a single assumption and the 2026 benchmark for where you should sit at your stage.

• The single number that tells them whether you’re buying growth or earning it, so you know your own number cold, without opening the file.

• The “tell” in a revenue line that immediately signals a founder who hasn’t built one of these before, and how to fix it without under-selling your growth.

• Where the bar actually shifts between a pre-seed model and a Series A data-room model, so you’re not over- or under-building for the stage you’re at.

• The one runway number that quietly kills deals that were otherwise going well.

• A self-audit scorecard to check your own model against every one of these before anyone else does.

• The pre-seed/seed financial model template we built to pass this exact test, free with this issue.

We’re going to walk through each of these with the actual 2026 benchmarks, show you what “pass” and “flag” look like at your stage, and hand you the model built to clear them - plus the self-audit scorecard to check your own numbers against before anyone else does.

What gets checked first, in order

Associates don’t read your model top to bottom. They jump to three numbers, in roughly this order, and everything else is read through the lens of what those three numbers already told them.

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - FRAMER

Framer’s AI agents can now help build, edit, and manage your website

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

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

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

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

Trusted by teams at Miro and Perplexity.

See what Framer’s AI agents can do →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Inside the VC backchannel: how investors really reference-check founders.

It’s one of the most common stories in venture, and almost nobody gets the real explanation.

A founder walks out of a partner meeting convinced it’s done. The room was warm. The lead partner said “we’re excited” - twice. By the time he’s home, he’s modelling the post-money.

Then: eleven days of silence. Not a pass. Not a term sheet. Just a polite “still working through our process”.

Every week, founders come out the other side of this window, some with a term sheet, some with a pass that makes no sense given how well the meetings went. And the ones who get passed on rarely learn why. The email says “not the right fit at this time.” The real answer is sitting in the firm’s deal file, two lines long, written after a phone call the founder never knew happened.

Here’s what actually goes on during those silent days.

The associate who’d been friendly in every meeting was on the phone with a former employee the founder hadn’t spoken to in three years. The partner had texted two investors who passed on the seed round. Someone on the team had found the ex-cofounder - the one who left in year one, the one who isn’t mentioned anywhere in the deck.

The deal didn’t stall. It went underground.

In what really happens in the 48 hours after your pitch, I walked you through the internal machine that starts running the moment you leave the room - the deal memo, the sponsor dynamic, the Monday partner meeting. That piece ended at the moment the partnership says “let’s move forward.”

This one covers what happens next. Because between “let’s move forward” and a signed term sheet sits the least understood, least visible, and most fatal stage of the entire fundraising process: the reference check. And unlike your pitch, you don’t get to be in the room for it.

Today’s deep dive covers:

• The real scale of VC reference-checking and the data showing it starts earlier than you think

• The five channels VCs use to check you out (only one involves the list you gave them)

• The exact questions investors ask on these calls - including the forced-ranking questions designed to make polite people say revealing things

• The kill decoder: the seven answer patterns that end deals, and why “he was great” can be a death sentence depending on how it’s said

• How to pre-run your own backchannel before any VC does - the exposure-mapping exercise that takes 90 minutes and has saved real rounds

• The reference prep kit: exactly how to brief your on-list references, with the script

• How to flip the backchannel and run it on your investor before you sign

• The 72-Hour Backchannel Audit - a checklist you can run this week, mid-raise or before one

Let’s get into it.

The diligence you never see

Start with the numbers, because they’re bigger than most founders assume.

The largest study ever run on VC decision-making - a survey of 885 institutional VCs across 681 firms by Gompers, Gornall, Kaplan, and Strebulaev - found that for every deal a VC closes, they spend an average of 83 days from first contact to close, invest 118 hours in due diligence, and make roughly 10 reference calls.

Ten calls. Think about your reference list for a second. You probably gave them three names, maybe four.

The gap between the list you handed over and the calls that actually happen is the backchannel. And at the top of the market, the gap is wider: top-tier firms routinely run 15 to 25 reference calls per deal, with backchannel sources - people you never named and will never know were contacted - treated as the highest-signal category.

One more number that explains why all this effort goes into checking you specifically. In the same study, 95% of VCs named the founding team as an important factor in their investment decision, and 47% ranked it as the single most important factor - ahead of product, ahead of market, ahead of traction. VCs also attributed eventual success or failure more to the team than to the business itself.

Follow the logic. If the team is the thesis, then diligence on the team is the diligence. Your metrics get an afternoon in a spreadsheet. Your character gets two weeks of phone calls.

The five channels (your list is only one of them)

VC reference work isn’t random. Across firms, it runs through five distinct channels, and understanding them tells you exactly where your exposure lives:

Read more

👋 Hey, Sahil here - welcome to today’s edition of Venture Curator, where we break down how great startups grow, how top investors think, and what’s shaping the future of tech.

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - BEZEL

💰 AI labs could pay your company $100K to $3M for data you already have.

Bezel (YC-backed) helps you partner with labs that buy anonymised operational data from companies like yours.

Your operational workflows, documentation, processes, and internal knowledge like Slack are extremely valuable. Now you can start a new recurring income stream for your business in less than a month while maintaining privacy.

We’re looking to partner with companies that have more than 20 employees and have existed for more than a year. Our partners have already paid out over $200 million for this data.

See if your company qualifies →

📜 DEEP DIVE

How do you build personal and business AI agents with Claude if you can’t code?

You open a new chat. You explain who you are, what the company does, who the customer is, and how you like things written. You get a decent draft. You copy it out and close the tab.

Tomorrow morning, you do the whole thing again.

That’s most founders’ relationship with AI right now, and it’s why the tools feel underwhelming. You’re not working with an assistant. You’re working with an intern who has amnesia and gets rehired every morning.

Here’s the part that should bother you: of all the companies experimenting with AI agents, roughly one in ten has one actually running. Everyone is in the chat box. Almost nobody has built the thing that replaces it.

And the gap between those two groups isn’t technical. It has nothing to do with coding. It’s setup - a one-time hour where you tell the AI who it is, what it needs to know, and what it’s allowed to touch. Do that once, and it stops forgetting you.

The reason nobody does it is that every guide explaining how is written for engineers. So you close the tab and go back to the slow way.

This one isn’t. No code, about an hour, two working agents by the end - one for your daily work, one for your business, with every prompt ready to paste.

What’s inside this guide

• How much time and money does an AI agent actually save me?

• What are the four ingredients every agent is built from? Learn these once, and you can build an agent for anything.

• How do I build a personal assistant that knows me?

• How do I build an agent for my business? Three complete, ready-to-use versions.

• What’s the prompt formula that separates an agent that works from one that gives you junk?

• What do I do when the output isn’t great? A two-minute fix almost nobody knows.

• When should I level up, and how do I stay safe?

By the end, you won’t just have read about agents. You’ll have two of them running, and you’ll know how to build a third for any job you can think of.

Let’s get into it.

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - MACKTEZ

The IT book for founders who aren't technical…

Most founders never decide to have an IT problem. It just shows up as the company grows: a tangle of tools, accounts, vendors, and security gaps that nobody really owns. Tame the Tech Beast, by Macktez founders Noah Landow and Michael Horst, draws on 30 years of guiding organisations through growth and change to show nontechnical leaders how to turn IT from a back-office burden into a strategic advantage.

"I wish I'd had this book 17 years ago." (Cindy Gallop, founder and CEO) Available on Kindle, paperback, hardcover, and audiobook.

Get the book →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

What is your startup actually worth in 2026? Plus a valuation calculator to price your next round.

Every founder walks into a raise with a number in their head. Usually it comes from a friend’s round, a headline about a company that sounds a bit like yours, or a rule of thumb from a few years ago, when “10x ARR” was the line most people used.

That way of thinking stopped working sometime in the last eighteen months. In 2021, your multiple mostly tracked your growth rate. Grow faster, get paid more per dollar of revenue. In 2026, two companies with the same revenue and similar growth can raise at prices five times apart, and the gap has very little to do with the numbers in their data rooms. It comes down to which story the investor files you under before the first meeting is over.

I wanted to see that clearly, so I pulled together every AI round I could find from 2024 to 2026 where the company’s annual recurring revenue at the time of the round was disclosed or credibly reported: 31 rounds across coding tools, legal, healthcare, voice, infrastructure, sales and consumer apps. I divided each post-money valuation by the ARR behind it, then compared that with what public software trades at and with the seed and Series A medians from Carta and PitchBook.

The pattern was sharper than I expected. Once you see what investors are actually paying for, you can position your company to be priced on it.

Here’s what we’re covering:

• What is a dollar of your revenue worth to a private investor right now, and how far is that from what public markets pay?

• Does more revenue get you a higher multiple? (Short answer: no.)

• If it isn’t revenue size or growth, what actually decides whether you get 20x or 54x?

• What are seed and Series A rounds really pricing at in 2026, AI and non-AI?

• How do you position your company so investors put you in the higher bracket? Six moves, and what to change in your deck.

• What is a “headline valuation”, and why might the number you announce not be the price you actually raised at?

• The valuation calculator: put in your ARR, growth, margin, retention and how you earn revenue, and get a low, base and high valuation plus the dilution at each.

• The positioning score: eight questions that tell you which end of that range to anchor your ask on.

A dollar of revenue is worth ten times more in private markets than public ones

Start with the baseline. In April, Meritech’s tracking of public software companies had the median public company trading at about 3.2x ARR. Even the top ten were at 11.7x. That’s 63% below the pre-2021 median, and the median stock was down about a third for the year.

Private AI rounds are in a different universe. Across my 31 rounds, the median company raised at 36.7x ARR. Look only at the rounds priced in 2026, and the median jumps to 54.6x.

A private investor paying 37x for revenue that public markets value at 3x isn’t paying for the revenue. They’re paying for what they believe the revenue turns into, and for the right to own a piece of a company that might own a category. Everything in this piece follows from that. Your valuation is an investor’s bet on your future position, and in a raise, most of what you control is how believable you make that position.

It also means the number is more fragile than it looks. PitchBook’s latest valuations report points out that the AI valuation thesis hasn’t yet been tested by public markets, and that a repricing could follow if public investors won’t pay what venture has. So a high price now is a bar you have to clear next round, not just a win you get to bank.

Bigger revenue doesn’t buy you a bigger multiple

The obvious assumption is that more revenue earns a higher multiple. It doesn’t, at least not in this data.

The clearest view is the cluster of companies that raised at almost exactly $100M in ARR. Same revenue, very different prices:

• Lovable, the app builder, raised at about 18x.

• Gamma, the AI presentation tool, raised at 21x.

• Clay, the GTM data platform, at about 31x.

• Harvey, in legal, at 50x.

• Legora, also legal, at 56x.

• Glean, enterprise search, at about 72x.

• Sierra, customer service agents, at 100x, and again at 100x a few months later on $150M.

That’s a 5.5x spread at identical revenue. And growth doesn’t close the gap either. Lovable was arguably the fastest-growing company on that list, going from launch to $100M in about eight months, and it priced near the bottom. Tomasz Tunguz noticed the same thing looking at a smaller set of AI companies this summer: the fastest grower priced near the bottom, and the premium tracked something else.

At the small end it’s even more extreme. Rox, a sales agent startup, reportedly raised at $1.2B on around $8M of ARR, which is roughly 150x. Cursor’s Series A in 2024 was priced at about 8x.

So if it isn’t size, and it isn’t speed, what is it? When I sorted the 31 rounds by one simple attribute, the gap mostly explained itself. And once you know what that attribute is, there are specific things you can change in how you describe your company, and in how you report revenue, that move you from one bracket to the other.

Read more

👋 Hey, Sahil here - welcome to today’s edition of Venture Curator, where we break down how great startups grow, how top investors think, and what’s shaping the future of tech.

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - THE NUANCE COLLECTIVE

What Does AI Tell Investors About Your Company?

The Nuance Collective measures how AI models describe your company across five signals: who gets named, rank, confidence, accuracy and direction.

Investors, partners and enterprise buyers now ask AI about you before they take the call. If the models tell a weaker or wrong story, you start every raise and every deal behind.

We find where their version drifts from yours and close the gap, so the company that shows up in diligence is the one that earns the term sheet and the partnership.

See What the Models Say About You →

Partnership With Us

Get your product in front of over 125,000+ audience - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Why are so many AI startups calculating ARR wrong?

Take current MRR, multiply by 12, put it on the slide. You’ve done this. Every founder has. It’s the fastest way to turn a month of revenue into a growth story, and for a decade it was close enough to true that nobody argued with it.

It stops being true the moment any part of your revenue is usage-based. And if you’re building anything AI-adjacent in 2026, some part of it almost certainly is.

Quick definition, because this matters for everything below:

Usage-based pricing (also called consumption pricing) means customers pay based on what they actually use - API calls, tokens, compute, seats-plus-overage - instead of a flat fee that’s identical every month.

It’s now the pricing model for 38% of SaaS companies, and it’s close to the default for AI products, where cost genuinely does scale with usage in a way a flat subscription can’t absorb.

Usage-based pricing is good for you. It’s also the exact thing that breaks the math you’re probably still using to report growth.

Where the math actually breaks

MRR × 12 assumes one thing: that this month looks like next month, and the month after that.

For seat-based software, that’s roughly true - nobody doubles their headcount overnight.

For usage, it’s rarely true.

A customer runs a big batch job. A retailer has a seasonal spike. A client onboards three new teams in one week and then plateaus. None of that is “recurring” in the way the word implies, but the moment it lands in a given month, your naive ARR calculation happily multiplies it by 12 and reports it as your steady-state business.

One infrastructure-metering vendor that works with usage-heavy companies put it plainly: presenting a single ARR number to your board means you’re claiming every dollar behaves like a predictable subscription. That assumption breaks the moment your largest customer throttles back consumption, and your “recurring” revenue drops with it.

Here’s what that actually looks like on a chart - This is a simplified model of a usage-based startup with one customer usage burst in November - not an unusual pattern for anyone selling API or compute-metered products with lumpy enterprise usage:

• Naive ARR - this month’s revenue × 12. Reacts to spikes, so it’s often wrong.

• Smoothed run-rate - 3-month average × 12. Evens out spikes, closer to the truth.

• Committed baseline ARR - only what’s guaranteed by contract. No usage, no spikes - the safest number.

The naive number (current month × 12) swings from $850K to $1.18M to $890K across three months. Nothing about the underlying business changed that fast. What changed was one customer’s usage in one month, multiplied by twelve and mistaken for a trend.

A separate framework for comparing the two metrics head-on makes the gap concrete: in a worked example where a company collects $450K in a month - $300K from actual subscription contracts, the rest from uncommitted usage overage and a one-time project - the contracted ARR comes out to $3.6M. Annualising the full month instead gives you $5.4M.

That’s a 50% gap between the two numbers, and it comes entirely from treating a one-off month as if it were guaranteed to repeat.

Why this is a you-problem, not just an investor-problem

Most of what’s been written about ARR inflation this year focuses on founders misleading investors. That’s real, but it’s the smaller risk for most of you.

The bigger one: you’re using this same broken number to run your own company.

Runway math, hiring plans, burn multiples - all of it gets built against whatever number sits in the ARR cell of your model. If that number is inflated by a single spike month, you’ll greenlight a hire you can’t actually support, or tell your board you have more runway than you do, without anyone lying to anyone. You did it to yourself, using a formula that was never built for how you get paid.

Two terms worth being precise about, since they get used interchangeably and shouldn’t be:

• Baseline (or committed) ARR - revenue from customers with an actual minimum commitment: a contracted floor, a subscription fee, something that doesn’t depend on behaviour changing next month.

• Variable (or usage) run-rate - everything above that floor: overage, consumption spikes, anything tied to how much a customer happened to do this month.

Blending these into one “ARR” line is the single most common way founders make their own numbers less useful to themselves.

Who this actually hits hardest

Not every business model is exposed equally. Before you check your own numbers, place yourself on this spectrum:

Read more

👋 Hey, Sahil here - welcome back to Venture Curator, where we explore how top investors think, how real founders build, and the strategies shaping tomorrow’s companies.

Big idea + report of the week :

• Who actually finds venture’s outliers? Here’s what the data shows.

• Nine years later, Most VC funds still haven’t returned the money. What happens next?

Frameworks & insightful posts :

• Where is the biggest money in AI: Frontier Models or the Middle Market?

• How much should your startup actually spend on AI? Here’s what the data shows.

• Why has venture due diligence got worse for deep tech?

Get 20% off forever

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

FROM OUR PARTNER - BEZEL

💰 AI labs could pay your company $100K to $3M for data you already have.

Bezel (YC-backed) helps you partner with labs that buy anonymised operational data from companies like yours.

Your operational workflows, documentation, processes, and internal knowledge like Slack are extremely valuable. Now you can start a new recurring income stream for your business in less than a month while maintaining privacy.

We’re looking to partner with companies that have more than 20 employees and have existed for more than a year. Our partners have already paid out over $200 million for this data.

See if your company qualifies →

START WITH

🧠 Big idea + report of the week

Who actually finds venture’s outliers? Here’s what the data shows.

Venture investors are usually taught that this is an outlier business. A handful of companies nobody saw coming produce most of the returns, so the firms with the biggest teams, the widest networks and the most capital should be best placed to find them.

But new research from Dan Grey at Odin, built on Dealroom data, suggests the biggest firms are now the least likely to back a company that looks different from everything else getting funded.

Grey scored every company in the 2023-25 seed portfolios of five megafunds (a16z, General Catalyst, Lightspeed, NEA and Accel) and a selected group of top-ranked emerging managers on how rare it was. Rarity was measured on business (industry, model, customer, geography) and technology, against every seed-funded company from 2018 to 2025. The most unusual fifth was counted as outliers.

• Emerging managers put 24.7% of their scorable seed rounds into outlier profiles (49 of 198). The megafunds put 11.5% there (32 of 278). About twice the share.

• The gap doesn’t come from every small fund being a contrarian. Plenty of emerging managers are as AI-heavy as the megafunds: Conviction is 100% AI, South Park Commons 93.5%. The range comes from specialists with different mandates, like Lowercarbon at 18.8% AI and Multicoin at 15.4%.

• That’s why pooling matters. Combine five unlike emerging-manager books and concentration typically falls by 22.6%. Combine the five megafunds and it falls by 2.0%, because all five already sit between 82.7% and 89.5% AI. Only 2 of 462 possible emerging-manager combinations failed to beat the megafund group.

There’s a reason this matters beyond portfolio statistics. Research on financing risk has long argued that whether investors expect later capital to be available shapes which experiments get a first check at all. Odin is careful to say its data doesn’t prove that mechanism. But if the largest pools of capital only back what is already widely fundable, a company outside that consensus depends on someone else to take the first bet.

So does this mean emerging managers are better at picking winners?

No. “Outlier” here means rare among funded companies, not more innovative and not more likely to succeed. Adding founder background to the scoring narrows the gap to 19.8% versus 14.4%. The emerging-manager group is a selected cohort, not the whole market. And smaller funds have their own herd moments: blockchain made up 46.7% of their earlier rounds before falling to 20.9%.

And this is where investors need to be careful.

If your thesis is that returns come from outliers, another brand-name allocation mostly adds more of the same bet. A basket of genuinely different mandates adds range. But range alone isn’t the case for writing the check. Odin’s own test is what a fund adds that’s missing from your existing portfolio, at what fees and carry, with what ownership after dilution, and with what follow-on risk.

The real question isn’t which fund is biggest. It’s which funds are actually looking where nobody else is, because on this data, that’s increasingly not the same list.

Nine years later, Most VC funds still haven’t returned the money. What happens next?

Founders are usually taught to think of their VC as patient capital. Venture is a long game; the fund is built to wait for the big outcome, and nobody on the cap table is in a hurry for cash.

But new data from Carta’s Q2 2026 VC Fund Performance report suggests that patience is running thin for a lot of funds, because most of them still haven’t paid their own investors back.

Carta tracked net DPI, the cash a fund has actually returned to its LPs for every dollar they put in, across roughly 3,000 US venture funds that started investing between 2017 and 2026.

• Funds that started in 2017 are now about nine years old, close to the end of a typical ten-year fund life. The median one has returned just 0.37x in cash. Even a top-quarter fund is only at 0.70x. Only the top 10% have handed back more than LPs put in, at 1.37x.

• It gets worse with younger funds. The median 2018 fund has returned 0.15x, and the median 2019 fund 0.04x, or 4 cents on the dollar after seven years.

• The value is there on paper. The median 2017 fund is worth 1.72x on its books, but only about a fifth of that has come back as cash. The rest is still locked in private companies that haven’t been sold or listed.

There’s a second detail that explains why this is hard to see from the outside. Most of these funds have paid out something: 86% of 2017 funds and 70.4% of 2018 funds have returned at least some cash. So almost every fund can tell its LPs that distributions have started. Very few can say they’ve returned the money.

So why should a founder care about their investor’s fund math?

Because the clock on your investor’s fund is not the same as the clock on your company. A fund nearing the end of its life, still owing its LPs real cash and trying to raise its next fund, has strong reasons to want liquidity soon. That pressure can show up as a push toward an earlier sale, a sale of its stake in a secondary, or more caution about putting reserve money into your next round.

And this is where founders need to be careful.

None of this means your investor will act against you. Funds can extend their life, and many top firms have strong reasons to keep backing their winners. Carta’s data shows the cash gap, not how every fund will behave. But the vintage of the fund that wrote your check is now a real input, not a footnote. A partner investing from a 2017 or 2018 fund is sitting in a very different spot from one investing from a fresh 2025 fund.

The real risk isn’t that your investor needs liquidity. It’s not knowing which fund your money came from, how much cash it still owes its own backers, and how that might shape their advice on your next raise or your exit.

📄 Must-Read Post

SOMETHING MORE

🧩 Frameworks & insightful posts

Where is the biggest money in AI: Frontier Models or the Middle Market?

Most AI coverage fixates on the frontier: the newest, smartest, most expensive model from each lab. But Tomasz Tunguz at Theory Ventures makes the case that the market that actually matters is the middle tier, and the spending data backs it up.

The mid tier already takes about 40% of AI spend and 30% of tokens.

The frontier, by contrast, is thin. Anthropic’s most capable model, Fable 5.1, took just 3.7% of gateway spending in its first twelve days. Its predecessor peaked at 13.2% before falling to 4.9% a month later, once Opus 5 shipped at half the price. Among large corporate accounts, frontier models’ share of token consumption dropped from 53% in early August to 45% by September.

That’s where the price war is being fought too. When Anthropic cut prices on a new model in September, OpenAI matched the cut roughly 90 minutes later. The Opus line had sat at $5 input and $25 output per million tokens across four straight versions before that cut. At the low end, OpenAI cut Luna’s price by 80% in July and by another 50% in September.

Three forces keep pushing mid-tier prices down:

• Lab rivalry. Labs now match each other’s cuts within hours, not quarters.

• Open-weight models. On gateways that publish their data, open models run the majority of token volume at an 86% discount to the blended price of closed models.

• Fine-tuning. Cursor’s Composer 2, fine-tuned from the open-weight Kimi K2.5, cut overall cost 86% versus its previous in-house model. Harvey cut cost per cell 55% against Sonnet 5 while scoring higher than Fable 5.

The reason this keeps happening is structural. What a business needs from AI, summarizing a contract or classifying a support ticket, barely changes year to year. The cost of the intelligence that meets that bar keeps falling fast. So the tier that clears a fixed requirement gets cheaper every quarter, and most real work sits in that tier, not at the frontier.

Demand looks less like a pyramid with the frontier on top and more like a bell curve with a fat middle, where buyers care about intelligence per dollar, not peak capability. The open question is whether that middle eventually becomes a commodity. If it does, the economics of the entire AI market shift with it.

For anyone building on top of these models, it’s worth asking which tier your product actually needs. Paying frontier prices for mid-tier work is now one of the easiest margins to give away.

How much should your startup actually spend on AI? Here’s what the data shows.

Ask most founders what they spend on AI, and you’ll get a confident number: a few thousand a month across ChatGPT and Claude seats, a Cursor bill, a creeping API line. It sounds precise. It isn’t, because it adds together three kinds of spending that have nothing in common except the vendor category.

Ramp’s payment data across 70,000+ US businesses shows how split “using AI” has become.

• The median company spends $11.38 per employee per month. The top 10% spend $611.

• The top 1% spend $7,449, roughly 650x the median. And when Ramp linked that spending to Revelio Labs’ workforce records across 21,559 companies, the companies spending heavily grew headcount 10.2% over two years, with entry-level hiring up 12%.

• Low-intensity adopters showed no significant change at all.

So spend more. But on what?

Most founders judge their AI bill against their software budget, because the charges sit next to Figma and Notion on the statement. That’s the wrong denominator. A growing share of AI spend doesn’t replace software. It replaces people. The support agent is competing with the support hire, not with your SaaS stack.

Run it on a typical seed-stage company: 12 people, about $180K a month in payroll, $4K a month on AI. Against the software budget, $4K looks heavy. Against payroll, it’s 2.2%. Same number, opposite conclusion.

The fix is to split AI into three budgets, each judged against its own number:

• Payroll-line AI (support, SDR and coding agents) is judged against the salary of the hire you didn’t make. If you can’t name that hire, it doesn’t belong here.

• Productivity-line AI (seats, notes and writing tools) is judged by usage. If usage has decayed by month two, cancel it.

• COGS-line AI (tokens and API calls inside your product) is judged by gross margin at 10x volume, and it has to sit in COGS in your books.

Blend the three and every signal cancels out. Overspending on seats makes the total look bloated, so founders hold back on agents, which is exactly where the returns are. The top 1% aren’t winning because they spend more. They’re winning because their spend is sorted: heavy where AI replaces labour, strict where it just speeds people up, and watched by margin where it lives inside the product.

We broke down the full sort, including a 30-minute audit you can run on your own statements this week, in What should your AI spend actually be measured against?

Why venture due diligence got worse for deep tech?

You’d expect the hardest technology to get the hardest scrutiny. A quantum computer, a fusion reactor or a new chip architecture either works under the laws of physics or it doesn’t, and checking that takes real expertise.

But Marin Ivezic at PostQuantum argues the opposite has happened: the bigger and more technical the deal, the less likely anyone independent has checked whether the science holds up.

It isn’t that investors got lazy. Several structural changes stacked on top of each other.

The expert layer disappeared.

Investors used to get independent technical judgment from bank research teams that employed specialist analysts. When Europe’s MiFID II rules in 2018 forced research to be paid for separately from trading, that model shrank, European equity research reportedly fell around 20%, and specialist analyst headcount at major banks dropped.

Expert networks, now a roughly $2.5 billion business, filled part of the gap. But they only work if you already know which expert to ask and what to ask them. In a frontier field, that’s exactly the knowledge a generalist investor doesn’t have.

The money moved to generalists.

In 2025, private quantum investment hit $4.9 billion, up 192%. The biggest investors were BlackRock and Nvidia, not specialist quantum funds. Sovereign wealth and pension money increasingly backs these rounds too, and one survey of sovereign funds found 58% lacked the resources to lead a deal. Capital that can’t run its own technical review ends up relying on someone else’s.

The lead’s name replaced the review.

A reputable lead investor certifies a company for everyone who follows. Followers assume the lead checked the physics, but they can’t see that review or verify it happened. Some rounds don’t even have that: Quantinuum’s $600 million round had no named lead at all.

Deadlines and herding reward speed over scrutiny.

US funds were sitting on a record $311.6 billion of uninvested capital at the end of 2023. A manager under pressure to deploy has more to gain from joining a hot round than slowing it down, and being wrong alongside peers does less damage to a reputation than being wrong alone.

Deep tech has no forced checkpoint.

Biotech is the useful contrast. FDA approval forces data into the open at each stage, so the industry built diligence norms around it: scientific advisory boards, specialist reviewers, staged checks.

Quantum has no equivalent gate. The most rigorous technical review of a major quantum company the piece could find came from the Australian government, released through a freedom-of-information request, not from any investor.

The results show up in the numbers.

Private quantum investment of $4.9 billion in 2025 compares with about $1.4 billion in total quantum computing revenue. Three quantum companies that went public via SPAC in 2021 projected about $1.2 billion in combined 2025 revenue and delivered around $138 million. And it was short sellers, not investors, who did some of the hardest verification work in the sector.

There’s a precedent for how this ends. Venture put more than $25 billion into cleantech between 2006 and 2011 and lost over half of it, and the researchers who studied that cycle warned that sovereign and pension funds without hardware experience would be the next to fund such companies.

The proposed fix is simple: before backing a deep-tech round above a set size, LPs should require an independent technical feasibility review, one not commissioned by the fund or picked by the founder. Worth knowing, the author invests in quantum startups and runs a firm that offers this kind of review. But the core point holds regardless: in deep tech, a logo on the cap table has quietly replaced the technical check, and at these check sizes, a few weeks of independent review is cheap.

📬 Upgrade to Venture Curator Premium

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

Get 20% off forever

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

• Partner - a16z | USA - Apply Here

• Associate / Senior Associate - Spyre Promptech fund | India - Apply Here

• Analyst - Pender Ventures | USA - Apply Here

• Associate - Plug & Play Tech Center | USA - Apply Here

• Associate - Harbourvest Partners | UK - Apply Here

• Investment Director - Robostrategy | USA - Apply Here

• Incubation Associate, Compute - Primary | USA - Apply Here

• Investment Analyst - Mela Venture | India - Apply Here

• VC Fellow - V11 | Remote - Apply Here

• Associate Counsel - Hivemind Capital Partner | USA - Apply Here

• VC Fellow - V11 | Remote - Apply Here

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

Upgrade to Venture Curator Premium

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

(Subscribe to Venture Curator Premium and get 20% off forever.)

Subscribe now

Share

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - FRAMER

Framer’s AI agents can now help build, edit, and manage your website

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

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

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

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

Trusted by teams at Miro and Perplexity.

See what Framer’s AI agents can do →

📜 DEEP DIVE

Is being a solo founder why investors keep passing? The complete guide to raising alone.

Building alone has never been easier. One person with Claude Code and a Stripe account can ship a product, sell it, and support it in a way that took a team of four in 2019.

The data agrees: solo founders were 17% of new startups ten years ago. Carta has them at 36% of companies founded in 2025 and 36 to 37% of new companies joining in 2026, and Stripe Atlas says 63% of the C corps it incorporated in Q2 2026 were started by one person. Raising alone hasn’t got easier at the same rate, and that’s the part nobody writes about honestly. The usual take is either “VCs hate solo founders” or “just get a cofounder.” Both are lazy, and both are wrong in the way that costs founders rounds.

I kept hearing from solo founders who’d been passed on and assumed it was the solo thing. So I sat down with a few who’d actually raised, and with a VC friend who has backed a couple of solo founders and passed on a lot more. What came out was not “solo founders can’t raise.”

It was that most solo founders who fail to raise are running the wrong process, with the wrong investors, saying the wrong sentence about why they’re alone. The VC put it in a way that stuck: “I’ve never passed on someone for being solo. I’ve passed on a lot of people for not having an answer to the question solo raises.”

Then I looked at the data, and the one thing everyone assumes about solo founders- that they raise at a discount- turned out to be the wrong place to look.

Here’s what we’re covering:

The data

• How big is the gap between solo founders starting companies and solo founders raising?

• Is there actually a solo discount on valuation or dilution?

• Who is actually writing solo cheques, and where is the gate opening fastest?

What investors are really asking

• What are the four objections an investor runs on a solo founder, and which one is new?

• What did the solo founders who raised have that the ones who didn’t lacked?

How to raise

• How do you position being solo so it reads as a thesis, not a gap? (The sentence that works and the one that kills you)

• What proof replaces a cofounder? (The bench, the bus-factor answer, the bandwidth evidence)

• Who do you raise from first, and in what order?

• How big should the round be, and what is it for?

• What are the seven mistakes that get solo founders passed?

• Are you ready to raise alone?

Solo founders doubled. Only 1 in 4 funded companies has one founder.

Here’s the number that starts every conversation about solo founders. About 36% of startups founded on Carta in 2025 were solo-led, up from 31% in 2024 and double the 17% of a decade ago. In 2026 it’s 36 to 37% of new companies, and Stripe sees 63% of its new C corps incorporated by one person. AI is the main reason: one person can now build and sell in a way that used to need two.

Here’s the number that should start the conversation instead. Among companies that actually raise venture money, the solo share is roughly 25%, up from about 10% a decade ago. Real progress, but still a 12-point gap. And the cohort data is harsher: in 2024, solo founders were about 35% of new startups but only 17% of the ones that raised that year, and took just 14.7% of the cash in priced rounds. That makes a solo founder roughly two and a half times less likely to close a round in year one.

Investors’ preferences show up the same way. 80% of companies that raised last year had more than one founder, and the most common team that closed a round was two people (36%, or 40% in SaaS).

So the gap is real, it’s big, and it’s closing from both ends. The question is what kind of gap it is, because that changes what you do about it.

Solo founders don't raise at a discount. Most just never get to the round.

Everyone, including most solo founders, assumes the penalty shows up as price: lower valuation, more dilution, worse terms. It’s the thing founders brace for.

Carta’s cap table data says otherwise. Once a solo founder closes a priced round, the valuation and dilution are statistically identical to a multi-founder team’s. No discount. And because there’s no cofounder on the cap table from day one, solo founders keep a median 75% more equity at exit than the lead founder of a multi-founder company.

Read that again from the investor’s side. Investors are not pricing solo founders down. They are either saying yes at the same price, or saying no. The penalty is binary. It’s a gate, not a discount, and that changes the whole problem. A discount you negotiate. A gate you have to clear.

And the gate is not the same height everywhere. In 2025, about 48% of angel investors made at least one investment in a solo-founder company, while over 75% of VC funds made none. Angels are open. Institutional funds mostly aren’t, yet. That’s the most useful fact in this issue for deciding who to call first, and I’ll come back to it.

And for the solo founders who do get through, 2026 looks different from 2016. A survey of 240 founders who raised seed or pre-seed in 2026 found solo founders closed faster, averaged 14 investor meetings to close, and reported fewer objections than two- and three-founder teams, 81% of whom were asked about equity splits or founder conflict. The objection didn’t disappear. It moved from the solo founder to the team.

Where the gate is opening fastest is also telling.

Y Combinator, the hardest filter in startups, had 8.5% solo founders in its Summer 2016 batch. Its Winter 2026 batch had 22 solo founders out of 199, about 11%. Independent counts of the Spring 2026 batch put it at 19%, and in September YC itself said nearly one in five of its companies is now solo-founded. Devtools, where a single builder can show working software fast, hit 22%. The filter that used to tell founders to go find a cofounder is now funding one in five without one.

So here’s the real picture. There’s no price to negotiate. There’s a gate; it’s highest at institutional VC and lowest at angels and accelerators, and it’s moving. Which leaves the only question that matters: what did the solo founders who got through it do differently from the ones who didn’t?

That’s the half of the issue. The four things an investor scores when you walk in alone (one is new this year and it’s the one sinking most AI solo founders), the sentence about being solo that works and the one that ends the meeting, the proof that replaces a cofounder, who to raise from first (almost the reverse of what most people do), how to size the round, the seven mistakes, and a scorecard to tell if you’re ready. If you’re building alone and raising in the next six months, this is the one to read.

Part 1: The four questions an investor asks when you walk in alone

The VC friend walked me through what goes through his head when a solo founder pitches. It’s four questions, in order, and the founder usually only prepares for the first.

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

📜 DEEP DIVE

Your competitor just raised millions. What do you do this week?

I've watched a lot of founders get this news, and it lands harder than almost anything else that happens while you're building. Your closest competitor just raised. The number is bigger than anything you’ve raised, maybe bigger than everything you’ve raised combined. For a day or two you feel like the ground moved: like the customers you’ve been working on are about to leave, like your existing investors are quietly re-reading their memo on you, like the fund you were about to pitch has already made up its mind. You feel confused, a bit behind, a bit shattered.

It isn’t like that. But it feels like that, and most founders act on the feeling.

Within a day, most of them have done one of three things, and all three are mistakes.

• They’ve drafted a “congrats to our friends at X” post.

• They’ve told their cofounder it’s time to raise, right now, at whatever price.

• Or they’ve opened the roadmap and started adding the features from the competitor’s announcement.

I wanted to know what the founders who handled this well actually did, so I went to two people. A VC friend who’s watched this play out across a portfolio, who told me the thing that reframed the whole issue for me: “When a competitor raises, I don’t worry about the founder who calls me that day. I worry about the one who doesn’t.” And a founder who had his closest competitor announce a round three times his total funding, and then raised his own round eight months later, from a fund that had lost the competitor’s deal. He walked me through what he did in the first week, what he got wrong, and what he’d do differently.

Then I went to the data, because the fear underneath the whole thing is “they have more money, so they win.” And the data on that is a lot less clear than the fear.

Here’s what we’re covering:

Their round

• What does a big round actually commit your competitor to?

• Does the best-funded company in a category even win? (I checked ten categories)

• Is their raise actually a threat to you at all? (Scorecard included)

Your people

• What are your customers thinking this week? (Two email templates: one for existing customers, one for live deals)

• What are your investors thinking? (The email template to send before they ask)

• Who on your team will the competitor call first, and what do you say at the all-hands?

Your moves

• What should you do in the first seven days, and in what order? (Day-by-day plan)

• Should you cut price or match their roadmap?

• Should you raise now, and which investors just did your diligence for you?

Let’s go.

What the round actually commits them to.

The headline is a number. The obligation underneath it is burn and time, and most founders never do that second calculation.

Take the typical case. A median Series B in 2026 is around $30M at roughly $120M post-money, raised at about $10M ARR, with the next round expected around 30 months later. The median Series B company burns about $900K a month, against $350K at Series A. So the $30M that landed on your competitor this morning is roughly 24 months of runway at the burn their investors expect, and it comes with an implicit target: close to 3x ARR before the money runs out, or the next round doesn’t happen.

That’s the first thing to understand about the raise. It is not a war chest. It’s a clock. From today, your competitor has around two years to grow roughly 3x, and every decision they make- hiring, pricing, which segment to chase- is now shaped by that clock.

The founder I spoke with said the thing that calmed him down was writing the competitor’s burn on a whiteboard: “They just committed to spending a million dollars a month. I was spending a hundred thousand. For the first time I understood that they had a problem I didn’t.”

Second thing: the money doesn’t buy what you think it buys. Tom Tunguz looked at headcount growth across companies by round size. He found that companies that raised top-quartile rounds grew headcount by about 6% in the following year, against 12% for everyone else, and the difference wasn’t statistically significant. A big raise does not reliably translate into a bigger team. Carta’s own data has the average Series B team at 45 people, down from 53 two years ago. The hundred engineers you pictured are mostly not coming.

Does the best-funded company win?

This is the fear underneath the whole week, so I checked it against the ten AI categories we’ve mapped in previous issues. For each one: who raised the most during the category’s funding wave, what happened to them, and who leads the category now.

Three out of ten. The company that raised the most in the wave ended up leading the category in Harvey’s, ElevenLabs’ and Sierra’s cases. In the other seven, the best-funded company lost half its revenue (Jasper), got acquihired (Inflexion, Adept), got sold for parts (Humane, Windsurf), shut down (Neeva), or watched its category lose credibility (11x). And in two of the three wins, the leader was the best-funded because it was already winning, not the other way round.

This isn’t just an AI pattern.

CB Insights looked at the biggest tech exits since 2012 and found that the most highly funded startups tended to underperform those that raised less after IPO, and that 32% of the 50 biggest exits came from companies that raised $100M or less. The expensive failures are famous for a reason: Jawbone raised $930M and liquidated; Quibi raised $1.75B and shut in six months.

The honest version of the data: capital matters, and the biggest winners usually did raise a lot eventually. But the company that raises the most first, in a category that hasn’t been decided, wins less than half the time. Their round is a bet that the category can be bought. The data says it often can’t.

Which is exactly why the week matters. The raise doesn’t decide the category. What you do in the seven days after it, with your customers, your investors and your team, is where the category actually gets decided. And most founders spend those seven days either panicking or posting.

That’s the rest of the issue is - the seven days in order and why that order, what your customers are thinking right now and the two messages that worked (one to existing customers, one for the deals the competitor’s sales team is already working), the investor note to send before they ask and what your investors actually think, the five people on your team the competitor will call and what to do before they do, the two moves that feel right and cost the most, whether to raise and the one group of funds that just did your diligence for you, and a scorecard for whether their round is even a threat. If you’ve got a competitor with a round in the works, read this before the announcement, not after.

Read more

Today’s deep dive is in partnership with Ace Pickleball Club Media, or APCM.

Pickleball is growing fast, but the more interesting story may be the business being built around its audience.

That’s what we’re looking at today.

Pickleball has spent the last several years becoming much bigger than a niche recreational sport. More people are playing, more clubs are opening, tournaments are expanding, and an increasingly valuable audience is forming around the sport.

But there is another opportunity developing alongside the courts.

Every large sport eventually creates an entire ecosystem around its audience. Golf has magazines, newsletters, podcasts, YouTube channels, personalities, events, sponsorships, and major advertising businesses. Football and basketball have built enormous media industries around the people who follow them.

Pickleball is beginning to develop the same ingredients.

Ace Pickleball Club Media, or APCM, is building that media layer.

First, an important distinction

APCM is currently raising capital through a Regulation Crowdfunding offering.

But this is not an investment in the physical Ace Pickleball Club locations.

Investors are buying into Ace Pickleball Club Media, the company building content, advertising, marketing, and experiential businesses around the pickleball audience, which is vastly larger than the number of players the clubs can serve.

Most media companies start with an audience problem

Building a newsletter, podcast, website, or video channel is relatively easy.

Building distribution is much harder.

Most new media companies start with zero readers, zero subscribers, and zero brand recognition. They may spend years trying to build an audience before they can seriously think about monetization.

APCM starts from a very different position.

Through its relationship with Ace Pickleball Club, the company says it already has access to:

• 205,000+ subscribers

• 30,000+ social followers

• 2.5M+ annual website visitors

• 25,000+ club members

• 1.5M+ annual club visits

That existing distribution gives APCM a head start.

Instead of building an audience from scratch, the company can focus on creating content and advertising products around people who are already deeply interested in pickleball.

The unusual advantage is that the audience exists offline too

Most emerging media companies can reach people through email, social media, podcasts, websites, and video.

APCM can do all of that.

But through its relationship with Ace Pickleball Club, it also has access to physical environments where pickleball players gather regularly.

That opens up a very different advertising opportunity. A brand could potentially reach the same player through:

• a newsletter in the morning

• a podcast or video during the week

• an advertisement inside a pickleball club

• product sampling while they play

• sponsorship of a tournament or event

• an ongoing digital campaign afterward

This combination of digital and physical distribution is much harder for a traditional online publisher to reproduce.

And it could become one of APCM’s most interesting advantages.

Three businesses built around one audience

Rather than relying on a single publication or advertising product, APCM is building three businesses that can work together.

Beyond the Bounce

Beyond the Bounce is the content side of the business.

The idea is to build media products pickleball players actually want to consume, including newsletters, podcasts, videos, websites, magazines, and personalities.

That creates several potential revenue streams:

• advertising

• sponsorships

• affiliate revenue

• subscriptions

• brand partnerships

The basic model is familiar.

Create useful or entertaining content, build a direct relationship with an audience, and help brands reach that audience.

Courtside XP

Courtside XP takes the advertising model into the physical world.

Instead of limiting advertisers to digital impressions, APCM can potentially create experiences inside the environments where players actually spend their time.

That could include:

• in-club advertising

• product sampling

• tournament sponsorships

• venue branding

• events

• experiential campaigns

For brands, there is a major difference between reaching someone who happens to have an interest in pickleball online and reaching someone while they are actually standing inside a pickleball club.

That context can make the audience much more valuable.

Hi Digital Group

The third piece is Hi Digital Group, the marketing-services business.

It helps businesses with areas such as advertising, campaign management, creative services, marketing strategy, and technology.

This adds another revenue stream, but more importantly, it can complement APCM’s other businesses.

Beyond the Bounce builds attention.

Courtside XP creates access to the audience inside physical locations.

Hi Digital Group can help brands build broader campaigns around that attention.

The result is one audience with several different ways to monetize it.

The real asset is the audience

One way to understand APCM is to temporarily forget that the company is focused on pickleball.

Imagine a business already had access to hundreds of thousands of people who shared the same interest, regularly consumed information about it, spent money around that interest, attended physical locations, and strongly identified with the community.

That audience itself becomes valuable.

Brands across categories such as apparel, travel, health, food, financial services, technology, equipment, and entertainment may all have reasons to reach those consumers.

APCM’s bet is that pickleball’s audience has grown faster than the dedicated media infrastructure serving it.

And there is room to build and expand upon the newsletters, podcasts, personalities, events, sponsorships, and advertising products that sit around the sport.

Physical distribution could be difficult to copy

This may be one of the most interesting parts of the business. Digital distribution is increasingly rented.

Publishers depend on platforms such as Google, Instagram, TikTok, and YouTube, where algorithms can change and reach can disappear quickly.

Email gives media companies a more direct relationship with readers, but almost anyone can launch a newsletter.

A network of physical venues is different.

Ace Pickleball Club already operates locations where members play, participate in leagues, attend events, and spend significant time.

That gives APCM potential access to something many emerging media brands do not have:

A physical environment where its audience repeatedly gathers.

For APCM, that could create advertising inventory that is harder for another pickleball newsletter or website to replicate.

Why raise from the community?

There is also a natural connection between this business and equity crowdfunding.

APCM is built around a highly community-driven sport, and now it is allowing people to invest in the media company behind that strategy.

Again, investors are not investing in the physical clubs themselves. The capital is being raised to help grow APCM’s content, advertising, marketing, and experiential businesses.

The foundational audience and direct access to the clubs is what makes it unique from other media companies competing for attention from both the audience and advertisers.

The bigger opportunity

Sports businesses rarely stop with the sport itself. As participation grows, businesses begin forming around that audience:

• media

• events

• advertising

• sponsorships

• creators

• equipment

• communities

• experiences

APCM is betting that pickleball has reached the point where more mature infrastructure can now be built.

Ace Pickleball Club gives it access to players and physical locations. Beyond the Bounce gives it a content engine.

Courtside XP creates real-world advertising and sponsorship opportunities. Hi Digital Group adds marketing services.

Now APCM is raising capital to try to connect those pieces into a larger media platform. The scale and promise of the opportunity is something prospective investors should evaluate themselves.

But the underlying opportunity is easy to understand:

Pickleball has already built a large and passionate audience. APCM is building the media business around it.

Explore the Ace Pickleball Club Media investment opportunity →

Learn more about Ace Pickleball Club →

*This is for informational purposes only and is not investment advice. Please review the offering materials and conduct your own research before making an investment decision.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Subscribe now

Share

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - SIMPLEX CHAT

Investors are backing SimpleX Chat, the messaging network built without any user identifiers

Most messaging platforms rely on some form of persistent identity, whether that is a phone number, email, username or public key.

SimpleX Chat is taking a different approach.

It is building a private messaging network with no user identifiers, where every conversation has its own network address and user data stays on people’s devices instead of being stored centrally.

Why investors are paying attention:

• 520,000+ monthly active users without paid marketing

• 3M+ app downloads and 20M+ messages sent per day

• 17x growth from 2023 round, only 2x valuation cap

• $650,000+ in community donations

• 1,000+ community servers and 19,000+ GitHub stars

• Open-source technology reviewed by Trail of Bits

SimpleX is building beyond private messaging.

The broader idea is a private communication layer for messages, communities, businesses and eventually agent-to-agent communication, while keeping privacy built into the network architecture.

SimpleX Chat has now opened its equity crowdfunding round to the community.

Invest in SimpleX Chat →

For this promotion we received 2 public names on SimpleX Network, venturecurator.simplex and venturedailydigest.simplex, registered for 5 years from the names launch on December 12. We will later write about what SimpleX names are and how they work.

📜 DEEP DIVE

How are new-age startups pivoting, and what do VCs make of it? An analysis of 18 AI-era pivots.

I’ve been talking to a few investor friends about something I kept noticing in the companies we’ve covered over the last month, across how AI startups got their first customers, which ones die after a model release, and which categories investors funded and then abandoned.

Cursor, Perplexity, Lovable, Mercor, Decagon. Five of the fastest-growing AI companies of the decade, and not one of them is doing what it started doing. Cursor was building AI for mechanical engineers. Perplexity was a text-to-SQL tool. Lovable was an open-source GitHub project with no revenue. Mercor was matching engineers to jobs.

That’s not a coincidence, and it’s not the old “every great company pivots” line either. When I put the dates next to each other, something more specific showed up: the ones that became category leaders all pivoted inside their first 18 months, before they had revenue worth protecting. The ones that pivoted after year three mostly survived, but smaller. And the ones that were forced into it by an acquihire or a cash crisis rarely came out the other side as an independent company.

One of the investors I spoke with said the thing that made me want to write this:

“The pivot used to be a conversation. Now it’s a notification. I find out when the product changes.”

That’s a different relationship than the one every founder guide describes.

So this issue is about what actually changed. Not “should you pivot,” which the data answers with an 81%. But how AI-era companies pivot differently, what the timing data says, which kinds of pivots produced winners, and how your investors read it now that the cost of building has collapsed and the price of your seed round has doubled.

Here’s what we’re covering:

• The survey numbers: pivoting is the default, and most founders think they did it late

• Three things that changed about pivoting since 2022, with the data

• The 18-month line: when the winners pivoted vs everyone else

• The full pivot map: 18 companies, what they changed, how it ended

• The four pivot types and which one actually produces category leaders

• How your existing investors read a pivot now: higher prices, less patience, milestone-gated bridges, and the AI-washing test

• The pivot conversation, and the memo that gets a bridge instead of “let’s stay close”

• A scorecard for your own situation

The survey numbers: pivoting is the default now, and most founders think they were slow

Start with the number nobody argues with. In Wilbur Labs’ February 2026 survey of 200 founders, 81% said their company pivoted from its original idea, and 57% made a major pivot or more than one. That’s higher than any pre-AI survey I can find. And the regret runs one way: 42% said they wished they had pivoted sooner. Almost nobody wishes they’d waited.

Two more numbers from the same survey matter for what comes next. When asked why they pivoted or failed, founders pointed almost equally to competition and market shifts (45%) and product or technology issues (44%). And half of them named technological disruption, including AI, as the top threat to their company. In 2023, the top reason was running out of money.

By 2026, money dropped to 25%, and the market moved to the top. That’s a different kind of pivot trigger, and it was the first thing to change.

Three things that changed about pivoting since 2022

Every pivot guide you’ve read assumes three things: building the new thing takes months, the trigger is “customers didn’t want the old thing,” and your investors are part of the decision.

All three have shifted.

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Which AI categories did investors fund and then abandon? An analysis of fund flows vs shutdowns by category, 2021 to 2026.

I’ve had a couple of calls with VC friends over the last week, and one thing came up in both that I haven’t been able to stop thinking about.

Neither of them is really evaluating AI SDR companies anymore. One told me he’d got a deck that morning and just didn’t open it. When I asked why, he said the category already showed him how it ends, so the deck wasn’t going to change much. The other said something similar about a couple of categories she’s stopped taking meetings in.

That’s a big shift for seed investors. Three years ago, the whole point of the job was that you didn’t know which company in a hot category would win, so you took a few shots. Now a chunk of the market has decided that some categories have a shape, and the shape ends badly regardless of who’s in it.

So I went looking for whether that’s true. I took every AI category with a clear funding wave between 2021 and 2026, dated the peak, and then traced what happened to the named leaders 12, 18, and 24 months later. Layoffs, valuation cuts, acquihires, fire sales, shutdowns, or, sometimes, a $60 billion exit. Dates from announcements and press. Category totals where a tracker published one.

Here’s what we’re covering:

• The one chart: how long a hot category takes to turn, and why that number is falling

• What actually happened to 14 AI categories after the money arrived

• The bit the “AI reckoning” headlines get wrong (fewer AI shutdowns by share, but older and better funded)

• The four tells that a category is 12 months from correcting

• Which 2025 categories are running the 2023 pattern, named

• A scored test for your own category, and what to do if it fails

Hot categories now turn in under a year

The thing that surprised me most wasn’t which categories died. It was how consistently the timing worked, and which way it’s moving.

AI writing peaked in October 2022, when Jasper raised $125M at $1.5 billion. ChatGPT launched six weeks later. The first visible damage- layoffs and a CEO change- took until mid 2023, and the full picture- revenue from roughly $120M down to $55M, four rounds of layoffs, an internal valuation cut, took about 21 months to become public.

AI browsers peaked in October 2025 with Atlas. Atlas was shut down in August 2026. Ten months.

That’s the pattern in one line. The gap between “hottest category in venture” and “first leader in trouble” has gone from around two years to under one. It’s not because startups got worse. It’s because the two things that end a category- a lab shipping the feature and the buyer working out the product doesn’t stick- both happen faster now. The lab release cycle is quarterly, and the churn data arrives at the 90-day renewal.

Which means the founder who raised into a hot category in 2026 doesn’t have the runway the 2022 founder had. The 2022 founder got two years to find a second act. The 2026 founder gets three or four quarters.

So which of this year’s hot categories are on the short clock? That’s what the rest of this issue answers.

Below the line: every category mapped, money in and what came out; the four signs a category is about to turn, one of which shows up in job postings months before anywhere else; the three 2025 categories I’d be nervous about, named; and a test to score your own. If you raised into anything hot this year, you want this before your next investor call.

Where the money went, and what it bought

Read more

In partnership with SoloGPs.vc, we’re looking at the rise of solo GPs and the community bringing these managers and LPs together.

There’s an interesting shift happening in venture that doesn’t get enough attention.

More investors are choosing to run funds independently, where the GP is often the brand and investment decisions can move quickly.

But running a fund solo comes with a different set of challenges. Workload is less distributed, networks can be smaller, and many decisions that would normally be shared across a partnership ultimately sit with one person.

That could mean evaluating a deal, thinking through portfolio construction, navigating LP relationships, comparing fund operations, finding the right service providers, or simply having another experienced manager to ask, “How are you handling this?”

That gap is what SoloGPs.vc is trying to fill.

Solo doesn’t have to mean alone

SoloGPs.vc is building a dedicated community for emerging solo managers, particularly GPs running Fund I through Fund IV.

The community already includes 50+ solo GPs sharing the practical side of running a fund: deals, LP relationships, fund operations, co-investments, tools, and the challenges that rarely make it into polished VC conversations.

Members connect through monthly office hours, quarterly virtual sessions, and a private community designed for the kind of peer learning and knowledge sharing that can be harder to access when you’re running a fund independently.

But this October, they’re taking that community offline.

A room designed around solo GPs and LPs

On October 19 and 20, SoloGPs.vc is bringing its community together in San Francisco for the Solo GP Summit.

The Solo GP Summit is curating programming where solo GPs will meet LPs interested in backing them, while continuing to foster a community designed to support managers throughout the lifetime of their funds.

And this is where I think it becomes particularly interesting for LPs.

Finding established venture firms isn’t difficult. Finding promising emerging managers early, understanding how they actually invest, and building relationships with them before they become widely known is much harder.

Rather than creating another massive venture conference, the Solo GP Summit is designed around a more curated environment for real conversations between managers and allocators.

The Solo GP Summit takes place just before RAISE Global, with the kickoff on October 19 and the main Solo GP Summit on October 20. Pricing goes up October 1. The first 5 registrations with coupon code VENTURECREW get 50% off.

The current lineup includes managers across Fund I through Fund VIII, including Charles Hudson (Precursor Ventures), Brianne Kimmel (Worklife Ventures), Shruti Gandhi (Array Ventures), Gale Wilkinson (VITALIZE Venture Capital), and Pat Matthews (Active Capital), with more speakers and LP guests still to be announced.

For LPs and family offices looking at emerging managers, that’s the real opportunity: meeting a concentrated group of fund managers in one place, getting beyond the fundraising deck, and starting relationships with managers they might otherwise never encounter.

Why I think this is worth watching

Being a solo GP offers independence, but independence doesn’t have to mean operating in isolation.

As more investors build smaller, values-driven firms, the ecosystem around them needs to evolve too. Peer communities, LP networks, operating resources, co-investment relationships, and places where managers can openly exchange what’s actually working become increasingly valuable.

SoloGPs.vc is building that layer.

And it brings another important part of that ecosystem into the room: LPs interested in discovering and backing solo emerging managers.

If you're an LP or family office looking for emerging venture managers, or a solo GP building your fund, the Solo GP Summit is worth being in the room for

Don’t wait on this one: Solo GP Summit pricing goes up October 1, and applying and requesting the invite before then locks in the current rate

Request an invite for the October 19-20 gathering in San Francisco.

Applying? The first 5 registrations will get 50% off. “VENTURECREW” Coupon Code for 50% off,  and in the “invited by” field of the community or event application, mention “Sahil S Venture Crew”.

🔴 Share Venture Curator

You currently have 0 referrals, only 5 away from receiving a 🎁 gift that includes 20 different investors’ contact database lists - Venture Curator

Subscribe now

Share

Big idea + report of the week :

• 537 US unicorns are still carrying valuations from the 2021-22 boom. Are they still real?

• Could calling your startup ‘AI-Native’ be hurting your valuation?

Frameworks & insightful posts :

• The Barbell-ification of Software: AI is about to kill the middle of the software market.

• How is your seed valuation actually decided? (It has almost nothing to do with your company).

• If ChatGPT can do what your app does for free, why would anyone pay you?

Get 20% off forever

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

FROM OUR PARTNER - SEMRUSH ONE

Your customers are searching on Google AND ChatGPT. Can they find you on both?

Search has changed. Ranking on Google still matters, but buyers are also asking AI tools which products to use, which companies to trust, and how different options compare.

Semrush One brings both sides into one place. You can track your Google rankings and AI mentions, see the prompts driving demand, compare your visibility against competitors, and find gaps you can actually act on.

It monitors 239M+ relevant LLM prompts globally, alongside the SEO data Semrush is already known for.

See where your brand shows up across Google + AI →

WORK WITH US

🤝 Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

START WITH

🧠 Big idea + report of the week

537 US unicorns are still carrying valuations from the 2021-22 boom. Are they still real?

Founders and early employees are usually taught to treat the last round’s valuation as the scoreboard. It’s the number on the cap table, the number in the press release, and the number everyone uses to estimate what their equity is worth.

But new data from PitchBook’s Q3 2026 Quantitative Perspectives report suggests that for most of America’s unicorns, that number hasn’t been tested by the market in years.

PitchBook looked at every active US unicorn and sorted them by the year they last raised a priced round.

• Of 964 active US unicorns, 312 last raised in 2021 and another 225 in 2022. That’s 537 companies, roughly 56% of the entire unicorn population, still carrying a valuation set during the most expensive funding market in venture history.

• The cash isn’t coming back to match those marks. Venture fund distributions sit at just 7.9% of net asset value, barely half the 14.5% long-run average, and PitchBook says funds are being held back specifically by inflated 2020 to 2022 markups.

• When these companies do test their price, it often doesn’t hold. Public investors are refusing to pay private market prices, Chime listed at a significant markdown from its private peak, and secondary buyers apply much steeper discounts to companies whose last round is years old than to recent raisers.

There’s a second trend hiding behind this. Many of these companies aren’t stuck because they’re failing. They’re choosing not to raise or list, because any new priced event would likely force them to confront a lower number. Staying private and quiet keeps the 2021 valuation on paper. That’s how the market ends up with record paper value and record trapped value at the same time.

So is the unicorn boom real, or mostly a number nobody has checked?

For a lot of these companies, both can be true. Some have genuinely grown into their 2021 price and would clear it easily today. But the market has no way of confirming which ones, because they haven’t raised or sold at a real price since. A four-year-old valuation isn’t a lie. It’s just unverified.

And this is where founders and employees need to be careful.

If your company last raised in 2021 or 2022, the valuation on your cap table is the most optimistic number your equity has ever had, not necessarily what it’s worth today. That matters for how you negotiate your next round, how you think about exercising options, and what you tell your team their equity means. It also matters for investors: a fund showing strong unrealised gains on these companies is reporting marks, not money.

The real risk isn’t that your valuation might be lower than it looks. It’s making decisions about raising, hiring, or exercising as if a number set in 2021 is still the price the market would pay you today.

Could calling your startup ‘AI-Native’ be hurting your valuation?

Calling yourself an AI startup can still command a valuation premium. But investors are increasingly asking a second question: how much of the company is actually defensible because of AI?

The data in this analysis suggests the market is beginning to separate companies that share the same “AI-native” label into very different buckets.

• The AI premium is still real: Carta’s H1 2026 data shows AI startups at seed raising similar amounts of capital as non-AI startups while receiving roughly 50% higher valuations.

• But not every AI company receives the same premium: The analysis places commodity AI wrappers around 3x to 8x revenue, vertical AI products with sticky proprietary data around 10x to 20x, and companies combining real IP with proprietary data around 25x to 40x.

• The scrutiny increases as companies mature: At seed, companies without protectable IP are described as receiving a 20% to 30% markdown versus comparable companies with stronger defensibility. By Series A, the analysis puts that gap at 30% to 40%.

z890-\This helps explain why simply adding AI to a product is becoming less meaningful.

Investors increasingly want to understand what happens after you remove the AI label.

Can another startup recreate the product by calling the same foundation-model API? Does every new customer generate proprietary data that improves the product? Is the software embedded deeply enough in a workflow that replacing it becomes painful? And do the economics still work when inference volume becomes 10x larger?

Those questions point toward four areas founders should probably examine before their next fundraise: technical differentiation, proprietary data, workflow lock-in and unit economics at scale.

The practical test for technical differentiation is surprisingly simple: swap the underlying model. If replacing your primary model provider with a comparable model leaves the product almost unchanged, investors may struggle to see where the technical moat exists.

For proprietary data, user count alone isn’t enough. A stronger signal is whether increased usage creates data that makes the product measurably better over time, improving things like accuracy, resolution time or match quality.

Workflow depth provides another clue. If customers who integrate the product more deeply consistently retain and expand more, founders have actual evidence of switching costs rather than simply claiming their product is sticky.

And then there are economics. An AI product with attractive margins at today’s usage may look very different at 10x the inference volume. Modeling those economics before investors do can reveal whether the business actually becomes stronger as it scales.

That makes the evolution of the “AI-native” premium interesting.

Eighteen months ago, simply being associated with AI could materially strengthen a fundraising story. As more startups adopt the same language, investors have more reason to look underneath it.

The AI label can get you into the conversation. The increasingly valuable part is proving why what you’ve built becomes harder to copy as you grow.

📄 Must-Read Post

SOMETHING MORE

🧩 Frameworks & insightful posts

The Barbell-ification of Software: AI is about to kill the middle of the software market.

AI is making software dramatically cheaper and faster to build. That sounds great for startups, but it could also weaken some of the moats that protected software companies for decades.

Mike Vernal from Conviction makes an interesting comparison: software may be heading toward the same “barbell” structure that transformed newspapers after the internet.

Before the internet, regional newspapers benefited from expensive distribution. Once distribution became nearly free, much of the middle disappeared. A few global publications became much larger, while thousands of independent newsletters and niche publications emerged.

Vernal argues AI could create something similar in software.

Traditional software moats often came from three things:

• Building the product was expensive

• Switching systems was painful, and

• Integrations created strong ecosystems.

AI weakens all three. Competitors can replicate features faster, migrations can become more automated, and AI can make integrations significantly easier.

The possible result is a market with two powerful ends:

• Huge platforms: A smaller number of companies could own entire buying categories such as sales, marketing, finance, HR, legal, or healthcare. Instead of selling one narrow tool, they keep expanding until they become the system customers use for almost everything.

• Tiny software businesses: At the other end, AI coding tools could enable millions of people to build highly specific software for themselves, their companies, or small groups of customers. Some could grow into profitable niche businesses.

• The uncomfortable middle: Mid-sized point solutions may face the most pressure. If large platforms can continuously add their features and small teams can cheaply recreate narrow products, being a standalone tool becomes harder to defend.

The Amazon analogy explains what the new moat could look like. Amazon started with something relatively easy to copy: selling books online. Its defensibility came from decades of relentless reinvestment into logistics, infrastructure, technology, and distribution.

AI may push software companies toward the same strategy: build faster, expand continuously, reinvest, and make the total product increasingly difficult to replicate.

For venture-backed software startups, that creates an interesting strategic question: If building software becomes almost free, is your moat still one great product, or everything you can build around it?

How is your seed valuation actually decided? (It has almost nothing to do with your company).

Founders often assume valuation is mainly about traction, market size, or how well they pitch the company.

But there’s another constraint most founders rarely see: the economics of the VC fund sitting across the table.

Imagine you’re pitching a $28M seed fund. It typically writes around a $600K check and wants roughly 6% to 8% ownership.

At 7% ownership, a $600K investment implies an ~$8.6M post-money valuation. At 6%, it implies $10M. So if you’re raising at an $18M valuation, that fund may simply be unable to make the investment work within its portfolio model. Pasted markdown

That creates a simple chain:

Fund size → Check size → Target ownership → Maximum workable valuation

And this matters more today because seed valuations have risen. Carta’s median seed post-money valuation cited in the analysis is $24M. A $50M fund investing $1.5M at that valuation gets only 6.2% ownership, potentially below its target. Pasted markdown

This also changes how founders should think about round size.

If you tell a fund you’re raising $3M and the lead wants 15%, you’ve effectively implied a $20M post-money valuation before anyone explicitly discusses valuation.

A more useful sequence is:

1. Calculate how much capital you need to reach the next milestone with 18 to 24 months of runway.

2. Add a buffer.

3. That becomes your round size.

4. Divide it by the dilution you’re comfortable accepting.

5. Then target funds whose check size and ownership model can support that valuation. Pasted markdown

There’s another number founders often overlook: the option pool. A lead may ask for a 10% to 15% pool to be created pre-money, which dilutes existing shareholders before the investment lands. The analysis argues that negotiating the pool based on an actual hiring plan can sometimes matter more to founder ownership than fighting over another $1M or $2M of headline valuation. Pasted markdown

The easiest way to avoid wasting weeks with the wrong fund is to ask this on the first call:

“What’s the typical initial check for this fund, and what ownership do you usually target?”

Those two numbers let you quickly estimate whether the fund’s economics can support your round. Pasted markdown

So, don’t treat every valuation pushback as a verdict on your startup. Sometimes your company isn’t the problem. Your round simply doesn’t fit the fund’s math.

If ChatGPT can do what your app does for free, why would anyone pay you?

This is becoming one of the biggest questions for consumer app founders.

If a user can open ChatGPT, Claude, or Gemini and get roughly the same result in seconds, why would they keep paying $10 or $20 every month for a separate app?

RevenueCat’s Daphne Tideman argues that the answer isn’t simply “add more AI.” AI-powered apps are already monetizing well, generating 41% more revenue per payer and 52% better trial conversion than non-AI apps. But their 12-month retention is only 21.1%, compared with 30.7% for non-AI apps. Pasted text

In other words: AI can get people through the door. It doesn’t necessarily give them a reason to stay.

Look at Chegg versus Duolingo.

ChatGPT can replace much of Chegg’s old homework-answer experience. But it can also teach you French, and Duolingo is still growing. In Q2 2026, Duolingo’s revenue increased 18%, daily active users grew 23% to 58.7M, and paid subscribers rose 17% to 12.7M. Pasted text

The difference is what exists around the answer.

RevenueCat suggests six things apps can build that are harder for a blank LLM chat to replicate:

1. Structure: Turn an answer into a complete workflow or experience.

2. Memory: Become more useful as the app learns about the user over time.

3. Habit: Use streaks, reminders, accountability, and gamification to bring users back.

4. Precision: Own specialized data or expertise that produces better results.

5. Connection: Build community, identity, personality, or emotional attachment.

6. Physical + digital: Combine software with sensors, devices, or real-world data. Pasted text Pasted text

Duolingo is a great example of stacking several of these together. ChatGPT can teach a language, but Duolingo has spent years building streaks, leaderboards, progression, reminders, and gamification. When Duolingo introduced leaderboards, learning time reportedly increased 17%, while the number of highly engaged learners tripled. Pasted text

There’s also a simple test founders can run: the Blank Box Test.

Open ChatGPT or another LLM, describe the exact problem your user comes to your app to solve, and compare its response with what your product delivers.

If most of your product’s value can be recreated with one prompt and one response, you have a differentiation problem. If your app adds structure, accumulated data, habit, precision, connection, or a real-world component, that’s where the moat may be. Pasted text

So, AI itself probably isn’t the moat. The stronger question for founders is: what does your product give users that an empty chat box cannot? RevenueCat’s argument is that the winners will use AI underneath the product while competing on the experience built around it.

📬 Upgrade to Venture Curator Premium

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

Get 20% off forever

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

• Research Analyst - Kindred Venture | USA - Apply Here

• Associate, Early-Stage Investments - Premji Invest | USA - Apply Here

• Investment Director(Strategic Investment) - YZi Lab | Remote - Apply Here

• Portfolio Management Associate - Cerity Partner | USA - Apply Here

• Associate - Venture Capital - Artha Group | India - Apply Here

• Partner 36, Corporate Controller - a16z | USA - Apply Here

• Investment Associate - Cerity Partner | USA - Apply Here

• Visiting Investment Analyst - Antler - Apply Here

• Senior Associate - NY Life Venture | USA - Apply Here

• Senior Principal - Entegris Venture | USA - Apply Here

• AI Analyst - Array Venture | USA - Apply Here

• SMB Program Associate - Plug and Play Tech Centre | USA - Apply Here

• VC Fellow - V11 | Remote - Apply Here

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

Upgrade to Venture Curator Premium

Get access to 150+ premium archive posts, 100+ startup & VC resources, investor databases, fundraising templates, and exclusive startup research - all in one place.

(Subscribe to Venture Curator Premium and get 20% off forever.)

Subscribe now

Share

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

How did emerging AI startups get their first customers? An analysis of 50 companies

There’s a question that every founder has: “Where do the first ten customers actually come from?” Not any random customers, but these emerging AI startups.

The honest answer most founders get is a shrug and “just launch.” Post on Product Hunt, do a Show HN, tweet the demo, wait. So they do, and a week later they have 400 signups, three Discord messages, and no revenue. Then they assume the product is the problem.

I wanted to know what the companies that made it actually did, so I did the boring thing again. I took 50 AI companies, mixed across enterprise, developer, and prosumer tools, and consumer and creative, and looked for the specific story of their first paying customers. Founder interviews, podcasts, company blogs, press. The first ten. Where a company’s early channel wasn’t documented anywhere I could find, I dropped it and picked another.

Here’s what we’re covering:

• The one chart: the primary first-customer channel across all 50

• The launch finding, and why “just launch” is the most expensive advice in AI

• How the pattern changes by segment (enterprise, dev tools, consumer)

• The three channels that exist now and didn’t in 2020

• The first-ten playbook for each segment, in order

• The outreach that actually worked, with the messages

• A Claude prompt that builds your first-ten list from your own network

Let’s go.

First, the map

Read the bottom of that chart before the top.

Five of 50 companies got their first paying customers from a public launch. Five. And of those five, four are products you can demo in a single screenshot or a 30-second clip: Lovable, Gamma, Tome, Udio.

The fifth, Artisan, is an enterprise company, and it didn’t get there with a launch. It got there with the “Stop hiring humans” billboards, which is a PR stunt with a sales team behind it.

Now read the top. The biggest bucket is community: Discord servers, subreddits, developer forums, LinkedIn creator circles. Then founder network. Then the viral demo. Those three account for more than half of the 50, and none of them is a launch.

The number one source was the founder’s own network, the number two was cold outbound, and “just launch” was last on his list of seven. That was pre-AI data. The AI companies I looked at follow the same shape, with three new channels added that I’ll get to.

The uncomfortable version of the finding: the launch isn’t a customer acquisition channel for most AI companies. It’s a signup acquisition channel. The first ten paying customers almost always came from somewhere the founder already had trust, or from somewhere the product could be seen working before anyone was asked to pay.

What the best-known stories actually say

Three companies most founders point to as “they just went viral.” None of them did, in the way people think.

Cursor - Bloomberg’s headline was that Cursor drew a million users “without even trying”, with zero marketing spend. True.

But the first users were developers the founders knew, who recommended it to developers they knew, with a Discord as the place they gathered. The company built its early team out of that same Discord. That’s not a launch. That’s a product good enough that its first ten users did the selling.

Harvey - The first customer story is the best cold email in AI. Winston Weinberg, a litigator, ran 100 questions from r/legaladvice through GPT-3, had three attorneys grade the answers, and emailed the results to Sam Altman and OpenAI’s general counsel. That got him early model access and OpenAI as first investor.

Then, instead of starting with small firms, Harvey went straight to Allen & Overy and signed a 4,000-lawyer rollout when the company had four people working out of an Airbnb. Investors told them it was a horrible idea. Harvey’s first two customers were London firms. Nothing about it was inbound.

Decagon - In a category with Sierra, Salesforce and Zendesk, Decagon’s early logos came from winning head-to-head contests against much larger vendors, running its agent against the incumbent’s on the customer’s real tickets and letting the numbers decide. Vapi did the same thing later, beating 40 rivals for Amazon Ring. The bake-off is now a first-customer channel in its own right, and I’ll come back to it.

The pattern across all three: the founder created a situation where the product could be judged on its work before anyone had to trust the company.

A graded proof for OpenAI. A 4,000-seat pilot. A contest on the customer’s own data. The launch skipped that step, which is why it produces signups and not customers.

By segment, it’s three different games

Read more

Get 20% off forever

P.S. Get access to 100+ startup & VC resources, investor databases, fundraising templates, 150+ premium archive posts and exclusive startup research - all in one place.

FROM OUR PARTNER - SEMRUSH

Your buyers don’t just search Google anymore.

I’ve been looking at how search is changing, and this is probably the biggest shift marketers need to pay attention to.

People still use Google, but they’re also discovering products through ChatGPT, AI Overviews, and other AI tools. Semrush now lets you track both from one place.

You can see where your brand ranks in search, how often it appears in AI answers, where competitors are gaining visibility, and what may be holding your site back.

See how visible your brand really is →

Partnership With Us

Get your product in front of an audience of 125,000+ - Our newsletter is read by thousands of tech professionals, founders, investors and managers worldwide. Get in touch today.

📜 DEEP DIVE

Which startups die after a model release? An analysis of every AI launch since ChatGPT

I mapped every major lab release since ChatGPT against what happened to the startups standing in front of it. Fourteen releases, thirty-odd named outcomes. The kill rate is real, the pattern is not what founders assume, and three positions came through every single one. Inside: the full blast map, the four ways a release actually kills you, a scored 90-day test, and a Claude watchlist that runs it for you.

When the VC’s screener issue went out, I expected the replies to be about the rubric or the prompts. A few were. But the one I kept seeing, in slightly different words each time, came through clearest from a founder building a research tool:

“How do I know if OpenAI is just going to build my product?”

And honestly, I never had a good answer. Everyone has a view. “Wrappers die.” “Distribution wins.” “Never build a feature.” None of it is checkable, and none of it tells you what to do.

So I did the boring thing. I took every major release from OpenAI, Anthropic and Google since November 2022 that had a startup category in front of it, and I tracked what happened to the named companies in that category over the following 90 days and the following year. Layoffs, valuation cuts, acquihires, shutdowns, and the ones that grew anyway. Dates from company announcements and press, not from memory. It took a couple of days.

Here’s what we’re covering:

• The one chart: how fast a release turns into damage (one day to ten months)

• What actually happened after fourteen releases, with names and dates

• The pattern, and why “wrappers die” is the wrong lesson

• The four mechanisms that do the killing, and which one is the deadliest (it isn’t the model)

• The 90-day blast radius test, eight questions, scored

• The three positions that survived every release since 2022

• A Claude watchlist that reads lab signals and scores you monthly

First, the speed

Before the pattern, the timing. Because the thing that surprised me most wasn’t who died. It was how unevenly fast it happened.

Read the two ends of that chart.

At the bottom: Huxe, an AI audio app built by three former NotebookLM engineers, backed by Conviction and Jeff Dean. It announced it was shutting down on May 21, 2026, one day after Spotify shipped a personal podcast feature that did roughly the same thing. Eight months after raising. One day after the platform moved.

At the other end: Jasper, the first AI writing unicorn, raised $125M at $1.5B in October 2022. ChatGPT launched a month later.

Nothing visible happened for seven months. Then came the first layoffs in July 2023, the CEO out in September 2023, an internal valuation cut, and revenue falling from around $120M to $55M.

Same cause, ten-month difference in when the bill arrived. That matters for you because most founders are watching for the fast version and missing the slow one. The slow one is the one that looks fine on a dashboard for two quarters.

What actually happened, in one table

Here’s the short version of the blast map. The full one, with the 90-day and 12-month columns for every release, is behind the wall.

And one row that doesn’t fit the story everyone tells: the labs’ own products die in the blast too. OpenAI’s Sora app shut down in April 2026, Atlas lasted under a year, Operator got folded back into ChatGPT. The lab shipping into your category doesn’t mean the lab wins your category. It means the category gets reset.

The pattern, and why “wrappers die”, is wrong

Here’s the thing: the data says that the vibe doesn’t.

The release rarely killed the category leader. In most of the fourteen, the leader came out bigger.

• Cursor crossed a billion in ARR nine months after Claude Code shipped and surpassed $2B by February 2026.

• Harvey raised at $11B two months after Anthropic’s legal plugins caused a $285B software selloff, then raised $550M at $15.5B in September on $400M of ARR.

The release killed everything below the leader. The number twos with no distribution. The thin layer that was a prompt plus a UI. The consumer apps built on one modality. And, in the newest pattern, the incumbents: LegalZoom fell 20% and Thomson Reuters 16% within 48 hours of a GitHub repo of Markdown files.

So “wrappers die” is the wrong lesson. Jasper was a wrapper, and it’s still here, projecting $180M for 2026. Harvey is a wrapper, by any honest definition, and it’s the most valuable legal software company on earth.

The lesson is narrower and more useful: the release removes the reason to buy from whoever isn’t the default.

If you’re the default in your category, you get a bigger market. If you’re not, you get 90 days.

Which raises the only question that matters: what made Harvey the default and Jasper not? And what about the one case where the leader did get destroyed, in 40 days, without a single model release?

That’s the paid half. The full map with dates, the four mechanisms (the deadliest one isn’t a model at all), the scored test you run on your own company, the three positions that survived every release, and the watchlist that keeps scoring you every month. If you’re building anything on a lab’s API, you should know your score before the next launch, not after.

Part 1: The full blast map

Read more