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.

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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

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

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• Open-source technology reviewed by Trail of Bits

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📜 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

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📜 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”.

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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?

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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.

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🧠 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.

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📜 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

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📜 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

Big idea + report of the week :

• Claude is already leading 26% of Anthropic’s AI R&D. What happens next?

• The hidden test AI startups may face during acquisition diligence.

• Is being a first-time founder becoming a structural disadvantage?

Frameworks & insightful posts :

• Is the SaaS crash narrative wrong? Here’s what 72,000 companies show.

• How do you build a seed deck that actually raises money?

• What does a VC really look for in your financial model? (Template you can use).

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🧠 Big idea + report of the week

Claude is already leading 26% of Anthropic’s AI R&D. What happens next?

Anthropic published new internal measurements showing how deeply AI is now involved in its own model research and development. The most striking number is how quickly Claude has moved from assisting researchers to leading parts of the work.

• Claude now “leads” 26% of Anthropic’s AI R&D: At this level, Claude can take a high-level instruction and complete most of a task end-to-end while a human supervises. Anthropic says this was below 1% in February 2026.

• More than 90% of AI R&D involves Claude at a collaborative level or higher: This means AI can already perform substantial chunks of the work across most of Anthropic’s model-development process. However, Anthropic says Claude is not yet fully autonomous on any measured subset of R&D.

• 30,000 AI agents are working internally at any given time: On Anthropic’s most-used internal research and engineering platform, roughly 30,000 agents were operating simultaneously as of August 2026. Their actions are monitored, with 100% passing through an online monitor before execution.

• Actual interventions are extremely rare: Anthropic analysed more than 1 billion agent decisions during August. Only 0.002%, roughly 1 in 47,000, were blocked by its real-time monitoring system. Its offline system escalates roughly 50 high-priority cases per week to humans.

• Anthropic is also measuring how much compute goes toward safety: During one week in July, about 6% of compute used for AI R&D went specifically toward safety work. Among compute used for AI-driven AI R&D, the safety share was roughly 12%.

The important shift here is that AI development is no longer simply humans using AI as a coding assistant.

Anthropic is trying to measure something much more consequential: how much of the process of creating the next generation of AI is itself being handed over to AI.

Today, humans are still supervising. Anthropic defines the final stage, AL5, as the point where AI can identify work, execute it, test it and deploy it without needing a human involved.

They say they haven’t reached that point.

But going from under 1% of R&D being AI-led in February to 26% by August makes the direction of travel worth watching closely.

The hidden test AI startups may face during acquisition diligence.

Founders heading into acquisition talks are usually taught that diligence is a fairly mechanical process. Financials, contracts, code review, reference calls. The tense part is supposed to be the negotiation, not the diligence itself.

But a single line buried in Fidelity and PitchBook’s 2026 VC trends report points to a far more adversarial process running in parallel for some AI-native companies, one most founders have no idea is happening to them.

The finding shows up almost as an aside in the report’s outlook section, not as a headline stat: acquirers evaluating AI companies are increasingly paying outside consulting firms to attempt to rebuild the product they’re considering buying, in parallel with the deal itself.

• The stated purpose is to pressure-test defensibility: checking whether what makes the startup valuable is a genuine, hard-to-replicate advantage, or something a well-resourced team could recreate in a matter of weeks.

• This isn’t a hypothetical threat model. AI consulting work has become, in the report’s own words, “a meaningful revenue driver” for consulting firms specifically because enterprises are paying for exactly this kind of evaluation.

• The company running this test is very often the same one sitting across the table in diligence meetings, asking friendly questions about your roadmap and architecture, while a separate internal or contracted team quietly tries to reproduce what you’ve built.

There’s a reason acquirers have started doing this now rather than a few years ago. AI bubble concerns are already reshaping how capital gets deployed across the market - the same report flags “spray and pray” AI investing as a real worry heading into 2026. Acquirers who’ve watched valuations detach from defensibility have gotten wary of paying a premium for a moat that turns out to be thin, so testing it directly has become a genuine diligence tool instead of a one-off paranoid move by a single buyer.

So why would a company go this far instead of just trusting what’s in the data room?

Because in AI specifically, the usual proof points of defensibility, retention, revenue growth, even patents, don’t reliably prove a product can’t be rebuilt. A team with resources, model access, and a few weeks can often approximate a young AI company’s core functionality. So acquirers have started testing that claim directly rather than taking the pitch deck’s moat story at face value.

And this is where founders need to be careful.

Being cloneable doesn’t automatically kill a deal. But it changes the terms of the conversation, and you can’t negotiate around a test you don’t know is happening. You may walk into a valuation discussion believing your technical moat is your strongest card, while the acquirer’s internal team has already run their own private experiment and reached a very different conclusion, one you never get the chance to counter because you never knew to ask.

The real risk isn’t that your product might be cloneable. It’s negotiating as if the acquirer only knows what’s in the data room, when they may already have their own answer to the exact question your valuation depends on.

Is being a first-time founder becoming a structural disadvantage?

The venture pitch has always come with a version of the same reassurance: great ideas win regardless of whether you’ve built a company before. Investors back the idea and the team in front of them, not just the résumé.

But new data from PitchBook’s Q3 2026 analyst note suggests the market’s actual behaviour tells a different story, and the gap between first-time and serial founders is getting wider, not smaller.

PitchBook tracked early-stage US VC deal value and deal count through August 2026, isolating how often at least one founder on a deal had already started a company before.

• Serial founders make up a shrinking 26.8% of early-stage deal count this year, down five points year over year. But they’re capturing a growing 55.1% of total deal value - up more than five points over the same period.

• The tilt is sharper inside the rounds led by the biggest checks. Among deals backed by multistage funds specifically, 39.2% involve at least one serial founder, well above the broader market’s 26.8%.

• That gap between multistage-backed deals and the broader market has widened from 8.9 percentage points in 2025 to 12.4 points in 2026 — the largest gap in six years.

There’s a real explanation behind this, not just bias. PitchBook’s own read is that serial founders, particularly those with a prior exit, tend to show exactly the traits large funds lean on when there isn’t much operating history to judge a company by: proven execution, an existing network, and credibility that offsets some of the uncertainty baked into early-stage investing.

There’s also a selection effect running the other way - the strongest serial founders often run competitive processes and choose investors capable of leading their future rounds too, meaning some of this pattern reflects founders picking multistage capital as much as multistage funds picking them.

So is this the market getting smarter about risk, or a shortcut that’s becoming more entrenched?

Probably both, and the two are hard to fully separate. Serial founders genuinely carry lower-variance signal at a stage where there’s little else to evaluate. But when the mechanism reinforcing that signal is check size and fund economics as much as it is founder quality, the gap compounds independent of whether the specific first-time founder in the room is actually the stronger bet.

And this is where first-time founders need to be careful.

None of this means you can’t raise. The traditional path - smaller funds, iterative fundraising, building traction round by round - remains genuinely viable, and PitchBook’s own data confirms it. What’s changed is that the ceiling looks different depending on which side of this line you’re on. If you’re a first-time founder raising into a competitive, multistage-adjacent round, your traction and specificity are doing double duty right now: proving the company and standing in for the track record your résumé doesn’t yet carry on its own.

The real risk isn’t being a first-time founder. It’s assuming the fundraising environment treats your first company the way it treats someone’s third — on this data, it doesn’t, and the gap is still growing.

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🧩 Frameworks & insightful posts

Is the SaaS crash narrative wrong? Here’s what 72,000 companies show.

Earlier this year, fears that AI would disrupt traditional software triggered what became known as the “SaaSpocalypse,” wiping nearly $1 trillion from software valuations in early February, with some estimates putting the broader decline as high as $2 trillion.

But new data from Stripe suggests the underlying SaaS businesses have been far more resilient than their stock prices implied.

Stripe created the Stripe SaaS Index using weekly payment volume from an average of roughly 72,000 non-AI SaaS businesses. Because it tracks the same businesses over time, it gives a useful view into how SaaS demand itself is changing.

The surprising finding: SaaS revenue growth accelerated through the sell-off rather than collapsing.

• Year-over-year growth in the index has climbed above 30%, around the strongest levels seen since 2023.

• From May through December 2025, the index grew about 14%. Over a comparable period beginning in January 2026, it grew another 21%.

• The overall index is now roughly 4% above the trajectory it was following before the SaaS sell-off.

The strength isn’t evenly distributed. Larger SaaS companies helped drive the aggregate numbers, while the typical SaaS company initially experienced slower growth. But even that group subsequently reaccelerated and has returned roughly to its pre-sell-off trend.

The most interesting signal may be young SaaS companies.

Businesses less than a year from their first Stripe transaction experienced particularly strong acceleration. Their growth began rising in mid-2025 and continued through the SaaSpocalypse. Stripe also found that younger SaaS companies have rapidly increased their use of AI tools.

At the beginning of 2026, mature SaaS companies were about five percentage points more likely to show Stripe’s proxy signals for AI adoption. By April that relationship had flipped, and young SaaS companies are now about four points more likely to show those signals.

There are also differences underneath the headline numbers. US SaaS growth has remained particularly strong, Europe is still above its pre-sell-off trajectory despite some slowing, and healthcare SaaS showed especially strong acceleration. Retail and professional-services SaaS have also performed above their earlier trends.

The important distinction is between what markets expect AI to do to SaaS and what is happening to SaaS demand today.

The sell-off reflected a forward-looking fear: if AI agents can perform work traditionally handled by software, existing SaaS products could eventually lose pricing power, users, or entire workflows.

Stripe’s payment data doesn’t prove that concern is wrong. It also doesn’t measure profitability or payments processed outside Stripe.

But so far, the feared disruption hasn’t appeared as a broad collapse in SaaS revenue.

So, AI may ultimately reshape SaaS, but the transition currently looks less like SaaS disappearing and more like SaaS companies adapting, with some of the youngest companies potentially benefiting from AI adoption themselves.

How do you build a seed deck that actually raises money?

Most founders know the standard pitch deck advice: explain the problem, show the solution, prove there’s a market, and keep the slides simple.

But after analysing 50+ Y Combinator startup decks from companies that collectively raised more than $450M, a much clearer pattern emerges. Strong seed decks tend to follow a surprisingly consistent structure.

Here’s the 9-slide framework:

1. Title: Company name + one sentence explaining exactly what you do.

2. Problem: Make the pain obvious. Use a real example, statistic, or customer story instead of a wall of text.

3. Solution: Show how your product solves that problem and why your approach is different.

4. Traction: Put your strongest evidence here, whether that’s revenue, users, retention, growth, pilots, or another meaningful signal.

5. Unique advantage: Explain what you understand, own, or can do that competitors cannot easily replicate.

6. Business model: Make it immediately clear who pays you, how much they pay, and how the company makes money.

7. Market opportunity: Show how large the opportunity can become without relying only on a giant top-down TAM number.

8. Team: Focus on why the founders are particularly suited to build this company.

9. The ask: State how much you’re raising, what the capital will fund, and what milestones it should help you reach.

But structure is only half the job.

YC’s pitch deck guidance comes back to three principles: legibility, simplicity, and obviousness.

A slide should be readable immediately. It should communicate one important idea rather than five competing ones. And someone unfamiliar with your startup should understand what you’re trying to say within seconds.

That means cutting dense paragraphs, tiny screenshots, unnecessary diagrams, excessive branding, and anything that forces the investor to decode the slide.

The bigger lesson: a seed deck doesn’t need to explain everything about your company. It needs to make the most important things impossible to misunderstand.

We also collected 50+ YC startup pitch decks that collectively raised $450M+, so you can study how real founders structured their fundraising stories rather than starting from a blank template.

So, your pitch deck is not a company encyclopedia. It is a sequence of evidence. Each slide should answer the next obvious question an investor has, then move them naturally to the next one.

What does a VC really look for in your financial model? (Template you can use).

Most founders think investors open a financial model to decide whether the five-year projections are believable.

That’s not really the test.

At pre-seed and seed, nobody expects you to predict exactly where revenue will be five years from now. Instead, the model is being used to answer a simpler question:

Does this founder actually understand how their business works?

An investor can often form that view surprisingly quickly.

Three things matter immediately:

• Burn multiple: How much cash are you burning for every dollar of new ARR? If growth requires increasingly more capital, investors notice.

• CAC payback: How long does it take to recover the cost of acquiring a customer? This helps separate efficient growth from growth that is simply being bought.

• The hockey-stick test: If historical growth is modest but your forecast suddenly accelerates, what specifically causes it? More salespeople? A new channel? Higher conversion? Pricing changes?

The problem isn’t projecting aggressive growth.

The problem is projecting aggressive growth without showing what creates it.

There’s also an important stage difference.

At pre-seed and seed, investors generally don’t need a giant 30-tab financial model. A bottom-up revenue model, basic P&L, cash runway, and clearly stated assumptions can tell them far more.

By Series A, expectations change. Historical numbers need to reconcile, retention and cohorts matter more, and unit economics need to withstand deeper diligence.

So before sending your model, ask yourself:

Can I explain exactly where growth comes from, what it costs, and how long my cash lasts without opening the spreadsheet?

If not, the problem probably isn’t the spreadsheet.

It’s that the assumptions underneath it still need work.

We also built a simple pre-seed/seed financial model that calculates burn multiple, CAC payback, runway, and the core metrics investors are likely to examine.

Download the financial model.

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📜 DEEP DIVE

Run your pitch deck through a VC’s AI screener before they do.

If you sent a deck to a seed fund in the last 90 days, there is a very good chance the first thing that read it was not a person. It was a model, running against a rubric, producing a one-page memo with a section titled something like “Reasons to pass.” A rubric, if the word is new to you, is just a scoring sheet: what the fund wants to see, points for each, and zero for anything missing.

That memo decided whether an associate spent ten minutes on you or whether you got the "not a fit right now" email three days later.

Nobody tells you this happened. The rejection reads the same as one written by a human who thought hard about it.

Last week I spent an hour on a call with a partner at a seed fund (roughly $80M fund, mostly B2B, US and Europe). He shared his screen and walked me through their intake pipeline. I’m not naming him or the fund, but he let me keep the structure, the rubric fields and the output format on the condition that I don’t quote the fund’s thesis file. Fair. Everything below is built from that, plus a few other investors I’ve spoken with who run some version of the same thing.

This one has a lot of practical stuff in it, so here’s what we’re covering:

• The pipeline your deck goes through, step by step, and where you actually get cut

• What the AI memo looks like when it’s about you (I’ll show you a real output)

• The 8-field rubric the screener scores against, with the weights

• The full three-prompt chain, copy-paste ready

• A Claude Project setup so you can run yourself through it in 20 minutes

• The five pass reasons that show up most, and the deck fix for each

• Downloadable rubric file and prompt file

Let’s go.

The part nobody told you about

Here’s the thing that stuck with me from the call. He said (I’m paraphrasing from memory, not a direct quote): “Founders think the deck is a story we read. It’s not. It’s a form we extract.”

That’s the shift. When you send a deck to most funds now, the first pass is extraction. A model pulls out fields: stage, round size, ARR, growth, team, market, thesis fit, red flags. Then it scores those fields against the fund’s rubric and thesis file. Then it drafts a short memo. Then it routes you to one of three piles.

Decks that clear the criteria move to a second stage where an analyst validates the main claims and a first-pass memo gets drafted. Borderline decks, usually 15 to 25% of inbound, get a short analyst look before they’re classified. By the time a partner sees anything, the screen has cut inbound down to roughly 5%, each one arriving with a memo already drafted.

So the real funnel isn’t “we get 2,000 decks a year and take 200 meetings.” It’s “we get 2,000 decks, a model kills 1,500 in the first minute, an associate spends ten minutes each on 400, and a partner reads 100 memos that were 70% written by software.”

And this isn’t a few elite firms with data science teams. A survey of nearly 300 private capital dealmakers this year found 85% now use AI to automate daily work, up from 76% a year earlier, and 82% use it for deal sourcing research. On the M&A side, Deloitte found 79% of dealmakers had digitised target screening and 97% had brought generative AI or advanced analytics into the process somewhere.

The partner I spoke with was blunt about why: their inbound tripled between 2023 and 2025, headcount didn’t move, and the two associates were spending 60% of their week reading decks that should never have reached them. So they built the screen. It took two weeks. It’s a Claude project with a rubric file, a thesis file and three prompts. That’s it. No custom software.

Which is exactly why you can build the same thing.

What the memo about you looks like

This is the part that made me want to write this issue. He ran a deck through it live (a real inbound they had already passed on, founder details stripped). Here is the top of what came out, lightly edited to remove identifying stuff:

Company: [redacted], workflow tool for mid-size logistics brokers
Stage / ask: Pre-seed, raising $1.2M on a $10M post SAFE
Traction: 4 pilots, 0 paying, “LOIs with 2 enterprises”
Thesis fit: Partial. Vertical SaaS is in scope, logistics is not a focus sector. Team: 2 founders, 1 domain (8 yrs brokerage ops), 1 technical (first startup). No prior founder exits.
Score: 41 / 100
Routing: Archive

Reasons to pass

1. No paying customers at $10M post; pilots are unpriced and no conversion timeline given.

2. Market size slide uses top-down TAM ($48B) with no bottoms-up path; serviceable market not stated.

3. “LOIs” not attached or quantified; screen treats unquantified LOIs as zero.

4. Deck has no slide on why now; thesis file requires a stated catalyst for vertical SaaS.

5. Ask implies 12 months runway on the model’s burn estimate; no milestone stated for the next raise.

What would change the score 1 paid pilot with a number attached, or a bottoms-up SAM with a named first segment, would move this to Needs Human Review.

Read that again from the founder’s side.

That founder probably has a great answer for every one of those five points. The LOIs are real. The pilots have a conversion date. There’s a why-now; it’s just spread across slides 3 and 9 instead of being called out. None of it mattered, because none of it was extractable from the deck as written. The model doesn’t infer what you meant. It scores what’s on the page, and an

ything missing is not a blank. It’s a flag.

And here’s the bit that should worry you: the founder got an email that said: “We loved the space, but it’s a bit early for us.” That’s the human translation of a 41. It tells you nothing about which of the five things to fix.

This is what I mean when I say the past reasons are being written about you in a room you’re not in. The difference now is that the room is a prompt, and you can rebuild it.

Everything below is the rebuild. The rubric, the three prompts, the project setup, and the fixes for the pass reasons that come up most. If you’re raising in the next six months, run yourself through it before you send a single deck. At $99 a year, that’s less than one hour of the lawyer you’ll hire to paper the round that this helps you get.

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 :

• Could AI handle a month-long project without human help by 2027?

• Why is $3 trillion of Venture Capital still waiting to be returned?

Frameworks & insightful posts :

• What did 7,200 developers reveal about AI coding?: The State of Web Dev AI 2026 report.

• How fast should founders ship features in the AI era? A framework from a former Facebook executive.

• Why nobody wants to build this boring AI category (Even though it’s worth billions.)

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🧠 Big idea + report of the week

Could AI handle a month-long project without human help by 2027?

Most people still think of AI as something that helps with small tasks - writing emails, generating code snippets, answering questions, or summarizing documents.

But what if the more important question isn’t how smart AI is, but how long it can work independently before needing human intervention?

A new study from METR suggests that this “task horizon” may be improving far faster than most people realize.

METR (Model Evaluation & Threat Research) recently published an updated version of its Measuring AI Ability to Complete Long Tasks research, tracking how quickly frontier AI models are improving at completing increasingly complex tasks from start to finish.

AI’s working time is growing faster than expected

The headline finding is striking.

METR’s earlier research estimated that AI task-completion capability was doubling roughly every seven months. After expanding the dataset and testing newer frontier models, the updated estimate is now 130 days (around 4.3 months).

In other words, the amount of work an AI can successfully complete before failing is growing significantly faster than researchers previously thought.

The acceleration is also recent.

The strongest improvements occurred between 2024 and 2025, suggesting the curve is still steepening rather than flattening.

Researchers made the benchmark harder and AI still improved

What’s interesting is that this wasn’t achieved by making the test easier.

METR expanded its evaluation suite from 170 tasks to 228 tasks, while more than doubling the number of long-duration challenges.

Some key changes:

• Total tasks increased from 170 to 228

• Tasks requiring 8+ hours of human work increased from 14 to 31

• More complex software engineering and reasoning tasks were added

Despite the harder benchmark, frontier models continued pushing the curve upward.

Claude Opus 4.6 achieved an estimated task horizon of roughly 718 minutes, meaning it can successfully complete tasks that would take a human nearly an entire working day.

Not all AI capabilities are improving equally

The research also shows a growing gap between digital work and physical-world tasks.

Software engineering and reasoning benchmarks are improving at an extraordinary pace, with task horizons doubling every few months.

Meanwhile:

• Visual computer-use tasks remain 40-100x behind software tasks

• Self-driving progress is improving much more slowly

• Physical-world interaction continues to be a major bottleneck

This suggests AI may transform knowledge work long before it masters the physical world.

The bigger implication

If current trends continue, today’s multi-hour AI agents could evolve into systems capable of handling multi-day and eventually multi-week projects.

METR notes that month-long task completion may become realistic around 2027.

That doesn’t mean AI will replace every worker.

But it does mean the conversation may soon shift from “Can AI do this task?” to “How much of this project can AI own from beginning to end?”

And that is a very different future than the one most people are imagining today.

Why is $3 trillion of Venture Capital still waiting to be returned?

For decades, venture capital followed a relatively simple cycle.

Investors backed startups, companies went public or got acquired, and capital flowed back to LPs who reinvested into the next generation of funds.

That cycle is now breaking down.

A new report from the World Economic Forum and Stanford GSB, titled The Future of Venture Capital: Unlocking Liquidity and Growth, highlights a growing bottleneck inside the venture ecosystem: trillions of dollars in startup value exist on paper, but very little of it is being converted into actual cash returns.

Venture has created enormous value, but much of it remains locked up.

The report estimates there are now roughly 1,920 VC-backed unicorns globally, representing more than $7.3 trillion in private-market value.

The challenge is that most of this value has not been realized.

According to the research, venture funds are currently sitting on approximately $3 trillion of unrealized NAV (Net Asset Value) - assets that exist on balance sheets but have not yet generated distributions back to investors.

What’s even more surprising is how old many of these companies have become.

• 59% of unicorns are now more than 10 years old

• 20% are more than 15 years old

• Many remain private despite reaching enormous scale

The traditional path from startup to public company is taking longer than ever.

LPs are waiting much longer to get their money back

This liquidity slowdown is beginning to show up in fund performance metrics.

The report highlights a sharp decline in DPI (Distributed to Paid-In Capital), one of the most important measures LPs use to evaluate venture returns.

Historically, funds in their prime return years typically distributed around 20% of value back to LPs. By the end of 2025, that figure had fallen to approximately 12%.

The trend is even more visible in recent vintages.

Among 2021 funds, roughly 75% had returned less than one-quarter of LP capital by year four, compared with about half of 2017 funds at the same stage.

The result is a growing mismatch between paper gains and actual cash returns.

Venture capital is becoming increasingly concentrated

It also highlights how capital is flowing toward a smaller number of firms and companies.

Fundraising has become significantly harder across the industry.

The 10 largest venture funds captured nearly 43% of all venture capital raised in 2025, while the median time required to close a US venture fund stretched to a record 15.3 months, up from 9.7 months just three years earlier.

At the company level, concentration is becoming even more extreme.

AI accounted for more than 50% of global venture deal value, with just five companies - OpenAI, Anthropic, xAI, Scale AI, and Project Prometheus - absorbing roughly 20% of total global VC funding through a handful of mega-rounds.

In many ways, venture capital is increasingly becoming a game of a few giant funds backing a few giant companies.

The industry’s biggest challenge isn’t funding - it’s liquidity

The common narrative is that venture capital has too little money. The data suggests the opposite.

There is enormous value sitting inside private markets, but far less liquidity than investors expected when those investments were made.

The real question facing venture over the next few years isn’t whether startups can raise capital.

It’s whether the industry can create enough exits - through IPOs, acquisitions, or secondary markets - to unlock the trillions of dollars currently trapped on balance sheets.

Because until that happens, much of venture’s success remains theoretical rather than realized.

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SOMETHING MORE

🧩 Frameworks & insightful posts

What did 7,200 developers reveal about AI coding?: The State of Web Dev AI 2026 report.

A few years ago, AI coding tools were mostly autocomplete on steroids.

Today, they’re becoming a core part of how software gets built.

The team behind the State of Web Dev AI 2026 surveyed more than 7,200 developers to understand how AI is changing software development. One finding stood out immediately:

Just one year ago, developers said AI generated about 28% of their code on average. Today that number has climbed to 54%.

In other words, the average developer now produces more code with AI than without it.

The biggest growth came from developers who say more than 75% of their code is AI-generated. At the same time, the number of people using AI “constantly” while coding doubled year-over-year.

But the more interesting shift is not just how much AI is being used.

It’s how developers are choosing to use it.

Coding agents are replacing chatbots

The early AI era was dominated by chat interfaces. Developers copied code into ChatGPT, asked questions, received suggestions, and manually implemented changes.

Now a new category is emerging: coding agents.

Among all coding tools surveyed, Claude Code received the highest positive sentiment score, ahead of OpenAI Codex and GitHub Copilot.

Instead of simply answering questions, these agents can navigate repositories, edit files, execute tasks, and work across larger parts of a codebase.

The interface is shifting from “ask AI a question” to “assign AI a task.”

That’s a very different product category.

Claude is winning where developers spend money

ChatGPT remains the most widely known AI product. But when respondents were asked which AI tools they actually pay for, Claude ranked first.

More developers reported paying for Claude than ChatGPT, Gemini, Copilot, Perplexity, or any other AI product.

This matters because consumer popularity and willingness to pay are not the same thing.

Developers appear to be rewarding products that directly improve workflow productivity rather than general-purpose assistants.

It’s one reason why Anthropic has been gaining momentum inside engineering teams over the past year.

Developers are spending more on AI

The era of cheap AI is slowly ending. As adoption grows, AI companies are becoming more aggressive about monetization.

The survey shows growing numbers of developers spending between $50 and $500 per month on AI tools.

Many teams now view AI subscriptions the same way they view cloud infrastructure, GitHub, or productivity software: a standard operating expense.

The question investors are asking is whether this spending growth can eventually justify the enormous valuations assigned to AI companies.

Developers aren’t convinced. A majority of respondents either agreed or strongly agreed that we’re currently living through an AI bubble.

The biggest fear isn’t technology. It’s jobs.

Despite widespread adoption, developers remain worried about what comes next.

Job displacement ranked as the most concerning AI risk, ahead of military applications, environmental impact, security risks, and AI-generated misinformation.

The concern isn’t necessarily that AI can already replace engineers. It’s that managers may eventually believe it can.

That distinction matters. Technology adoption often changes labor markets long before technology fully replaces labor.

AI still has a trust problem

For all the progress made by modern models, developers continue to report the same core frustration.

Hallucinations remain the number one pain point. Respondents also highlighted poor code quality, lack of context, privacy concerns, and rising costs as major issues.

The industry has largely solved the “can AI generate code?” question. The next challenge is solving “can developers trust the code it generates?”

That may end up being a far bigger challenge than model intelligence itself.

What this signals

The most important takeaway from this year’s survey is that AI adoption is no longer a future trend. It’s already happening.

Developers are generating more than half their code with AI, paying for AI tools at increasing rates, and integrating coding agents directly into their workflows.

The debate is shifting away from whether AI will become part of software development.

The real questions now are:

• Which AI companies capture the developer workflow?

• Can AI businesses justify their valuations through monetization?

• And how much of software engineering eventually becomes agent-driven?

Those questions will define the next chapter of the AI industry.

How fast should founders ship features in the AI era? A framework from a former Facebook executive.

AI has dramatically reduced the time it takes to build software.

A solo founder can now ship features in days that previously required entire engineering teams. As a result, many startups are treating speed itself as the competitive advantage.

But faster building doesn’t automatically mean faster shipping.

Recently, Gokul Rajaram - who held leadership roles at Google, Facebook, and Square, and is now a founding partner at Marathon Management shared an interesting framework for deciding how often companies should ship products.

The insight came from his own experience.

When he left Facebook for Square in 2013, one thing immediately surprised him. Facebook engineers shipped updates multiple times a day, while Square often released customer-facing updates only every few weeks.

His first reaction was simple: Square needed to move faster. Over time, he realized the opposite.

Both companies had intentionally chosen different shipping philosophies because their customers had very different needs.

At Facebook, speed mattered.

Users primarily visited the platform for entertainment, social interaction, and discovery. New features were usually welcomed. Customers had time to explore new experiences, learn new functionality, and adapt quickly to changes.

At Square, reliability mattered far more than novelty.

Small businesses depended on Square to process payments, manage transactions, and run daily operations. A bug wasn’t merely annoying - it could directly impact a merchant’s revenue.

For those customers, reliability was the product.

That led Square to build extensive quality assurance processes, human review stages, alpha testing, beta testing, and deliberate release schedules before new features reached customers.

There was another important difference. Many Square customers simply didn’t have time to absorb constant product changes.

A new feature often required updating workflows, retraining staff, adjusting business processes, or reconfiguring third-party software integrations.

Even if Square could ship daily, their customers couldn’t keep up.

This led Gokul to propose a simple framework built around two questions:

1. How critical is your product?

If your product is mission-critical to your customers, quality and reliability should outweigh speed.

Examples include:

• Payments

• Banking

• Healthcare

• Infrastructure

• Security software

In these categories, getting something wrong can be extremely costly.

If your product is primarily used for entertainment, communication, or productivity, speed becomes more valuable because the consequences of mistakes are lower.

2. How much time do your customers have?

Some customers are busy operators. They don’t have time to learn new workflows every week.

Others actively enjoy exploring new features and experimenting with products.

This distinction matters more than most founders realize.

Combining these two variables creates four different shipping models.

The Square model - Critical product + Time-strapped customers

Examples:

• Payments

• Financial infrastructure

• SMB operating systems

Prioritize reliability.

Use extensive QA processes, human review, staged rollouts, and carefully package new functionality into larger releases.

Bundled releases - Non-critical product + Time-strapped customers

Examples:

• B2B productivity software

• Workflow tools

Move quickly internally, but bundle customer-facing releases.

A feature customers never notice may provide less value than founders assume.

The Facebook model - Non-critical product + Time-rich customers

Examples:

• Social media

• Consumer apps

• Entertainment platforms

Optimize for speed. Ship features as soon as they are ready. Users enjoy discovering what’s new.

Reliable and continuous - Critical product + Time-rich customers

Examples:

• Consumer banking

• Financial apps

Maintain strong quality controls, but release features once they’re ready because customers have enough bandwidth to adopt them.

The broader lesson is simple. AI has changed how fast software can be built. It hasn’t changed how customers consume software.

The best founders won’t simply ask: “How quickly can we ship?”

They’ll ask: “How quickly should our customers receive change?”

Those are often very different answers.

Why nobody wants to build this boring AI category (Even though it’s worth billions.)

Most founders want to build the next AI coding tool, AI design platform, or AI productivity app.

Very few wake up thinking about payroll compliance, anti-money laundering reviews, regulatory filings, audit trails, or employee monitoring.

That’s exactly why the opportunity is interesting.

Behind the scenes, every large company is spending enormous amounts of money on compliance. Every employee payroll run, customer onboarding process, bank transfer, insurance claim, mortgage application, tax filing, and regulatory report has compliance requirements attached to it.

As companies become larger and regulations become more complex, the default solution has been simple: hire more people.

Recently, James da Costa and Angela Strange shared a fascinating breakdown on why compliance may become one of the biggest AI categories in enterprise software over the next decade.

The numbers are staggering.

There are now more than 400,000 compliance officers in the United States alone, representing over $40 billion in annual labour costs.

The U.S. Bureau of Labour Statistics projects more than 33,000 new compliance openings every year for the next decade.

Yet despite all this hiring, the industry still struggles.

Compliance teams face constant backlogs, talent shortages, rising regulatory complexity, and costly mistakes. In 2024, TD Bank received a $3 billion fine after failing to monitor the vast majority of its transactions and accumulating tens of thousands of unresolved alerts.

The problem isn’t a lack of people. The problem is that much of compliance work still looks exactly the same as it did years ago.

• People manually read documents.

• People copy information between systems.

• People compare records across databases.

• People monitor regulatory updates.

• People write reports.

In other words, much of compliance consists of repetitive, document-heavy workflows that AI is increasingly good at handling.

What’s changing now is that AI has crossed an important trust threshold. For years, technologies like OCR could extract information from documents, but “mostly correct” wasn’t good enough for compliance.

A mortgage application can’t be 90% accurate. A regulatory filing can’t be mostly complete. An anti-money laundering review can’t miss critical information.

Modern AI systems can now read, understand, extract, summarise, and reason over complex documents with near-human accuracy. A 400-page regulatory document that once required days of review can now be analysed in minutes.

That changes the economics entirely.

The authors break the opportunity into three major startup categories.

1. Turn regulation into code

Every year, governments publish thousands of pages of new regulations. Today, humans read those documents, interpret them, update internal policies, and manually ensure compliance.

AI allows regulation itself to become machine-readable.

Instead of treating regulations as PDFs, companies can convert them into structured rules that software can monitor automatically.

One example is Tako, which helps businesses navigate Brazil’s notoriously complex labour regulations. Rather than forcing HR teams to manually track thousands of union rules and hundreds of annual changes, the system continuously audits compliance and flags potential issues in real time.

The bigger idea is that regulations become executable software rather than static documents.

2. Replace legacy compliance systems

One of the most overlooked realities in enterprise software is that many compliance departments still operate on infrastructure built decades ago.

The integration layer isn’t software.

It’s a human.

Employees spend their day moving information between systems because the systems themselves don’t communicate effectively.

This creates a massive opportunity for AI-native replacements.

Companies like Valon, Vesta, and Sardine are rebuilding compliance infrastructure from scratch.

Sardine, for example, can reduce the time required to prepare a Suspicious Activity Report from more than 30 minutes to less than one minute by automatically gathering information from multiple systems and generating the required documentation.

That’s not just efficiency.

That’s an entirely different operating model.

3. Augment compliance workers with AI agents

The most immediate opportunity may simply be helping existing compliance teams do more work with the same number of people. A compliance officer reviewing a customer onboarding case typically needs to:

• Review documents

• Extract information

• Verify identity

• Check sanctions databases

• Cross-reference internal systems

• Prepare reports

AI agents can handle most of these repetitive tasks automatically and escalate only exceptions that require human judgment.

Instead of replacing compliance professionals, AI allows them to focus on decisions rather than paperwork.

One insight from the article stood out.

Every compliance function is built from three layers:

• Regulation

• Software

• People

Most startups will initially attack one layer.

The biggest companies may eventually own all three.

They’ll translate regulation into code, build the system of record, and deploy AI agents that operate on top of it.

That’s what makes this opportunity so interesting.

Compliance doesn’t look exciting from the outside.

But neither did payroll software, accounting software, or cloud infrastructure in their early days.

Sometimes the largest opportunities are hidden inside the workflows everyone else wants to avoid.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Ghost Angels, a venture fund launched by a group of 20 former Snap employees and led by ex-Snap partnerships executive Max Rivera, is backing the next generation of AI-powered social and consumer startups. (Link)

• Atomus, a Menlo Park, CA-based newly formed venture capital firm, is reportedly raising a $500m fund. (Link)

• Mouro Capital, a Madrid, Spain- London, UK- and San Francisco, CA-based venture capital firm, has secured $400m from Banco Santander for the first close of its third fund. (Link)

• Veriten, a Houston, TX-based research, strategy and investment firm, held the initial close of its second flagship energy venture fund at over $105m. (Link)

• Haun Ventures, a Menlo Park, CA-based venture capital firm, closed Fund II at over $1 billion. (Link)

New Startup Deals

• Invertix, a Munich-based startup building an autonomous AI workforce platform for the renewable energy sector, raised €1.7M in Pre-Seed funding. (Link)

• Stellar Alpina, a Zurich-based aerospace startup developing compact rotating detonation rocket engines, raised CHF 3.5M in Pre-Seed funding. (Link)

• Daloopa, a NYC-based financial data infrastructure company powering AI workflows, raised $47M in Series C funding. (Link)

• Triomics, a NYC-based oncology AI company, raised $22M in Series B funding. The round was led by Battery Ventures. (Link)

• Caudal Energy, an Oxford-based renewable energy company, raised £4.3M in funding. (Link)

• WeRoad, a Milan-based travel-tech company, raised $58M in Series C funding. The round was led by Airbnb. (Link)

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📜 DEEP DIVE

Your VC called your competitor before passing on you. How to spot it, stop it and protect your deck.

You had a good first meeting. The partner leaned in, asked real questions about your pricing and your wedge, pushed on your ICP in a way that told you he’d looked at the category before. He closed with “this is interesting, let me bring it to the team,” and you walked out thinking you had a lead.

Then nine days of silence, and a polite two-line pass with no reason in it.

Here’s what most likely happened in those nine days. Before the partner meeting, an associate ran your company against the portfolio, the way they run every company that gets past a first call. Your name came up next to one of their existing investments, not a direct competitor, but close enough that the partner who sits on that board had to make a call before anyone spent more time on you. So he messaged that founder, or caught him on a check-in that was already on the calendar, and asked some version of “we’re looking at a company in your space, does this bother you?”

That founder said yes. Or said “not really, but I’d want to know more,” which in practice means the same thing. Either way, your deal was done before the partner meeting started, and the partner who liked you never had to argue for you in the room.

You were never on that call. Nobody was going to write you an email that said “we passed because someone we already own asked us to,” so what you got instead was the version with no reason in it.

I’ve watched this play out enough times now that I can usually spot it from the shape of the pass. Meeting goes well, partner is engaged, deal evaporates without a substantive reason. When a founder tells me that story, my first question is always the same: did you check their portfolio before you walked in?

Almost nobody has.

The call nobody tells you about

Every fund that takes portfolio conflicts seriously runs some version of this check. The mechanics vary, but the shape doesn’t.

Before a partner spends real time on you, someone on the team screens you against the existing portfolio. If there’s an overlap, direct or adjacent, the partner who sits closest to that portfolio company picks up the phone. Sometimes it’s a five-minute courtesy call. Sometimes it’s a real conversation about whether the fund should be anywhere near you.

Three things can come out of that call, and you don’t get a vote in any of them -

The first is a clean pass. The portfolio founder says “yeah, that’s us in eighteen months,” the fund backs off, and you get the two-line email. This is the polite version. It’s also the most common one, because the best investors don’t want to be in a position of conflict. They’d rather kill your deal early than have to choose later.

The second is worse. The portfolio founder says “no, not competitive, go ahead,” but the call itself has now put you on their radar. Your positioning, your pricing, the wedge you spent the meeting explaining. There’s no formal leak, but there doesn’t need to be. Investors have a very clear interest in helping their portfolio companies succeed, and if what they learned from you is useful to a company they already own, it moves.

The third is the one that used to be rare and isn’t anymore. The fund decides you’re the better bet and invests anyway. Good for you, until you find out your new lead investor is also sitting on the board of the company you’re trying to beat.

Why this is getting worse, not better

For most of venture’s history, the rule was simple. VC firms do not invest in startups that compete directly with existing portfolio companies, and most went out of their way to leave enough room between portfolio companies that a pivot wouldn’t cause a collision.

That norm is breaking, and the reason is fund size. A mega-fund can’t make its math work unless it’s in every outlier, and right now there are more dollars chasing outliers than there are outliers. Something has to give, and what’s giving is the rule that kept two competitors out of the same portfolio.

You can already see it at the top of the market. Lightspeed holds a sizable stake in Anthropic and is also a big backer of Mistral. a16z’s AI portfolio lists OpenAI, Anthropic and Mistral side by side. SoftBank funded multiple sides of the same fight years before anyone else was willing to say it out loud.

Those are frontier labs, and the founders involved have enough leverage to negotiate around it. You don’t. Most founders lack the hard power to block a competitive investment, and the small set who have soft power are the ones with the biggest companies. That’s not you at seed.

So the situation you’re actually walking into in 2026 is this: the conflict check still happens, it still kills deals, and it still leaks information. But the old guarantee that a fund won’t back your competitor after seeing your deck is gone. Both risks are live at the same time.

The mistake I see over and over

Here’s the pattern. A founder builds a target list of forty funds, sorts by “invests in our stage and sector,” and starts sending emails. The sector filter is the problem. The funds most likely to show up in a sector search are, by definition, the funds most likely to already own something near you.

One founder I know pitched a well-regarded seed fund for a vertical SaaS product. Great meeting - two weeks later, a pass. Six months after that, the fund led a round in a company that had quietly pivoted into the founder’s exact category. Nobody did anything illegal. The founder had just spent forty-five minutes explaining their playbook to the one fund in the market that had a reason to pass it along.

Another founder went a step further and pitched a partner who sat on a direct competitor’s board, on the theory that “they know the space, they’ll get it fast.” They did get it fast. The partner has a fiduciary duty of loyalty to the company they sit on the board of, and that duty doesn’t switch off because you asked for feedback.

The third mistake is subtler. The founder describes the market so broadly, “we start with X, then expand into Y and Z,” that they walk straight into an adjacency with a portfolio company that’s nowhere near their actual product. The fund hears “Y” and calls the founder of the Y company. Deal dead over a slide about year three.

Even the funds themselves know how costly the check is. Hunter Walk at Homebrew has written that his fund explicitly passed on companies related to current investments at least twice, and in both cases the startup they passed on outperformed the portfolio company. That’s the honest version. The check kills good deals, and the fund would rather eat that cost than the conflict.

What almost none of these founders did is the one thing that would have caught it. You know, at best, 1 out of every 100 founders asks a direct conflict question at the start of the meeting. One in a hundred.

The other ninety-nine walk in blind, pitch for forty-five minutes, and find out from the silence.

Below the line: how to map a fund’s portfolio for conflicts in about twenty minutes before you pitch, the exact question to ask in the first two minutes and how to read the four answers you’ll get, what to do if you’re already conflicted and don’t know it, and the pre-pitch checklist.

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 :

• Anthropic modelled the AI economy of 2030. Who actually wins? The answer might surprise you.

• Are AI startups getting acquired too early? Here’s what the data shows.

Frameworks & insightful posts :

• Scaling vs. Profitability Problem: Where Venture Capital often goes wrong.

• Can vertical AI startups still win when incumbents own the data?

• How much should founders actually pay themselves? - NFX framework.

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🧠 Big idea + report of the week

Anthropic modelled the AI economy of 2030. Who actually wins? The answer might surprise you.

The biggest question around AI may not be whether it creates economic growth. It may be who actually captures that growth.

Anthropic’s new Economic Scenario Explorer models what the US economy could look like by 2030 under three different paths for AI adoption: modest, substantial and extreme. Rather than assuming entire jobs disappear, the model treats each occupation as a bundle of tasks that AI can augment, automate, leave unchanged or help create.

The differences between the scenarios are enormous.

• Modest: US GDP reaches $34.1T by 2030, just 1.6% above the economy without AI.

• Substantial: GDP reaches $36.3T, an 8.3% boost. AI can perform roughly half of knowledge work, although adoption remains incomplete.

• Extreme: GDP reaches $44.4T, 32.4% higher than without AI, with annual GDP growth eventually reaching roughly 15%.

But the most interesting part isn’t GDP. It’s what happens to workers as AI becomes more capable.

In Anthropic’s substantial scenario, knowledge-worker wages are essentially flat at -0.3% relative to a world without AI, while wages for other workers rise 5.9%.

In the extreme scenario, the divergence becomes dramatic: knowledge-worker wages fall 11.5%, while wages for other workers rise 33.6%. Knowledge-worker unemployment rises to 17.9%, pushing overall unemployment to 11.9%.

There is an equally important shift in who captures the economic value.

Today, roughly 60% of economic output goes to labour and 40% to capital. Anthropic estimates capital’s share could rise to 43.9% in the substantial scenario and 54.8% in the extreme scenario, leaving labour with just 45.2%.

So AI could create the strange situation where the economy becomes dramatically more productive and wealthier, while many knowledge workers simultaneously experience weaker wages and fewer job opportunities.

Of course, these aren’t forecasts. Anthropic explicitly describes the model as a simplified scenario tool, and the extreme case requires assumptions such as recursively self-improving AI, rapid adoption and very limited creation of new knowledge-work tasks.

Still, it raises a fascinating question.

If AI creates trillions of dollars of additional economic value but increasingly shifts that value from labour toward capital, owning productive assets may become much more important than simply having a highly valuable skill.

Are AI startups getting acquired too early? Here’s what the data shows.

Founders are usually taught to plan around a long runway to exit - build for years, not quarters. The standard model assumes a decade or so of grinding before an acquisition or IPO even becomes realistic.

But new research from Madrona suggests that timeline has quietly collapsed for AI-native companies specifically and most founders haven’t repriced their own plans around it yet.

Madrona tracked five years of its Intelligent Applications 40 list alongside its own portfolio and exit data, looking at how and how fast - AI companies are actually getting bought.

• Roughly half of GenAI acquisitions in 2026 involve companies under five years old. In 2022, that number was close to zero.

• This isn’t a fluke sitting off to the side of the data - it shows up directly in Madrona’s own exit examples. Statsig was acquired by OpenAI for $1B; Neon was acquired by Databricks for $1B. Neither is a decade-old company grinding toward a traditional outcome.

• Hyperscalers aren’t waiting for AI companies to mature into obvious acquisition targets anymore. They’re buying teams and capability years earlier than the old M&A timeline assumed.

There’s a second trend that explains why this is happening.

Staying independent in AI right now is unusually hard: capital is concentrating at the very top, with 90% of the $366B raised by the last two years’ top AI companies going to just three names - OpenAI, Anthropic, and Databricks - while being a “category leader” on Madrona’s own list lasts about a year before 68% of it turns over.

In a market that reshuffles that fast, a two-year-old company with real technical differentiation may already represent as much strategic value to an acquirer as it ever will.

So why are hyperscalers moving so much earlier than they used to?

It’s not that young AI companies are worth less and getting bought cheap out of desperation. It’s closer to the opposite - in a market moving this fast, waiting five extra years for a company to “mature” the way SaaS companies used to often means watching it get displaced instead. Buying early is a bet on capability and speed, not a discount play.

And this is where founders need to be careful.

Faster exits sound like good news on the surface - less time to liquidity, less risk of getting displaced before you cash out. But it isn’t automatically better, and it isn’t automatically available to you just because you’re AI-native. Cap tables, vesting schedules, and investor return timelines are still mostly built around the decade-long hold period this data says is disappearing. A cap table structured for a long grind can leave both founders and early employees with weaker terms if an acquisition offer shows up in year three instead of year eight.

The real risk isn’t that your company gets acquired too early. It’s building and negotiating, and structuring your cap table - for a timeline the market has already proven it no longer runs on.

📄 Must-Read Post

SOMETHING MORE

🧩 Frameworks & insightful posts

Scaling vs. Profitability Problem: Where Venture Capital often goes wrong.

Startups are constantly told to scale. Raise more capital. Hire faster. Enter new markets. Grow revenue. Figure out profitability later.

But Aswath Damodaran makes an important point: not every business becomes better when it gets bigger.

The real question is whether the economics improve as the company scales.

A business is naturally easier to scale when it has a large and growing market, low capital requirements, strong unit economics, limited customer inertia, and real competitive advantages.

If those conditions are missing, growth can create the illusion of progress while making the underlying business worse.

Damodaran describes a few very different outcomes -

• Lightning in a Bottle: Revenue and profits scale together. Early Google and Facebook are examples.

• Field of Dreams: The company loses money while scaling, but eventually the economics improve. Amazon is the classic example.

• Field of Nightmares: Revenue scales, capital keeps coming in, but the business model never becomes profitable.

• Niche Star: The company deliberately stays smaller because scarcity, pricing power, or specialisation creates more value than maximum scale.

The problem is that venture capital naturally pushes companies toward the first three paths.

VC returns follow a power law. A small number of investments generate most of the returns, so investors are incentivised to look for companies capable of becoming enormous.

That can create a mismatch.

A founder might have a healthy $50M business opportunity, but the VC model may need that company to pursue a $5B outcome.

More capital then makes aggressive expansion possible, even when the underlying economics do not justify it.

And this has become easier over time.

Private companies can now raise much more money and stay private longer. They can reach huge revenue numbers and valuations before proving that the business can consistently generate profits.

So capital can sometimes delay the moment when a weak business model has to confront reality.

So -

Scaling is valuable only when growth improves the economics of the business. Otherwise, you are not fixing the problem. You are financing a larger version of it.

For founders, a better question than “How fast can we grow?” is:

“What happens to our unit economics, competitive advantage, and profitability when we become 10x bigger?”

If those things improve, scale aggressively. If they don’t, staying smaller may actually create more value.

Can vertical AI startups still win when incumbents own the data?

One of the biggest questions in vertical AI right now is surprisingly simple:

If Salesforce, DocuSign, Atlassian, ServiceNow and other incumbents already own the customer data and workflows, why does a startup need to exist?

The argument has become stronger as general AI agents improve. A customer could theoretically connect its existing systems to Claude or another general-purpose agent and let that agent work across them.

Seema Amble from a16z recently shared a useful framework for thinking about where vertical AI startups can still win.

The key distinction is between owning the record and owning the job.

An incumbent might own the CRM record, contract, support ticket, or employee data.

But the actual work customers need completed is usually much bigger.

A contract is not the entire legal matter. A CRM opportunity is not the sale. A support ticket is not the complete customer resolution.

Those jobs involve information, decisions and actions spread across multiple systems and people.

That creates three potential layers:

• Incumbents own the record: data, permissions and actions inside systems like CRM, HR or legal software.

• AI labs can own the front door: general agents can potentially coordinate work across several applications.

• Vertical AI startups can own the job: they can specialise in completing one specific workflow from beginning to end.

The opportunity becomes clearer when you look at how AI agents are evolving.

Amble describes an agent hierarchy with four levels.

• Retrieval: The AI reads. It searches information, summarises documents, analyses data and drafts responses.

• Process: The AI does. It updates records, invokes tools, routes approvals and completes predefined workflows.

• Policy: The AI decides within rules. It uses company playbooks, precedents and thresholds to handle situations requiring some judgment.

• Principal: The AI decides what should be done. It makes open-ended decisions involving strategy, risk and resource allocation.

Most enterprise AI products started around retrieval.

But incumbents are moving upward.

DocuSign is moving from contract review toward applying legal playbooks. Salesforce is building specialised sales and quoting agents. ServiceNow is moving from workflow agents toward governed execution.

That means simply building a better chatbot on top of an incumbent system is becoming a much weaker startup strategy.

The stronger opportunity is learning how to perform the entire job better than either the incumbent or a general-purpose agent.

And this is where vertical specialisation becomes important.

A vertical AI startup can observe not only the final result, but also the decisions, corrections and expert feedback that produced it.

Consider legal work.

A signed contract tells you what everyone eventually agreed to.

It doesn’t necessarily tell you which alternatives were considered, why a lawyer rejected certain language, which risks required escalation, or why an exception was approved.

Capturing those decisions creates a learning loop.

The AI performs work → an expert reviews it → corrections are captured → outcomes are measured → the system improves on the next job.

That can become much more valuable than simply having access to historical documents.

Harvey offers an interesting example.

Instead of waiting years to accumulate customer data, it created roughly 1,750 simulated legal-task environments using synthetic data, public legal information and expert-created examples. Each environment represented realistic legal work and included detailed criteria for evaluating whether the result was good.

In other words, Harvey manufactured its own curriculum.

That suggests a useful framework for founders evaluating vertical AI opportunities.

Ask four questions:

• Can an expert quickly identify what the AI did right or wrong?

• Does the work require real judgment rather than simple rules?

• Does the job happen frequently enough for the system to keep learning?

• Can you start with one task and gradually expand until you own the entire job?

Markets where all four are true could be particularly attractive.

Legal, accounting and tax are obvious examples, but the same pattern can exist in manufacturing, healthcare operations, logistics, construction, and many other industries where work crosses multiple systems and expert judgment still matters.

The bigger takeaway is that access to data alone may not be the moat vertical AI founders should chase.

Incumbents already have enormous amounts of data. General AI agents will increasingly be able to access that data.

The harder advantage may be owning the workflow deeply enough to understand what good work looks like, why experts make particular decisions, and how to improve the next result.

So the emerging AI stack may look something like this:

• Incumbent owns the record.

• AI lab owns the interface.

• Vertical AI startup owns the job.

For vertical AI founders, that last layer may still be very much up for grabs.

How much should founders actually pay themselves? - NFX framework.

Founder salary is one of those decisions that sounds simple until you actually have to make it.

Pay yourself too little and personal finances start becoming a distraction. Pay yourself too much and you burn runway while slowly starting to think more like a salaried executive than an owner.

NFX recently shared a useful framework for thinking about founder compensation, and the interesting part is it starts with one principle:

Pay yourself enough that money doesn’t distract you from building, but keep equity and the long-term outcome as the real financial reward.

That means the right salary will be different for every founder.

Someone supporting a family and paying a mortgage has different needs from a 22-year-old founder living cheaply. Trying to copy another founder’s salary misses the point.

NFX breaks the philosophy into three ideas.

1. Feel like an owner, not an employee

Founders shouldn’t benchmark themselves against what they could earn at OpenAI, Anthropic, or another large technology company.

You chose a different economic model. An employee primarily gets paid through compensation. A founder takes more risk because the potential reward sits in the equity.

The goal is therefore not to maximize salary every time the company raises more capital. It is to preserve enough ownership and upside that increasing the value of the company remains far more meaningful than increasing your paycheck.

2. But don’t make yourself unnecessarily poor

The opposite extreme can be just as damaging.

Some founders believe paying themselves almost nothing demonstrates commitment.

But if you’re accumulating personal debt, worrying constantly about bills, or creating financial pressure for your family, that stress eventually affects how you run the company. It can even change your incentives.

A founder under serious personal financial pressure may become more willing to accept an early acquisition simply because it solves their immediate financial problem.

The better approach is to pay yourself enough to live comfortably, maintain a reasonable personal buffer, and stay focused on building for the long term.

3. Don’t turn fundraising into a salary increase

Closing a funding round feels like progress, but NFX makes an important distinction:

Fundraising success is not the same as company success.

Raising $5M doesn’t automatically mean the founders should suddenly start paying themselves much more.

Founder compensation can change, but there should be an underlying reason.

NFX suggests that legitimate reasons might include a meaningful life change, becoming heavily diluted, the company genuinely reaching escape velocity, or completing a strategic secondary when the company is performing strongly.

What shouldn’t drive the decision?

“We just raised.” Or: “Other founders are earning more.”

There’s also a second-order effect founders sometimes overlook.

Your own compensation influences how the rest of the organization thinks about compensation. A very high founder salary can make it harder to maintain discipline when negotiating packages with senior hires.

The same thinking applies to secondaries.

Taking some money off the table isn’t necessarily bad. At the right stage, it can actually align founders and investors by removing enough personal financial pressure that the founder doesn’t feel compelled to sell the entire company too early.

But doing a secondary in every round defeats the purpose.

The broader lesson is that founder compensation shouldn’t be optimized around maximizing or minimizing salary.

It should be optimized around alignment.

You should earn enough that personal finances don’t interfere with your ability to build the company, while keeping enough of your economic upside tied to creating something genuinely valuable.

A useful question to ask yourself is:

“Does my compensation make it easier for me to think like the long-term owner of this company?”

If the answer is yes, the exact salary number matters a lot less.

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This one’s in partnership with Papermark - thanks to their team for sharing the data and customer stories that made this edition possible.

The deck’s ready, so you hit send. Then you can’t stop checking your inbox: did they open it, did they get three slides in and jump on a call, or close the tab in ten seconds? You don’t know, and you won’t, not unless you ask.

A week goes by. You send a “just following up” email into a silence that could mean anything.

I’ve seen this from both sides. Founders who can’t put their phone down for a week after sending. Investors who open the deck, skim it in under a minute, and move on without a second thought.

This is the real dark stretch of any raise, the days between sending and hearing back. I know this is right for almost every founder out there, and almost none of them can see what’s happening on the other side.

Papermark recently pulled the numbers on this across 24,541 pitch decks and 358,672 real investor views: anonymised data from decks sent through their platform.

A few of their numbers are worth sitting with before you send your next one.

• 44% - the share of investors who actually reach your last slide, which is usually where the ask lives. More than half never get there.

• 79% of founders send the exact same link to every single investor. So when that link shows “26 views,” there’s no way to know who any of those 26 people were.

• Day 3 is when silence starts to mean something. A quarter of the decks are still unopened by then.

• The strongest signal in the whole dataset isn’t a warm call - it’s an investor coming back to reopen the deck, or downloading it. Only 18% of founders even have download tracking turned on, so most never see this signal at all.

Check your own deck against these before you read on: the typical opened deck gets 5 views, not the 26 you’d guess from the average. Total reading time across a deck runs about 18 minutes; decks that go on to close a round get almost double that, at 36 minutes.

The sweet spot for length is 9 to 16 pages, and the single best exact length is 12. And financials hold above-average attention when investors reach that slide, yet 6 out of 10 founders leave it out entirely.

None of this is a slide-writing problem. It’s the fact that your raise is happening in a room you can’t see into, and the only real fix is being able to see into it.

That’s the entire premise behind Papermark: tracked sharing and page-by-page analytics on a free plan and a full data room with NDA gating and watermarking once things get serious enough to need it. Skip the feature list, though.

Here’s what three very different founders actually did with that visibility -

Oscar tested 10 data rooms before he trusted one with his raise

Oscar Levy is building River Markets, a prime brokerage for prediction markets. Before he sent a single deck, he signed up for ten different data room tools and ran the identical test on each one: same files, shared the way he’d actually share them with a real investor.

Once he picked Papermark, he used the page-by-page analytics the way an operator reads a dashboard. Slides that consistently held investor attention stayed in the deck. Slides that got skipped got cut or rewritten.

Follow-ups went out the moment a repeat view came in, not on a fixed weekly cadence. His words: it was “extremely good to see which slides were eye-catching or boring.”

River Markets closed an $8.5M seed round led by Haun Ventures, with several other funds and angels from Google, Nvidia and Citadel participating.

George’s raise took 9 months. His data room outlived it entirely.

George Catinean co-founded LaDepozit, a self-storage operator. His raise wasn’t a two-week sprint - it was a 9-month private round, with site economics and the build-out plan shifting the entire time, and a team of ten people with zero spare hours for document logistics.

He started on Papermark’s open-source, self-hosted version, because he’s an engineer and figured he could run it himself. It didn’t stick.

In his words: “It’s worth paying for a good data room and not having a headache.” He moved to the paid product, used granular permissions to open more of the room as each investor’s conviction grew, and closed €1.5M.

The part I actually like most: the round closing didn’t end the data room’s job. LaDepozit still uses that same room today for investor updates and earnings calls. It’s not archived - it just changed jobs.

Martin needed to know the moment a conversation was actually starting

Martin Patz runs Differential Bio, a Munich biotech startup using AI and lab automation to de-risk bioprocess scale-up. Sensitive IP, sensitive data, and a small team meant he couldn’t afford to guess when to follow up. He set up Papermark’s webhooks to fire the instant an investor engaged with the materials, instead of checking a dashboard every hour.

He raised a €2M pre-seed round led by Ananda Impact Ventures and ReGen Ventures, and credits the tracking directly: “Papermark’s tracking features were instrumental in our fundraising success.”

Oscar’s fast rules for anyone raising right now

A few things Oscar said he’d tell any founder starting a raise today, and none of them are about the tool itself:

• Be clear about what you do and where the money goes. Ambiguity is the most expensive thing in a deck.

• Investor sequencing matters more than people admit. Who you talk to first changes the outcome of the round, not just the speed.

• A small angel check can lead to a much bigger intro. Who’s writing matters more than the size.

• Track what happens after you send. Knowing which slides actually landed is the difference between guessing and iterating.

What this actually looks like once you set it up

Twenty minutes. Most of it is just breaking the habit of attaching a PDF instead of sending a link.

• Free plan, no card needed. Send a tracked link, one per investor. Not a rule, just math: the second two investors share a link, “26 views” tells you nothing.

• Turn notifications on. 92% of founders in Papermark’s own data already do this. It’s the whole difference between a same-day follow-up and a guess two weeks later.

• Once the deck has real numbers, a cap table, unreleased traction, or you’re raising a bigger round and an investor asks for more than just the deck, it’s time to move up to a full data room. The NDA sits before the first page loads, gets timestamped and saved automatically- no chasing anyone for a signature. Watermark and per-investor folders live here too, same setup George used at LaDepozit.

• When the round closes, don’t archive it. LaDepozit’s room is still open right now, just doing investor updates instead of a raise.

None of this fixes a weak pitch, and it won’t warm up a cold list. But if your deck is good and your list is warm, this is the difference between wondering why someone went quiet and actually knowing.

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📜 DEEP DIVE

What are VCs really asking AI founders in due diligence?

Over the last few weeks, I’ve been talking to investors actively writing checks to AI companies. The ones doing $400K to $4M into companies that are nine months old - four people, one paying customer, a demo that works about 80% of the time.

I asked all of them the same question: what are you asking now that you weren’t asking a year ago?

Everyone had an answer ready. Several pulled up an actual list.

One described a call where the founder had answered everything on market, team and traction well - genuinely well and then got asked what happens to his product when the next frontier model ships the feature he’d built the company around. The founder said they’d move faster than the model companies. The partner wrote three words in the memo. The deal died in a week, and the founder still thinks he lost it on valuation.

Another now asks to see the eval harness run live in the meeting. She said maybe one in fifteen companies can do it.

This isn’t a stylistic shift. There is a whole new section of AI diligence; it has five distinct parts, and most founders raising right now have never seen the questions in it - let alone built the documents it expects.

So they gave me the questions - all of them.

Why it changed this fast

The money got easier, and the diligence got harder at once. Same cause.

AI crossed 50% of all pre-seed dollars in Q1 2026, up from roughly 30% a few years ago. When one category absorbs that much capital, a fund can’t differentiate on category anymore - only on which company inside it. The only lever left is forensics. Carta now puts the AI Series A threshold near $3.5M ARR, roughly triple where it sat three years ago, and ICONIQ puts average AI gross margin at 52% - inference alone eating around 23% of revenue.

Every investor knows that last number. So when you say “software margins,” they don’t hear a business model. They hear a claim that needs a spreadsheet behind it.

The actual shift: they stopped wanting answers

Old diligence tested your narrative. AI diligence tests your artefacts. Not what you say about data rights - the inventory. Not your accuracy claim - the harness. Not your margin story - the waterfall.

Which is why founders keep failing questions they could answer fine in conversation. The question isn’t “what’s your gross margin?” It’s “open the model.” And the tell isn’t a wrong number - it’s the pause before someone says they’ll follow up over email.

The follow-up arrives four days later. By then the memo is written.

What’s inside today’s deep dive -

All 21 questions, in five blocks. For each: how it gets asked, what’s actually being tested, the answer that quietly ends the conversation, how to answer it well, and the document you’re expected to open.

• Model dependency - 4 questions. The block that kills the most deals. Founders answer it with confidence, and the confident answer is the fatal one.

• Data rights - 4 questions. Where the moat you described twenty minutes earlier quietly evaporates.

• Evaluation - 5 questions. One in fifteen companies can do the thing asked here. The other fourteen find out live.

• Inference economics - 5 questions. There’s a specific month coming that most founders haven’t modelled.

• Deployment and exposure - 3 questions. Everyone read the AI Acts headline. Almost everyone read it wrong.

Plus the self-test with scoring, a stage triage for pre-seed versus Series A, the data room structure to build before your first meeting, and the one move that flips diligence from evaluation to closing.

Before you start

Don’t dump the artefacts. The point of building the file isn’t to send it - it’s that having built it changes what comes out of your mouth. Founders who’ve done the work answer in specifics. Founders who haven’t answer in adjectives.

Read more

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📜 DEEP DIVE

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👋 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 :

• Are higher-valuation YC startups better bets? Here’s what the data shows.

• Do VCs actually reward profitability?

Frameworks & insightful posts :

• Why are so many SaaS companies struggling to grow, even after adding AI?

• What really happens after you pitch a VC and leave the room?

• Are you calculating ARR wrong if customers pay based on usage?

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🧠 Big idea + report of the week

Are higher-valuation YC startups better bets? Here’s what the data shows.

Seed investors are usually taught one simple rule: get into great companies as early and cheaply as possible.

But new data from Rebel Fund suggests that, at least inside Y Combinator, paying a higher price can sometimes be a signal that you are looking at a stronger company.

Jared Heyman and Rebel Fund analysed 642 YC startups from the 2021 to 2024 batches where they had Demo Day SAFE valuation-cap data. The relationship between price and near-term startup success was surprisingly strong.

• Higher-priced YC startups were 3x more likely to reach Series A+: 25% of companies priced above their batch’s median valuation reached Series A or beyond, compared with just 8% of companies below the median.

• They were also far less likely to fail: 16% of below-median companies had shut down, versus only 6% of companies priced above the median.

• The relationship persists across valuation ranges: When Rebel plotted Demo Day SAFE caps against Series A+ graduation, the probability of reaching Series A increased consistently as valuation caps increased.

There is another important trend behind this. YC itself is getting much more expensive. Among companies Rebel interviewed, the median SAFE cap increased from roughly $15M in W21 to $40M by P26, nearly tripling in five years.

So why might expensive startups perform better?

The valuation itself probably isn’t causing better outcomes. Strong teams, traction, large markets and investor demand can push valuations higher. In other words, price may be capturing information that investors have already discovered about the company.

And this is where investors need to be careful.

Reaching Series A is not the same as generating a great venture return. A company bought at a $40M valuation that eventually exits for $200M could produce a worse return than a $10M company that reaches the same outcome. Rebel’s data shows that expensive YC startups have been safer, but it does not yet prove that they produce better long-term returns.

That creates an interesting tension for seed investors: a high valuation shouldn’t automatically be a reason to walk away, but neither should it be mistaken for quality.

The real opportunity is still finding the company where the fundamentals are significantly better than what its current price implies.

Do VCs actually reward profitability? Here’s what the data shows.

CFOs at VC-backed startups hear the same thing in almost every board meeting: get to profitability, tighten the burn, show discipline. It’s been the standard advice since the market reset of 2022.

But new data from Silicon Valley Bank’s H2 2026 State of the Markets report suggests that advice and what actually gets funded are two very different things.

SVB tracked median revenue growth and profit margin for VC-backed tech companies doing more than $10M in annual revenue, split by sector, from Q1 2022 through Q2 2026- comparing the typical company in each sector against the subset that had just closed a new round.

• Companies that recently raised capital look nothing like the median company in their own sector. They’re running meaningfully higher revenue growth and meaningfully deeper losses - the opposite of the “get efficient” advice most founders are hearing from their own boards.

• Since Q1 2022, the broader population in every sector - fintech, enterprise, frontier tech, consumer internet - has moved steadily toward profitability, margins improving quarter after quarter as companies cut costs to extend runway.

• But that same cost-cutting has pulled growth down with it. And growth, not margin, is what’s actually opening the door to a new check right now.

There’s a trap hiding in that gap.

The companies that followed the standard advice in 2022–2023 - cutting burn, tightening spend, pushing toward breakeven - are now the ones stuck.

They hit the target their board asked for, and it still isn’t enough to raise on.

Meanwhile, companies still burning hard but growing fast are the ones closing rounds.

So why are VCs funding exactly the opposite of what they say they want?

Profitability advice isn’t wrong for company health - it’s genuinely the safer path if capital dries up. But it isn’t what’s winning term sheets in 2026. Capital is chasing growth right now, especially anything that looks AI-native, and a clean set of margins doesn’t compensate for a soft growth line.

And this is where founders need to be careful.

Fixing your burn rate is still the right call if your goal is survival without a raise. It is not the same move as making yourself fundable. Those used to be the same advice. Right now, they aren’t.

The real risk isn’t running an inefficient business. It’s optimising for the metric your board keeps asking about while missing the one that actually decides whether you raise your next round.

Is your startup actually AI-Native? The valuation data says it matters.

SVB Bank analysed roughly 9,000 VC-backed companies that describe themselves using AI language and found something striking: 42% showed little evidence that AI was actually core to what they were building.

But the more interesting finding is what happened to their valuations.

Companies were grouped into Low, Medium and High AI relevance based on whether their descriptions contained signals of real technical AI development rather than generic phrases like “AI-powered.”

In 2026:

• High AI relevance: 349% valuation premium versus the median US tech company

• Medium relevance: 135% premium

• Low relevance: 21% discount

That creates a 370-percentage-point gap between companies perceived as genuinely AI-native and those with weak AI relevance.

And that gap has exploded. In 2022, High-relevance companies traded at roughly a 20% premium while Low-relevance companies were at a 26% discount, a spread of only 46 percentage points.

Four years later, the spread is more than 8x larger.

There is another interesting shift happening underneath this.

In 2016, 44% of AI-labeled companies fell into the High-relevance category and only 16% were Low relevance. By 2026, High relevance had fallen to 34%, while Low relevance had grown to 42%.

In other words, more startups are calling themselves AI companies, while a smaller percentage appear to have AI at the core of what they actually build.

The important caveat is that this score isn’t measuring whether a startup is good or bad. An application company built on third-party models can still become an exceptional business.

What the data suggests is that investors are becoming much better at distinguishing between companies doing substantive AI work and companies simply adopting the language.

For founders, that makes positioning surprisingly important. Calling yourself “AI-native” or “AI-powered” isn’t necessarily helping if the rest of your company description can’t explain what makes the technology meaningfully different.

As the AI label becomes more common, specificity may be becoming more valuable than the label itself.

📄 Must Read Post

SOMETHING MORE

🧩 Frameworks & insightful posts

Why are so many SaaS companies struggling to grow, even after adding AI?

Almost every B2B software company now has AI features. Yet growth hasn’t suddenly come back.

That’s the uncomfortable part. You can ship copilots, agents, AI search, automation, and new pricing tiers, but if the rest of the company still operates like a 2022 SaaS business, AI alone probably won’t change the trajectory.

Recently, Jason Lemkin shared a useful breakdown of what this looks like across public B2B software companies, and the numbers are pretty revealing.

Out of 58 public B2B companies with available data:

• 18 are growing below 10%

• 23 are growing between 10% and 20%

• 11 are growing between 20% and 30%

• Only 6 are growing above 30%

So 41 of 58 companies are growing below 20%.

And the valuation penalty is significant. Companies growing 20% to 30% trade around a median 5.5x revenue multiple. At 10% to 20%, that falls to 3.1x. Below 10%, it drops to roughly 1.9x.

The bigger thing is that adding AI features and rebuilding a company around AI are completely different things.

A real rebuild usually touches four areas:

1. Product: AI can’t just sit beside the old workflow. Eventually it needs to remove or fundamentally change parts of that workflow.

2. Pricing: If AI lets 25 employees do the work of 40 but you still charge per seat, your product may actually shrink its own billing base.

3. Data: Agents need clean context. Stale CRM records, duplicate data, missing fields, and undocumented processes become much bigger problems once AI starts acting on them.

4. GTM: Selling AI outcomes is different from selling software access. The buyer, pricing conversation, implementation process, and value proposition can all change.

Fin, formerly Intercom, is a useful example of how deep this can go.

The company started betting heavily on AI support around 2023. By 2026, it had built Fin into an agent approaching $100M ARR, with 30,000+ customers and roughly 76% of support volume reportedly resolved end to end. Eventually, the company even renamed itself around the AI product.

That wasn’t an AI feature launch.

It was roughly four years of rebuilding the product, pricing, positioning, and company around a different way of delivering value.

That’s probably why many established SaaS companies feel stuck somewhere in the middle right now. They’ve shipped AI, but they haven’t rebuilt everything around what AI changes.

The useful question for founders isn’t “Do we have AI?”

It’s: If we started this company today with AI available from day one, would we still build, price, sell, and operate it the same way?

If the answer is no, that gap is probably where the real work begins.

What really happens after you pitch a VC and leave the room?

A founder can walk out of a VC meeting feeling like everything went well. The partner liked the product, asked detailed questions, nodded at the traction numbers and ended with, “We’re excited to keep talking.”

Then nothing happens for a week.

What founders rarely see is that the fundraising process keeps moving without them. Within hours, someone at the fund may be writing an internal memo that compresses your company, team, traction, market and risks into a few pages.

That document can become more important than the pitch itself because many of the people ultimately voting on the investment were never in the original meeting.

We recently broke down what happens during those first 48 hours inside a venture firm, and the numbers explain why this stage matters so much.

A large study of 885 institutional VCs found that for every investment eventually made, a firm typically:

• Looks at roughly 100 opportunities

• Meets about 28 founding teams

• Discusses around 10 at the partner level

• Moves fewer than 5 into serious diligence

• Negotiates roughly 1.7 term sheets

• Invests in 1

So simply getting a meeting means very little. The bigger battle is getting someone inside the fund willing to take your company into the partner meeting and defend it.

And that is where the memo matters.

Most investment write-ups cover roughly the same things: the company and round, founding team, product and wedge, market, traction, risks, and deal terms. But the important part is that the memo is not a transcript of your pitch. It is someone else’s interpretation of it.

If you struggled to explain why your product is different, that can become “unclear wedge.”

If you dismissed a competitor too casually, that may become a risk.

If your ARR definition sounded questionable, the number may get discounted before the partnership ever sees it.

And if your founder-market fit was obvious to you but never clearly articulated, the person writing the memo now has to build that argument for you.

That leads to a useful fundraising principle:

Don’t just make your startup impressive. Make it easy for an investor to explain when you’re not in the room.

There are a few practical ways founders can do this.

1. Send a short post-meeting memo.

Within 24 hours, recap what the company does, why your team is unusually suited to build it, your wedge, precise traction, why now, and what you are raising. Not a generic thank-you email. Give them language they can actually reuse internally.

2. Give your champion answers to the obvious attacks.

Think about the three questions a skeptical partner is most likely to ask. “Why won’t OpenAI build this?” “Why is this market large enough?” “Why does this team win?” Send concise, evidence-backed answers before those questions get asked without you there.

3. Treat silence as information.

A progressing deal usually creates activity: more meetings, data requests, references, customer calls, diligence. Silence after a good meeting can simply mean nobody has decided to spend their internal credibility championing the deal.

One of the most useful reframes is that your pitch is raw material for the internal story investors tell about your company.

You may spend weeks preparing the deck, but the final investment decision can come down to a short discussion where someone else has ten minutes to explain why the company should exist, why you should win and why the risks are worth taking.

The better you make that story before leaving the room, the less gets lost when your pitch becomes someone else’s memo. (Read more here)

Are you calculating ARR wrong if customers pay based on usage?

A lot of AI startups still report ARR using the old SaaS formula: Current monthly revenue × 12 = ARR.

That worked reasonably well when customers paid the same subscription every month.

But AI software is increasingly usage-based. Customers pay for tokens, API calls, compute, overages, or actual consumption. And usage can move sharply from one month to another.

That makes the old formula surprisingly dangerous.

Recently, one breakdown showed why: if a customer has a large usage spike in November, multiplying that month by 12 can make the company look like it suddenly added hundreds of thousands of dollars in recurring revenue.

Then December arrives, usage returns to normal, and ARR appears to collapse. Nothing meaningful happened to the underlying business. The metric created both the growth and the slowdown.

A simple example:

• 40 customers each guarantee $2,000/month = $80K committed monthly revenue

• Normal additional usage adds around $24K/month

• One customer suddenly runs a large project and creates an extra $11.5K in one month

That month might produce roughly $113.5K in revenue. Annualise it directly, and you get about $1.36M ARR.

But the more useful view is:

• $960K committed ARR

• Around $288K annualised variable revenue based on a recent average

• Roughly $1.25M sustainable run-rate

The difference comes from treating one unusual month as if it will repeat twelve times.

That matters beyond fundraising. Founders use ARR to make hiring decisions, calculate runway, judge burn efficiency, and set growth targets. An inflated number can quietly convince you that the company can afford spending it actually cannot.

The better approach is to separate revenue into two buckets:

1. Committed ARR

Revenue customers are contractually required to pay, regardless of usage.

Think subscription fees, minimum commitments, or contracted floors.

2. Variable usage run-rate

Everything driven by actual consumption.

Instead of annualising the latest month, use a rolling average, such as the previous three months, to smooth unusual spikes.

So rather than saying:

“We’re at $1.36M ARR.”

You can say:

“We have $960K in committed ARR plus roughly $288K in annualized usage revenue based on the trailing three-month average.”

That gives investors, boards, and founders a much clearer picture of what is actually recurring. There’s another useful insight here: usage revenue often becomes more predictable as the customer base grows.

One customer may spike in November while another slows down. Across enough customers, those movements can partially cancel each other out. So volatility is often most painful when the customer base is still small or concentrated.

A simple reporting rule for usage-based startups:

• Start with contracted minimum revenue

• Separate all usage above that minimum

• Average variable usage across several months

• Report committed and variable revenue separately

• Don’t change hiring or spending plans because of one unusually strong usage month

And if you’re fundraising, go one step further: show whether usage from previous customer cohorts is still there months later.

For usage-based AI companies, investors increasingly care less about one impressive annualised number and more about whether the consumption behind it is durable.

So -

MRR × 12 tells you how big this month would be if it repeated perfectly. It does not automatically tell you how much recurring revenue your business actually has.

For traditional SaaS, those numbers can be close. For usage-heavy AI companies, they can be very different.

You can access the Excel sheet here.

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📜 DEEP DIVE

Could calling your startup 'AI-Native' be hurting your valuation? The data might surprise you.

I’ve sat in on enough pitch practice sessions this year, and talked to enough investors after they’ve passed on a deal, to notice the same gap showing up every time.

The founder says “AI-native” on slide one with total confidence. The investor, three slides later, is already mentally sorting the company into a bucket the founder doesn’t know exists, and that bucket, not the slide, is what the term sheet gets priced against.

You’ve probably done this without knowing it. You put “AI-native” on your deck eighteen months ago because every operating partner told you to. It worked. It’s still working, on average - but “on average” is hiding the fact that the same three words now sort you into a 3x company or a 30x company, and nothing on your title slide tells you which.

The premium is real; that part hasn’t changed

Start with the number that’s still true. On Carta’s H1 2026 data, AI companies at seed are raising similar cash amounts to non-AI peers but closing at roughly 50% higher valuations - meaningfully less dilution for the same check.

That premium compresses to around 30% right at Series A, the exact round where investors stop taking the label at face value, before widening back out to 193% by Series E+ once a company has actually proven it deserves the tier.

That’s the number every founder I talk to this year has memorised. It’s also the number making a lot of them careless - because the number that’s dropping, not the number that’s still growing, is the one sitting right in front of them at their next round.

The correction is happening underneath the average

Here’s what the aggregate number hides: the AI premium isn’t one number anymore. It’s split into three, and which one applies to you has nothing to do with whether you call yourself AI-native.

• Commodity AI wrappers - thin interfaces on top of a foundation model API, no proprietary data, no defensible workflow - are trading at 3x to 8x revenue.

• Vertical AI products with sticky, proprietary data are trading at 10x to 20x.

• Companies with real IP plus proprietary data are commanding 25x to 40x. Same label, same slide - a 5x spread in what the market will actually pay.

The compression is worse the further you go. At seed, a startup with no protectable IP is already taking a 20- 30% markdown against a comparable company that has it - the difference between a 22x and a 16x multiple on paper.

By Series A, that penalty widens to 30–40%, because that’s the round where investors stop taking the AI-native label at face value and start running a formal defensibility check on it.

Investors know they’ve been burned doing the opposite.

None of this shows up as a rejection. It shows up as a number on a term sheet lower than the one your friend at a comparable company got - and neither of you will ever see the memo that explains why.

What I want to show you next is the actual checklist investors are running to sort you into one of those three buckets, the self-check I built so you can score yourself against it before your next raise, and - the part that actually moves your number - what to go build in the next 90 days to move buckets.

The test nobody shows you

I asked around this quarter - associates at three funds, one partner who runs technical diligence calls specifically to catch thin AI wrappers and the scoring logic is more consistent than founders assume. It isn’t vibes. It’s four weighted categories that produce the multiple your term sheet gets priced against before you’re in the room.

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.

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📜 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

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 :

• Is AI creating a new era of VC herding?

• Are we measuring the “Best VCs” the wrong way?

Frameworks & insightful posts :

• Why is SaaS’s “Per Seat” pricing starting to break in the AI era?

• Is the AI company you’re investing in actually defensible or just a wrapper with ARR?

• Before negotiating your seed valuation, do this VC’s fund math.

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Is AI creating a new era of VC herding?

With hundreds of billions flowing into AI, venture capital increasingly looks like a market where the biggest investors are all chasing the same handful of companies.

The same funds appear across massive AI rounds. Capital is concentrating around a small group of perceived winners. But is the entire VC industry really becoming more crowded around the same bets?

SVB looked at this in its State of the Markets H2 2026 report, using Foresight cap table and ownership data.

The numbers show something more nuanced: herding is clearly increasing at the very top, but it is still relatively uncommon across the broader venture market.

Among the five largest US VC-backed startups in 2019, just 9% of known investors had invested in two of the five companies.

For the five largest startups in 2026, that number has jumped to 23%.

AI makes the concentration particularly striking. The five largest AI startups in the analysis have collectively raised around $350 billion and reached roughly $2 trillion in aggregate valuation. When companies reach this scale, many large investors want exposure to the same perceived category leaders.

But this isn’t necessarily a new VC behaviour.

A similar pattern appeared during the consumer technology boom. In 2016, around 20% of investors in the five largest startups had invested in two of them, with capital clustering around companies such as Uber, Airbnb, Snap and WeWork.

So the pattern may be less “VC suddenly became a herd” and more “capital concentrates around perceived winners during major technology cycles.”

The broader startup market looks very different.

SVB examined roughly 1,600 startups that raised funding since 2023. Only 167, around 10% of the sample, shared five or more institutional investors with another company.

Even among startups backed by one of the ten most active investors, 74% had only one of those top investors on their cap table. Just 6% had more than three.

There is a small cluster where genuine concentration appears much stronger. Twenty-four companies shared at least five institutional investors with ten or more other startups. And 38% of these companies were already Series B or later.

That makes sense. As startups mature, rounds become larger and the universe of investors capable of writing $50 million, $100 million or larger checks becomes much smaller. The same large funds naturally begin appearing together.

The important distinction is therefore between capital concentration and industry-wide herding.

VC doesn’t appear to be one giant herd. Instead, the market increasingly looks like a funnel. Thousands of startups begin with relatively diverse investor bases, but as a small number emerge as potential category leaders, capital starts converging around them.

For founders, this matters because the investors appearing on OpenAI-sized rounds are not necessarily representative of the fundraising market you are operating in. Most startups still compete across a much more fragmented investor landscape.

For investors, there is a harder question hiding inside the data: when 23% of investors start overlapping around the same five companies, are they independently identifying exceptional businesses, or is growing consensus itself making those companies feel safer to back?

That distinction only becomes obvious after the returns arrive.

Are we measuring the “Best VCs” the wrong way?

Every time a ranking of the “best VC firms” comes out, people immediately look for the familiar names.

If Sequoia, Andreessen Horowitz, Benchmark or other major firms aren’t near the top, people question the ranking. But there is a deeper problem: what exactly does “best” mean in venture capital?

Dan Grey explored this in his piece on the futility of ranking VC firms, using Ilya Strebulaev and Blake Jackson’s 2026 venture ranking as a starting point. The interesting part comes when the ranking is adjusted for how much capital each firm actually deploys.

The original venture score rewards accumulated outcomes. Bigger firms have more capital, make more investments and therefore have more opportunities to produce major portfolio companies.

But once the score is divided by estimated capital deployed, effectively asking how much venture impact a firm generates per dollar of capital, the leaderboard changes dramatically.

• SV Angel moves from #31 to #1.

• Ribbit Capital moves from #18 to #2.

• Benchmark moves from #14 to #3.

• First Round jumps from #45 to #9, a 36-place improvement.

Meanwhile, some of the largest firms move sharply in the opposite direction.

• Andreessen Horowitz falls from #2 to #32, a 30-place drop.

• Thrive falls from #8 to #34.

• Lightspeed moves from #7 to #27.

• Tiger Global drops from #5 to #22.

Only three firms remain in the top ten under both approaches: Sequoia, Index Ventures and DST Global.

That difference exposes an important problem with VC rankings.

Scale and investment quality are not necessarily the same thing.

Imagine Fund A deploys $1 billion and produces $5 billion of value, while Fund B deploys $100 million and produces $1 billion. Fund A created more absolute value, but Fund B generated much more value relative to the capital it needed.

A ranking based on absolute outcomes could favour Fund A. An efficiency-based ranking could favour Fund B.

Neither ranking is automatically wrong. They are answering different questions.

This becomes even more important because actual VC performance is difficult for outsiders to measure. DPI, which tells you how much cash a fund has actually returned to investors relative to the money invested, is generally private.

Public rankings therefore rely on things we can observe: capital raised, AUM, deals completed, portfolio valuations, follow-on funding, exits, board seats and other proxies.

That can create a structural advantage for large, active firms.

For example, Grey notes that 50% of TIME’s 2026 VC ranking methodology explicitly relates to scale, including capital raised, fundraising momentum, dry powder, AUM and deal activity. Another 40% covers performance-related measures that include private valuations and follow-on capital, rather than simply measuring cash returned to LPs.

This doesn’t mean large firms are bad investors. It means a ranking can partly answer “who is biggest and most influential?” while readers interpret it as “who generates the best investment returns?”

And the efficiency-adjusted ranking has limitations too. It starts with the firms already included in the original dataset. A small seed or micro-VC with extraordinary returns could therefore be missing entirely.

There is a broader lesson here for anyone evaluating venture funds.

Don’t start with the ranking. Start with the methodology.

Ask what is actually being rewarded: capital deployed, famous portfolio companies, valuation creation, exits, DPI, fund multiples, consistency or efficiency.

A firm can be excellent at building a huge venture platform without being the most capital-efficient investor. Another can quietly generate exceptional returns from a small fund while barely appearing on mainstream rankings.

The most interesting number on this chart might therefore be 3.

Out of all the firms shown, only Sequoia, Index Ventures and DST Global remain, top-ten investors when measured both by raw venture score and capital efficiency.

That ability to combine scale with efficiency may be a much harder achievement than simply topping either ranking alone.

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SOMETHING MORE

🧩 Frameworks & insightful posts

Why is SaaS’s “Per Seat” pricing starting to break in the AI era?

For decades, SaaS had a beautifully simple business model: charge customers based on how many employees use the software.

AI is making that model much harder to justify.

Jason Lemkin argues that the problem goes beyond AI. Software renewal prices have already risen aggressively, companies are consolidating vendors, and AI spending is increasingly competing with existing software budgets.

Some striking numbers:

• Enterprise software renewal increases reached 16.4% in June 2026, versus roughly 2.7% G7 inflation.

• 79% of IT leaders experienced a price increase at renewal.

• 78% faced unexpected AI or consumption-related charges.

• 45% of CIOs say AI budgets are coming from existing software budgets rather than entirely new money.

• 54% are actively consolidating vendors.

But the bigger problem is that seats no longer represent value very well.

Imagine a customer-support team has 40 employees and your software helps them process 3x more tickets. Under seat pricing, your revenue barely changes.

Even worse: if AI lets the company reduce that team from 40 people to 25, your product is creating more value while your SaaS revenue actually falls.

That’s why Lemkin sees three pricing models becoming increasingly important:

• Consumption pricing:

Customers pay for what they use - tokens, API calls, credits, searches, tasks or compute. It works when value increases with usage, but vendors need spending caps and predictable commitments to prevent bill shock.

• Resolution pricing:

Customers pay when the software successfully completes a clearly defined task. For example, an AI support agent might charge only when it actually resolves a customer issue. This is easier to measure because the result is binary: resolved or not resolved.

• Outcome pricing:

Customers pay based on the business result created - revenue generated, costs saved, claims processed, deals completed, etc. It aligns price most closely with value, but attribution and measurement can become difficult.

For established SaaS companies, the transition probably won’t mean eliminating subscriptions overnight. A more realistic model is platform fee + usage/resolution/outcome pricing, introduced gradually.

The larger shift is simple:

SaaS used to charge for access to software. AI software increasingly needs to charge for work performed by software.

That changes not only pricing, but how SaaS companies measure value, forecast revenue, design products, and sell to enterprises.

Is the AI company you’re investing in actually defensible or just a wrapper with ARR?

AI companies can look incredible in a spreadsheet. Strong ARR. Fast growth. Good customer references. Healthy margins.

But Mark Ajzenstadt makes a useful point for anyone evaluating AI businesses:

Those numbers tell you whether customers are paying. They don’t tell you what the company actually built.

He shared one diligence example where an AI company claimed to have a “proprietary AI platform,” had $4.2M ARR growing 40% YoY, and was asking roughly 12x revenue.

Even most AI startups calculate their ARR in a very wrong way.

Then the codebase was opened.

Behind the deck was GPT-4o, a system prompt, a React frontend, and roughly 600 lines of glue code. No proprietary model. No meaningful proprietary data.

The revenue was real. The defensibility wasn’t.

For AI companies, four risks deserve much more attention during diligence:

1. Model dependency: How much of the product depends on OpenAI, Anthropic, Google or another third-party model? If the model can be swapped in an afternoon, the underlying model probably isn’t the moat.

2. Data moat: Does the company actually own unique data that improves the product? Is the provenance documented? Could a competitor reproduce similar performance using public data?

3. Talent concentration: Does the core AI system depend on one or two engineers who understand how everything works? If they leave, can someone else retrain, maintain and improve the system?

4. Substitution risk: Could a competent team rebuild the core product using frontier APIs and modern coding tools in weeks or even days? If yes, the valuation should probably reflect that.

A simple way to think about it is to score each category from 0 to 10, where 10 means very high risk.

A company might have spectacular revenue growth while simultaneously scoring 9/10 on model dependency, 8/10 on substitution risk and 9/10 on talent concentration. That doesn’t necessarily make it a bad business.

But it makes it a very different asset from a company with proprietary data, deeply embedded workflows and technology that is genuinely difficult to reproduce.

The broader lesson is useful beyond PE and M&A:

ARR tells you whether customers value the product today. Technical diligence tells you whether competitors can erase that value tomorrow.

For traditional SaaS, financial diligence could tell you much of the story. With AI-native companies, investors increasingly need to understand the codebase, data, model dependencies and workflow advantage before deciding what the ARR is actually worth.

The spreadsheet is still important.

It just shouldn’t be the whole diligence process.

Before negotiating your seed valuation, do this VC’s fund math.

Founders often hear some version of: “We like the company, but we’re not there on valuation.”

It sounds like feedback on the business. Maybe the traction isn’t strong enough. Maybe the market story needs work. Maybe you need another few months of growth.

But sometimes the answer was already determined before the pitch started.

Take a $28M seed fund. If it typically writes a $600K initial cheque and wants roughly 6-8% ownership, the math puts its workable post-money valuation somewhere around $7.5M–$10M.

So if you’re raising at an $18M post, there may be nothing to negotiate. The fund simply can’t write its normal cheque and get enough ownership for its portfolio model to work.

That’s the useful reframe from this Venture Curator deep dive:

Fund size → typical cheque → target ownership → maximum workable valuation.

And today, that matters more because seed pricing has moved up quickly. Carta’s median seed post-money is around $24M. A $50M fund writing a large $1.5M cheque gets only 6.25% at that valuation.

If its model needs 8–10%, the problem isn’t necessarily your company. It’s fund economics and the market price simply don’t match.

This creates an important filter for founders before starting a raise.

1. Work backwards from your round size - If you need $3M and are comfortable selling roughly 20%, you’re implicitly targeting about a $15M post-money valuation.

2. Understand what size lead can support that - The lead may need to write $1.5M–$2M of the round. A small seed fund built around $500K checks is probably better suited as a follower, no matter how much they like you.

3. Ask the fund math early - One useful question on the first call is: “What’s your typical initial check, and what ownership do you usually target?” If they say $500K and 7%, their natural valuation range is roughly $7M post. You now know whether continuing the process makes sense.

There’s another wrinkle founders often overlook: round size itself helps set valuation.

If a fund wants 15% and you tell them you’re raising $3M, the implied post-money is $20M. Raise $1.5M with the same investor and ownership target, and the implied post becomes $10M.

That means the better sequence is often:

Milestone you need to reach → capital required → acceptable dilution → implied valuation → funds capable of leading it.

Not: pick an attractive valuation first and then try to convince every VC that it’s justified.

There’s also a quieter dilution lever worth watching: the option pool. A lead may ask for a 10–15% employee pool to be created pre-money, meaning that dilution comes from the existing shareholders before the investment lands. Founders can spend days negotiating another $1M of headline valuation while giving away more economic value through an oversized pool.

So, before pitching a seed fund, understand its fund size, recent cheque sizes, whether it actually leads rounds, and the ownership it typically targets.

You may discover that some of the investors on your list were never capable of meeting your round on the terms you wanted.

That’s useful information to have before spending six weeks trying to change their mind. Read in more detail here.

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Why your CV keeps getting ignored for VC jobs (and how to fix it): How to reframe your experience into clear venture signals, add an investing exposure section, and create proof of work that shows you are already doing the job before anyone hires you.

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👋 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.

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📜 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.

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