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Big idea + report of the week :

• AI Hiring Paradox: What does Ramp’s study of 21,000 companies reveal about AI and hiring?

• Why are even great startups struggling to raise Series A?

• Why most Private Equity firms still aren’t seeing returns from AI?

Frameworks & insightful posts :

• How to kill churn: A playbook from a founder who built a $50M SaaS business.

• Could an AI crash actually create the next golden age?

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🧠 Big idea + report of the week

AI Hiring Paradox: What does Ramp's study of 21,000 companies reveal about AI and hiring?

One of the biggest fears around AI is that companies will use it to replace workers.

A new study suggests the opposite may be happening -at least for companies that are adopting AI most aggressively.

Researchers from Ramp and Revelio Labs analysed AI spending and workforce data across 21,000+ U.S. businesses to understand what actually happens after companies roll out AI.

Their conclusion: companies that invest heavily in AI don’t shrink - they grow.

Companies with the highest AI adoption increased total headcount by 10.2% over two years, while low AI adopters saw no statistically significant change in hiring.

Entry-level hiring grew even faster. High AI adopters expanded entry-level headcount by 12% over two years, suggesting firms are recruiting new graduates who already know how to work with AI rather than eliminating junior roles.

The hiring effect wasn’t immediate. Most companies saw little change during the first 6-12 months, with workforce growth accelerating only after employees had time to integrate AI into daily workflows.

The researchers were careful to account for an important criticism: fast-growing companies naturally adopt new technology earlier.

Instead of comparing AI companies with everyone else, they matched early adopters against businesses following nearly identical growth paths that simply hadn’t adopted AI yet. Even after controlling for those differences, the hiring advantage remained.

The study also found AI adoption spreads through networks.

VC-backed companies and firms connected to strong technology ecosystems were far more likely to adopt AI than peers in similar industries. Interestingly, small businesses adopted AI less frequently, but when they did, they spent more per employee than larger companies - suggesting AI can unlock capabilities that smaller teams previously couldn’t afford.

Rather than replacing workers outright, the evidence points toward a different pattern: companies using AI effectively become more productive, expand faster, and then hire more people to pursue opportunities that weren’t previously possible.

Why are even great startups struggling to raise Series A?

Raising a seed round used to feel like the hardest part of building a startup. Today, many founders are discovering that Series A has become the real filter.

Even companies with millions in revenue and healthy growth are struggling to raise, while investor attention remains concentrated on AI, defence, robotics and a handful of other hot sectors.

Recent data from Carta, combined with observations from FPV Ventures partner Nikunj Kothari, shows just how much the market has changed.

The odds of reaching Series A have fallen sharply. Among the strongest seed cohorts, roughly 30-35% of startups reached Series A within three years. Newer cohorts are tracking below 20%, making graduation from seed increasingly difficult.

The seed ecosystem itself is shrinking. The number of active seed-stage startups peaked in Q3 2022 and has steadily declined as acquisitions, shutdowns and Series A graduations now outpace the creation of new seed companies.

Strong businesses are still getting funded - but on tougher terms. Founders building outside today’s hottest categories are often raising flat rounds or modest step-ups, even with a few million dollars in ARR. In today’s market, securing a quality Series A investor may matter far more than maximising valuation.

Many founders compare themselves to AI startups raising at massive valuations with little revenue. But those deals represent a small part of the market. Most venture-backed companies are operating in a much tougher fundraising environment where investors are becoming increasingly selective.

The reality is that Series A is no longer just a milestone - it’s becoming the biggest bottleneck in venture. Founders who survive this stage are doing so by building durable businesses, demonstrating clear traction and optimising for long-term company building rather than headline valuations.

Why most Private Equity firms still aren't seeing returns from AI?

AI has quickly become one of private equity’s biggest priorities, with firms encouraging portfolio companies to adopt it in the hope of improving efficiency, accelerating growth and ultimately increasing exit valuations.

But according to a new PitchBook report, most PE firms are still in the experimentation phase. While AI adoption is widespread, very few firms can point to meaningful financial results.

The biggest obstacle isn’t the technology - it’s implementation.

Data readiness was cited as the largest barrier to AI adoption across portfolio companies, followed by unclear ROI, limited management bandwidth and talent shortages. Regulatory concerns ranked much lower, suggesting execution is a much bigger challenge than compliance.

Most AI projects are saving time, not creating revenue.

Today’s most common use cases include financial planning, reporting, contract management and other back-office workflows. These improve productivity, but only 8% of firms say AI is already making a material impact on EBITDA or strengthening their exit story.

The biggest opportunity lies in revenue generation.

Bain Capital shared examples of using AI inside industrial and aerospace businesses to automate order processing, improve pricing decisions and respond to customers faster. Rather than simply reducing costs, these applications are designed to win more customers, grow revenue and expand margins over time.

The next phase of AI adoption won’t be defined by who uses the most AI tools, but by who integrates AI into core business operations in ways that directly improve financial performance. For most private equity firms, that transformation is still underway.

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🧩 Frameworks & insightful posts

How to kill churn: A playbook from a founder who built a $50M SaaS business.

Every founder obsesses over growth. More traffic. More signups. More revenue.

But after scaling VEED to $50M in annual revenue, founder Sabba Keynejad says the metric that matters most isn’t acquisition - it’s churn. If customers leave as fast as they arrive, growth becomes a treadmill that’s almost impossible to sustain.

After spending nearly a decade studying churn across consumer apps, SMB software, enterprise products, and APIs, he shared the framework that changed how he thinks about building software businesses.

1. Your market matters more than your product

One of the biggest misconceptions is that churn is mainly a product problem.

In reality, it’s often a market problem.

A product people use every working day naturally becomes sticky. A product someone opens once to create a birthday invitation probably won’t.

That’s why different categories have completely different expectations:

• Enterprise SaaS: under 3% monthly churn is considered healthy.

• SMB SaaS: under 6% monthly churn is a strong benchmark.

• Consumer products: under 12% monthly churn is often acceptable.

He also shared one number every founder should remember:

The math compounds quickly. A business losing 10% of customers every month replaces roughly 70% of its customer base every year, meaning growth increasingly becomes about replacing lost users instead of creating new value.

2. Your average churn rate hides your biggest problem

A single churn number rarely tells the full story. Imagine your company reports 10% monthly churn.

That sounds useful - but what if one customer segment churns at 5%, while another churns at 20%?

The average hides where the real problem lives.

Instead of trying to reduce churn across the board, he recommends identifying the customers who naturally stick around the longest.

Ask questions during onboarding:

• What role do they have?

• What company size are they from?

• What are they trying to accomplish?

Then compare those answers against long-term retention.

The goal becomes surprisingly simple:

Find your stickiest users. Build for them.

Acquire more people like them.

He shared that he personally onboarded customers until VEED reached $6M ARR, speaking with 10-15 users every day instead of relying entirely on dashboards. Those conversations revealed which customers became long-term users and which were never likely to stay.

3. Activation is usually a bigger churn lever than retention

Many founders spend months trying to reduce churn when the real issue happened much earlier.

Users never experienced the product’s value.

Every product has an “aha moment” - the point where someone understands why they’ll keep using it.

Anything that delays that moment increases churn:

• Asking for credit card details too early.

• Complicated onboarding.

• Long surveys.

• Confusing interfaces.

The faster users reach value, the more likely they are to stay.

Cursor is a great example. Instead of aggressively selling upfront, it lets users build real value first. By the time usage limits appear, many users already depend on the product enough to upgrade naturally.

4. The 3 hidden levers you aren’t using.

While your market largely determines your long-term churn ceiling, there are several practical ways to improve it.

Some of the highest-leverage tactics include:

• Annual plans, which reduce churn mechanically while improving cash flow.

• Exit flows that offer pauses or temporary discounts instead of immediate cancellations.

• Recovering failed payments through smart retries and dunning emails, since expired cards alone account for around 2% of churn in many SaaS businesses.

Individually, these changes may seem small, but together they can reduce churn by 10–30%.

5. The best SaaS companies eventually achieve negative churn

The ultimate goal isn’t simply retaining customers. It’s negative churn.

That’s when existing customers expand faster than the business loses customers through cancellations.

Companies like Figma achieve this because teams naturally invite colleagues, purchase additional seats, and adopt adjacent products over time.

If new revenue from existing customers consistently exceeds lost revenue, the business continues growing even before acquiring a single new customer.

That’s why he ranks SaaS businesses in three broad categories:

• Tier 1: Low or negative churn businesses embedded into customers’ daily workflows. These are the strongest companies to build.

• Tier 2: High-churn but profitable businesses that can still become excellent bootstrapped companies, though scaling becomes harder.

• Tier 3: High churn combined with low margins - the most dangerous combination, especially for AI businesses with expensive inference costs.

The takeaway isn’t that churn should be eliminated. It can’t.

It’s that the strongest software companies don’t win by constantly fixing churn 0 they win by building products people naturally return to every single day. That’s a much stronger competitive advantage than any pricing experiment or cancellation flow.

Could an AI crash actually create the next golden age?

Everyone is talking about the AI bubble.

Model valuations are soaring. Data centre spending is exploding. Capital is flowing into AI faster than almost any technology in history. Most people assume the goal is to avoid a crash at all costs.

But venture capitalist Vijay Pande argues the opposite:

His argument isn’t that recessions are good. It’s that every major technological revolution - from railways to the internet - followed the same pattern: innovation, speculation, a financial bubble, a crash, and then decades of real economic value.

According to economic historian Carlota Perez, the bubble isn’t a detour. It’s often what finances the infrastructure that later powers an entire generation of businesses.

Why bubbles matter more than most people realise

History shows that while investors often lose money during bubbles, society usually keeps the infrastructure they paid for.

• Railway speculation left Britain with an enormous rail network.

• The dot-com crash wiped out billions in market value but left behind the fibre-optic infrastructure that powered the internet economy.

• AI is following the same path today through massive investments in chips, data centres, energy infrastructure, and AI talent.

The companies funding this buildout may not all survive, but the infrastructure almost certainly will.

A crash isn’t the real danger

One of the strongest ideas is that a market crash and a depression are distinct.

A crash simply resets unrealistic valuations.

A depression happens only when governments, businesses, and institutions fail to rebuild afterward.

He explains it through a simple framework with four possible outcomes:

1. Mild crash + weak response → “Gilded Age.” Growth resumes, but the underlying problems remain unresolved.

2. Mild crash + strong response → “False Dawn.” Some reforms happen, but not enough pressure exists to create lasting change.

3. Deep crash + weak response → “The Graves.” Economic pain turns into political instability, extremism, and long-term damage.

4. Deep crash + strong response → “Golden Age.” Society uses the crisis to rebuild institutions, adopt new technologies, and unlock decades of growth.

The crash doesn’t determine the future. The response does.

Why AI could follow the same historical cycle

During a boom, easy money discourages difficult conversations. Questions like:

• Who should own AI?

• How open should models be?

• How should governments regulate them?

• Who benefits from productivity gains?

Often get ignored because markets are focused on growth.

A crash forces those conversations.

History shows many of the institutions that shaped decades of prosperity - financial regulations, deposit insurance, competition rules - were created only after major crises exposed the weaknesses of the previous system.

Don’t redistribute the profits. Spread the tools.

Perhaps the most original argument in the article is about AI ownership.

Instead of simply taxing successful AI companies after the fact, he believes society should focus on giving more people direct access to the productive tools themselves.

That means:

• Open AI models.

• Affordable compute.

• Broad ownership.

• Easy access for builders rather than concentrating power among a handful of companies.

So, owning the tools creates far more long-term prosperity than redistributing the profits they generate.

Why founders should pay attention

Most founders think about AI as a product opportunity.

Every major technology cycle eventually shifts from financial speculation to real productivity. The companies that survive aren’t necessarily the ones that raised the most money during the boom - they’re the ones that build enduring value once the hype disappears.

If AI follows the same historical pattern as railways, electricity, automobiles, and the internet, the biggest winners may emerge after the bubble bursts, not before.

The real question isn’t whether an AI correction happens.

It’s whether the infrastructure, institutions, and access we build today are strong enough to turn that correction into the foundation for the next economic renaissance rather than another lost decade.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Harpoon Ventures, a San Diego, CA-based early-stage venture capital firm, closed Fund IV at $155m. (Link)

• Tapestry VC, a London, UK-based venture capital firm, has launched an $80m Fund III. The $80M Fund III is co-anchored by a $40m commitment from new sovereign investor. (Link)

• Ashton Kutcher, an actor and investor, is leaving Sound Ventures to launch a new early-stage venture firm with former NFX general partner Morgan Beller. (Link)

• Vicus Ventures, a New York and San Francisco-based early-stage venture firm founded by brothers Raj Singh Sandhu and Sunny Singh Sandhu, closed its debut $55 million fund. (Link)

New Startup Deals

• Dawnguard, an Amsterdam, Netherlands-based developer of an AI-native cloud security architecture platform, raised an additional $3.3M in Pre-Seed funding. (Link)

• Appnigma AI, a San Francisco, CA-based developer of an AI-powered enterprise software integration platform, closed its Pre-Seed funding round. (Link)

• Taxwire, a New York City-based AI-powered sales tax automation platform, raised $25M in combined Seed and Series A funding. (Link)

• Arcturus, a Los Angeles, CA-based developer of carbon-infused advanced conductor materials, raised $8M in Seed funding. (Link)

• Axon, a Dubai, UAE-based institutional financial orchestration platform, raised approximately $1M in funding. (Link)

• 8090 Labs, a Menlo Park, CA-based AI-enabled software manufacturing company, raised $135M in Series A funding. (Link)

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

• Fund Operations Analyst - Indogen Capital | Indonesia - Apply Here

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• Associate - Engine Venture | USA - Apply Here

• Senior Venture Studio Analyst - Atlantic Health System | USA - Apply Here

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📜 DEEP DIVE

How to automate your startup fundraising with Claude (And avoid this autopilot trap).

I recently spoke with a VC who invests exclusively in pre-seed and seed rounds, and he sounded almost tired talking about his inbox. Most of what lands now, he said, splits into two piles within seconds - genuinely worth a reply, or something he’s learned to move past without finishing the sentence.

What surprised me wasn’t the split. It was what actually separated the two piles.

I talked to a handful of founders raising right now - same AI tools, same market, wildly different outcomes. One closed her round in under two weeks. Another sent sixty personalised-looking investor emails, a deck that looked polished but said nothing specific, and follow-ups that referenced numbers from an old version of his own model and heard back from exactly one, a polite pass, with a mention that his message had “that AI smell.”

It wasn’t a writing problem. It wasn’t even a design problem - the deck looked fine on its own. Something else was different, and it wasn’t creativity.

That gap is the whole story right now, and it starts with what’s actually happening to the money.

The money is real. The odds aren’t.

$300 billion flowed into roughly 6,000 startups globally in Q1 2026 - an all-time high, up more than 150% quarter over quarter and year over year. That single quarter absorbed close to 70% of everything venture investors deployed in all of 2025.

Read the fine print and the picture changes fast. Seed funding did rise 31% year over year to $12 billion - but the number of seed deals fell 30% over the same period, down to roughly 3,800. More dollars.

Fewer winners. The money isn’t spread thinner; it’s stacked higher on fewer companies.

That’s the environment you’re raising into right now:

A market with record capital and a shrinking number of seats at the table. Which is exactly why so many founders have started reaching for AI to widen their odds - better investor research, faster decks, outreach at a scale no human could type by hand.

Here’s the part almost nobody’s telling them.

The same automation that’s winning rounds is getting other founders quietly blacklisted

AI-assisted investor matching is producing real results for the founders using it well - some platforms building on this approach report meeting rates well above what cold, unassisted outreach typically converts at, simply because the targeting is sharper and the research that used to take a week now takes an afternoon.

Investor inboxes have adjusted just as fast. Investors can now spot a templated AI email - one that name-drops a portfolio company in an obviously generic way - within seconds. The good ones flag it and move on. The frustrated ones mention it to other investors, and a founder’s name starts showing up in group chats and “pass on this one” flags between funds, in places they’ll never see.

Nobody sends you a rejection email that says “this felt automated.” You just... stop hearing back. From everyone. At once.

And here’s what makes this harder to navigate: the fundraising tool market has exploded. Deck generators, investor-matching platforms, outreach CRMs, call-prep assistants - a new one launches almost weekly, most promising to “automate your raise.” Some of that is genuinely useful.

Some of it is the exact mechanism getting founders quietly flagged, and the tools rarely tell you which is which.

And a lot of founders end up paying for these tools before they’ve even closed the round they’re trying to automate - burning scarce pre-funding cash on subscriptions meant to save them money later. That’s backwards, and it’s avoidable.

The good news: most of what actually needs automating doesn’t need a new tool at all - Claude, which you’re probably already using, does it in a plain conversation. No new subscription, no black box deciding what “personalised” means.

Here’s the framework I give founders raising in this market:

There’s a right way to do this - what to fully automate, what to never touch, and exactly how to build pitch deck content, follow-ups, and outreach the right way, using Claude.

Here’s what we’re going to cover:

• Which fundraising tasks should you hand to Claude completely? (Copy-Paste Prompts).

• What’s the prompt that turns 40+ investor databases into a ranked shortlist in minutes?

• Where do deck generators, investor-matching tools, and outreach CRMs actually fit, and which one should you trust least?

• Why doesn’t “sound more human” fix an AI-smelling outreach email, and what actually does?

• What’s the follow-up cadence that keeps you top of mind without looking automated?

• Is your own outreach already setting off alarms? (Run this checklist and find out.)

• How do you pressure-test your pitch before a real investor does it for you?

• How do you turn this into a one-time Claude Project so it’s ready for the rest of your raise?

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📜 DEEP DIVE

The Founder’s guide to secondaries: getting liquid before the exit.

A founder I’ll keep anonymous crossed $40 million in net worth two years ago, on paper. Her company had just closed a Series C, the 409A (the formal appraisal that decides what her shares are officially worth) had caught up to the new round, and on a spreadsheet she was rich. That same month she was running the numbers on whether to renew her apartment lease or move somewhere cheaper, because her actual bank balance was a founder’s salary minus rent in a major city.

That gap - wealthy on the cap table, tight in the checking account - used to be the toll you paid to build something big. You accepted it going in. Build for a decade, wait for the IPO or the acquisition, and somewhere on the far side the paper turns into money.

The waiting was the deal.

The deal has changed. Quietly, and a lot faster than most of the people writing about startups have noticed.

Why does $40 million on paper still leave a founder broke?

Because the old clock broke: companies are staying private far longer than the model assumed. Of the roughly 1,920 unicorns in the world as of March 2026, 59% were founded more than ten years ago, and 20% more than fifteen years ago, according to World Economic Forum / Jay Ritter data.

The five-to-seven-year round trip from seed to liquidity that defined venture for thirty years is gone. Top companies now meet their capital needs privately and stay there, sometimes skipping the public markets entirely.

Which leaves a strange new problem at the centre of a “successful” startup:

enormous created value, locked inside shares nobody can spend. Founders feel it. Early employees feel it worse - they took below-market salaries for equity that, for years, has been a number on a screen they can’t touch.

And the longer the private window stretches, the longer that money stays frozen.

For a while, there was no release valve. Now there is one, and it’s swallowing the market whole.

What actually replaced the exit?

Here’s the number that should reframe how you think about your own equity.

In the twelve months ending June 2025, founders, employees and early investors sold $61.1 billion of existing shares on the secondary market (selling your already-owned shares to another investor, instead of the company issuing new ones to raise money) - more than the $58.8 billion raised by every single VC-backed IPO over the same period, according to Carta.

Selling your shares privately, without an exit, is now a bigger source of liquidity than the entire IPO market.

It’s not a one-quarter blip, either. Step back to the full picture of how money actually left venture-backed companies in 2024, and the old playbook looks almost quaint: an estimated 71% of all venture exits ran through the secondary market rather than IPOs or acquisitions (PitchBook, via Chronograph).

The trajectory is the loud part. Annualised direct secondary value climbed from roughly $50 billion at the end of 2024 to about $91.7 billion a year later - an 83% jump in five quarters, per The State of Venture.

Across the whole market (venture plus fund-level), total secondary volume ballooned to about $160 billion in 2024 and is projected to have cleared $210 billion in 2025. And it hasn’t cooled in 2026 - the US venture secondary market hit fresh record levels in the first quarter of 2026, per PitchBook. There is now a deep, well-capitalised bid waiting for private startup stock.

The clearest sign this has gone from rare event to routine plumbing: the average gap between tender offers at a given company collapsed from 899 days in 2022 to just 132 days by 2025 (PitchBook, via Crowdfund Insider). Companies used to open a liquidity window once every two and a half years. Now it’s roughly every four months.

So the finish line moved. The thing you assumed you’d wait ten years for - turning equity into spendable money - is now something that happens during the build, on a repeatable schedule, for a growing list of companies.

But the headline number hides three things that decide whether you get any of it.

• The first is that $61 billion is not spread evenly. A startling share of it flows to a tiny handful of names, and whether you make that list depends on factors most founders have never been told to optimise for.

• The second is that the mechanics have traps - a wrong move can blow up your 409A, spook your board, or kill the deal before it clears.

• And the third is timing: there’s a specific window in a company’s life when this is possible, and a specific way to open it without looking like you’re heading for the door.

That’s what the rest of this issue is for.

What you’ll find below the line

• How does a founder actually get a secondary done - the two real paths, and how much of your stake you can realistically sell?

• What’s the catch the headline number hides, and why 86% of the money is locked to just twenty companies?

• What are the three traps - ROFR, 409A, and the signalling problem, that quietly kill founder secondaries before they close?

• What do you actually keep after tax, and why did a 2025 law quietly hand founders a much bigger break on exactly this kind of sale?

• When is the window actually open, and how do you time a sale around a round so it reads as confidence, not exit?

• If you’re building right now, what do you do this quarter to put yourself on the right side of all of this?

So how does a founder actually get a secondary done?

There are two doors. They look similar from the outside and behave completely differently once you walk through them.

Door one: the company-run tender offer.

This is the one you want. The company itself sponsors a structured liquidity window - it sets a price (usually pegged to the most recent round), decides who’s eligible and how much they can sell, and runs the whole thing through a formal process that, by rule, stays open for a 20-business-day window.

Because the company controls the buyer list and the price, it’s the cleanest, fairest way to get liquidity at scale, and crucially, it’s structured so it doesn’t wreck the company’s 409A valuation. Tenders are how employees and founders at the OpenAIs and Stripes of the world have been cashing out.

Door two: the direct, bilateral sale.

You find a buyer yourself (often through a platform like Hiive, Forge, or Nasdaq Private Market), and the two of you negotiate a price. Faster in theory. Messier in practice, because your company almost certainly holds a right of first refusal and transfer restrictions that let it step in, block the sale, or buy the shares itself, and a high bilateral price can knock your 409A out of line, which raises the strike price on every new option grant and quietly makes hiring harder. More on that trap below.

The good news for sellers is that the buy side has never been healthier. After a brutal 2022–2023 reset, the market has genuinely repriced. Median discounts on direct VC secondaries compressed from 46% below the last round in December 2023 to just 3% a year later, according to Zanbato data via Behind Genius Ventures, and by Q1 2025 the strongest names were trading at premiums.

Translation: a year ago, selling meant eating a near-halving of your shares’ value. Today, for a healthy company, you can get something close to par. The cost of liquidity dropped through the floor.

As for how much you can sell - tenders almost always cap participation, typically letting holders sell a slice of their vested position (commonly in the 10-20% range, though it varies by company and round). The design intent is deliberate: enough to take real money off the table and de-risk your life, not so much that it looks like the people who know the company best are running for the door.

What’s the catch nobody puts in the headline?

This one matters, and it’s where most of the breathless coverage misleads you.

That $61 billion is brutally concentrated. On the Hiive platform in Q4 2025, the top five companies accounted for 55.6% of all secondary trading value, and the top twenty soaked up 86.4% (The State of Venture).

Caplight put it even more starkly: 83% of Q3 2025 trading volume happened in just fifteen companies. OpenAI’s single $6.6 billion tender, on its own, was 6.2% of the entire year’s secondary volume.

So the honest version of this story is not “every founder can now cash out.” It’s this: a deep, professionalised liquidity market exists, and it pays full freight - if you’re building something the buy side is fighting to own.

If you’re a median seed-stage company, you are not running a tender next quarter, and no one is DMing you on Hiive. Even at the very top it isn’t automatic: in early 2026, roughly $600 million of OpenAI shares that institutional holders tried to sell found no buyers, while demand for Anthropic stock ran hot. Demand is specific, not general.

But the on-ramp is widening, and that’s the part worth planning around. In 2025, 70 companies saw their shares trade on the secondary market for the very first time ($492 million in fresh names, per The State of Venture).

And 2026 is bringing real structural democratisation: tender offers are firing far more often, OpenAI built retail access into its latest round, two no-accreditation venture funds listed on the NYSE, and the London Stock Exchange launched the first regulated private-stock market.

The set of companies and sellers for which this is real grows every quarter. The move isn’t “wait until you’re OpenAI.” It’s “understand the mechanism early, so the moment your company crosses the line into demand, you’re ready to act instead of scrambling.”

What are the three traps that kill founder secondaries?

Plenty of founders technically qualify for liquidity and still botch it. Almost always on one of these three.

1. The right of first refusal (ROFR).

Buried in your financing docs is, in all likelihood, a clause that lets the company and its existing investors match any outside offer before your shares can change hands.

Try to run a quiet bilateral sale, and you can spend weeks negotiating a buyer only to have the company exercise its ROFR and unwind the whole thing or simply block it under transfer restrictions. The fix is to stop treating a secondary as something you do to the company and start treating it as something you do with it. Tenders exist precisely because they route around this problem by design.

2. The 409A landmine.

Your 409A valuation sets the strike price on employee options. Sell a chunk of stock bilaterally at a rich price, and you can drag that 409A up - which raises strike prices on new grants, makes your offers less attractive to recruits, and irritates the very employees you’re trying to retain.

This is the single most underrated reason boards prefer structured tenders: a well-run tender can deliver founder liquidity while managing the 409A impact, where a sloppy direct sale just detonates it.

3. The signalling problem.

A founder selling shares can read, to the wrong audience, as the founder is checking out. This is the fear that keeps most boards from saying yes. The counter is both narrative and structural.

Narratively, the honest case - backed by every investor who’s watched it play out is that a founder who’s banked a modest slice makes better long-term decisions: they can turn down a mediocre acquisition offer, take rational risks, and keep building without their entire net worth held hostage to one outcome.

Structurally, you sell a small percentage, you tie it to a moment of strength, and you bring the board in early rather than asking forgiveness. Sell 10% of your stake in a round everyone’s celebrating and it signals durability. Try to dump 60% in a quiet quarter, and it signals exactly what they’re afraid of.

What do you actually keep after tax?

The second thought after “how much can I sell” should be “what’s left after the IRS takes its cut”, and for founders, the answer changed dramatically in 2025, in a way that’s almost custom-built for pre-exit liquidity.

The mechanism is QSBS - Qualified Small Business Stock, Section 1202 of the tax code. For years it ran on a brutal all-or-nothing cliff: hold shares in a qualifying C-corp for five years, and you could exclude up to $10 million of gain from federal tax; sell a day early and you got nothing.

The One Big Beautiful Bill Act, signed July 4, 2025, scrapped the cliff for stock issued after that date and replaced it with a graduated schedule: hold three years and exclude 50% of the gain, four years and exclude 75%, five-plus years and exclude the full 100%.

The same law raised the lifetime exclusion cap from $10 million to $15 million (or 10× your basis, whichever is greater), and lifted the company-size ceiling from $50 million to $75 million in gross assets - so larger startups now qualify too.

Here’s why it matters for this conversation specifically. The old rule actively punished early liquidity: take money off the table before year five and you forfeited the entire exclusion. The new graduated schedule means a founder doing a secondary at year three or four can shelter half or three-quarters of the gain instead of paying full freight on all of it. The tax code stopped fighting the exact thing this whole issue is about.

Three things to keep straight, because this is where founders lose money they didn’t have to:

• There are two parallel regimes. The new tiers, the $15M cap, and the $75M ceiling apply only to stock issued after July 4, 2025. Shares you already hold from before then are grandfathered under the old rules - five-year cliff, $10M cap, no partial exclusions (Keystone Global Partners). Most founders selling today are still under the old regime, so don’t assume the 3-year break applies to your existing stock.

• The partial tiers aren’t as sweet as the headline. The portion of gain that isn’t excluded at year three or four gets taxed at 28%, not the usual 20%. Net it out and the effective federal rate lands around 16% at three years and 8% at four - still a huge saving versus a fully taxable ~24%, but not zero.

• Your state may not play along. QSBS is a federal exclusion. Several states - California most prominently - don’t recognise it, so you can owe full state tax even on a federally tax-free sale.

None of this is tax advice, and the qualification rules (C-corp status, the active-business test, excluded sectors like consulting and financial services) are genuinely fiddly. The move is to know whether your stock qualifies and which holding tier you’re in - before you’re staring at a tender deadline, and to have a real advisor run your specific numbers.

The founders who lose here aren’t the ones who pay tax. They’re the ones who sold without realising they were twelve months away from keeping a far bigger slice.

When is the window actually open?

Liquidity isn’t available at random. It clusters around specific triggers — a new financing round, an employee-retention or option-refresh cycle, or a pre-IPO positioning moment.

The cleanest of these by far is the priced round. When you’re raising, three things are true at once: there’s a fresh, defensible valuation; there are sophisticated buyers already at the table; and the company is structurally set up to bolt a small secondary component onto the primary raise.

Lead investors increasingly expect this and will often carve out room for founder and employee secondary as part of the deal. That’s the moment. Trying to engineer a secondary in the dead air between rounds, with a stale valuation and no buyers in the room, is doing it on hard mode.

The bigger mental shift - the one the best-run companies have already made - is to stop treating liquidity as a someday event and start treating it as a recurring operating process. Boards at top companies now run liquidity on a calendar: an annual or semiannual window, clear eligibility rules, set caps, a repeatable structure.

Liquidity becomes a retention and recruiting tool - senior hires from public tech are far more willing to join, and stay, when they can see a real path to realising value before an exit a decade out. It stops being a favour and becomes infrastructure.

If you’re building right now, what do you do this quarter?

You’re probably not running a tender next week. But the founders who get liquidity when the window opens are the ones who did the boring work before they needed it. Five concrete moves:

Read your own paper.

Pull your charter, your financing agreements and your stock plan and find your ROFR language, transfer restrictions, and any existing liquidity provisions. Know what you’re actually allowed to do before you need to do it. Most founders have never read these clauses; the ones who have move fast when it counts.

Build the board relationship around this now, not later.

The time to align on a liquidity philosophy is a year before you want to sell, in a calm moment, framed around retention and team alignment - not in a tense one-off ask that reads as “the founder wants out.”

Tie your thinking to your next round.

If a raise is on the horizon, that’s your window. Raise the idea of a modest secondary component with your lead early in the conversation, when you have leverage, not after terms are set.

Watch your own pricing.

If your company is established enough to trade, your shares may already have an implied price on the secondary platforms. Knowing where you’d clear and whether you’d sell at a discount or a premium turns a panicked decision into a planned one.

Size it like an operator, not a lottery winner.

The whole point is de-risking your life enough to make better decisions, not cashing out. A modest slice, taken from a position of strength, tied to a moment everyone’s celebrating. That’s the version boards approve, and investors respect.

One honest caveat before you treat any of this as permanent. A big share of today’s secondary volume rides on a handful of giants now lining up to go public by early 2026; five of the six most-traded private companies (Anthropic, SpaceX, OpenAI, Databricks, and Anduril in various combinations) were considered credible IPO candidates inside 12–18 months, a potential “$1.4 trillion IPO wave.”

PitchBook expects overall secondary volume to dip when those names list, since they soak up a disproportionate share of activity. Read only the headline number and that looks like the window closing.

It isn’t. More IPOs mean fresher public pricing, which narrows the bid-ask spread that makes private shares hard to value in the first place, and they free up capital that flows back toward the next tier of companies. The mega-cap trades shrink; the on-ramp for everyone else widens.

That’s exactly why the 2026 infrastructure build - more frequent tenders, retail access, no-accreditation venture funds, a regulated private-stock exchange - matters more than any single quarter’s volume. The structural shift - liquidity as a recurring operating process, available years before an exit - is the permanent part. The specific names trading this quarter are not.

The exit was never really the finish line - it was just the only door we knew about. That’s no longer true. Liquidity has quietly become the third pillar of how venture works, sitting right alongside primary fundraising and the eventual exit, and it’s available years earlier than the old model promised.

The founders who understand this aren’t waiting a decade to find out whether the paper was ever real. They’re turning a piece of it into security now, on their own schedule and building better, longer, and calmer because of it.

The money left the building a long time ago. The question is whether you know which door it went through.

This issue is for information only and isn’t legal, tax, or financial advice. Talk to your own advisor before acting on any of it - especially on QSBS eligibility and timing, where the details are specific to your stock.

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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 :

• Why is it getting harder than ever to launch a new VC fund?

• Only 99 of 619 Unicorns have exited. What changed?

• What does a fund-returning company actually look like?

Frameworks & insightful posts :

• The buy-vs-build framework for building defensible SaaS in AI era.

• Framework: The 3-Layer AI sales stack every founder should build.

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🧠 Big idea + report of the week

Why is it getting harder than ever to launch a new VC fund?

Breaking into venture capital has always been difficult. But today, the challenge isn’t just proving you can pick great startups - it’s convincing LPs to back a new fund when most of the industry’s capital keeps flowing to the same established firms.

Dan Grey from Odin recently shared data showing that this shift isn’t a temporary consequence of the 2022 downturn. It’s a structural change that has been building for more than a decade.

The chart tells the story.

• In 2015, emerging managers raised nearly 60% of all US venture capital.

• By Q1 2026, that share had fallen to just 9.1%, meaning more than 90% of venture fundraising is now captured by experienced, incumbent firms.

Grey argues this happens because venture behaves asymmetrically during both booms and busts.

When capital floods into the asset class, most of it doesn’t create new managers - it simply makes the largest firms even larger. And when markets contract, LPs become more conservative, pulling capital away from first-time funds and concentrating it further among established brands.

That creates a structural “ratchet effect.” Every fundraising cycle pushes the industry toward greater concentration, but very little pulls it back.

Several forces reinforce this dynamic:

• Large LPs naturally favour large funds. Pension funds, sovereign wealth funds and endowments need to deploy hundreds of millions of dollars efficiently. Writing a handful of large cheques to proven managers is operationally much easier than backing dozens of emerging funds.

• Incumbents benefit from stronger fundraising flywheels. Bigger funds have longer track records, deeper LP relationships and larger teams, making each successive fund easier to raise - even during difficult markets.

• Emerging managers face liquidity pressure first. As exits slow and distributions decline, the smaller LPs that traditionally back first-time funds often pull back the fastest.

The result is an industry where capital increasingly flows through a smaller group of firms.

Grey believes this matters because emerging managers have historically been the investors willing to back unconventional founders, explore overlooked markets and develop conviction before everyone else notices an opportunity.

As capital becomes more concentrated, investment decisions become more concentrated too. More firms end up competing for the same founders, the same AI companies and the same market narratives, while fewer investors have the flexibility to pursue ideas outside the consensus.

This doesn’t necessarily mean the largest firms will generate worse returns. But it does change how innovation gets financed. Venture has always relied on diverse opinions and independent conviction to discover outlier companies. When capital pools around fewer decision-makers, the industry risks becoming better at funding obvious opportunities while becoming less effective at discovering the unexpected.

Only 99 of 619 Unicorns have exited. What changed?

Becoming a unicorn used to be the beginning of the exit journey. Within a few years, many companies would either go public or get acquired, returning capital to investors and allowing it to flow back into the startup ecosystem.

That cycle has broken down.

Stanford GSB professor Ilya Strebulaev recently shared new data showing that today’s unicorns are staying private far longer than any previous generation, deepening the venture industry’s liquidity crunch.

The numbers are striking.

Between 2020 and 2022, 619 companies reached unicorn status. Five years later, only 99 have exited, leaving 520 unicorns still private.

That’s an exit rate of just 20%.

Compare that with earlier generations of unicorns:

• Companies that became unicorns between 1999 and 2015 reached a 54% exit rate within five years.

• The 2016–2019 cohort reached 52% over the same period.

• The 2020–2022 cohort has managed just 20%.

The slowdown appears almost immediately.

After two years, previous unicorn cohorts had already seen roughly 20–23% of companies exit. The 2020–2022 cohort was sitting at just 6%. Three years in it was 7%, and after four years, only 8% had exited.

This isn’t just about founders delaying IPOs.

Every delayed exit means founders wait longer for liquidity, employees hold stock options they can’t easily monetise, VCs struggle to return capital to LPs, and pension funds, endowments and family offices receive fewer distributions to reinvest into the next generation of startups.

It’s one reason why the venture industry is sitting on an estimated $3 trillion of unrealised value, while secondary markets have become an increasingly important source of liquidity as traditional IPOs and acquisitions remain scarce.

The venture ecosystem depends on capital constantly recycling from successful exits into new funds and new startups. When that recycling slows, the effects ripple across the entire market - from LP fundraising to founder financing.

Today’s unicorns aren’t disappearing. They’re simply staying private much longer than the venture industry was built to expect.

What does a fund-returning company actually look like?

Venture capital is built on one promise: wait long enough, and a single company can return an entire fund (power law).

Few companies have ever demonstrated that better than SpaceX.

After spending 24 years as a private company, SpaceX has finally gone public, giving investors, employees and founders their first meaningful path to liquidity. The IPO valued the company at roughly $2.1 trillion, making it one of the world’s most valuable businesses.

The scale of the wealth creation is extraordinary.

• SpaceX raised $30.8 billion across 61 financing rounds from more than 420 investors over its lifetime.

• The company grew into a 22,000-person business before reaching the public markets.

• Elon Musk’s stake is now worth roughly $1.03 trillion, while he continues to control 84.4% of the company’s post-IPO voting power.

But Musk isn’t the only winner.

Some of venture capital’s biggest firms are sitting on returns that could define entire decades of performance.

Valour Equity Partners, one of SpaceX’s earliest believers, now holds a stake worth approximately $81 billion after investing just a few hundred million dollars over the years—representing well over a 100x paper return.

Other major winners include:

• Founders Fund, whose SpaceX stake is worth more than $50 billion.

• Sequoia Capital has holdings exceeding $20 billion.

• Andreessen Horowitz, which still owns roughly $10 billion worth of shares.

• Luke Nosek, co-founder of Gigafund and one of SpaceX’s earliest institutional backers, whose personal stake alone is worth more than $5 billion.

The IPO also creates liquidity for thousands of employees who spent years building the company with stock compensation. Many of those newly wealthy operators are expected to become angel investors, founders or startup backers, injecting fresh capital into the next generation of aerospace, defence, and industrial technology companies.

But there is one important catch.

Almost none of these shareholders can sell immediately.

Like most IPOs, SpaceX has imposed lock-up restrictions that stagger when investors can exit. Most shareholders will only be able to gradually sell portions of their holdings over the coming year, while Elon Musk cannot sell any shares for 366 days after the IPO.

That means the paper wealth exists today, but the actual liquidity will arrive slowly.

For the venture industry, though, this is still a landmark moment.

After years of frozen IPO markets and delayed exits, one of venture capital’s greatest investments is finally beginning to return capital—providing long-awaited liquidity for funds, LPs and employees across the ecosystem.

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SOMETHING MORE

🧩 Frameworks & insightful posts

The framework for knowing whether customers will buy your SaaS in the AI era.

One question keeps coming up whenever people talk about building SaaS today.

“If AI can generate software in hours, why would anyone continue paying for it every month?”

It’s a fair question.

Every week, there are new stories of teams replacing expensive software with internal tools built using Claude or ChatGPT. Something that once required months of engineering can now be prototyped over a weekend, making many founders wonder whether traditional SaaS businesses are becoming harder to defend.

Recently, software engineer Brandur Leach shared one of the most thoughtful takes. Instead of asking whether AI will kill software companies, he asks a more useful question:

His answer revolves around something he calls the Minimum Viable Unit of Saleable Software.

AI has made software cheaper - but not free

It’s easy to assume that if AI writes code, companies should simply build every internal tool themselves.

In reality, that’s rarely how software gets built. Even with AI, someone still has to:

• Review the generated code.

• Test edge cases.

• Fix bugs.

• Maintain the product over time.

• Keep improving it as business needs evolve.

AI dramatically reduces development time, but it doesn’t eliminate the ongoing cost of ownership.

That’s an important distinction.

A simple example explains the economics

A company was paying $400 per month for Jira and decided to replace it with an internally built version using Claude.

At first glance, the decision sounds obvious. Why keep paying every month when AI can build something similar?

But once you factor in engineering time, the picture changes.

Assuming an engineer earns around $200,000 per year, every hour spent maintaining that internal tool has a real cost. Even if AI handles most of the coding, someone still owns the product.

According to the rough math:

• A $400/month subscription only justifies about 4 hours of engineering time per month.

• After accounting for initial development and ongoing maintenance, it would still take roughly three years before rebuilding Jira actually breaks even.

For many companies, continuing to pay the subscription is simply the cheaper decision.

The equation changes as software becomes more expensive

Now compare that with something like Salesforce.

A larger company paying around $25,000 per month for CRM licenses is effectively spending the equivalent of more than one full-time engineer every month.

At that price point, building an internal alternative starts looking much more reasonable.

The interesting idea isn’t that companies should always buy or always build.

It’s that every product sits somewhere on a spectrum.

The “zone of viability”

One of the strongest ideas from the article is the concept of a zone of viability.

Software remains valuable when two conditions are true:

• It solves problems that aren’t trivial for AI to recreate.

• Its pricing stays reasonable enough that customers don’t feel compelled to build their own version.

Move outside that zone in either direction, and the economics change.

If the product is too simple, AI makes rebuilding it relatively easy.

If it’s priced too aggressively, companies become increasingly willing to invest engineering time instead of paying recurring subscription fees.

The winners are likely to be products that stay comfortably between those two extremes.

The new question every SaaS founder should ask

For years, founders mostly worried about whether customers wanted their product.

AI introduces a second question.

Is my product valuable enough that customers will continue buying it instead of building it themselves?

That doesn’t just depend on features.

It depends on how much expertise, maintenance, reliability, and continuous improvement your product provides customers without them having to own.

So, AI hasn’t eliminated the need for software businesses - it has simply raised the bar.

The companies that survive won’t necessarily have the most features. They’ll be the ones whose products remain more expensive to recreate than they are to buy. That may become one of the defining competitive advantages of SaaS in the AI era.

Framework: The 3-Layer AI sales stack every founder should build.

Almost every sales team has embraced AI.

Teams are using AI SDRs, email generators, account research tools, intent signals, call summaries, and automated prospecting. On paper, this should translate into more pipeline, higher conversion rates, and significantly more productive sales reps.

Yet for many companies, the results have been surprisingly average.

Reps still spend hours researching accounts. Prioritisation often feels like guesswork. And despite all the automation, buyers continue receiving cold emails that sound exactly like every other AI-generated message in their inbox.

So, where is AI actually falling short?

Recently, GTM leader Cam Wright shared one of the most practical explanations I’ve read. His argument isn’t that today’s AI models aren’t capable.

It’s that most companies are asking AI to make sales decisions without giving it the two things every great salesperson relies on: context and judgment.

Instead of improving decision-making, many companies are simply automating execution.

The biggest opportunity isn’t writing better emails

Most AI sales tools focus on the last step of the process. They help teams:

• Write outbound emails

• Generate call scripts

• Summarize accounts

• Draft LinkedIn messages

• Automate repetitive tasks

Those are useful features, but according to Wright, they’re not where the real competitive advantage comes from.

The biggest gains happen much earlier. Before any email is written, someone has to answer questions like:

• Which account deserves our attention?

• Why is this company likely buying right now?

• Which person inside the organisation actually feels the pain?

• What business problem should we lead with?

If those decisions are wrong, even the best AI-written email won’t produce results.

Targeting and positioning matter far more than perfect copywriting.

AI knows signals. It doesn’t automatically understand what they mean.

Imagine two companies that both announce they’re hiring SDRs. Most AI tools see the same buying signal and recommend reaching out to both.

An experienced salesperson doesn’t. One company may:

• Already use your biggest competitor.

• Have recently signed a long-term contract.

• Lack the integrations your product depends on.

The other might:

• Be actively expanding its sales team.

• Have recently hired someone who previously used your product.

• Already use tools that fit naturally into your ecosystem.

• Show multiple buying signals across CRM activity, website visits, and hiring trends.

On the surface, both companies look identical. In reality, one is a high-priority opportunity while the other is almost impossible to win.

The difference isn’t better AI.

It’s a better context.

Your competitive advantage isn’t the data - it’s how you interpret it.

Today, almost every GTM team can buy the same data. Everyone has access to:

• Job posting data

• Intent signals

• CRM enrichment

• Website visitor tracking

• Contact databases

• AI-generated messaging

Those inputs are becoming commodities.

The real edge comes from how your company combines and interprets those signals.

For example, a hiring announcement alone shouldn’t trigger outreach.

But combine that with recent website activity, a closed-lost opportunity from last year, a new RevOps hire, and a technology stack you integrate with—and suddenly the account tells a completely different story.

That’s not something an off-the-shelf AI model understands automatically.

That’s the knowledge your business has built over years of selling.

The companies seeing the biggest AI gains build three layers before they automate

The strongest GTM organisations don’t simply plug AI into their workflow. They first build the foundation AI needs to make better decisions.

1. Build a data foundation

Bring together everything your company already knows - CRM history, product usage, opportunity data, customer conversations, website activity, enrichment, job postings, and market signals, so both humans and AI work from the same source of truth.

2. Own your GTM decision logic

This is where your competitive advantage lives.

Define your ideal customer profile, account scoring, buying signals, personas, playbooks, disqualifiers, and prioritisation rules internally rather than outsourcing that thinking to an AI vendor.

3. Let AI orchestrate execution

Once the first two layers are in place, AI becomes dramatically more useful.

Instead of guessing who to contact, it retrieves the right context, applies your playbooks, recommends next steps, and generates messaging based on your company’s own decision-making framework.

The result is AI that follows your strategy instead of inventing one.

So, access to powerful AI models is no longer an advantage. Almost every company has access to similar models, similar data providers, and similar automation tools.

The real differentiation is moving upstream - building better judgment about which accounts matter, why they matter, and what your business uniquely understands about them. AI can execute that strategy incredibly well, but it still needs humans to define it first.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

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

• Valour Equity Partners, the Chicago-based growth investment firm founded by Antonio Gracias, is looking to raise at least $2.5 billion for its new Fund VII. (Link)

• Menlo Ventures, a Silicon Valley venture firm known for backing AI startups, has raised $3 billion across new funds, its largest fundraise in 50 years. (Link)

• Los Angeles, CA-based Mantis VC is raising a fourth fund targeting $100m. (Link)

New Startup Deals

• Valence AI, a San Francisco, CA-based emotional intelligence infrastructure company, raised an undisclosed Seed round, bringing total funding to $5M. (Link)

• Seltz, a San Francisco, CA-based provider of a web retrieval infrastructure layer for LLMs and AI agents, raised $12.5M in Seed funding. (Link)

• Timefold, a Ghent-based vehicle routing and shift scheduling platform, raised $13M in Series A funding. (Link)

• Prosper AI, a New York-based AI platform for patient journey management, raised $30M in Series A funding. (Link)

• Nura Bio, a South San Francisco-based neuroprotective therapeutics company, raised $73.8M in Series B funding. (Link)

• Cargofy, a Chicago-based AI-powered freight operations platform, raised $11M in Series A funding. (Link)

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• VC Investment Intern - First Momentum Venture | USA - Apply Here

• Investment Associate - Antler | UK - Apply Here

• Growth Intern (Merantix AI Campus) - Germany - Apply Here

• VP — Finance & Compliance - Transition VC | India - Apply Here

• Senior Associate - ICONIQUE CAPITAL | USA - Apply Here

• Investment Associate / Analyst - Flashpoint | UK - Apply Here

• Venture Capital Analyst - MicroVentures | USA - Apply Here

• Hard Tech Investment Associate - Upfront Venture | USA - Apply Here

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• Senior Venture Studio Analyst - Atlantic Health System | USA - Apply Here

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📜 DEEP DIVE

Below this price, AI products churn 3x worse. Most founders price below it.

There’s a number quietly haunting AI founders this year, and it isn’t ARR.

It’s the share of last month’s revenue that’s still here this month. Unglamorous, hard to fit in a launch tweet and the cleanest signal of whether what you’re building compounds or quietly bleeds out.

We spent two years cheering the zero-to-$100M-in-a-year stories. They’re real. The part that rarely gets said out loud: that growth gets nearly impossible to hold once a bigger and bigger install base walks out the door every month. There’s a blunt name for it - burning through your TAM. You’re not growing; you’re refilling a bucket faster than it drains and calling the inflow “growth.”

So someone went and got the data - roughly 3,500 software companies, sorted into B2B SaaS, B2C SaaS, and AI-native, and compared on how well each held onto revenue. The first cut looks damning.

• B2B SaaS: a median net revenue retention of 82%. Healthy.

• B2C: 49% - leakier, as you’d expect.

• AI-native: 48% net, but just 40% gross - worse than consumer apps.

AI was shedding customers faster than B2C, with no upsell to cover the loss.

If you stopped there, you’d conclude AI products are just structurally bad at retention. A lot of people stopped there. Cassie Young at Primary Venture Partners has been warning about an incoming “gross retention apocalypse,” and the chart above is the kind of thing she’s pointing at.

But the average is hiding the actual story. Because when you split those same AI companies by price, the “AI churn problem” doesn’t hold up as one problem. It splits cleanly into two, and which one you have was mostly decided by a pricing-page decision you made before launch.

Here’s the split:

• AI products under $50/month: 23% gross retention, 32% net.

• AI products at $50–249/month: 45% gross, 61% net.

• AI products over $250/month: 70% gross, 85% net - essentially indistinguishable from healthy B2B SaaS.

Same underlying models. Same wrappers, half the time. What separates a 32%-net death spiral from an 85% compounding machine isn’t the quality of the AI - it’s the price in front of it.

Which flips the diagnosis. “AI has a churn problem” sends you off to rebuild the product. But this is a packaging problem, and packaging is something you decide before you ever ship.

That’s the good news and the uncomfortable news at once: you can fix churn without a heroic roadmap, but only if you can see what’s happening under your topline. Most founders can’t. They’re reading the wrong number, off the wrong month, and drawing exactly the wrong conclusion about who their customers are.

Here’s what this issue gets into:

• Why is the retention number on your dashboard almost certainly overstating the truth, and which month should you actually be reading it from?

• What’s quietly bleeding cheap, broadly-applicable AI products dry and why won’t a slicker onboarding flow stop it?

• Where does the durable line really sit, if crossing it has almost nothing to do with the number on your pricing page?

• Which three levers lift retention 10-20 points without shipping a single feature?

• And what exactly should you run against your own numbers this week?

If you’re building anything AI-native or signing off on the budget for it, this is the part that decides whether the ARR you’re celebrating is the kind that stays.

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📜 DEEP DIVE

How to Build Your Personal and Business AI Agents With Claude (No Coding Required).

No code. About an hour. Two working agents by the end, one for your daily work, one for your business. Every step and prompt ready to paste.

Five to fifteen hours a week.

That’s what founders who use AI well quietly hand back to themselves. Yet of all the companies experimenting with AI agents, only about one in ten has one actually running. Everyone’s dabbling. Almost nobody’s built the thing.

That gap is what this issue is about, and if you don’t code, you’re standing right in the middle of it.

Here’s how it’s felt: everyone’s talking about “agents,” but every guide is written for engineers, so you close the tab and go back to the slow way. Open a chat, re-explain who you are, copy the answer out, start over tomorrow. That’s not an agent. That’s an intern with no memory.

The difference between chatting with AI and having an agent isn’t technical skill. It’s setup. You set it up once, and from then on it knows you, reaches your tools, and does the job the moment you ask. No code, no developer. Let me show you.

This guide is going to walk you through it slowly and completely. We wrote it the way a founder who has already done this would sit down and explain it to a friend over coffee.

What’s inside this guide

• What is an AI agent, really? How much time and money does this actually save me?

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

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

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

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

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

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

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

Let’s get into it.

So what is an “AI agent”?

Forget the jargon.

An AI agent is just an assistant you set up once so it already knows your situation and can do a specific job on command, without you re-explaining everything.

Think of a great human assistant. What makes them valuable isn’t genius; it’s that they know your context: how you like your emails to sound, which meetings matter, what your past work looks like.

You don’t brief them from scratch every morning. You say “handle this,” and they do. An agent is the same idea. Give it that context once, and it works the same way.

What one can do for you starting this week: draft emails in your voice, prep you for meetings, sort and reply to your inbox, research a prospect before a call, turn messy notes into clean docs, and write first-draft support replies. Not someday. A Tuesday afternoon of setup.

And it pays off.

• AI-using small businesses save five to fifteen hours a week on content and admin alone; at $25 an hour, that’s $6,500 to $19,500 of your time back every year.

• Productivity jumps 26% to 55% in the areas where AI is actually deployed, and AI-enabled owners are nearly twice as likely to report growth.

The catch, and the reason this guide exists: those gains go to founders who set it up properly, not the ones typing into a blank box and hoping. That gap, between “I tried AI a few times” and “I have an agent that does this job,” is the whole game. Let’s close it.

The one idea behind every agent: four ingredients

Before we touch a single button, here’s the idea that makes all of this simple.

Every agent you will ever build, whether it’s for your own inbox or for your business, is made of exactly four things.

1. A role.

Who it is and what it’s for. “You are my executive assistant.” “You are our customer support specialist.” This is you handing it a job title.

2. Context.

What it needs to know to do the job well. Facts about you, your company, your customers, and the way you like things done. This is the onboarding.

3. Tools.

What it’s allowed to reach into. Your email, your documents, your calendar. This is giving it the keys to the right drawers.

4. A trigger.

The instruction you give to make it run the same way every time. “Prep me for this meeting.” “Draft a reply to this customer.” This is you saying “go.”

That’s the whole thing. The reason most people feel like AI “doesn’t really work for them” is that they only ever give it ingredient four, the trigger, and skip the first three. They ask a stranger to do a job without telling it who it is, what it should know, or what it can use.

We’re going to give it all four, and that’s what turns a chatbot into an agent.

Everything above is the why. Below is the build part:

• The 5-step setup, click by click

• Your personal assistant, with the exact prompt to paste in

• Three ready-to-use business agents: customer support, sales-call research, and content repurposing, each with its full prompt

• The two-minute fix for when the output isn’t great

• When to level up, and how to stay safe

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Read more

Big idea + report of the week :

• Is AI’s winner-takes-all era quietly ending? What the State of AI 2026 report reveals.

• Why are so many 2021 venture funds underperforming?

Frameworks & insightful posts :

• What does YC’s latest batch reveal about the future of startup building?

• Why are only 5% of companies seeing real returns from AI?

• Did we get the Google vs. ChatGPT story completely wrong?

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🧠 Big idea + report of the week

Is AI’s winner-takes-all era quietly ending? What the State of AI 2026 report reveals.

For two years, the AI story was almost boringly simple: one app ran away with everything, and everyone else was a rounding error. If you were building, the only real strategic question was “how do we not get flattened by OpenAI?”

That framing just stopped being true, and the data behind it is worth sitting with.

Sensor Tower recently published State of AI 2026, which tells a very different story from what's getting narrated in the AI world:

The moat cracked - on schedule, not in theory.

ChatGPT’s True Audience share fell below 50% for the first time in March 2026, sliding to roughly 46.4% globally. It’s still enormous - over 1.1 billion monthly active users, with Gemini at 27.7% (662M) and Claude at 10.3% (245M) - but the single-player era is visibly turning into a three-player one.

Claude is the fastest-growing challenger, up 452% YoY in May, with US share climbing from 4.4% to nearly 14%.

Loyalty is softer than anyone priced in.

The most underrated data point in the whole report: when OpenAI signed an agreement with the Department of War, ChatGPT uninstalls spiked roughly 200% above average in mid-March - and a lot of those users moved to Claude, which actually out-downloaded ChatGPT from March 1–5.

A single brand/values event moved users at scale. If your product sits on top of one model, that’s not someone else’s problem.

The game shifted from growth to money.

Download and spend growth are slowing, and the industry is pivoting hard to monetisation. Projected H1 2026: ~2.3B AI app downloads and $4.2B+ in consumer spend. Engagement is deepening too - ChatGPT is at ~215 minutes per user per month, with Gemini (100 min) and Claude (120 min) climbing fast.

Notably, Claude leads on subscription conversion at ~13%, a reminder that intensity of use beats raw reach for revenue.

This is the part most operators are sleeping on - AI is becoming a discovery layer.

It’s turning into a third channel alongside Search and Social, and it’s already moving commerce:

• GenAI referral traffic to shopping sites rose across every major retail category; Computers & Electronics saw its GenAI traffic share grow nearly fourfold.

• Walmart and Target - both integrated with AI assistants - now see GenAI referral shares above 1.5%, while Amazon lags because of its more closed, crawler-restricted ecosystem.

• Amazon’s Rufus users convert at nearly twice the rate of non-users.

Translation: “being findable inside AI” is becoming the new SEO. The retailers that opened up are getting the traffic; the walled gardens are getting skipped.

And it’s becoming an ad channel fast.

ChatGPT started testing ads in early 2026, and impressions jumped more than 7x between March and May.

Early advertisers cluster in Shopping, Software, Travel, and Financial Services - the research-heavy purchases.

Meanwhile, the AI labs themselves are spending like crazy to defend turf:

GenAI ad spend more than tripled in the US in Q1 2026, with OpenAI up 800%+ and Anthropic up 1,184% YoY.

For founders and operators, that cashes out to three things: distribution is genuinely up for grabs again, you need an AI-discoverability strategy the way you once needed SEO, and you should assume your users can and will switch models on a dime.

Why are so many 2021 venture funds underperforming?

For years, venture capital operated on a simple promise.

Invest in the next generation of startups, wait patiently, and eventually the winners will return the entire fund multiple times over.

But what happens when almost everyone invested at the same time, at the same peak valuations?

Peter Walker from Carta recently analysed performance data across 2,689 US venture funds, and one chart stands out.

The 2021 vintage is struggling.

Five years into its life cycle, the median 2021 venture fund sits at just 1.04x Net TVPI.

In simple terms, the average fund has barely created value beyond the capital it invested.

Some of the numbers are striking:

• The median 2021 fund sits at 1.04x.

• Even the top 25% of funds have only reached 1.29x.

• The top 10% of funds are at 1.54x.

• The top 5% of funds have reached 1.93x.

That might sound reasonable until you compare it with older vintages.

The 2017 cohort tells a very different story.

At roughly the same stage of maturity, the median 2017 fund reached 1.68x, while the 90th percentile reached 3.47x.

Perhaps the most surprising comparison:

The 95th percentile fund from 2021 (1.93x) is still below the 75th percentile fund from 2017 (2.21x).

In other words, some of today’s best-performing funds would have looked merely above average a few years ago.

So what happened? Walker points to four forces colliding at the same time.

• Record numbers of investors entered venture during the boom.

• Startup valuations reached historic highs.

• Interest rates reversed sharply in 2022.

• AI emerged as a major platform shift shortly after many 2021 funds had already deployed capital elsewhere.

The result was a perfect storm.

Many investors bought into companies at peak prices just before capital became more expensive and public market multiples compressed.

The venture industry is still working through that adjustment.

Interestingly, newer vintages appear to be recovering.

The 2022 vintage, despite launching during a difficult fundraising environment, is already showing stronger numbers. Its 90th percentile sits at 1.70x, while the top 5% have crossed 2.0x.

That suggests the problem may not be venture capital itself. It may simply be that 2021 was one of the most expensive moments in startup history to put money to work.

The broader lesson is an uncomfortable one.

Venture returns are often determined long before a company exits.

They’re heavily influenced by the price investors pay on the day they enter.

And for many funds raised during the 2021 boom, that entry price may prove to be the defining story of the entire vintage.

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🧩 Frameworks & insightful posts

What does YC’s latest batch reveal about the future of startup building?

Every six months, hundreds of founders apply to Y Combinator hoping to build the next billion-dollar company.

Most people look at the batch and ask: “Which startup will become the next Stripe?”

A more interesting question is: “What patterns show up when you analyse all of them together?”

That’s exactly what investor and builder Chris Lu did after studying all 196 companies and 395 founders in YC’s Spring 2026 batch.

The obvious headline is AI. 95% of startups in the batch use AI in some form.

But that’s actually not the interesting part anymore.

AI is rapidly becoming infrastructure. The more interesting question is what founders are building on top of it.

Everyone is building agents

A year ago, “AI agents” sounded like a startup category. Today it looks more like the default.

• 137 of 196 companies (70%) are building AI agents

• Data infrastructure came in a distant second

• Robotics, computer vision, voice, and workflow automation were much smaller categories

The shift is important. Founders are no longer asking:“How can I use AI?”

They’re asking: “Which job can an AI agent do better than a human?”

That subtle change is shaping where startup creation is heading.

B2B continues to dominate

Despite all the excitement around consumer AI, most founders are still chasing business problems.

• 62% of companies are B2B

• 44% of the entire batch are AI-native B2B SaaS companies

• Consumer startups represent only a small portion of the cohort

This creates an interesting challenge. When dozens of startups have access to similar models and similar tooling, the advantage is rarely the technology itself.

The winners will likely be determined by:

• Distribution

• Domain expertise

• Customer acquisition

• Execution speed

Not model access.

Hardware is making a comeback

One of the biggest surprises wasn’t AI. It was hardware. Several startups in the batch are building physical products across:

• Defense

• Aerospace

• Drones

• Robotics

• Nuclear energy

• Medical technology

Around 13 startups fall into the defence, drone, and aerospace category alone.

A few years ago, these would have been considered niche bets.

Today they’re becoming one of the most interesting areas for startup creation.

Small clusters often reveal bigger opportunities

Looking beyond the headline categories reveals a few emerging themes. There are:

• 6 startups building infrastructure for prediction markets

• 6 deep-tech and bio-hardware startups

• 4 AI security and agent safety companies

None of these categories dominates the batch.

But they represent something interesting: founders building the infrastructure around new markets rather than competing directly inside them.

Historically, some of the biggest startup outcomes have come from selling picks and shovels during a platform shift.

The founder profile is changing

The stereotypical startup founder is often portrayed as a college dropout building from a dorm room. The data paints a different picture.

• 70% of founders are technical

• 49% of companies have all-technical founding teams

• Only 3% are dropouts

• Only 5% hold PhDs

The most common previous employer wasn’t Google or Meta. It was Amazon. Amazon and AWS produced 33 founders in the batch, ahead of Meta and Google.

The modern startup pipeline increasingly looks like:

Big Tech → Startup rather than College → Startup

Solo founders are becoming increasingly viable

Two founders remain the dominant setup. But one number stood out.

38 companies were started by solo founders. And 29 of those are AI-native businesses.

AI is reducing the amount of work required to launch and operate software products.

Research, coding, design, support, marketing, documentation, and customer onboarding can increasingly be handled by a much smaller team.

The result is that a single founder can now build products that previously required several employees.

What’s happening inside YC is likely a preview of what’s happening across the broader startup ecosystem.

AI is becoming standard. Agents are becoming the default product. Technical founders continue to dominate.

And startup creation is becoming faster, cheaper, and more accessible than ever before. The founders who stand out over the next few years probably won’t be the ones with access to better technology.

They’ll be the ones who move faster once everyone has access to the same technology.

Why are only 5% of companies seeing real returns from AI?

Everyone is trying to figure out how to get more value from AI.

Yet according to research from Bain, McKinsey, BCG, and MIT, only around 5% of companies are generating meaningful returns from their AI initiatives.

The surprising part?

The biggest predictor wasn’t the model they used, the amount they spent, or even their data infrastructure.

It was whether they redesigned their workflows before introducing AI.

This insight comes from a detailed analysis of AI adoption across hundreds of companies, and it highlights a mistake many founders are making right now: automating broken processes.

A founder recently shared a story that captures the problem perfectly.

His company automated support triage, lead qualification, and reporting. Everything looked great on the dashboard. Then CAC increased, NPS dropped, and enterprise conversions slowed.

After weeks of investigation, they discovered their AI qualification system had been routing high-value prospects into low-priority nurture sequences while prioritising low-intent signups.

The AI wasn’t broken. The workflow was. The AI simply scaled the problem.

A few insights stood out from the research:

1. Only 5% of companies are creating meaningful AI value

Three separate studies reached almost the same conclusion:

• McKinsey: 5.5% of companies report significant EBIT impact from AI

• BCG: roughly 4–5% generate value at scale

• MIT: only around 5% of AI deployments produce millions in value

Different firms. Different datasets. Same result.

2. Workflow redesign matters more than model selection

McKinsey found that top-performing companies were nearly 3x more likely to redesign workflows before automation.

55% of high performers redesigned workflows first.

Only 20% of everyone else did.

Most companies are simply layering AI on top of existing processes.

3. AI makes workflow debt more expensive

The report introduces an interesting concept: workflow debt.

It’s the operational equivalent of technical debt.

Examples include:

• Approval chains nobody questions anymore

• Spreadsheet processes everyone accepts

• Handoffs between teams that exist for historical reasons

• Manual reporting systems held together by workarounds

Humans naturally compensate for these inefficiencies. AI doesn’t. It executes exactly what it’s told.

As Bain puts it: “AI doesn’t fix workflow debt. It locks it in, speeds it up, and makes it more expensive to unwind.”

4. Some workflows are much safer to automate than others

The research highlighted a clear pattern. Good candidates:

• Reporting

• Document processing

• Regulatory monitoring

• Structured review tasks

Riskier candidates:

• Customer support

• Lead qualification

• Customer-facing decision-making

These workflows often contain exceptions, judgment calls, and undocumented rules that humans handle instinctively.

AI struggles when those hidden assumptions aren’t explicit.

5. The companies winning with AI all do the boring work first

One of the best examples came from Amazon.

Its tax team built an AI system to monitor VAT regulation changes globally.

Instead of trying to automate everything, they focused on a single structured workflow.

The result:

• 26 minutes per update became 2 minutes

• 92% reduction in effort

• 80% of outputs required no edits

Not because the AI was magical. Because the process was well understood before automation began.

The biggest takeaway from all of this is surprisingly simple:

Most companies think they’re running an AI problem. They’re actually running a workflow problem. The winners aren’t necessarily using better models.

They’re spending more time understanding how work should flow through the business before they automate it.

And right now, that’s creating a widening gap between the 5% seeing real returns and everyone else still wondering where the ROI went.

Did we get the Google vs. ChatGPT story completely wrong?

For the last three years, one of the most popular AI predictions was simple: Google was about to have its Nokia moment.

ChatGPT had the better product.

People could ask questions in plain English, get direct answers, and skip the endless list of blue links that defined search for two decades.

The logic seemed obvious. If ChatGPT keeps improving, why would anyone still use Google?

What’s surprising is that the opposite appears to be happening. From Sherwood News -

Google Search is growing.

According to Similarweb data, Google’s global traffic has remained remarkably stable since ChatGPT launched and recently reached new highs. At the same time, Google’s Search business grew 19% year-over-year in its latest quarter.

That’s not what disruption usually looks like.

What’s happening instead is that Google is quietly turning AI into an upgrade for Search rather than a replacement for it.

When Google launched AI Overviews, many people thought it would cannibalise its own business. If users get answers instantly, wouldn’t they click fewer links and generate less revenue?

So far, the data suggests otherwise - Google says users who interact with AI-powered features actually perform more searches.

AI helps people get started faster, which often leads to even more questions, more exploration, and more engagement.

A few numbers stand out:

1. AI Overviews now reach more than 2.5 billion users.

2. Google’s AI Mode has crossed 1 billion monthly active users.

3. Search queries hit an all-time high last quarter.

4. Search revenue grew 19% year-over-year.

The bigger lesson here isn’t about AI. It’s about distribution.

OpenAI built the breakthrough product. Google already owns the distribution.

Every improvement Google makes can instantly reach users across Search, Chrome, Android, Gmail, Maps, and YouTube. That’s a moat very few companies in history have ever had.

And Google is doubling down.

The company plans to spend roughly $180–190 billion on infrastructure this year alone to support its AI ambitions.

Most startups simply can’t compete at that scale. Ironically, the company under the most pressure from AI may not be Google.

It may be publishers. As Google answers more questions directly inside Search, fewer users need to click through to websites.

Search is becoming more useful for users while becoming less valuable for many publishers.

That’s the tradeoff nobody talked about when AI search first emerged.

The easiest story was “ChatGPT will kill Google.” Reality turned out to be more complicated. OpenAI proved AI-native search could work.

Google proved that having billions of users is still one of the strongest advantages in technology.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• 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)

• PayPal Ventures, the corporate venture arm of PayPal, has paused new investments and is winding down operations as part of the company’s broader restructuring efforts. (Link)

• Animal Capital, a NYC-based early-stage venture capital firm, closed Animal Capital Fund III at $33m. (Link)

• Base10 Partners, a San Francisco, CA-based venture capital firm, closed $850m in new capital throughout 2025, with funds now actively deploying. (Link)

New Startup Deals

• Optiak, a Madrid-based modular operating system for enterprise AI, raised €4M ($4.7M) in Pre-Seed funding. (Link)

• Clario, a Menlo Park-based platform helping enterprises manage redundant, obsolete, and insignificant data, raised $6M in Seed funding. (Link)

• Aston Power, a Raleigh-based power delivery and energy infrastructure platform for data centres, raised $20M in equity funding. (Link)

• SpliSense, a Jerusalem-based clinical-stage biotechnology company, raised up to $13M in funding. (Link)

• Human Continuum, a NYC-based developer of platelet-derived and plant-based exosome therapeutics and diagnostics, raised over $5.13M in Seed funding. (Link)

• ChatSee.ai, a San Francisco-based intelligence layer platform for autonomous AI systems, raised $6.5M in funding. (Link)

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• Senior Analyst - ICONIQUE Capital | USA - Apply Here

• Events Manager - Bain Capital Venture | USA - Apply Here

• Venture AI & Operations Specialist - Blum Venture | Austria - Apply Here

• VC Investor - Entourage | Belgium - Apply Here

• Senior Associate - New York Life Venture | USA - Apply Here

• Senior Investment Manager - Resolution Impact Venture | USA - Apply Here

• Program Manager - a16z | USA - Apply Here

• Visiting Analyst (Investment Team Intern) - DN Capital | UK - Apply Here

• Associate - Manhattan Venture | USA - Apply Here

• Portfolio Reporting Team Lead - Cerity Partner | USA - Apply Here

• VP — Finance & Compliance - Transition VC | India - Apply Here

• Senior Associate - ICONIQUE CAPITAL | USA - Apply Here

• Venture Capital Analyst - MicroVentures | USA - Apply Here

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📜 DEEP DIVE

Why are even great startups still struggling to raise capital despite record venture funding?

In February 2026, U.S. startups raised $62.5 billion across 462 deals. It was the largest single month of venture funding in recorded American history.

And it wasn’t a one-off. You’ve watched a version of that headline land every few weeks since - another record quarter, another ten-figure round, another “venture is back” victory lap - right up to the month you’re reading this. The drumbeat hasn’t stopped. If anything, it’s gotten louder.

If you’re a founder and then opened your own pipeline - the four VCs who ghosted you after the second call, the term sheet that’s been “with the partners” for three weeks and felt a quiet, specific kind of crazy.

Because the numbers say it’s the best funding environment in a decade. Your bank account says otherwise.

You’re not imagining it. And you’re not bad at fundraising.

That $62.5 billion was almost entirely two companies. Anthropic raised $30 billion. Waymo raised $16 billion. Two names took roughly three-quarters of the biggest funding month in history before the other ~460 startups split what was left.

This is the story nobody tells you cleanly: venture capital is having its best year on paper and one of its hardest years in practice - at the same time. The headline number and the founder’s reality have completely decoupled.

This edition is about that gap. Specifically:

• The anatomy of the barbell - exactly how concentrated the money has become, with the numbers

• Why it’s happening - the mechanics pulling capital toward a handful of names (it’s not a fad; it’s structural)

• The famine on the other end - what the data says about your raise: seed, Series A, the graduation cliff

• What to actually do about it - a positioning playbook for founders who will never be the $30B headline, and don’t need to be

• The opportunity hiding in the barbell - where the fleeing capital leaves white space: cheaper talent, the wide-open application layer.

• What to watch - the live signals that tell you when the bar is about to widen back out.

If you raise in the next 18 months, the difference between understanding this market and fighting the last one is the difference between a clean round and a slow death by “we’d love to see more traction.”

Let’s get into it.

The anatomy of the barbell

In Q1 2026, just four companies - OpenAI, Anthropic, xAI, and Waymo - raised $188 billion between them. That was 65% of all global venture capital for the quarter. Sixty-five per cent. To four companies.

Everyone else on the planet - every SaaS startup, every biotech, every fintech, every consumer app, every climate company, in every country - split the remaining third.

This isn’t a one-quarter fluke. It’s the steep end of a curve that’s been bending for years.

Go back to 2022: rounds of $1 billion or more made up just 5% of all venture dollars. By 2023, that share hit 15%. By 2024, 19%. Into 2025 and 2026, billion-dollar rounds came to represent roughly half of all the money invested in AI - and AI is now most of the market. The money didn’t shrink. It pooled.

The concentration is geographic too.

In 2025, the San Francisco Bay Area pulled in $126 billion of global AI funding - but $113 billion of that went to just 92 companies raising rounds of $100 million or more. A few dozen addresses within a few square miles absorbed more capital than most countries’ entire startup ecosystems.

So when you read “record venture funding,” translate it. It does not mean capital is widely available. It means a small number of companies are absorbing staggering sums, and the average reported deal is being dragged upward by a handful of giants. $62.5 billion across 462 deals is an average of $135 million per deal - a number that describes almost no real company’s actual round.

The barbell is real. Now the more useful question: why?

Why this is happening (and why it won’t reverse soon)

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📜 DEEP DIVE

Some AI companies are about to disappear. Here’s how to tell if yours is one of them.

A few weeks ago, a founder we know - Series A, decent revenue, the kind of company that gets written up as an “AI success story” - got a call from his lead investor for a routine portfolio check-in. Then the investor asked one question that the founder couldn’t answer cleanly:

If your model provider doubled its prices tomorrow, what happens to your margins?

He didn’t know. Not exactly. He knew his burn rate to the dollar. He knew his CAC, his churn, his NRR. He’d never actually sat down and worked out how much of his product’s value lived inside an API call he didn’t control.

That conversation has been happening a lot lately, in different forms, at different stages. It’s not really about pricing. It’s about a blind spot that’s gotten enormous while almost nobody was watching it - because for the last three years, the AI boom rewarded speed, not ownership. Ship the feature. Wire up the API. Worry about the rest later. Later kept getting pushed out, and now a lot of “later” is arriving at once.

Then Satya Nadella gave the blind spot a name

Recently, Microsoft’s CEO posted something on X -

Just a framework, offered plainly, for a thing a lot of operators have felt but never quite said.

"Every company is going to have to build what I think of as human capital and token capital.

Human capital comprises the knowledge, judgment, relationships, ingenuity, and pattern recognition of its people, while token capital is the firm's AI capability it builds and owns."

Satya Nadella on X

Strip away the corporate phrasing and what he’s pointing at is pretty simple -

Companies have always tracked one kind of capital - the people, the relationships, the institutional knowledge sitting in a few senior heads. Nobody’s been tracking the other kind. The AI layer itself: do you actually own it, or are you just renting access to someone else’s?

Nadella isn’t warning about something that might happen. He’s describing something that already has, to companies that found out the hard way, usually right around the time a funding round or an acquisition conversation forced the question.

So that’s what this issue is actually about. Not a recap of his post. Instead, we went and found the actual numbers: what it costs a company when its AI is rented instead of owned, what the market pays extra for the ones that own it, and a five-minute test that tells you which one you are right now.

What we cover in this issue

• The rented land problem - what API dependency really costs you

• The defensibility spectrum - where does your company sit right now?

• The model swap test - one question that reveals everything about your AI moat

• What investors are about to ask you - The due diligence questions on AI ownership.

• How to start building token capital this quarter - even if you're not a technical founder

The rented land problem - What API dependency actually costs you.

Here’s a scenario that played out in hundreds of AI startups across 2024 and 2025.

A founder builds a great product on top of GPT-4. Customers love it. Revenue grows. The team builds product intuition, the go-to-market works, and a Series A gets done at a solid multiple.

Then OpenAI

• releases a native feature that does 80% of what the startup does, for free, inside ChatGPT.

• Or the API pricing jumps, and it did, three times in 2025 alone.

• Or a cheaper model from a competitor becomes available, but the entire product is so tightly coupled to OpenAI’s specific output format that switching would require a rebuild.

In each case, the startup discovered the same hard truth: the intelligence was never theirs. The economic consequences are real and measurable.

Bessemer Venture Partners found that AI wrapper startups - companies with no proprietary model layer - require 3.2 times more funding to reach profitability versus traditional SaaS businesses.

Traditional SaaS runs gross margins of 70-90%. AI wrappers run 50-60%, on a good day. And every time an API provider adjusts pricing, those margins shrink further - with no recourse and no leverage.

The valuation story is equally stark.

In 2025-26, AI startups with proprietary data moats and defensible model layers commanded 25-30× revenue multiples. While wrapper companies -

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📜 DEEP DIVE

The $20 Trillion Blind Spot: Bezos Just Showed You Where to Build.

Something unusual is happening right now in the world of venture and technology, and most people are looking at the wrong part of it.

This week, Jeff Bezos’s startup Prometheus raised $12 billion at a $41 billion valuation. The backers weren’t just Andreessen or Sequoia.

They were JPMorgan Chase, Goldman Sachs, and BlackRock. The three largest financial institutions on the planet are now writing billion-dollar venture checks.

The company has 150 employees - no public product.

And a goal that sounds like science fiction: build an “artificial general engineer” - AI that can design jet engines, drug compounds, and complex physical systems the way GPT-4 writes emails.

Everyone covered the fundraise. Almost nobody asked the obvious follow-up question.

This is not a rhetorical question. We spent a few days pulling the data. What we found was a gap so large it is hard to believe nobody is talking about it.

Three industries - construction, pharma manufacturing, and power grid infrastructure- together represent more than $20 trillion in annual economic activity. They employ hundreds of millions of people globally. They are the literal foundation of how civilisation runs. And they are about to face the most significant technological disruption since the Industrial Revolution.

The number of AI-native startups building for them? Relative to their size, negligible.

This is either the most obvious trap in tech or the most obvious opportunity. We believe it is the latter. This issue explains why, shows you who is moving, and maps what needs to be built.

We are going to cover:

• The World Right Now: Why physical infrastructure is suddenly the centre of everything.

• The Productivity Paradox: How big these industries are and how untouched they’ve been.

• The Startup Density Gap: The data showing where founders have gone, and where they haven’t.

• Why Nobody Has Built Here: The real structural reasons and why they’re moats, not blockers.

• The Prometheus Signal: What a $41B bet tells founders who can’t raise $12B.

• Founders to Watch: Who is actually moving in these spaces right now.

• The Unlock Map: What needs to be built, in what order, in each industry.

The World Right Now

It is June 2026. Here is what the macro picture looks like.

AI has won the software layer. The race for foundation models is functionally over as a competition of new entrants - OpenAI, Anthropic, Google DeepMind, and a handful of others have consolidated the top.

Enterprise software is being rebuilt around AI agents. Developer tools, legal research, financial analysis, customer support - these are being automated faster than most analysts predicted two years ago.

The next frontier is physical. And the physical world is not ready.

The data centre crisis.

Large technology companies are expected to commit more than $1 trillion in infrastructure spend in 2025-2026 alone.

As of early 2026, 190 gigawatts of hyperscale data centre capacity has been announced across 777 projects globally - roughly 148 GW planned, 21 GW in construction, 12 GW already operational.

The problem:

• Data centres can be built in 12 to 18 months. Connecting them to the power grid takes five to seven years.

• Of the 110 data centre projects slated to open in 2025, more than a quarter were delayed - not because of chips or software, but because of power, permitting, and construction. The physical world is the bottleneck.

The infrastructure spending boom.

Governments globally are deploying historic levels of capital into physical infrastructure.

• The US Bipartisan Infrastructure Law committed $1.2 trillion.

• The EU’s NextGenerationEU fund is deploying €800 billion.

• India’s infrastructure spend has grown at double-digit rates.

• The Middle East is building entire cities.

The global construction market, already at $17 trillion in 2025, is projected to reach $19.6 trillion by 2032. Every one of those trillions requires engineering, permitting, construction, and supply chain work that is still largely done by hand.

The pharma manufacturing gap.

AI has revolutionised drug discovery. Companies like Recursion, Insilico, and Formation Bio have used machine learning to compress what used to be decade-long molecule discovery processes into years or months. Formation Bio reportedly cut clinical trial lead times by half.

AI pharma investment has crossed $485 billion in total disclosed capital. But the factory- the industrial process of actually making drugs at scale, maintaining quality, releasing batches, and meeting FDA compliance- has barely changed. Most facilities still run on paper. The discovery is AI-native. The manufacturing is 1970s infrastructure.

The labour shortage.

Across construction, energy, and advanced manufacturing, the skilled workforce is shrinking faster than industries can replace it. AEC (architecture, engineering, and construction) has 440,000 unfilled vacancies in the US alone, nearly double the 2019 level. Energy infrastructure is facing a similar wave of retirement.

This is the context in which Bezos raised $12 billion. He is not making a speculative bet. He is looking at $20+ trillion of industries, a global infrastructure boom, an AI model that is ready to automate engineering tasks, and a labour market that desperately needs it.

The bet is not that physical AI will eventually matter. The bet is that it matters right now.

The Productivity Paradox

Now consider what these industries look like from the inside.

There is a number that should bother every founder in tech. Since 1945, productivity in US manufacturing has grown by roughly 1,500%. Agriculture transformed beyond recognition. Retail, logistics, finance, media - all reshaped by digital tools, often multiple times over.

Construction? The Richmond Federal Reserve’s 2025 research found that US construction labour productivity fell more than 30% from 1970 to 2020 - a fifty-year decline- while the rest of the American economy doubled output per worker.

A 2022 academic paper estimated that the construction sector alone accounted for about one-third of the decline in US trend GDP growth since World War II. The compounding cost is approximately $1 trillion in lost value every five years.

McKinsey translates this into opportunity: if construction productivity merely caught up with the rest of the economy, the industry’s annual value added would rise by $1.6 trillion - enough to meet half the world’s annual infrastructure needs and boost global GDP by 2%.

This is not a sector that tried technology and found it didn’t work. It is a sector that technology has almost entirely skipped. AEC companies spend just 1 to 2% of revenue on technology - versus 3 to 5% across industries generally. The industry has been under-investing in software for so long that the gap between what is possible and what exists is measured not in years but in decades.

Pharma manufacturing tells a similar story. While AI drug discovery companies collectively raised nearly $1 billion in disclosed rounds just in the 12-month period to mid-2026, the broader pharma digital manufacturing market - estimated at $42 billion globally - is being served primarily by legacy enterprise vendors. Most pharmaceutical production facilities run batch release processes that involve manual review of paper records.

A contamination event or quality failure is caught by a human reading a printout. The AI revolution in pharma has been entirely front-loaded toward discovery and has barely touched production.

And the grid: the US interconnection queue - the waiting list for new projects to connect to the power grid - currently holds more than 2,400 gigawatts of pending projects.

For scale, total US generating capacity today is roughly 1,200 GW. There is twice the current grid sitting in a queue, waiting.

The bottleneck is not technology, not capital, not demand. It is the absence of modern software systems to model, manage, and accelerate the process of connecting new infrastructure to existing infrastructure.

The Startup Density Gap

Here is the most precise way to see the opportunity.

Take any major industry. Plot it on two axes: the size of that industry against the number of AI-native startups actively building for it. The result is a map of where founders have gone and, more importantly, where they haven’t.

• Fintech: multi-trillion-dollar industry, thousands of AI-native companies.

• Healthtech software: same.

• Legal tech, marketing automation, enterprise SaaS, developer tools - all densely populated with venture-backed AI-native startups, across every subsegment, at every stage.

Now look at construction, pharma manufacturing, and grid infrastructure. Enormous industries. Tiny startup footprints.

The numbers make this concrete, which makes it even more interesting:

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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 :

• Why are billion-dollar VC funds racing into seed deals?

• The truth behind OpenAI’s $852 billion price tag (What you need to know before the IPO).

Frameworks & insightful posts :

• Where is Consumer AI actually headed and who gets to win?

• Anthropic reveals how close AI is to building itself?

• What did hundreds of founders reveal about VCs? - A must-read thread.

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🧠 Big idea + report of the week

Why are billion-dollar VC funds racing into seed deals?

One of the most common beliefs in venture capital is that seed investing is still protected territory.

The logic sounds reasonable.

Mega-funds manage billions of dollars. They should be spending their time writing large Series B, C, and growth checks, while emerging managers compete for the earliest deals.

But new research from Pavel Prata at Murph Capital, using Harmonic data, suggests the market has already changed.

The analysis tracked the early-stage investing activity of 10 mega-funds ($10B+ AUM) across three distinct eras: the SaaS era (2015-2019), the ZIRP era (2020-2022), and the AI era (2023-2026).

The conclusion is difficult to ignore:

Mega-funds are doing far more seed investing today than they ever have before.

Some of the numbers are staggering.

• Andreessen Horowitz increased from 16.6 early-stage deals per year during the SaaS era to 75.3 deals per year in the AI era. That’s a 4.5x increase.

• General Catalyst grew from 15.2 deals per year to 61.5 deals per year, a 4x increase.

• Sequoia moved from 19.6 deals per year to 50.6 deals per year.

• Lightspeed increased from 11.6 deals per year to 32.1 deals per year.

What’s most interesting is that this wasn’t supposed to happen.

Many investors assumed the explosion in seed activity during 2020-2022 was simply a consequence of free money and zero interest rates.

The expectation was that once capital became more expensive, activity would normalise.

Instead, the opposite happened. a16z and General Catalyst are now investing at levels that exceed their ZIRP peaks.

In other words, this isn’t a temporary distortion. It’s a new baseline.

The research identifies three distinct fund behaviours emerging:

• Accelerators: a16z, General Catalyst, and Khosla Ventures are investing in more seed deals today than ever before. They didn’t pull back after ZIRP ended. They accelerated.

• Stabilisers: Sequoia, Accel, and Lightspeed have moderated from peak activity levels, but still operate at roughly 2-3x their SaaS-era pace.

• Disciplined operators: Bessemer, Index Ventures, and Lux Capital avoided extreme spikes but still increased their seed activity materially compared to a decade ago.

The result is a dramatic shift in market structure.

During the SaaS era, these 10 firms collectively completed roughly 140-150 early-stage deals annually.

Today they complete roughly 370-400 deals per year.

That’s almost triple the volume.

For founders, this means there are more well-capitalised investors willing to write seed checks than ever before. But for emerging managers, the implications are more complicated.

The competitive landscape is no longer a handful of seed specialists competing against each other.

It’s seed specialists competing against firms with billions in capital, massive platform teams, global networks, and established brands.

The takeaway isn’t that emerging managers can’t win. It’s that they can no longer rely on “we invest earlier” as a differentiator.

The firms that succeed will be the ones that can clearly answer a harder question:

Why should a founder choose you when Sequoia, a16z, General Catalyst, and Accel are all playing the same game?

That’s becoming the defining question for the next generation of venture firms.

The truth behind OpenAI’s $852 billion price tag (What you need to know before the IPO).

OpenAI is racing toward a $1 trillion IPO with 905 million weekly active users, 92% Fortune 500 penetration, and ~$30B in trailing ARR. On paper, it looks like the undisputed AI leader. But a fresh analyst note from PitchBook tells a different story - one where the price tag is running far ahead of the business.

PitchBook scored OpenAI using their AI Business Quality (AIBQ) framework across five dimensions - capital efficiency, revenue quality, compute independence, governance, and moat durability.

OpenAI landed 4.8 out of 10, dead last in its peer group, while trading at $177.5B per AIBQ point - 11.7x the rate of Databricks at $15.1B per point. The brand is everywhere. The fundamentals are another conversation.

Here’s what the numbers actually show:

• Q1 2026 revenue was $5.7B - but OpenAI burned ~$7B that same quarter, spending $2.22 for every dollar earned, with a -122% adjusted operating margin

• 67 cents of every revenue dollar goes to compute costs before a single dollar reaches R&D or sales - against an internal gross margin target of 46%, the actual figure sits at 33%

• Break-even ARPU is ~$80/month. ChatGPT Plus costs $20. Only the $200 Pro and custom Enterprise tiers actually cover their cost of delivery

• Total infrastructure commitments sit at ~$1.2 trillion across Azure, Oracle, AWS, and others - with committed compute alone ramping to $80B in 2028, which exceeds OpenAI’s projected revenue that year

• Annual subscriber churn runs at 41%; nearly half the user base turns over every year, yet net revenue retention sits at 88%, well below the 120%+ benchmark institutional investors expect from SaaS businesses

The Microsoft deal is simultaneously OpenAI’s biggest relief and its biggest unknown. A renegotiated $38B revenue-share cap saves OpenAI an estimated $70B–$97B through 2030 - without it, profitability is mathematically impossible.

But post-2030 terms are completely undisclosed, and that’s precisely when OpenAI expects first to turn cash flow positive.

The competitive picture is shifting fast too:

• 18 months ago, OpenAI led enterprise LLM spending at 50%. Today it’s at 27%, with Anthropic surging from 24% to 40%

• Claude Code now holds ~54% enterprise coding market share with ~$2.5B ARR, and coding is where enterprise relationships are won

• ChatGPT’s share of AI web traffic fell from 86.7% to 64.5% in just 12 months

• Anthropic, now valued at $965B, just filed its confidential S-1 on June 1 and is projecting its first profitable quarter - while OpenAI projects $665B in cumulative cash burn through 2030

If Anthropic lists before OpenAI, OpenAI prices against a profitable competitor at a higher valuation and a better AIBQ score. PitchBook sees no scenario where that favours OpenAI.

Working backwards from the $852B valuation, the implied 2030 free cash flow needed is $95B–$105B. The current trajectory points to a range between -$10B and -$30B.

Closing that 3x–7x gap requires gross margins to nearly double, $1.2T in infrastructure commitments to shrink, consumer monetisation to accelerate, and a Microsoft renewal that hasn’t even been negotiated yet - all simultaneously.

The scale is real. The window to justify the price is narrowing.

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🧩 Frameworks & insightful posts

Where is Consumer AI actually headed and who gets to win?

Consumer AI is growing fast. GenAI app downloads went from roughly 200 million in 2022 to 3.8 billion in 2025, and mobile in-app purchase revenue scaled from under $200 million to $5 billion over the same period.

But PitchBook’s latest State of Consumer AI report digs past the headline numbers and finds a market that is deeply concentrated, structurally bifurcated, and increasingly unforgiving for anyone outside the top tier.

The demand signal is real - but so is the monetization gap.

Content creation leads usage across both Claude (43% directive share) and ChatGPT (28% message share), with $3.8 billion in VC deal value growing 85% YoY.

Education shows the starkest disconnect: strong engagement on both platforms but VC funding down 65% YoY.

The reason is simple - OpenAI’s paid conversion rate sits at just 5%, which actively suppresses willingness to pay across the category rather than building toward it.

A few things reshaping how this market gets built and funded:

• Consumer AI pricing has broken into new territory.

Before ChatGPT, $100+/month was commercially nonviable for consumer products. Now 57 startups charge above that threshold. OpenAI’s $200/month Pro tier and Perplexity’s $167/month plan both exceed what consumers pay for Amazon Prime annually ($139).

The catch: inference costs remain high, unit economics are strained, and only a narrow cohort of productivity-oriented platforms can hold those prices.

• The K-shaped economy is shaping the market structure.

The top decile of US earners owns 87% of domestic equities, while 90% of households face rising debt burdens, credit card balances up ~6% YoY, and $18.8 trillion in total household debt.

This forces B2C AI to monetize at the extremes: premium productivity tools for high earners, or free-tier mass market products competing directly against labs subsidizing hundreds of millions of users indefinitely.

• Late-stage return math is quietly breaking.

Median Series D+ ownership has fallen from 13.3% to 6.2% since 2016. A 6% stake in a $5 billion platform now requires a $15 billion+ exit just to 2x.

The practical implication is that growth-stage consumer AI has migrated from an ownership-driven return model to a price-appreciation-driven one - which means a lot more rides on picking the right companies early.

• The value is extremely concentrated.

The top 10 consumer AI unicorns now represent 79.4% of total category value, up from 61.7% in 2024.

The entire “consumer unicorn” asset class is functionally a bet on 10 companies. Everything else is competing for the remaining 20%.

• Series B is the sweet spot.

Seed posts the highest annualized returns at 93.4%, but dollar failure rates hit 34.8% driven by overfunded rounds, not failed companies.

Series B offers a better risk-adjusted profile: 97.1% company survival rate, 63.5% annualized return, and only 5.4% dollar failure rate.

PitchBook’s suggested playbook is disciplined seed checks for product-market fit, then concentrated Series B positions in the survivors.

The IPO window hasn’t opened for most B2C names. Of recent VC-backed IPOs, only -

• CoreWeave has meaningfully outperformed - up 98.9% since listing.

• Klarna is down 70.4%, Figma down 74.6%, StubHub down 56.5%.

The market is demanding proof of profitability before rewarding consumer platforms, and the gap between profitable and unprofitable B2C companies in public markets - now roughly 1.5x to 2x in EV/TTM multiple, is only widening.

The question isn’t whether consumer AI is growing. It clearly is. The question is whether the growth is translating into durable businesses and right now, that answer belongs to very few companies.

Anthropic reveals how close AI is to building itself?

Something quietly significant dropped this week. Not a model launch, not a valuation headline - Anthropic published an internal data dump on how much of their own work AI is already doing. And it reads less like a PR piece and more like a rare moment of honesty from one of the most important labs in the world.

The number that stops you cold: over 80% of the code merged into Anthropic’s codebase today was written by Claude.

A year ago it was low single digits. That’s not a gradual shift - that’s a flip.

The piece traces exactly how this happened and where it’s heading. The timeline is worth sitting with:

• 2021–2024 - Engineers write code, Claude occasionally helps with snippets

• 2025 - Claude Code launches, Claude starts writing and editing entire files

• 2026 - Claude runs autonomously for hours, delegates to other agents

• 20XX? - Claude potentially designs and trains its own successors

That last step is what they call recursive self-improvement. Not here yet. But the internal data points toward it more clearly than anyone outside the lab would have guessed.

A few things that matter specifically if you’re building or investing right now:

The productivity jump is real and the bottleneck keeps moving

Anthropic engineers are merging 8x as much code per quarter as they were in 2024. But the more interesting signal is what happened after.

Once Claude wrote the code, human review became the constraint. Once Claude ran experiments, choosing which ones to run became the constraint.

Every time one layer gets automated, the ceiling drops to whatever humans are still doing. That loop will repeat across every knowledge-work company, not just AI labs. The teams that spot the new bottleneck fastest are the ones that pull ahead.

The task horizon is expanding faster than most people track

• Early 2024 Claude handles 4-minute tasks reliably

• Early 2025 - Claude Sonnet 3.7 handles 90-minute tasks

• Today - Claude Opus 4.6 handles 12-hour tasks

The doubling rate is every four months. Week-long tasks are in range this year. Month-long tasks by 2027. That’s not a feature update - it’s a fundamentally different category of tool arriving every few months. Products and workflows built around today’s capabilities will feel dated fast.

The research autonomy experiment is the most underreported thing in this piece

Anthropic gave Claude-powered agents an open AI safety problem - no hand-holding, just a question and compute access.

Two humans working for a week recovered 23% of the performance gap on the problem. The agents recovered 97%, designing every experiment themselves. The only thing humans did was pick the problem to work on.

The gap that still exists is judgment - knowing which problems matter, which results to trust, when an approach is a dead end. Real gap, genuinely still human. But the honest read is that this might just be the next thing on the curve.

What this actually means if you’re building

The 100-person company doing the work of a 1,000-person one isn’t a pitch deck line anymore - Anthropic is already living it internally. But the unlock isn’t just speed. It’s surface area. Things that were permanently on the backlog get done. In one example from the piece, Claude shipped over 800 fixes that reduced a class of API errors by 1,000x.

The engineer overseeing it estimated a human would have taken four years. That’s not productivity - that’s a different category of what’s possible.

What this means if you’re investing

If engineering output per person scales 8x in two years, the advantage of having more engineers shrinks fast. Distribution, taste, trust, and proprietary data start mattering more than headcount.

The companies still pricing deals around team size as a proxy for execution capacity are using the wrong model. That rerating is already underway - it just hasn’t fully shown up in how term sheets get written yet.

The governance piece at the end - whether labs can actually coordinate a slowdown, whether verification regimes for AI training are even buildable - is the part most coverage will skip. Worth not skipping.

What did hundreds of founders reveal about VCs? - A must-read thread.

For years, founders have swapped VC horror stories in private group chats, dinners, and late-night founder calls.

Rarely do they say them publicly.

That changed this week when a simple post from founder Greg Isenberg triggered hundreds of founders to share their most memorable fundraising experiences. The stories ranged from hilarious to shocking, offering a rare look into the other side of venture capital.

Here’s a quick rounded up many of the stories, and the thread quickly became one of the most talked-about conversations in startup circles.

Some of the stories stood out:

Greg Isenberg shared that while pitching a $15M Series A to one of Silicon Valley’s top VC firms, a partner fell completely asleep for more than 30 minutes. Nobody acknowledged it. Everyone just continued the meeting.

• Zynga founder Mark Pincus experienced something similar. One investor slept through his pitch, and his friend encouraged him to keep presenting anyway.

• Former WayUp founder Liz Wessel said a well-known investor slept during her Series A pitch, yet the firm later sent a term sheet. Her team ultimately rejected the offer.

• Uber founder Travis Kalanick shared that a VC tried to quietly leave a meeting before hearing his pitch. Kalanick followed him all the way to his car and continued pitching from the passenger seat.

• One founder described investors signing term sheets and later disappearing before wiring money. Some reportedly continued asking for company updates afterward as if they were investors.

The most surprising stories came from Cloudflare founder Matthew Prince.

• Prince said one investor passed on Cloudflare because he allegedly believed a woman couldn’t lead a cybersecurity infrastructure company. That woman was Cloudflare co-founder Michelle Zatlyn. Today, Cloudflare is worth tens of billions of dollars.

• Prince also shared another story involving legendary investor Vinod Khosla. According to Prince, after offering to invest, Khosla allegedly suggested he remove his co-founders and keep their equity for himself. Prince said the conversation ended any possibility of working together.

Not everyone joined in to criticize investors.

Several founders pointed out that many VCs are thoughtful, supportive, and genuinely helpful partners. But the sheer volume of stories revealed something important: strange fundraising experiences are far more common than most people realize.

The deeper lesson isn’t about bad investors. It’s about power.

Most founders only feel comfortable sharing these stories after they’ve built successful companies, sold businesses, or no longer need venture capital. The founders naming names today are often the ones who already have leverage.

For early-stage founders, the thread serves as a reminder that fundraising is a two-way diligence process. Investors are evaluating founders, but founders should be evaluating investors just as carefully.

The right investor can accelerate a company for a decade.

The wrong one can become a problem long after the money hits the bank account.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• 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)

• Gigascale Capital, a Palo Alto, CA-based venture capital firm focused on the physical economy, closed its first institutional fund, at $250m. (Link)

• Benchmark Capital, the Silicon Valley venture firm behind early bets in eBay, Uber, Snap, and Twitter, has raised $2 billion across two new funds, including its first-ever $1.25 billion growth fund. (Link)

New Startup Deals

• AethexAI, a Cambridge-based startup building localized voice AI models, raised $3M in Pre-Seed funding. (Link)

• Willow, an Israel-based startup building an agentic access platform, raised $7M in Seed funding. (Link)

• Ramp, the financial operations platform, raised $750M in Series F funding at a $44B valuation. (Link)

• WasabiCard, a Singapore-based stablecoin payment infrastructure company, raised ~$10M in Pre-A funding. (Link)

• Quobly, a France-based quantum computing startup building silicon-based quantum computers, raised €115M in Series A funding. (Link)

• Terra AI, a Palo Alto-based AI platform helping solve subsurface uncertainty for mineral and energy development, raised $20M in Series A funding. (Link)

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📜 DEEP DIVE

Is workflow debt silently killing your AI returns? What the 5% who actually win do differently.

Recently I met a founder. We got talking about AI the way you do these days - not if, but how much, how fast, how far.

He wasn’t complaining. That’s the thing I keep coming back to. He was genuinely proud of what his team had built. Eight months earlier he’d walked into his board meeting and told his investors something that made everyone in the room feel good about the future.

“We’ve automated our support triage, lead qualification, and weekly reporting. AI has given us the leverage of a 60-person team.”

The board loved it. He loved it. The dashboards looked clean. The story was working.

Then, slowly, quietly, the way these things always happen - it wasn’t.

The AI had done exactly what he asked it to do. The process was broken long before he touched it. All he’d done was find a faster, more consistent, considerably more expensive way to be wrong.

He said it quietly, almost to himself: “I didn’t automate our operations. I automated our mess.”

He’s not alone. Not even close.

This is the conversation happening behind closed doors at almost every startup right now. Not in the board decks, press releases or founder Twitter threads about shipping velocity. In the quiet moments when the dashboards don’t add up, and nobody wants to say it out loud.

The technology isn’t the problem. The technology has never been the problem. Bain put it better than anyone:

“The single most costly mistake in AI deployment is automating a broken process. AI doesn’t fix workflow debt - it locks it in, speeds it up, and makes it vastly more expensive to unwind.”

- Bain & Company, Automation and AI Pathfinder Survey 2026

Bain’s 2026 survey of 951 global companies found that while 37% of companies targeted cost reductions of 11–20% from their AI programs, nearly 40% of those who actually measured outcomes landed in the 0–10% bucket. And 90% of those same underperformers are now raising their budgets again for the next wave. Not pausing to figure out why. Just spending more.

That’s the AI ROI gap everyone’s dancing around. And for founders, it’s worse than it looks in the data - because the enterprise numbers hide the startup failure rate, and founders have three specific vulnerabilities that make them more exposed than any Fortune 500 IT department.

In this edition:

• What workflow debt actually is and why your team has been hiding it from you without knowing it

• The data three consulting firms independently agree on and why only 5% of companies are winning

• Why founders are structurally more exposed to this than any enterprise CIO

• The five workflows founders automate first and which three are quietly landmines

• Klarna, Cursor, Air Canada, Amazon what each one got right or catastrophically wrong

• A five-step framework to audit your workflow debt before your next automation sprint

• Why 44% of companies are funding their next AI wave from savings that never arrived.

What the data actually says

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Read more

👋 Hey, Sahil here - welcome to this 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

The AI-Native Startup Playbook: How to Build, Launch and Scale Faster With AI.

Something irreversible happened to the startup landscape this decade. The wall between “people who can build” and “people with ideas worth building” is gone.

For the first time in the history of technology, the most interesting founders are not necessarily the ones with engineering backgrounds. They are the ones with the deepest understanding of a real problem.

• The healthcare professional who has spent a decade watching a broken system from the inside.

• The lawyer who knows exactly where contract review falls apart.

• The logistics operator who has manually fixed the same supply chain failure every quarter for five years.

In 2026, these founders can build the product that career engineers never thought to make - because they never lived the problem.

That is the most underrated shift in the startup world right now. And it changes what being a founder actually means.

Historically, founders spent most of their time in execution mode: writing code, managing people, handling day-to-day operational work.

In an AI-native startup, that role changes fundamentally. The founder becomes an orchestrator - directing specialized AI agents that read files, run commands, execute code, and automate operations - rather than an individual contributor doing the work directly.

Your attention moves up the stack, toward the higher-order decisions: what to build, why, and which systems carry that vision out.

But here is the part that most playbooks skip over: the same tools that make building faster also make failing faster.

Agentic coding tools will generate, test, debug, and refactor a codebase around a fundamentally flawed premise with the same enthusiasm they bring to a great idea. They do not know if your market is real. They do not know if your customer wants this. The intelligence in the system is still yours.

42% of startups fail because they built something nobody wanted. That number was established before agentic coding existed. It is going up.

One more thing before we get into the stages, and this applies especially if you have deep domain expertise in a specific sector. That expertise is not just useful context. It is a compounding competitive moat.

• An AI medical billing tool built by a generalist breaks on 340B drug program claims. Yours does not, because you encoded that edge case.

• A contract review tool built by someone who has never been in-house legal misses the workflows that actually matter. Yours does not.

Over time, that specificity - built into your product session by session - becomes something no well-funded competitor starting today can replicate. We will cover exactly how to build and protect that moat in Stage 04.

This playbook maps the four core stages of the AI-native startup journey - Idea, MVP, Launch, Scale - with the specific failure modes, exit conditions, and practical exercises you need at each one.

As an AI-native startup, you can use tools like GPT or Claude for this. Based on my experiment, would suggest Claude.

In this issue:

• The new founder playbook: how AI has completely rewritten who can build a startup in 2026

• Idea to MVP to Launch to Scale - the exact stage-by-stage framework for building an AI-native startup

• The biggest mistakes founders make at each stage (and how to avoid them before they kill your startup)

• The CLAUDE.md habit every AI-native founder needs before writing a single line of production code

• How to build a defensible moat using AI that no competitor can replicate - even with unlimited budget

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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🗞️ 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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• Executive Assistant - Pear VC | USA - Apply Here

• Technical Investment Operations Associate - Alpaca VC | USA - Apply Here

• Program Manager - Yale | USA - Apply Here

• Head of Platform - Springdale Venture | USA - Apply Here

• Venture Capital Expert - Mercer | Australia - Apply Here

• Venture Fellow - Entrepreneur First | France - Apply Here

• Investment Intern - 6 Degree Capital | UK - Apply Here

• Investment Team - Antler | Germany - Apply Here

• Investor (Senior Associate/Principal) - Square Peg | USA - Apply Here

• Founding Investor - Discipulus Ventures | USA - Apply Here

• Senior Analyst - ICONIQUE Capital | USA - Apply Here

• Venture AI & Operations Specialist - Blum Venture | Austria - Apply Here

• Senior Associate - New York Life Venture | USA - Apply Here

• Program Manager - a16z | USA - Apply Here

• Associate - Manhattan Venture | USA - Apply Here

• VP — Finance & Compliance - Transition VC | India - Apply Here

• Senior Associate - ICONIQUE CAPITAL | USA - Apply Here

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What should founders say when investors ask about valuation?

[A few weeks back, I had a call with an investor who has a decade of experience investing in startups globally as a VC (let’s call them Investor-A). We discussed how founders can approach the topic of valuation when investors ask. I thought I’d share a framework with you to tackle this question!]

One of the hardest things about the fundraising process for entrepreneurs is that you’re trying to raise money from people who have “asymmetric information.”

VC firms see thousands of deals and have a refined sense of how the market values them because they receive price signals across these deals.

As an entrepreneur, it can feel as intimidating as going to buy a car where the dealer knows the price of every make & model of a car and you’re guessing at how much to pay.

Of course, unlike cars, there is no direct comparison across each startup, so these are just some general guidelines to try to even the information field. So, grab your coffee and let’s deep dive into it.

What expectations do you have about valuation?

It is not uncommon for a VC to ask about your price expectations in this fundraising process.

It’s a legitimate question, as the VC is in “price discovery” mode and wants a sense of whether you’re within their valuation range.

It’s a tough dance, but here is what Investor-A suggest:

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Big idea + report of the week :

• Has Venture Capital quietly become a bigger-fund game?

• What 500 unicorns reveal about where great founders come from?

Frameworks & insightful posts :

• Why are Investors betting billions on Brain-computer interfaces despite an adoption problem?

• Why the next billion-dollar sales companies won’t own the database.

• How Anthropic builds its sales organisation?

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🧠 Big idea + report of the week

Has Venture Capital quietly become a bigger-fund game?

For years, emerging managers could build successful venture firms with relatively modest fund sizes.

A $20M-$50M fund could write meaningful seed checks, reserve capital for follow-ons, and still maintain ownership in the best companies.

That math is becoming much harder.

Peter Walker, Head of Insights at Carta, recently shared data from more than 18,000 venture rounds that highlights a structural shift happening across the venture ecosystem: startup round sizes are getting bigger, lead checks are getting bigger, and the amount of capital required to execute the same strategy is rising almost everywhere.

The question many managers now face is simple: Does your fund size still match the market you’re investing in?

Startup round sizes have expanded significantly

The numbers are striking. In 2019, the median seed round was roughly $2.5M. By 2026, it had reached $4.5M.

Series A rounds have grown even faster. The median Series A was approximately $9M in 2019. Today, it sits at $16M.

This isn’t simply a story about higher valuations.

It means investors who want to maintain the same ownership percentages they achieved five or six years ago must deploy substantially more capital into each company.

And that creates pressure throughout the entire fund model.

Leading rounds are becoming more expensive

One consequence of larger rounds is that lead investors need much larger checks.

According to Carta’s data:

• Median seed lead checks increased from $1.2M to $2.3M.

• Median Series A lead checks increased from $4.5M to $8M.

For managers who position themselves as lead investors, this dramatically changes fund construction.

A fund that could comfortably lead multiple rounds a few years ago may suddenly find itself stretched thin if it wants to maintain the same pace of investing.

The result is that many firms either need larger funds or need to accept smaller ownership positions.

Follow-on investing has become harder, too

The challenge doesn’t stop after the first check.

Many venture firms want to double down on their best-performing companies as those businesses raise larger rounds at higher valuations.

But when startups are raising more capital every round, reserve requirements grow alongside them.

Maintaining ownership in winners requires increasingly large follow-on checks, particularly in today’s AI-driven market, where companies often scale valuations rapidly between financings.

What used to be a manageable reserve strategy can quickly become a capital allocation problem.

Bridging portfolio companies creates another layer of pressure

The third challenge involves bridge rounds. When portfolio companies need additional capital between financings, managers face difficult choices.

Every bridge check competes directly with: New investments, Follow-on investments and Future reserves.

As round sizes increase, supporting existing portfolio companies becomes more expensive, forcing firms to make harder tradeoffs than they did during previous venture cycles.

Why are many managers being pushed toward larger funds

Taken together, all three forces point in the same direction.

Larger rounds require larger lead checks. Larger lead checks require larger reserves. Larger reserves require larger funds.

That is one reason many venture firms have steadily increased fund sizes over the past several years.

The logic is understandable. If the market itself requires more capital to execute the same strategy, managers naturally feel pressure to raise more money.

The hidden risk behind bigger funds

Just because the market encourages larger funds does not mean larger funds are always the right answer.

A fund size should reflect:

• A manager’s sourcing advantage

• Their ownership targets

• Their stage focus

• Their ability to deploy capital effectively

When a firm raises significantly more capital than its strategy can support, performance can suffer.

The danger is that larger funds often feel rational in the short term because LPs generally welcome growth. But a mismatch between fund size and actual investment strategy can create problems that take years to become visible.

So many venture firms are still operating with assumptions built for the market of 2019.

The market of 2026 looks very different.

Round sizes are larger. Ownership is more expensive. Follow-ons require more capital. Bridging companies cost more. Everything points toward deploying larger amounts of money.

But the lesson isn’t simply “raise a bigger fund.”

The real lesson is that every manager should periodically revisit the assumptions behind their fund model.

Because in today’s venture market, the most dangerous strategy may be running yesterday’s playbook with tomorrow’s capital requirements.

What 500 unicorns reveal about where great founders come from?

The narrative around American innovation is often framed as a domestic success story. But the data tells a different story.

Research from Stanford professor Ilya Strebulaev, covering 500 US unicorns and 1,078 founders, found that 44% of all US unicorn founders were born outside the United States.

Nearly half of America’s most valuable startups were built by entrepreneurs who started their journey somewhere else.

The implication is hard to ignore: immigrant talent isn’t just participating in the US startup ecosystem - it is one of its core growth engines.

India leads the global unicorn founder pipeline

Among foreign-born founders, India ranks first by a wide margin.

The study identified 90 Indian-born founders behind US unicorn companies, making India the largest source of immigrant unicorn entrepreneurs in America.

Israel ranked second with 52 founders, followed by Canada (42), the United Kingdom (31), and China (27).

What’s notable is the breadth of countries represented.

The dataset includes founders from 65 different countries across six continents, reinforcing that breakthrough entrepreneurship is increasingly global, even when the companies themselves are ultimately built in the US.

Moving to America dramatically increases the odds of success

One of the most interesting findings isn’t about founders - it’s about companies.

Roughly 8% of US unicorns were originally founded outside the United States before relocating.

And the relocation effect appears significant.

According to the research:

• Israeli startups that moved to the US were about 9x more likely to become unicorns.

• Indian startups saw roughly a 6.5x increase in unicorn outcomes after relocating.

• UK startups experienced approximately a 2.5x advantage.

The pattern suggests that while great companies can be started anywhere, the US continues to offer unique advantages in capital access, talent density, customer reach, and scaling infrastructure.

California remains the centre of gravity

When international founders relocate, most still gravitate toward California.

The state continues to function as the primary destination for ambitious startups looking to scale globally.

However, the research also highlights interesting regional patterns.

Israeli-founded unicorns, for example, often choose New York over California, while other founders increasingly spread across ecosystems such as Massachusetts and Texas.

In total, around 15% of US unicorns changed headquarters at least once between founding and achieving unicorn status.

Location remains a strategic decision, not simply a logistical one.

Israel produces the most unicorn founders per capita

While India leads in total founder count, Israel stands out on a per-capita basis.

When adjusted for immigrant population size, Israel generated approximately 43.4 unicorn founders per 100,000 first-generation immigrants, the highest rate among all countries studied.

For comparison:

• New Zealand: 37.3 founders per 100,000 immigrants

• Belgium: 24.4

• Canada: 5.3

• India: 2.5

America’s advantage is attracting global talent

America’s startup leadership isn’t solely a result of domestic talent or capital. It comes from being a magnet for ambitious people worldwide.

Nearly half of US unicorn founders were born elsewhere.

Some arrived as students. Some are engineers. Some founders are looking for a larger market.

What they found was an ecosystem capable of turning global talent into globally significant companies.

The lesson for policymakers, investors, and founders is straightforward: America’s startup dominance isn’t built only on what happens inside its borders. It’s built on its ability to attract exceptional people from outside.

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🧩 Frameworks & insightful posts

Why are Investors betting billions on Brain-computer interfaces despite an adoption problem?

Brain-computer interfaces may become one of the most important technologies of the next few decades.

The vision is enormous: restoring movement to paralysed patients, helping blind people see again, treating neurological diseases, and eventually giving humans new ways to interact with computers.

Investors are betting heavily on that future. More than $3 billion has flowed into brain-computer interface startups since 2024, with companies like Neuralink attracting billions in funding and reaching multi-billion-dollar valuations.

Rosie Bradbury from PitchBook recently explored one of the biggest unanswered questions in neurotechnology: even if brain-computer interfaces (BCIs) work, how many people will actually want them?

Investor enthusiasm is clearly accelerating. Since 2024, more than $3 billion has flowed into BCI startups, helped largely by the progress and visibility of Neuralink.

But behind the excitement sits a much tougher challenge: adoption.

Today, the technology remains highly invasive. Neuralink, for example, requires surgery to implant electrodes into the brain. Despite raising over $1.3 billion and achieving a valuation above $9 billion, the company reportedly had just 21 clinical trial participants worldwide as of early 2026.

That highlights a reality many investors acknowledge privately: building the technology may be easier than convincing millions of people to use it.

Researchers studying patient behaviour continue to find hesitation around brain implants. Even among people suffering from serious neurological conditions, many are reluctant to place foreign devices inside their brains unless symptoms become severe.

A useful example is deep brain stimulation, one of the most established forms of brain-computer interface technology. Despite proven medical benefits for certain Parkinson’s patients, only a small percentage of eligible patients undergo the procedure each year.

The hesitation is understandable.

Unlike wearing a smartwatch or carrying a smartphone, BCIs (Brain-Computer Interface) involve surgery, long-term safety considerations, and psychological questions about identity and autonomy. For many people, the brain feels fundamentally different from any other part of the body.

Still, the healthcare opportunity alone is enormous. Potential patient populations include:

• More than 90 million stroke survivors globally

• Over 11 million people live with Parkinson’s disease

• 20-25 million people with non-cataract blindness

• Millions of people are suffering from paralysis and motor disabilities

For many of these patients, BCIs could restore lost capabilities rather than simply enhance existing ones. That’s why most investors underwriting the category today are focused on medical applications first.

The more ambitious vision comes later.

Some investors believe BCIs could eventually become consumer products used by healthy individuals to improve memory, cognition, productivity, or even keep pace with increasingly powerful AI systems.

The argument is that humanity may eventually augment itself in the same way smartphones augmented communication.

In that world, brain implants would evolve from medical devices into mainstream computing platforms. But there are several hurdles before that becomes reality:

• The technology must prove safe over long periods.

• Surgical procedures need to become simpler and less invasive.

• Costs need to decline significantly.

• Clear consumer benefits must emerge.

• Regulatory approval pathways must mature.

Perhaps most importantly, people need a compelling reason to undergo surgery.

As one expert quoted in the article noted, technology adoption doesn’t happen simply because something is possible. The assumption that “if you build it, they will come” rarely works in healthcare.

That’s why even bullish investors are careful about timelines.

Most aren’t investing based on a future where healthy consumers rush to implant brain chips. They’re investing because they believe hospitals, rehabilitation centres, and patients with severe neurological conditions represent a realistic first market.

The broader lesson is one that founders often forget when evaluating frontier technologies: Technical breakthroughs don’t automatically create mass markets.

The history of technology is filled with products that worked but never achieved widespread adoption because user behaviour, trust, regulation, or economics lagged.

Neurotechnology may ultimately transform medicine, computing, and even human cognition. But the companies that win won’t just solve engineering challenges.

They’ll solve distribution, trust, safety, and user adoption as well.

And in many ways, those may be the harder problems.

Why the next billion-dollar sales companies won’t own the database.

For the last two decades, most of the value in sales software has accumulated around one simple idea: own the database.

Salesforce built a $140B company. HubSpot built a $9B company. Thousands of startups came and went, but the winners owned the system of record - the place where customer data, deal history, contacts, and company knowledge lived.

Recently, a16z shared an interesting perspective: AI isn’t replacing the CRM. It’s changing where work happens.

The shift feels similar to what happened in social media.

Years ago, Facebook’s most valuable asset was the friend graph. Then the News Feed became the primary experience. The graph still existed, but it became an input powering something more valuable.

The same thing may be happening to CRM systems.

Instead of spending their day inside Salesforce or HubSpot, sales teams are increasingly working through AI-powered intelligence layers that sit on top of those systems.

A sales rep’s day is starting to look very different:

• AI researches accounts before meetings.

• AI summarises earnings calls and company filings.

• AI drafts outbound emails.

• AI updates CRM records automatically after calls.

• AI identifies which deals need attention.

• AI surfaces risks before managers notice them.

The CRM remains the database, but the AI layer becomes the interface where decisions are made.

One chart from a16z’s GTM survey illustrates this clearly.

Today, account research, meeting preparation, outbound drafting, and follow-ups are already heavily shifting toward AI tools. Pipeline forecasting and CRM management still lean more heavily on traditional systems, but even those workflows are beginning to move.

The interesting part is that CRM usage hasn’t collapsed. In fact, CRM usage has slightly increased.

Nearly half of respondents said their CRM usage stayed the same over the past year, while 49% reported using CRM tools more frequently.

Why?

Because AI agents are actually creating more data than humans ever did.

Every call transcript, meeting summary, email interaction, and account update now flows automatically into CRM systems. The database becomes richer, even if humans spend less time manually updating it.

That creates an important distinction:

The system of record remains valuable. The system of intelligence becomes even more valuable.

Historically, enterprise software created value by helping people store information.

The next generation of software may create value by helping people decide what to do with that information.

That’s why a growing number of AI-native GTM startups aren’t trying to replace Salesforce. They’re building orchestration layers above it.

These systems pull information from:

• CRM platforms

• Email

• Calendars

• Call recordings

• Product analytics

• Slack

• Internal documents

Then combine everything into recommendations, actions, and workflows.

In other words, the AI layer becomes the operating system for revenue teams.

Another surprising takeaway from the survey challenges a common narrative around AI replacing sales jobs.

When asked how sales headcount will change over the next two years:

• 39% expect headcount to increase.

• 37% expect it to stay roughly the same.

• Only 23% expect reductions.

The reason is simple. If AI helps sales teams become more productive, many companies would rather grow faster than simply cut staff.

This mirrors what happened throughout software history. Better tools often expand markets before they shrink jobs.

So, AI isn’t always destroying existing categories. Sometimes it’s reorganising them.

Salesforce and HubSpot still own some of the most valuable datasets in enterprise software. That advantage isn’t disappearing anytime soon.

But the next generation of billion-dollar companies may not own the database. They may own the intelligence layer sitting on top of it.

And just as the News Feed became more important than the friend graph, the system that tells you what to do next may become more valuable than the system storing the data itself.

How Anthropic builds its sales organisation?

Most companies approach AI adoption the wrong way.

They buy another AI product. Add another dashboard. Create another workflow. And end up with seven disconnected tools instead of six.

Anthropic took the opposite approach.

After demand for Claude exploded following the release of Claude Opus 4.6, the company faced a problem most startups would love to have: more inbound demand than its sales team could handle.

Recently, Jason Lemkin (Founder of SaaStr) shared a YouTube video on this channel -

Instead of hiring hundreds of new reps or ripping out their existing software stack, they rebuilt the sales organisation around one idea:

Make AI the connective tissue between every system that already exists.

The result was remarkable.

54% of Anthropic’s new enterprise customers in 2026 came through a self-serve funnel.

Real enterprise accounts with contracts, invoicing, onboarding, and meaningful annual contract values.

The interesting part is that Anthropic didn’t replace its existing tools.

Their core stack remained:

• Salesforce for CRM and opportunity management

• Go for call recordings and coaching

• Ironclad for contracts

• Slack for coordination

• LeanData for lead routing

• Clay for enrichment and qualification

Claude sits in the middle and connects them all. Think about how most sales reps work today.

A rep jumps between Salesforce, Slack, LinkedIn, email, Gong recordings, company websites, internal documents, pricing pages, and spreadsheets just to prepare for one customer conversation.

The actual selling often takes less time than gathering context. Anthropic’s insight was that AI should eliminate those transitions.

A rep now starts the day with a Claude-generated briefing.

It pulls information from Salesforce, Gmail, Slack, calendars, Gong, customer conversations, and internal documents, then produces a prioritised list of actions:

• Which deals need attention

• Which customers require follow-up

• Which emails matter

• Which opportunities are at risk

Instead of opening ten tabs, the rep starts with a single source of truth.

Before a customer call, reps use a “Call Prep” skill.

Rather than spending 30 minutes researching the account, Claude generates a briefing that includes:

• Customer history

• Stakeholders involved

• Previous conversations

• Discovery questions

• Competitive positioning

• Public company information

Five minutes of preparation replaces half an hour of manual work. What’s even more interesting is how Anthropic handled internal operations.

One of the biggest bottlenecks in enterprise sales isn’t selling. It’s getting answers.

• Legal approvals.

• Security reviews.

• Pricing exceptions.

• Vendor onboarding.

• Contract redlines.

Every sales organisation eventually gets slowed down by support functions that can’t scale as fast as revenue teams.

Anthropic’s solution was simple: Slack became the front door for everything.

An employee submits a request in Slack. Claude reviews it first. If the answer already exists in policy or precedent, Claude resolves it immediately.

If escalation is needed, Claude packages all relevant context and sends it to the appropriate human. Instead of endless email threads, approvals move through a structured workflow.

The broader lesson is that AI’s biggest impact inside enterprises may not be automation.

It may be orchestration.

For years, enterprise software revolved around systems of record. The company that owned the database won.

Now we’re moving toward systems of intelligence. The winner may not be the company storing the data. It may be the company helping employees make decisions using that data.

Anthropic also did something many organisations still haven’t figured out: They turned their best sales reps into software.

The company identified high-performing workflows and packaged them into reusable Claude “Skills.”

Every new rep gets access to:

• Morning Briefings

• Call Preparation

• Customer Follow-Ups

• Competitive Intelligence

• Custom Asset Creation

Instead of hoping new hires learn from top performers, best practices become part of the system. A new employee effectively starts with years of organisational knowledge already embedded into their workflow.

That’s probably the most important takeaway from this story. Most companies think AI adoption means replacing people.

Anthropic used AI to amplify people. They connected everything together. And that’s a pattern we’re starting to see across modern organisations.

The biggest AI opportunity isn’t creating another tool. It’s eliminating the friction between the tools companies already use.

Now you might be able to connect the previous write-up line - But the next generation of billion-dollar companies may not own the database. They may own the intelligence layer sitting on top of it.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

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

• Transition Ventures, a London, UK and NYC-based early-stage investment firm, closed its second fund at $150m. (Link)

• Bridgewest Ventures, an Auckland, New Zealand- and Miami, FL-based venture capital arm of Bridgewest Group, held the first close of Bridgewest Venture Fund I LP, at approximately NZ$60.2m. (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)

New Startup Deals

• Saris, a San Francisco-based agentic workflow platform for banks and credit unions, raised $28.8M in Series A funding.(Link)

• Rep AI, a NYC-based AI platform for ecommerce brands, raised $6.2M in funding.(Link)

• Slamcore, a London-based spatial intelligence software company, raised $14M in funding backed by ROKStar Ventures.(Link)

• ClearNote Health, a San Diego-based cancer early detection company, raised $52M in Series D funding.(Link)

• Stride, a Vietnam-based clean energy fintech startup, raised $15M in Series B funding. The round was co-led by Lightrock and TRIREC.(Link)

• GEEIQ, a London-based analytics platform for games and virtual worlds, raised $6.8M in funding. The round was led by YFM Equity Partners.(Link)

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• Associate - Manhattan Venture | USA - Apply Here

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📜 DEEP DIVE

The end of cheap AI - Why AI startups may face their AWS moment sooner than expected.

For the last two years, the AI industry operated inside one very powerful assumption:

Inference costs - the cost of running AI models every time a user sends a prompt - would keep collapsing forever.

Models would become smarter every quarter. Open-source competition would commoditise intelligence. Hardware improvements would lower serving costs. And eventually, AI companies would inherit the same beautiful economics that made SaaS one of the greatest business models in history.

That belief shaped an entire generation of startups.

Founders launched “unlimited AI” plans before understanding long-term usage behaviour. Investors rewarded explosive adoption over contribution margins. Enterprises rolled out copilots across entire organisations, assuming economics would naturally improve with scale.

And honestly, during the early wave, that assumption looked correct.

The models improved dramatically. Costs per token kept falling. Every new release made the previous generation cheaper. AI adoption exploded faster than almost any software category in history.

(A token is simply a small unit of text that AI models process. Every prompt, response, workflow, memory layer, or agent action consumes tokens - which means more usage directly increases compute costs.)

But underneath all that excitement, another trend was quietly growing even faster: Usage.

That is the part most founders underestimate.

Because while inference costs were falling, token consumption was exploding. Autonomous workflows were becoming heavier. Context windows were becoming larger. Agents were chaining together increasingly expensive operations behind the scenes.

And now the industry is starting to discover something uncomfortable:

Many AI products were priced like software while being consumed like infrastructure.

That distinction matters enormously. Traditional SaaS businesses become more profitable as engagement increases.

Many AI businesses are becoming more expensive.

That is the real story beneath the recent signals from Microsoft, Uber, GitHub, OpenAI, Anthropic, Cursor, and Replit. Not that AI demand is collapsing. Demand remains incredibly strong. The problem is that AI is leaving its subsidy era and entering its cost-accounting era.

And once that transition begins, entire business models start getting exposed.

In this deep dive, we’ll break down:

• Why Microsoft and Uber’s recent AI decisions matter more than people realise?

• Why are token economies becoming more important than model intelligence?

• The hidden pricing illusion behind AI agents and copilots.

• Why flat-rate AI pricing is starting to collapse?

• Why many AI startups may never reach traditional SaaS margins?

• Which AI startups become vulnerable, and which become stronger?

• The new AI founder playbook for pricing, infrastructure, and margins.

• What investors will likely start looking for over the next 24 months?

The Microsoft + Uber signal

The Microsoft story matters because this was not some small startup reducing experimentation.

According to reports, Microsoft is removing most internal Claude Code licenses across major engineering groups and redirecting developers toward GitHub Copilot CLI instead.

Importantly, Microsoft still maintains deep ties with Anthropic. The Azure partnership remains intact. Anthropic models are still available in Azure AI Foundry. Microsoft still believes frontier AI models matter strategically.

Which is exactly why this decision is important.

Microsoft was not rejecting Claude’s quality.

It was rejecting - the economics of unlimited third-party AI usage running inside massive organisations where platform control, infrastructure optimisation, and procurement discipline increasingly matter.

Then came Uber.

And Uber’s situation made the economic problem impossible to ignore.

Reports suggested Uber burned through its entire 2026 AI coding tools budget in roughly four months after internal adoption exploded. Monthly AI tooling costs reportedly reached around $500-$2000 per engineer, while almost 95% of engineers were actively using AI tools every month.

That is not weak adoption. That is extraordinary adoption.

But the important part was not the adoption itself. It was the reaction afterwards.

Uber leadership reportedly started questioning - “whether increasing token spend was actually translating into proportional business outcomes.”

That single shift in thinking represents the next era of the AI market.

For the last two years, most AI products were evaluated through:

• excitement

• experimentation

• speed gains

• developer enthusiasm

• productivity narratives

Now, enterprises are starting to evaluate AI the same way they evaluate:

• payroll

• cloud infrastructure

• software procurement

• operational efficiency

• contribution margins

That changes the conversation entirely.

Even Nvidia’s vice president of applied deep learning reportedly admitted that, for some teams, compute costs are now exceeding employee costs.

That sentence alone explains where the market is heading.

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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 :

• Is the AI funding boom creating a venture return problem?

• Why are LPs turning bullish on China’s AI market again?

Frameworks & insightful posts :

• The all-in-one startup operating system for founders & investors.

• Could AI observability become the next billion-dollar category?

• What 80,000 AI users revealed about the next billion-dollar opportunity for founders.

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🧠 Big idea + report of the week

Is the AI funding boom creating a venture return problem?

The AI funding boom has created a strange situation in venture capital.

On one hand, many AI companies are growing faster than almost any startups in history. Investors see enormous markets, real enterprise demand, and the possibility that a handful of these companies could become trillion-dollar businesses.

On the other hand, the math behind venture returns is starting to look increasingly uncomfortable.

A new PitchBook analysis from Kyle Stanford highlights just how aggressive pricing has become across AI startups and why more investors are quietly beginning to question whether the eventual exit market can support these valuations.

AI startups are now commanding enormous premiums at every stage

The data shows the valuation gap between AI and non-AI startups widening rapidly.

In Q1 2026:

• Median AI Series A pre-money valuations reached $78M, up 84% versus non-AI peers.

• Median AI Series B valuations climbed to $270.8M, roughly 55% higher than non-AI companies.

• Median AI Series C valuations reached $606.5M, levels not seen since the peak of the 2021 market cycle.

The key point is that this is not isolated to a few outlier companies anymore. The premium now exists across nearly every stage of the AI venture ecosystem.

And the speed of that expansion is accelerating.

The real question is not growth - it is liquidity

Most investors agree that AI is a transformational technology. The harder question is whether today’s private market pricing will eventually hold up when companies face public market scrutiny.

That concern matters because venture returns are ultimately determined by liquidity events:

• IPOs

• Acquisitions

• Secondary transactions

And right now, the market still lacks strong evidence that public investors are willing to support the multiples being created in private AI rounds.

PitchBook notes that companies going public in 2025 often accepted meaningful valuation resets simply to complete their IPOs. The median IPO step-up was just 1.1x from the last private round, an unusually weak outcome by historical venture standards.

That creates an uncomfortable possibility: AI startups may eventually face the same valuation compression that hit SaaS companies after the 2021 peak - only at a much larger scale.

DPI data is becoming a warning sign for the entire venture market

One of the strongest signals in the report comes from venture fund performance itself.

PitchBook highlights that 2019 and 2020 vintage VC funds currently show five-year DPI levels of just:

• 0.13x for 2019 funds

• 0.12x for 2020 funds

Those are among the weakest liquidity outcomes seen at this stage since before the global financial crisis.

That matters because today’s AI investors are deploying capital into an ecosystem where the broader exit market has not yet fully recovered.

In other words, venture firms are underwriting massive AI valuations before the industry has proven that liquidity can support them.

Even the secondary market is highly concentrated

The secondary market is currently the closest thing venture has to real-time pricing discovery.

But even there, liquidity appears concentrated in a tiny group of companies.

PitchBook notes that the top 20 startups on secondary platform Hiive represented 86.4% of total trading value in Q4.

That concentration reveals something important:

Investors are still willing to aggressively buy exposure to a very small number of elite AI companies.

But confidence drops sharply outside that top tier.

The implication is that many AI startups may eventually discover that capital availability and liquidity are not nearly as broad as current fundraising conditions suggest.

So, the AI boom is creating some of the fastest-growing companies venture capital has ever seen.

But rapid growth alone does not guarantee strong investor returns. Eventually, every private valuation collides with public market reality.

And right now, nobody truly knows whether today’s AI pricing levels are justified, too high, or potentially still too low.

That uncertainty is what makes this moment so unusual. Investors are not simply betting on company growth anymore.

They are betting that future public markets will accept valuation assumptions that private markets are aggressively pricing in today.

Why are LPs turning bullish on China’s AI market again?

For the last few years, most global conversations around AI investing have centred around the U.S.

OpenAI. Anthropic. NVIDIA. Massive infrastructure spending. Trillion-dollar narratives.

Meanwhile, China’s private markets were largely viewed through a very different lens - regulatory pressure, geopolitical tension, weak domestic consumption, and declining fundraising activity.

But that narrative may be starting to shift.

A new PitchBook analysis on China’s AI market, combined with comments from investors at a Hong Kong industry event, suggests Western LP sentiment toward China is improving again - especially around AI, robotics, infrastructure, and manufacturing-driven technologies.

And the reason is not hype. It is an industrial advantage.

China’s AI advantage may come from the physical layer, not the model layer

While much of the U.S. AI ecosystem is concentrated around frontier foundation models, investors increasingly believe China’s edge may emerge from the infrastructure and hardware side of AI.

That includes:

• Manufacturing scale

• Robotics

• Energy efficiency

• Industrial automation

• AI applications tied to physical systems

One of the more interesting comments came from Gaocheng Capital’s Jing Hong, who argued that China is effectively “exporting low-cost electricity in the form of tokens.”

The point sounds abstract, but the implication is important. Large language models ultimately depend on:

• Chips

• Compute infrastructure

• Electricity

• Low-cost inference

And China appears structurally competitive in several of those layers.

According to Hong, China has roughly double the electricity productivity of the U.S. at nearly half the cost per kilowatt. If inference costs become one of the defining economic bottlenecks in AI, energy efficiency itself becomes a strategic advantage.

Capital is already flowing back into Chinese AI

PitchBook data shows AI and machine learning deal activity in Greater China remains resilient despite broader weakness across private markets.

Even after four straight years of fundraising declines, investors at the event repeatedly suggested that Chinese private markets may already be bottoming out.

LGT Capital Partners’ Brooke Zhou noted that Chinese private equity still generated approximately 14% net returns, despite the difficult macro environment.

That matters because many Western LPs had sharply reduced exposure to China over the past few years due to geopolitical uncertainty and concerns around liquidity.

Now, some of that capital appears to be cautiously returning - particularly toward sectors where China maintains clear industrial advantages.

Robotics may become the next major AI trade

Several investors also suggested that the next phase of China’s AI boom may shift away from large language models and toward robotics and embodied AI.

That transition makes sense.

China already dominates many parts of the global manufacturing supply chain. Combining that infrastructure base with increasingly capable AI systems creates a natural pathway into:

• Industrial robotics

• Factory automation

• Autonomous systems

• AI-powered manufacturing workflows

InnoVision Capital’s Lane Zhao said that while the first half of 2026 saw heavy focus on large language models, the second half is expected to see significantly more activity around robotics applications.

That may ultimately become one of China’s strongest positions in the global AI race.

Hong Kong’s IPO market is helping sentiment recover

Another major factor improving investor confidence is liquidity.

Hong Kong has reclaimed the top global spot for IPO activity this year, giving investors renewed confidence that exits are still achievable in Chinese markets.

That is particularly important after years of concerns around delayed liquidity and shrinking public market opportunities.

The combination of:

• Improving IPO conditions

• Strong manufacturing capabilities

• Competitive infrastructure costs

• AI-driven industrial demand

is beginning to shift sentiment from defensive positioning toward selective optimism.

So, the global AI race is no longer just about who builds the best chatbot.

It is increasingly about who controls the infrastructure underneath AI itself. The U.S. still dominates frontier models and software ecosystems.

But China may be developing a very different kind of advantage - one tied to manufacturing scale, robotics, energy efficiency, and physical AI systems.

And if AI increasingly moves from software into the real world, those advantages may become far more important than many investors currently realise.

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Could AI observability become the next billion-dollar category?

There’s a strange disconnect happening inside enterprises right now. AI agents are moving fast - from experiments to production, but the systems to measure their impact are still catching up.

A recent report from CB Insights highlights this gap clearly: in a survey of executives, 80% said AI agents are a priority… yet 40% admitted they either can’t track ROI or don’t even know how to measure it.

That’s not a small problem. It’s a signal. Whenever adoption moves faster than measurement, a new infrastructure layer is built.

And that’s exactly what’s happening here.

The next wave of AI startups won’t just build agents - they’ll build the systems that explain whether those agents are actually working.

Three categories are quietly emerging as the backbone of this shift:

• Observability & evaluation (knowing what your agents are doing)

• Memory infrastructure (helping agents retain and use context)

• Cost + ROI attribution (linking spend to real business outcomes)

Each of these solves a different bottleneck - but together, they define how AI agents scale inside real companies.

Take observability first.

Right now, agents fail silently more often than people think. They hallucinate, mis-handle workflows, or break in edge cases - and without proper tooling, no one even notices.

That’s why this has become the most active AI market right now by deal count. What’s interesting is how fast this layer is becoming “must-have”:

• Startups are building automated evaluation systems that test agents before deployment

• Others are using reinforcement learning from real-world failures to continuously improve agents

• Simulation tools are running thousands of scenarios to stress-test agents at scale

Even incumbents like Snyk and Coralogix are acquiring in this space - clear signal that monitoring won’t stay a feature, it becomes core infrastructure.

Then comes memory.

Most AI agents today are still surprisingly “forgetful.” They handle tasks well in isolation but struggle in real enterprise environments where context matters - past conversations, internal data, workflows across teams.

That’s why memory is emerging as a separate category. Not just storing chat history - but building systems where agents can:

• Decide what information matters

• Update their memory over time

• Retrieve the right context across sessions and systems

This directly addresses one of the biggest adoption blockers companies report: integration complexity and lack of internal expertise.

If agents can “remember” the business, they become far more useful. Finally, the hardest piece: cost and ROI attribution.

Most companies today track AI success using proxy metrics:

• Time saved

• Productivity gains

• Cost reduction

But only ~25% are actually measuring revenue impact. That’s a huge gap.

Because AI doesn’t become a budget priority until it ties to outcomes. This is where a new generation of tools is emerging:

• Platforms that map model usage and token spend to business metrics

• Systems that track costs across multiple AI providers and agents

• Tools that show how changes in prompts or models impact revenue, latency, and quality

Right now, this category is still early - but it’s arguably the most important one because visibility without outcomes doesn’t drive decisions.

What’s really happening here is a shift in where value gets created. The first wave of AI was about building capabilities.

The next wave is about making those capabilities measurable, reliable, and economically justifiable.

And that’s where the biggest opportunities now sit. If you’re building in AI today, it’s worth asking a simple question:

Are you building the agent… Or the layer that helps companies trust, scale, and pay for those agents?

Because history suggests the second layer often ends up just as valuable.

What 80,000 AI users revealed about the next billion-dollar opportunity for founders.

Most conversations about AI get stuck in abstractions. We hear big claims about job loss, AGI, productivity, safety, and existential risk, but much less about a simpler question: what does “AI going well” actually look like for real people already using it?

Anthropic recently ran one of the most interesting studies I’ve seen on this. It invited Claude users to describe how they use AI today, what they hope it could unlock in their lives, and what worries them most. More than 80,000 people across 159 countries and 70 languages participated, which makes this less like a normal product survey and more like a global snapshot of how people are beginning to fit AI into everyday life.

What makes the findings valuable is that they move beyond generic optimism or fear. They show where AI is already delivering value, where it still feels shaky, and what kinds of products or companies are likely to matter next.

The biggest thing people want from AI is not magic. It’s a relief.

When Anthropic asked people what they most wanted from AI, the answers were surprisingly grounded. The top categories were:

That tells you something important. Most people are not asking AI to become some futuristic super-being. They want it to help them do better work, reduce mental overload, manage life logistics, learn faster, and reclaim time.

That’s a useful correction for founders. The most immediate demand is not for “AI for everything.” It’s for products that remove friction from real life.

A lot of the strongest responses had the same emotional structure underneath them:

AI helped me do what I already needed to do, but with less stress and more breathing room. Someone used it to reduce documentation burden at work. Someone used it to learn coding despite a learning disorder. Someone used it to ask questions they felt embarrassed asking other people.

So, the best AI products may not be the ones that feel the most futuristic. They may be the ones that quietly remove cognitive load.

Work is still the main wedge, but quality of life is the real promise

The largest category people mentioned was “professional excellence.” That makes sense. Work remains the easiest entry point for AI because the ROI is easier to spot. If AI saves time, improves output, or helps someone get through repetitive tasks faster, the benefit is obvious.

But what’s more interesting is what sat underneath that answer. Many people started by talking about productivity, then revealed that what they really cared about was what productivity enabled outside of work: more time with family, more emotional energy, less admin, less mental clutter.

That distinction matters.

A founder might think they’re building a productivity product, when in reality the real value proposition is:

• more time freedom

• less anxiety

• less friction in daily life

• more space for better work and better living

That is a much richer product insight than “help users move faster.”

AI is already delivering in a few very specific ways

When Anthropic asked whether AI had already taken a step toward users’ goals, 81% said yes. That’s a meaningful number. But the shape of delivery matters more than the headline.

The main buckets where people felt AI had already helped were:

This mix is fascinating because it shows that AI is not just being used as a faster search engine. It is already functioning as a kind of thought partner, teacher, translator, research assistant, and in some cases, even an emotional support layer.

Some of the strongest responses came from people who felt AI gave them access to things they previously couldn’t reach. Not because the knowledge didn’t exist, but because the format was inaccessible, judgment-heavy, too expensive, or too difficult to navigate. That’s a powerful clue about where AI creates the most real value: not just speed, but access.

For builders, that opens up a big product opportunity. AI seems especially strong when it does one of three things:

• reduces intimidation

• removes judgment from learning

• translates complexity into something people can act on

That’s a better lens than simply asking whether a task can be automated.

The most common fear is not “AI takes over the world.” It’s that AI becomes unreliable at exactly the wrong moment.

Public AI debates often drift to dramatic long-term fears. But the top concern in this study was much more immediate: unreliability.

Users worry that AI will sound convincing while being subtly wrong. In some categories, that’s a mild annoyance. In others, it’s dangerous. Law, finance, healthcare, and government workers especially raised this concern because the cost of a confident error is high.

This is a critical insight for founders. Reliability is not a “nice to have” layer you add later. In many AI products, it is the product.

Other major concerns included:

What’s striking is how practical most of these fears are. People are not mainly debating whether AI is philosophically good or bad. They are asking whether it will make them less employable, less independent, less thoughtful, or too dependent on a system that is always available.

The real story is not optimism vs pessimism. It’s tension.

One of the best concepts in the report is what Anthropic calls the “light and shade” of AI.

The same capabilities people love are often the ones they fear. For example:

• AI helps people learn faster, but they worry it may weaken their own thinking.

• AI saves time, but they worry it increases expectations and speeds up the treadmill.

• AI offers emotional support, but they worry it could become a substitute for human connection.

• AI creates economic opportunity, but it also raises fear about displacement.

That is a much more honest frame than dividing people into “AI believers” and “AI sceptics.” Most users are both at once. They see upside and risk together.

That’s an important product lesson. Great AI products will not win simply by maximising usefulness. They’ll win by managing the tension around usefulness. The winners won’t just provide capability. They’ll create trust, boundaries, and clarity around how the product should fit into a person’s life.

The regional split is worth paying attention to

The report also found a noticeable pattern across geographies.

Users in lower- and middle-income countries were generally more optimistic about AI than users in wealthier regions. In many emerging markets, AI is seen less as a threat and more as a ladder, a way to start businesses, access education, or overcome infrastructure gaps.

That matters if you’re building global products.

In richer markets, AI is often framed around life management, overload reduction, and economic anxiety. In many developing regions, it is framed more around entrepreneurship, learning, and access. Same technology, very different emotional job-to-be-done.

Founders who understand that distinction will position themselves much better across geographies.

For founders -

The strongest message from this report is that people do not just want faster outputs. They want better lives.

That sounds obvious, but it is surprisingly easy to forget when building in AI. Many teams optimise around what the model can do instead of what the user is trying to become. Anthropic’s findings suggest that the most enduring AI products will be the ones that sit at the intersection of usefulness and human aspiration.

The near-term winning categories likely look less like vague general-purpose intelligence and more like products that help people:

• do meaningful work with less administrative burden

• learn without fear or embarrassment

• manage overloaded lives

• turn curiosity into action

• gain economic leverage

• access systems that previously felt closed off

At the same time, founders need to respect the downside. If your product saves time but increases dependency, gives emotional support but weakens real relationships, or accelerates output while making judgment worse, users will eventually feel that tension.

That’s why this study is so useful. It reminds us that AI is not entering a vacuum. It is entering messy human lives. And the companies that win won’t just be the ones with better models. They’ll be the ones who understand what people are actually trying to protect, improve, and reclaim.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Lauxera Capital Partners, a Paris, France- and San Francisco, CA-based growth-buyout and growth equity firm focused on Healthtech companies, closed Lauxera Growth II, at €520m. (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

• Oorja Bio, a Houston-based clinical-stage biopharmaceutical company, raised $30M in Series A funding. (Link)

• Avian, a Zurich-based industrial AI startup, raised $2.6M in Pre-Seed funding. The round was led by Founderful. (Link)

• StitcherAI, a Seattle-based IT finance intelligence platform, launched with $3M in Pre-Seed funding. (Link)

• Kin Health, a Los Angeles-based patient adherence platform, raised $9M in Seed funding. (Link)

• Leadbay, a San Francisco-based AI prospecting platform, raised $4.3M in Seed funding. (Link)

• Elliptic, a London-based digital asset decisioning company, raised $120M in Series D funding at a $670M valuation. (Link)

• Xpanner, a California-based construction automation company, raised $18M in Series B bridge funding. (Link)

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👋 Hey, here’s today at a glance -

Big idea + report of the week :

• Why are LPs fighting for access to the most valuable AI deals?

• Did OpenAI win attention while Anthropic built a better company?

Frameworks & insightful posts :

• What every founder needs to understand about AI economics?

• Where AI seed investors are most likely to find outliers.

• What’s driving the surprising decline in founder dilution?

START WITH

🧠 Big idea + report of the week

Why are LPs fighting for access to the most valuable AI deals?

For years, co-investments were treated as a useful side benefit of backing top venture funds.

Today, they have become one of the most important battlegrounds in private markets.

A recent PitchBook analysis by Jessica Hamlin explains why. As companies like OpenAI, Anthropic, and xAI race toward valuations that could eventually justify trillion-dollar outcomes, limited partners are fighting aggressively for direct exposure alongside their GPs.

The logic is simple: if a handful of frontier AI companies end up becoming the defining businesses of this decade, even a relatively small ownership stake could materially improve an LP’s overall portfolio returns.

That possibility is creating a new divide between institutions with the relationships, resources, and underwriting capabilities to access these deals and those that do not.

The biggest AI companies are attracting unprecedented amounts of capital

The scale of valuation growth in late-stage AI is unlike anything venture investors have seen in recent memory.

According to PitchBook, U.S.-based AI and machine learning startups raising Series D rounds and beyond reached a median pre-money valuation of $4.7 billion in Q1 2026. That is nearly four times higher than comparable non-AI companies and represents a 447.8% increase from 2024.

These numbers explain why LPs are so eager to participate.

If companies like Anthropic and OpenAI eventually grow into multi-trillion-dollar public businesses, direct co-investments made today could become some of the highest-returning allocations in an institution’s portfolio.

The market has shifted from capital scarcity to access scarcity

One of the most interesting insights in the report is how quickly the fundraising environment has changed. In early 2024, startups were competing intensely for capital. By 2026, the opposite is true.

PitchBook’s data shows that the demand-to-supply ratio for U.S. AI venture capital fell from 4.5x in Q1 2024 to just 0.9x in Q1 2026.

In practical terms, investors now have more money available than high-quality AI startups need to raise. That means the challenge is no longer finding capital.

The challenge is gaining access to the very small number of companies attracting extraordinary investor interest.

Fundraising cycles are accelerating

AI companies are also raising capital at a much faster pace than traditional startups. The median time between rounds for AI startups fell to 1.3 years in Q1 2026, compared with 1.9 years for non-AI companies.

This compressed timeline favors LPs with sophisticated co-investment programs that can evaluate opportunities quickly and commit large amounts of capital on short notice.

For smaller institutions, this creates a structural disadvantage.

Without dedicated internal teams and strong GP relationships, it becomes much harder to participate in the most competitive transactions.

Why co-investments matter so much

Co-investments are particularly attractive because they often come with little or no additional management fees and carried interest.

That allows LPs to increase exposure to their highest-conviction opportunities while reducing the overall fee burden on their portfolios.

A survey from Coller Capital found that:

• 44% of LPs said co-investments are becoming more important in their private markets strategy.

• Nearly 20% said they still do not have access to attractive opportunities.

In a market where a small number of AI companies may drive outsized returns, access to co-investments is increasingly becoming a competitive necessity rather than a nice-to-have.

The largest institutions are already positioned

Many of the world’s most sophisticated allocators have moved aggressively to secure exposure.

Examples include:

• Qatar Investment Authority participating in xAI’s $20 billion financing.

• GIC helping lead Anthropic’s $30 billion round.

• UC Investments joining OpenAI’s $122 billion financing.

UC Investments is particularly notable. Over the past decade, it reduced its manager relationships from 280 to just 28, reflecting a deliberate strategy of concentrating capital and increasing direct investment activity.

So - the AI boom is reshaping how LPs think about portfolio construction.

Historically, superior returns came primarily from selecting the right venture managers.

Increasingly, the greatest source of alpha may come from gaining direct exposure to the small number of companies building the foundational infrastructure of the AI economy.

That is why competition for OpenAI and Anthropic allocations has become so intense.

If these companies ultimately fulfill their potential, access to a single co-investment could meaningfully influence an institution’s long-term returns.

And for LPs without the necessary relationships and internal capabilities, the most important investment opportunity of the decade may remain frustratingly out of reach.

Did OpenAI win attention while Anthropic built a better company?

For most people, OpenAI still feels like the default winner of the AI era. It has the consumer brand, ChatGPT, massive usage, and the kind of mindshare very few startups ever reach.

But new PitchBook and Financial Times reporting shows the race is no longer that simple. Anthropic has reportedly signed a term sheet that would value it at $930B, ahead of OpenAI’s $852B valuation for the first time.

The bigger story is not just the valuation flip. It is what investors are choosing to reward.

OpenAI still has unmatched consumer reach, with reported weekly active users near 900M. But much of that usage remains free. Anthropic, on the other hand, is gaining ground inside enterprises, where contracts are stickier and margins matter more.

Ramp’s AI Index showed 34.4% of U.S. businesses using Anthropic versus 32.3% using OpenAI, a major shift from a year ago when Anthropic was below 10%.

That enterprise momentum is why investors may be underwriting Anthropic differently.

Claude Code has helped pull developers into workflows that can expand into broader company-wide contracts. If those accounts retain and expand, Anthropic starts to look less like a chatbot company and more like a high-quality enterprise software platform.

There’s also a capital efficiency angle. PitchBook notes that Anthropic’s expected $930B valuation comes on roughly $103B of equity raised, compared with OpenAI reaching $852B on about $173B.

That implies investors are paying more per equity dollar deployed into Anthropic, because they believe the path to public-market quality may be cleaner.

A few things seem to be driving that confidence:

• Strong enterprise adoption

• Reported gross margins above 70%

• Less dependence on a single strategic partner

• Cleaner corporate structure compared with OpenAI

• Developer-led adoption through Claude Code

The round itself also shows how concentrated late-stage AI investing has become. Greenoaks, Altimeter, Dragoneer, and Sequoia are reportedly leading the deal, each expected to commit at least $2B.

For Greenoaks and Altimeter, that would represent more than 10% of their AUM - a huge concentration bet even by growth-stage standards.

This is where the story gets bigger than Anthropic vs. OpenAI.

The AI frontier labs are now being valued like future public-market anchors. Whoever goes public first at a higher multiple could set the benchmark for the entire category.

That will influence how investors price model labs, AI infrastructure, enterprise copilots, and every company claiming to sit near the AI value chain.

For founders: attention is not the same as enterprise value. OpenAI won the first wave of consumer mindshare, but Anthropic is showing that enterprise adoption, margins, retention, and governance can matter just as much - maybe more when investors start thinking about IPO readiness.

The AI race is no longer just about who has the most users. It is about who can become the highest-quality business.

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What every founder needs to understand about AI economics?

For the past two decades, software has been one of the best businesses in the world.

Once you built the product, the cost of serving each additional customer was tiny. That’s what made SaaS so attractive. Companies could spend heavily upfront on engineering and go-to-market, then enjoy exceptionally high margins as they scale.

Domen Jemec recently shared an analysis explaining why AI may fundamentally change this equation.

His core argument is: AI makes software cheaper to build, but potentially much more expensive to operate.

That distinction matters more than most founders realise.

Why traditional SaaS became such a powerful business model

In classic SaaS, most of the cost is fixed.

You invest millions of dollars building the product, hiring engineers, and acquiring initial customers. But once the software is live, each additional user costs very little to support.

That creates a beautiful economic model:

• Revenue grows linearly as customers increase

• Cost per user declines over time

• Gross margins often exceed 70–80%

• Mature companies can reach 20–30% net margins

This dynamic is what made strategies like blitzscaling possible.

A company could lose money early, knowing that each new customer would become increasingly profitable as scale improved.

AI flips the cost structure

AI changes this because every meaningful interaction carries a real variable cost. Each prompt, agent workflow, reasoning step, and API call consumes tokens and infrastructure.

Unlike traditional software, where features become cheaper per user over time, AI-heavy features often remain usage-based and personalised.

That means costs scale much more directly with customer activity. In practical terms:

• More users generate more inference costs

• More complex workflows consume more tokens

• Personalised outputs reduce caching opportunities

• Heavy users can materially impact margins

The result is that software begins to look less like pure SaaS and more like a hybrid of software and services.

A simple way to think about it

Imagine two companies that both charge $10 per user per month.

The first is a traditional SaaS company named, GainZ.

It spends heavily upfront to build the product, but by the time it reaches scale, gross margins stabilise around 80%. At 1 million users, net margins can approach 20%.

The second is an AI-native company named AIBoosts.

It may cost far less to build because a smaller team can move faster with AI tools. But every user interaction incurs ongoing inference costs.

The company becomes profitable earlier because development costs are lower, yet long-term margins remain significantly lower because AI costs continue to grow with usage.

In Domen’s example, an AI-heavy product reaches only about 11% net margin at 1 million users, versus roughly 19% for traditional SaaS.

That difference has major implications for valuation and strategy.

The hidden shift founders need to understand

Many founders focus on how AI reduces engineering costs. That is real and important. Small teams can now build products that once required dozens of engineers.

But lower build costs do not automatically translate into better businesses.

In many cases, the true constraint moves from development cost to operating cost.

The new question becomes: Can you monetise the value of AI faster than your inference costs grow?

That is the central challenge of AI-native software.

Why this matters for valuations

Software historically commanded premium valuations because investors believed margins would expand dramatically over time. If AI compresses those margins, valuation frameworks may evolve as well.

Companies with structurally lower gross margins may be valued more like operational businesses than pure software companies. This does not make them unattractive.

It simply means investors will place greater emphasis on:

• Pricing power

• Proprietary data

• Distribution advantages

• Brand trust

• Operational efficiency

The best AI companies will still be extremely valuable, but the economics may look different from the SaaS playbook of the last decade.

How founders can respond

There are several ways to build durable AI businesses despite lower margins.

1. Align pricing with usage. If costs scale with activity, pricing should reflect that reality through tiers, overages, or premium plans.

2. Focus on high-value workflows. AI works best when it solves problems customers are willing to pay significantly more to address.

3. Build defensible moats. Proprietary data, network effects, and workflow integration matter more than ever.

4. Expand beyond software. Many of the strongest businesses will bundle software with services, compliance, operations, or real-world execution.

The emerging opportunities

Domen highlights several categories that become especially interesting in this new environment:

• Ultra-niche products that were previously too small to justify development costs

• Lower-cost challengers to established SaaS incumbents

• “Luxury software” with strong brand positioning and premium pricing

• Vertical businesses that combine software with real-world services

One of the most compelling implications is that smaller teams can now build profitable lifestyle businesses serving specialised markets that were once uneconomical.

So - AI is lowering the cost to create software, but it is also reshaping the economics of delivering it.

For years, software enjoyed a rare combination of high gross margins and near-zero marginal costs. That assumption no longer holds for many AI-native products.

The winners of the next decade will not simply build impressive AI features.

They will understand how those features affect the P&L, design pricing around real usage, and build businesses that remain profitable as inference becomes a core operating cost.

In other words: The future of software belongs not just to teams that ship AI fastest, but to those that understand the economics deeply enough to scale it sustainably.

Where AI seed investors are most likely to find outliers.

Every AI cycle produces noise. What matters is where outcomes compound, not just where capital is flowing. A recent PitchBook analysis looked at AI subsectors through one narrow but revealing lens: IPO exit predictor scores, a signal for where venture-scale outcomes are statistically more likely.

A few patterns stand out.

• First, agentic commerce infrastructure is quietly emerging as the strongest outlier candidate.

Commerce has historically been the on-ramp for every major platform shift, internet, mobile, cloud, and AI are following the same script. What changes this time is autonomy.

Payments, identity, fraud, loyalty, and inventory systems are being rebuilt so software can transact without humans in the loop. That makes the infrastructure layer, not consumer apps, the long-term value capture point.

• Second, AI-driven drug discovery is moving from promise to economic inevitability.

As AI improves trial design and success rates, the bottleneck shifts downstream. More trials mean more demand for tooling, data infrastructure, and clinical operations software.

Analysts expect this to expand the total addressable market aggressively through the decade, not because drugs get cheaper, but because more drugs actually make it to market.

• Third, AI protection and defence-adjacent systems are benefiting from a similar dynamic: Autonomy replaces humans in high-stakes environments.

Edge computing and real-time decisioning are enabling faster response systems, whether in cybersecurity or physical defence.

The complexity here is not a bug; it’s the moat.

• Finally, autonomous drones and swarms represent a classic early-cycle opportunity.

Fully coordinated land, sea, and air swarms are still hard to execute, but that difficulty creates asymmetry. As AI-first players replace human-piloted systems, legacy hardware and sensor providers face margin and relevance pressure.

Taken together, the signal is consistent:

• Outlier returns are clustering below the application layer

• Infrastructure that enables autonomy, not just intelligence, is where durability lives

• Complexity and regulation are acting as filters, not deterrents

For seed investors, the takeaway isn’t to chase what’s loud. It’s to focus on where AI changes system behaviour, not just user experience. That’s where platform-scale outcomes tend to form.

What’s driving the surprising decline in founder dilution?

One of the most interesting trends in venture right now is hiding behind a simple chart from Carta. Despite all the headlines about startups raising massive rounds, founders are actually giving up less equity than they did a few years ago.

Peter Walker, Head of Insights at Carta, recently shared data from more than 9,000 U.S. software financing rounds, and the numbers tell a fascinating story.

In 2022, the median seed round required founders to sell about 21% of their company. In 2026, that figure has dropped to 18%. The same trend is visible across every stage:

• Series A dilution fell from 20% to 17%

• Series B dilution declined from 17–18% to 13%

• Series C dilution dropped from 13% to 9%

At first glance, this seems counterintuitive. Round sizes are getting larger, especially in AI. Yet founders are keeping more ownership.

So what’s going on?

The fundraising bar has gone up

The most obvious explanation is that fundraising has become much harder.

While social media is filled with announcements of startups raising $40 million or $100 million rounds, those stories represent a very small subset of companies.

Behind the scenes, venture firms are more selective than they have been in years. That means only a small percentage of startups are able to raise institutional capital. But the ones that do often have much stronger leverage.