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

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📜 DEEP DIVE

What is your investor actually trying to win in a term sheet negotiation?

You’ve probably already read a term sheet guide. Maybe three of them.

So you know what a liquidation preference is. You know 1x non-participating is standard, you know what pro rata means, you could define anti-dilution if someone asked.

That’s the problem. Almost everything written about term sheets explains what the terms mean. Almost none of it tells you which ones your investor can actually move - and when you’re sitting across the table, that’s the only thing that matters.

I’ve sat on the investor side of these conversations, and I’ve talked it through with people who still do. What strikes me every time is how much of the negotiation is decided by information that never leaves that side of the table. Not because anyone’s hiding it. There’s just no reason to volunteer it, and the people writing the guides are teaching vocabulary rather than describing a negotiation.

Here’s the shape of it. There are around twenty terms on that page, and to the fund they are nowhere near equal. A handful are effectively fixed - the partner has already committed to them internally and genuinely cannot move. Several are wide open. And a few are in there because it’s standard paper, and they fully expect to hand some back.

So the same pattern repeats. A founder pushes hardest on the term with the least give in it, spends two weeks and all their goodwill getting a small split, and signs the three that compound over the next four years.

You find out what that cost later. Usually the first time you need to make a decision and realise you need permission to make it.

What your investor is actually solving for

Ownership. Not price. And it binds harder than founders think - many funds carry an internal ownership target required to do a deal at all, anywhere from 5% to 20%+ depending on stage, because they’d rather own more of fewer companies.

Here’s the part that changes how you negotiate -

That number isn’t a preference your partner formed on the drive in. It’s a number they took into a room and got approved. They stood in front of their partnership and said: we’re doing this one, here’s the price, here’s our ownership, here’s the board. That approval is the only reason a term sheet exists.

So when you push on ownership, you’re not asking them to be flexible. You’re asking them to reopen a decision that was partly about them. Most won’t.

Which is exactly why everything else on the page has more give than it looks like.

So here’s the mistake, precisely

Your valuation falls out of the fund’s cheque size divided by their ownership target - arithmetic you don’t control and can’t argue with. (Full model in how seed valuations actually get set.)

The option pool, the protective provisions, the pro rata language are all genuinely open, all cost you real ownership or control, and all get signed as written because they read like paperwork.

You’re pushing on the one thing welded shut and waving through the three that aren’t.

What’s inside today’s deep dive

• Which four terms are effectively fixed? And why pushing on them costs you more than it returns.

• Which terms have real room? The tradeable middle, where the actual money is.

• What’s in there just to be given back? The padding and how to spend it deliberately.

• How do you trade instead of just asking? Six specific swaps that work, in the exact framing to use.

• Which terms quietly cost more than valuation? Four of them, with the arithmetic.

• What do you ask on the first call to find out which kind of investor you’re dealing with?

Plus the one-page negotiation map - every major term, which bucket it’s in, what to ask for, and what to trade it against.

Let’s get into it.

Read more

Big idea + report of the week :

• What makes one AI startup worth 30x ARR and another 100x+?

• What separates an average VC fund from the top 1%? (Carta analysed 400+ funds).

• 195 new unicorns in six months. What’s driving the surge?

Frameworks & insightful posts :

• Is your seed valuation actually about your startup or the fund’s math?

• Can AI agents finally use computers well enough to replace back-office work? - a16z’s data shows something unexpected.

• Do the biggest startups actually come from the hottest trends? Sequoia analysed 20 years of tech trends.

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🧠 Big idea + report of the week

What makes one AI startup worth 30x ARR and another 100x+?

AI companies are reaching $100M ARR at extraordinary speed, but investors aren’t valuing every fast-growing company the same way.

Tom Tunguz analysed valuation and ARR data across Harvey, Legora, Sierra, Ramp, and Decagon. The interesting part isn’t simply how high the multiples are - it’s how differently the market prices companies with similarly exceptional growth.

• Sierra reached roughly a 79x ARR multiple, while Legora sits around 56x and Harvey around 37x. Decagon’s estimated multiple is even higher at 129x, though its revenue figure is based on third-party estimates.

• Multiples have also started expanding again. Legora moved from roughly 36x to 56x, Sierra from 67x to 79x, and Ramp from 23x to 31x as the fundraising environment improved.

• Harvey is the interesting exception: despite strong revenue growth, its multiple compressed from roughly 50x to 37x. That suggests growth rate alone isn’t determining valuation.

So - category position may increasingly determine the premium investors are willing to pay.

Two AI companies can both grow incredibly fast and still receive dramatically different revenue multiples depending on how investors view their market position, defensibility, and potential to own the category.

We’re effectively seeing 50x–100x ARR multiples return for some elite private AI companies - but this time, they’re being supported by businesses growing much faster than the SaaS companies that commanded similar multiples in 2021.

One caveat: several ARR figures are estimates rather than audited disclosures, so the direction of the valuation trends matters more than the exact multiples.

What separates an average VC fund from the top 1%? (Carta analysed 400+ funds).

Venture capital is built around a simple but brutal reality: a small number of investments generate most of the returns. Carta’s latest fund data shows that the same power law applies to VC funds themselves.

Carta looked at more than 400 U.S. venture funds from the 2016, 2017 and 2018 vintages and compared their net TVPI.

TVPI (Total Value to Paid-In Capital) simply measures how much a fund’s investments are currently worth compared with the money investors put into it.

A 2x TVPI means every $1 invested is now worth about $2, including both realized and unrealised value.

The difference between an average fund and an exceptional fund is enormous.

• At the 25th percentile, net TVPI is just 1.06x. Put simply, $1 invested is worth roughly $1.06 today. After years of taking startup risk and locking up capital, that’s essentially no meaningful return.

• The median fund sits at 1.38x. That means $1 invested has become roughly $1.38. It sounds positive, but venture funds typically hold capital for many years, so the time involved makes that return far less impressive than the multiple initially suggests.

• Even reaching the 75th percentile only gets a fund to 2.09x. In other words, being better than roughly three quarters of the funds in the dataset still doesn’t necessarily produce exceptional venture returns

Then the curve changes dramatically.

The top 10% of VC funds reach at least 3.37x TVPI, the top 5% reach 4.72x, and the top 1% jump to 13.12x.

Put differently, a hypothetical $1 million investment would correspond to roughly $1.38M at the median, $3.37M at the 90th percentile, $4.72M at the 95th, and more than $13M at the 99th.

This is why simply saying a VC fund “made money” doesn’t mean much. Investors are taking illiquidity, startup failure risk, and long holding periods, so returns need to justify that risk.

For LPs, access to exceptional managers can dramatically change portfolio outcomes. For VCs, consistently picking good startups isn’t enough. A few extraordinary companies often drive most of the fund’s returns.

The power law is clear: median performance looks ordinary, top-decile performance becomes attractive, and the top 1% operates in a completely different universe.

Being a VC is relatively easy. Generating exceptional venture returns is incredibly hard.

195 new unicorns in six months. What’s driving the surge?

After two years of slower funding and valuation resets, the global unicorn market is accelerating again. But this time, the rebound is being driven heavily by AI, robotics, chips and other capital-intensive technology companies.

Crunchbase counted 195 new unicorns globally in the first half of 2026, already surpassing the 193 companies that became unicorns during all of 2025. It is the strongest six-month period since the first half of 2022, although still below the extraordinary 2021 peak.

The acceleration is clear. New unicorn creation rose from 49 in H2 2023 to 62 in H1 2024, 80 in H1 2025, and 195 in H1 2026.

The 2026 cohort added roughly $440 billion in value to the Crunchbase Unicorn Board and has raised around $80 billion in funding. DeepSeek leads at a $50 billion valuation, followed by OKX at $25 billion and OpenAI Deployment Company at $14 billion.

Valuations are also moving faster. Crunchbase found that 19 new unicorns raised follow-on rounds within roughly six months, often at sharply higher valuations.

The market remains concentrated geographically. The U.S. produced 110 of the 195 new unicorns, followed by China with 38 and the U.K. with 13.

But the bigger story is where investors are placing their bets.

AI labs, robotics, AI infrastructure, semiconductors, defence, aerospace, and developer tools led the new unicorn cohort.

The recovery is real, but selective. Capital is flowing toward companies investors believe can dominate strategically important markets, often through successive rounds at rapidly rising valuations.

For founders, the 195-unicorn headline doesn’t mean fundraising is easy again. It shows that the gap between companies with strong investor momentum and everyone else is widening. Category, growth, strategic importance, and a credible path to market leadership matter more than ever.

📄 Must Read Post

SOMETHING MORE

🧩 Frameworks & insightful posts

Is your seed valuation actually about your startup or the fund’s math?

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

It sounds like feedback on the business. Maybe traction is too early. Maybe the market story isn’t strong enough. Maybe another few months of growth gets the number up.

But often, the disagreement started before the meeting.

A fund has a fixed amount of capital, a typical initial cheque, and an ownership target it needs to make its portfolio economics work. Once those three numbers are known, there is already a rough ceiling on what it can pay.

For example, if a fund typically invests $600K and wants roughly 7% ownership, the math implies an ~$8.6M post-money valuation. If you’re raising at $18M, a better pitch probably isn’t solving the problem.

The problem is that the fund may simply not be built to lead that round.

This idea was broken down recently in a useful framework for understanding seed pricing: instead of starting with “what is my startup worth?”, start with what price can this specific fund structurally afford?

The chain is surprisingly simple:

• Fund size influences how much capital is available for initial investments.

• That determines the fund’s typical cheque size.

• The cheque is divided by the ownership the fund wants.

• That gives you the approximate post-money valuation where its model works.

So a $1M cheque targeting 10% ownership implies roughly a $10M post. A $2M cheque targeting 10% gets you closer to $20M.

That framing explains why two investors can look at the same company and come back with completely different valuations. They may not disagree much about the startup at all. They may simply be running different fund models.

And today, this matters more because seed prices have moved faster than many smaller funds’ portfolio construction.

At higher seed valuations, a smaller fund can find itself unable to hit its target ownership even when writing one of its largest normal cheques. When that happens, it generally has three choices:

1. Stay below market. Keep the ownership requirement and only lead companies at lower valuations.

2. Become a follower. Write smaller checks into rounds priced by somebody else and accept lower ownership.

3. Concentrate the portfolio. Make fewer investments so the fund can write larger checks into each company.

For founders, knowing which version you’re talking to changes the entire fundraising process.

There’s also a useful way to reverse the math from your side.

Round size ÷ dilution = implied post-money valuation.

If you need to raise $3M and are willing to sell 20%, you’re implicitly targeting a $15M post-money valuation. Once you know that, you can work backwards again and ask: what size fund normally writes the cheque required to lead this round?

That may be a much better way to build a target list than starting with brand names.

A few implications:

• Research a fund’s typical cheque and ownership target before spending weeks in diligence.

• Look at its recent deals, especially whether it actually leads or mostly follows.

• Set your round size from the capital required to reach the next milestone, then calculate the valuation implied by acceptable dilution.

• Don’t spend six weeks trying to convince an investor to violate the economics of their own fund.

And there’s one simple question founders can ask surprisingly early:

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

Those two numbers can tell you more about whether a deal is possible than another hour discussing TAM.

Remember - seed valuation isn’t purely a judgment of what your company is worth. It’s the intersection of your financing needs and the portfolio math of the investor sitting across from you.

Sometimes “we’re not there on valuation” really means exactly that. They aren’t. (Read More)

Can AI agents finally use computers well enough to replace back-office work? - (a16z’s data shows something unexpected)

For a long time, computer-use agents looked impressive in demos but broke down the moment they had to do real work. Clicking through portals, filling forms, updating CRMs, moving data between legacy systems - the kinds of tasks millions of people still do manually - required too much supervision to make the economics work.

That is starting to change.

Fabrizio Serafini, an investment partner at a16z focused on early-stage AI, recently shared a detailed look at how far computer-use agents have progressed and what teams are actually seeing in production.

The most striking number is the performance jump.

A year ago, the best computer-use model scored roughly 42% on OSWorld-Verified, a benchmark that tests agents on real desktop tasks across Windows, Ubuntu, and macOS. Today, the leading score is around 85%, above the roughly 72% human baseline on the same benchmark.

That doesn’t mean agents are suddenly perfect. An 85% score still means 15 failures out of every 100 tasks, and real business workflows rarely tolerate that kind of error rate.

But the capability has improved enough that companies are now deploying agents on narrow, repetitive workflows where the steps are predictable, and success can be verified.

Some of the best current use cases look surprisingly boring:

• Updating systems of record and CRMs

• Processing IT tickets

• Pulling data from government, insurance, or retailer portals

• QA and record checking

One example is a data platform running roughly 15–20 million automated portal interactions per month. Agents act as a fallback when traditional scrapers break after a retailer changes its interface, diagnosing the issue and repairing the automation before engineers need to step in.

Another company is running 27 computer-use workflows handling roughly 1,500-2,100 IT tickets a day.

So - the model itself is quickly becoming less important than everything around it.

If every frontier model can eventually click, type, and navigate software, then “computer use” itself isn’t much of a moat.

The durable value moves into the surrounding system:

• What context does the agent have?

• What permissions can it use?

• How does it know what success looks like?

• What happens when the workflow changes?

• How does it verify its own work?

• When should it escalate to a human?

• Can repetitive portions be cached and turned into deterministic code?

That last point matters economically.

The article estimates a computer-use agent at roughly $6-8 per hour of inference, with a broader range of around $3–15 depending on the workflow and architecture.

Compare that with roughly:

• Offshore BPO labour: ~$10/hour fully loaded

• U.S. back-office labour: ~$30–45/hour fully loaded

Even in relatively expensive fully-agentic mode, the cost is already approaching offshore outsourcing economics and can be dramatically cheaper than U.S. labor.

And good systems don’t keep calling an expensive model for every click. One emerging pattern is to let the agent perform the workflow, convert repeatable portions into deterministic automation, then bring the model back only when something breaks.

That can push the blended cost much lower over time.

There is still a major limitation, though: verification.

Computer-use agents work best when success is immediately observable. If an agent updates a CRM field, the system can check the new value. If it files something into a portal and receives a confirmation, that’s measurable.

They struggle when the real outcome happens later or outside the system.

Imagine an agent submits an insurance claim successfully, but two days later an adjuster calls asking for a missing policy number. A human worker naturally resolves that call. The agent may never even know the process failed.

That’s why the current sweet spot is not “replace every office worker.”

It is much narrower:

High-volume workflows with repetitive steps, stable rules, legacy interfaces, clear verification, and obvious escalation paths.

The bigger opportunity for startups may therefore sit above the raw model layer.

As computer navigation becomes commoditised, the winners are likely to be the companies that understand a specific workflow deeply enough to package the context, credentials, guardrails, validation, and exception handling required to make the agent trustworthy in production.

The question is quickly moving from:

“Can an AI agent use a computer?” to:

“Can it reliably do this particular job without someone watching every step?”

That second question is where a very large new software market could be built.

Do the biggest startups actually come from the hottest trends? Sequoia analysed 20 years of tech trends.

Konstantine Buhler at Sequoia analysed nearly 20 years of Hacker News data, comparing the internet’s most-hyped technology topics each year with the most valuable company founded during that same year.

The biggest company created in a given year was rarely built around that year’s hottest trend.

• Airbnb was founded in 2008 while Google dominated online discussion.

• Uber launched in 2009 when low-level programming was among the hottest topics.

• Anthropic started in 2021 while crypto dominated attention.

But hype isn’t useless. Major trends often appear 5-6 years before they become mainstream.

• LLMs entered the top 15 in 2016 before reaching #1 in 2022;

• AI coding appeared in 2021 and now tops the ranking in 2026.

The stronger signal may be persistent attention rather than sudden hype. Topics that remain relevant for years can indicate a deeper technological shift, while short-lived spikes are easier to mistake for opportunity.

So as founders or investors: don’t automatically build where everyone is looking today. Watch what small technical communities keep talking about long before the rest of the market cares.

The best opportunities may not sit at peak hype - they may be hiding in the trends quietly compounding underneath it.

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

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📜 DEEP DIVE

What should your AI spend actually be measured against?

Ask most founders what they spend on AI, and they’ll give you a number without blinking. A few thousand dollars a month - some ChatGPT and Claude seats, a Cursor bill, an API line that’s been creeping, a couple of tools the team swears by. It sounds precise. It isn’t.

The question “what are we spending on AI?” produces a genuinely meaningless answer, because it adds together three unrelated kinds of spending that happen to share a vendor category. It’s like asking “what are we spending on electricity?” and lumping in the office lights, the product servers, and the salary of the person you didn’t hire because a machine does their job now.

The data: 70,000 companies, one cliff

Ramp sees actual payments across more than 70,000 US businesses - not surveys, money leaving accounts.

• The median company spends $11.38 per employee per month on AI.

• The top 10% spend $611.

• The top 1% - what Ramp’s economists call “AI-pilled” - spend $7,449 per employee per month, growing 14.1% month over month.

That’s not a bell curve with a long tail. It’s a cliff - a roughly 650x gap between the median and the top.

“Using AI” has split into two economies: a small group treating it as a core input to production, and everyone else buying subscriptions. Kruze Consulting’s data across 1,000+ venture-backed startups shows the same pattern from the inside - the average AI line item nearly tripled in 18 months, from $2,000/month in early 2023 to $5,000-6,000 by 2024, and almost nobody restructured how they account for it.

Dabbling returns nothing. Literally nothing.

Ramp linked its payment data to Revelio Labs’ workforce records across 21,559 companies - the first study to connect actual firm-level AI spending to what happened afterwards.

Companies that adopted AI grew headcount 10.2% over the next two years, entry-level hiring up 12%, gains spread across sales, admin, finance, and customer service.

But the gains belonged entirely to high-intensity adopters.

Low-intensity adopters - companies technically “using AI” but spending little per employee - showed no significant change at all. The $200-a-month, few-seats, we’re-experimenting posture - the one the median $11.38 represents - is indistinguishable from doing nothing.

So: spend more. On what? This is where nearly every founder makes the same mistake - not a spending mistake, an accounting one. The question “are we spending enough” can’t be answered while AI is one line in your head. Most founders are benchmarking that number against their software budget. For the part of the spend that actually matters, that’s the wrong denominator entirely.

The rest of this issue:

• The denominator error - the number your AI spend should actually be judged against (it’s not your software budget).

• The three budgets - the sort that shows where you’re overspending and where the returns actually live.

• The kill-criteria - the cancellation signal from 70,000 companies that tells you a tool failed before you’ve admitted it.

• The 30-minute audit - run it on your own statements this week and find the $500–$2K/month you’re burning on nothing.

Read more

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

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📜 DEEP DIVE

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

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

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

Your investors are going to call your customers.

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

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

What you’re actually spending

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

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

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

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

The mistake almost everyone makes

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

That’s it. That’s the mistake.

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

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

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

What’s inside today’s deep dive

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

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

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

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

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

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

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

When should you actually agree to a customer intro?

Read more

Big idea + report of the week :

• Why is Venture Capital breaking records while fundraising still feels so hard?

• Why do 70% of startup employees walk away from their vested equity?

Frameworks & insightful posts :

• Which layer of the AI Stack will capture the most value? - Chamath’s Six-Layer AI Investing Stack

• How should AI startups sell to enterprises? (a16z’s Lighthouse vs. Landgrab Framework)

• What did OpenAI learn from analysing 800,000+ work-related ChatGPT messages?

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🧠 Big idea + report of the week

Why is Venture Capital breaking records while fundraising still feels so hard?

The headline numbers make it look like venture capital is having another 2021-style moment. Startup valuations are climbing, mega-rounds are everywhere, and more companies are reaching $10 billion valuations.

But underneath that boom is a much more concentrated market.

PitchBook data shows that 19 U.S. startups became decacorns in 2026 through July 21, meaning they reached valuations of at least $10 billion. That has already surpassed the 18 new decacorns created during all of 2025 and puts 2026 on pace to beat the previous record of 22 set in 2021.

There are now 63 active U.S. decacorns, compared with 53 in 2025 and just 26 in 2021.

What makes this cycle different is where the capital is going.

During the first half of 2026, VCs invested $412.7 billion into startups, already exceeding the $319.2 billion invested during all of last year. But mega-deals, rounds of at least $100 million, represented an extraordinary 87.5% of that capital.

AI is driving much of the concentration. AI startups attracted $355.9 billion between January and June, representing roughly 86% of all U.S. venture investment during the period.

That capital is helping create a new generation of enormous private companies. SambaNova Systems became a decacorn after raising $1 billion at an $11 billion valuation. Shield AI, Cerebras, Clear Street, ElevenLabs, Harvey and Notion are among the other companies that crossed the $10 billion threshold this year.

There is an interesting dynamic behind these numbers. Investors aren’t simply funding more startups. They’re increasingly placing much larger bets on a smaller group of companies they believe can become category leaders.

AI makes that strategy particularly visible. Building frontier models, chips and infrastructure can require enormous amounts of capital, while the market is moving quickly enough that investors may prefer backing companies that have already demonstrated scale rather than spreading capital across dozens of smaller bets.

There is another reason these private valuations can keep growing: companies no longer need an IPO to provide some liquidity. Tender offers and secondary transactions increasingly allow founders, employees and early investors to sell portions of their holdings while companies remain private.

Strong late-stage startups can therefore delay going public while continuing to raise at progressively higher valuations.

But not every new decacorn looks the same. Some have substantial revenue and years of operating history. Others are reaching enormous valuations while still early in commercialisation or burning heavily to establish a position in fast-moving markets. The $10 billion label increasingly tells you how much investor conviction exists, not necessarily how mature the underlying business is.

For founders, that distinction matters. Record venture investment does not necessarily mean fundraising has become easier. Much of the new capital is being absorbed by a relatively small number of AI and late-stage companies. For everyone else, the market can remain highly selective even while aggregate funding statistics look spectacular.

The strange reality of the 2026 venture market is that record amounts of capital and a difficult fundraising environment can exist at exactly the same time.

Why do 70% of startup employees walk away from their vested equity?

Startup equity is often sold as one of the biggest reasons to join an early-stage company. Take a lower salary, help build something valuable, and if the company succeeds, your options could become meaningful wealth.

But there is a big gap between receiving stock options and actually owning the shares.

Peter Walker shared Carta data tracking the percentage of vested employee options that returned to startup option pools because employees did not exercise them before expiration. The numbers show how much startup equity employees ultimately leave behind.

In 2025, more than 70% of vested options went unexercised when employees left their companies. By the latest point shown in the data, the figure was roughly 70% for AI companies and 72% for non-AI companies.

That means startups are effectively getting a large portion of already vested employee equity back.

The historical trend is interesting too. At AI companies, the percentage of options returning to the pool fell from roughly 63% in early 2020 to just 42% around the peak of the 2021 startup boom. Employees had much stronger reasons to exercise when valuations were soaring, fundraising was abundant, and IPOs or secondary sales felt much closer.

As the market changed, the behaviour reversed. By 2024 and 2025, the non-exercise rate had climbed back toward 70%.

There are several reasons employees walk away from vested equity. Some don’t realise they need to exercise after leaving. Others cannot justify paying the exercise price and potentially facing taxes for shares in a private company. And even employees who believe in the company still have to ask a difficult question: when will I actually be able to sell these shares?

That last point matters because startup options are not cash. An employee can spend real money today to exercise shares while waiting years for an acquisition, IPO or secondary transaction that may never happen.

The AI boom hasn’t fundamentally changed this either. Despite huge secondary transactions and rapidly rising valuations at some high-profile AI companies, Carta’s broader data shows AI and non-AI companies ending up remarkably close, at roughly 70% and 72% respectively.

There is an important lesson here for founders designing compensation packages. A large option grant can look generous on paper while delivering little value if employees cannot afford to exercise it or don’t understand how it works. Longer exercise windows, better equity education and opportunities for employee liquidity can sometimes matter almost as much as the headline size of the grant.

And employees should probably evaluate startup equity differently too. The number of options in an offer letter tells you very little by itself. Exercise price, ownership percentage, dilution, exercise window, company valuation and realistic paths to liquidity determine whether those options might eventually become meaningful.

Startup equity can create extraordinary wealth. But Carta’s data is a useful reminder that vesting equity and realising value from equity are two very different things.

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🧩 Frameworks & insightful posts

Which layer of the AI Stack will capture the most value? - Chamath’s Six-Layer AI Investing Stack

AI is attracting enormous capital, but not every layer of the stack will produce equally attractive returns. As models improve and become increasingly interchangeable, some of today’s most valuable businesses could face commoditization while value moves elsewhere.

Chamath Palihapitiya recently shared his six-layer AI investing framework for thinking about where that value could accumulate:

• Land + power + shell: The physical foundation of AI infrastructure. As data centres consume more electricity and face permitting constraints, energised land and reliable power could become increasingly scarce and valuable.

• Silicon (GPUs): Huge value exists here, but building a new chip company has become extraordinarily difficult. Manufacturing precision, capital requirements, performance expectations, and supply-chain dependencies create enormous barriers for startups.

• Clouds: Potentially lucrative, but expensive and technically complex to build. As AI infrastructure becomes more regulated, identity, KYC, security, and compliance requirements could make operating these platforms even harder.

• Models: The most uncertain layer. If models continue improving while becoming easier to switch between, differentiation and margins could compress even as overall token consumption keeps growing.

• Harnesses: Potentially one of the most interesting layers. A harness connects models with an enterprise’s proprietary context - its data, workflows, evaluations, rules, and internal knowledge - while allowing the underlying model to be swapped when something better appears.

• Applications: Another potentially durable layer. AI could move enterprise software away from standardised, one-size-fits-all SaaS toward software customised around each company’s proprietary workflows and knowledge.

The framework points to an interesting barbell.

At the bottom of the stack, value comes from physical scarcity: land, electricity, chips, and infrastructure.

At the top, value comes from customer proximity and proprietary context: workflows, data, business rules, and software deeply embedded inside an organisation.

The middle - particularly models - could face greater commoditization as capabilities converge and switching becomes easier.

For founders and investors, this creates a useful way to think about AI opportunities: either own something genuinely scarce, or build something so deeply embedded in the customer’s workflow that replacing it becomes difficult.

How should AI startups sell to enterprises? (a16z’s Lighthouse vs. Landgrab Framework)

Founders often assume landing a prestigious enterprise logo is the best way to sell AI: win one big customer, turn it into social proof, and use that credibility to unlock everyone else.

But that can also waste months chasing a logo your actual buyers don’t care about.

Joe Schmidt and Julian Marx from a16z argue that AI companies should think about enterprise sales through two strategies: Lighthouse and Landgrab.

• Lighthouse: Use when buyers face high risk and need proof before adopting something new. A few respected customers can unlock an entire market. Harvey’s early adoption by major law firms is a good example.

• Landgrab: Use when buyers already understand the problem and purchasing risk is lower. Here, ROI matters more than prestige. Companies like Decagon and Stuut benefited from moving quickly, proving the economics, and selling broadly rather than waiting for a handful of marquee logos.

The framework comes down to two questions:

• How exposed is the buyer?

If choosing the wrong vendor could create regulatory, reputational, or career risk, they want credible proof that others have gone first.

• Does social proof travel?

In tightly connected markets like law or finance, one respected customer can influence hundreds of similar buyers. In fragmented markets, that logo may mean almost nothing.

That creates four possible GTM motions:

• High exposure + proof travels → Lighthouse

• Low exposure + proof doesn’t travel → Landgrab

• Low exposure + proof travels → PLG can work

• High exposure + proof doesn’t travel → a difficult market

One useful signal is how customers behave during sales. If they ask “Who else uses this?”, you probably need a lighthouse. If they mostly ask “How much money will this save me?”, speed and distribution may matter more.

And these strategies aren’t permanent. A startup can win a few lighthouse customers to establish credibility, dominate that vertical, and then switch into landgrab mode once buyers stop asking who went first.

So as an AI founder: don’t chase famous logos by default. Figure out whether your buyer needs proof or math - then build your sales strategy around that.

What did OpenAI learn from analysing 800,000+ work-related ChatGPT messages?

Most jobs are built around specialisation. Marketers handle marketing, engineers handle technical work, finance handles analysis, and legal handles contracts. When something falls outside your role, you usually hand it to another team.

AI is starting to change that.

New research from OpenAI Economic Research analysed more than 800,000 work-related ChatGPT messages in the U.S. and found something interesting: workers are increasingly using AI to perform tasks that traditionally belonged to entirely different occupations.

• 43.5% of occupation-specific AI use crosses job boundaries.

Once generic tasks like writing and summarising are removed, nearly half of specialised work done with AI belongs to another occupation.

• Some roles are becoming much more generalist.

Outside-occupation tasks account for 77% of occupation-specific messages from customer experience workers, 75% for designers, 69% for HR, 56% for legal, and 53% for marketers.

• Marketing and engineering skills are spreading everywhere.

Workers across departments are using AI for things like creating marketing material, financial calculations, technical troubleshooting, and other work that previously required specialist help.

• Small teams may benefit the most.

Task crossover is higher in smaller organisations, suggesting AI lets employees solve problems themselves instead of waiting for another department or specialist.

The bigger shift isn’t simply that AI makes existing jobs faster.

It may change what a job actually contains.

A salesperson can analyse customer data. A marketer can troubleshoot a website. A small-business owner can draft copy, review a contract, and perform basic financial analysis.

That means the future organisation may have fewer rigid boundaries between functions and more people who use AI to operate across several disciplines.

For founders, this is worth watching closely: some of the biggest AI opportunities may come from eliminating the handoffs between teams, not simply automating the work inside one team.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Index Ventures, a London and San Francisco-based venture capital firm, raised $2 billion across three new funds. (Link)

• U.S. Venture Partners, a Menlo Park, CA-based early-stage venture capital firm, is raising up to $400m for its 14th fund. (Link)

• Jump Capital, a Chicago, IL-based early-stage venture capital firm, raised $350m for its eighth fund. (Link)

• Align Ventures, a NYC-based venture capital firm investing in consumer brands and technologies, closed its Early-Stage Fund II at $125m. (Link)

New Startup Deals

• Proxy Foods AI, a Washington, DC-based food and beverage R&D platform, raised $6M in Seed funding. (Link)

• Intelligence, a San Francisco, CA-based developer of the AI design benchmarking platform Design Arena, raised $7.9M in Seed funding. (Link)

• Decade, a São Paulo, Brazil-based AI-native wealth advisory platform, raised $85M in Seed funding. (Link)

• Aavalynx, a London, UK-based AI-powered risk and capital management platform for enterprise dispute resolution, raised £1.5M in Pre-Seed funding. (Link)

• Marquee, a New York City-based developer of an AI-powered decision platform for professional sports organisations, raised $4M in Seed funding. (Link)

• Harmony, a New York City-based AI-powered enterprise service management platform, raised $34M in Seed funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

Looking to Break Into VC? Must be subscribed to our other newsletter - BREAK INTO VC for more tips and VC job updates.

• Senior Associate - Yamaha Motor Venture | USA - Apply Here

• Associate or Senior Associate - Bessemer Partner | USA - Apply Here

• VC Fellow - V11 | Remote - Apply Here

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

• Associate - Mount Judi Venture | USA - Apply Here

• Technical Investment Operations Associate - Alpaca VC | USA - Apply Here

• VC Research Lead - Murph Capital | Remote - Apply Here

• Trade Operations Analyst - RA Capital | USA - Apply Here

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

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

• Investment Associate - Cerity Partner | USA - Apply Here

• Head of Finance - Hivemind Capital Partner | USA - Apply Here

• Senior Associate, Talent Sourcing - Redesign Health | India - Apply Here

• Program Manager - Community - Plug and Play | USA - Apply Here

• AI Workflow Engineer - Alpaca VC | USA - Apply Here

• Investment Analyst - Forum Venture | Canada - Apply Here

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📜 DEEP DIVE

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

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

Tomorrow morning, you do the whole thing again.

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

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

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

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

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

What’s inside this guide

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

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

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

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

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

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

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

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

Let’s get into it.

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📜 DEEP DIVE

What does a VC really write in their internal notes after they reject you? (Most founders never see it).

You have a VC rejection sitting in your inbox right now. Most founders do.

Three or four sentences. Warm, slightly apologetic, no reason you can act on. “Really enjoyed the conversation. It’s a bit early for us at this stage. Would love to stay close as you grow.”

You’ve probably read it more than once, looking for the part that says what went wrong.

It isn’t in there. It was never in there. That email was written to politely end a conversation, and it did its job.

But it isn’t the only thing that got written. When a fund passes, a second record goes into their system - a reason code, a short note, and often a date. That one wasn’t written for you (many founders never get a chance to see this). It was written so a colleague who has never met you can open your file in eighteen months and know exactly what to do with it.

You’re reacting to the rejection email. The firm is operating off the other document.

Most passes aren’t verdicts at all.

Here’s what changes how you should read every rejection you’ve ever had.

Stanford’s Ilya Strebulaev, who has spent a decade surveying how VCs actually behave, describes the funnel plainly: roughly 70 of 100 opportunities die in the initial screen - usually within minutes, without any meeting at all. Conversion from first contact to cheque runs about 1% at Homebrew, 0.7% at a firm like a16z.

Read that with your own inbox in mind. If seven in ten deals are killed before a conversation, most of the passes you’ve collected were never assessments of your company. Nobody formed a view. There was no evaluation to fail.

So the honest framing is this: a pass is the default outcome. It is what happens when nothing overrides it. Getting one tells you almost nothing on its own. What tells you something is which kind you got - and that information exists, in writing, in their system.

The mistake this produces

Founders treat a pass as feedback. So they change something.

They rebuild the deck. They rewrite the market slide. They wait for a metric to move before going back out. Sometimes they change the actual company - a pivot triggered by a pattern of rejections that was never about the product.

And the pattern was real, in a sense. Twenty funds said no. But if fifteen of those were killed in the screen for stage, sector, geography or cheque size, then the pattern you’re responding to is a pattern in your target list, not in your business.

The genuinely useful signal is buried in the handful of passes that came after real work - the ones where a partner spent hours, took it to a Monday meeting, maybe started references. Those are verdicts. Those contain information.

Nobody can tell the two apart from the outside, because the email is identical either way. That’s not an accident. The same three sentences get sent whether they spent four minutes or four weeks on you.

Which means the skill isn’t getting fewer passes. It’s reading the ones you get.

What’s inside today’s deep dive

• What’s actually in the record? The four fields a firm keeps on a passed deal and which one decides whether you ever hear from them again.

• Why is the reason you’re given never the reason that’s written? The internal note has a different audience, and a different job.

• What do the eight standard phrases actually mean? Each one decoded, with the tell that separates the polite version from the real one.

• Which passes can be re-opened, and which are permanent? Three categories. Only one of them is worth your energy.

• What do you say back? The two-line reply that gets a real answer roughly a third of the time and why the obvious version of that question never works.

• How do you get back in eighteen months later? The trigger their system is actually waiting for.

Plus the pass decoder - all eight phrases, what’s written, and what to do and the audit to run on every rejection you’ve already collected.

Let’s get into it.

What’s actually in the record

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📜 DEEP DIVE

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

You have almost certainly been told, “we’re not there on valuation.” Possibly recently. Possibly by someone who seemed to like everything else about the company.

And you probably read it as a verdict. Not enough traction, too early, the story isn’t landing yet. Most founders respond by going back and building a better case for the number.

Here’s what’s usually happening instead.

Say the fund across the table is a $28M fund. Funds that size write cheques of about $600K, and they need to own 6-8% of what they back for the fund to work.

$600K at 7% prices the company at $8.6M. Even at 6%, it’s $10M.

If you were asking $18M, there was never a version of that meeting where the answer was yes.

Not a better deck, not more revenue, not a sharper story. The highest price that fund could pay was fixed before you walked in, and it was half your ask.

Nobody says this out loud. What you hear is “we’re not there on valuation,” which sounds like a judgment about your company and is usually a statement about their spreadsheet.

Your seed valuation is mostly arithmetic, and it starts with the size of the fund across the table.

• Fund size sets cheque size.

• Cheque size sets how much they need to own.

• That sets the highest price they can pay you. All of it, before your deck.

And that arithmetic has quietly stopped working for a lot of the funds you’re pitching.

The market ran away from the model

Carta has median seed post-money at $24M, an all-time high, up from $18M a year earlier.

Now put that next to how funds are built. A $50M fund writes cheques of roughly $750K to $1.5M, and needs 8–10% ownership. That’s not a guess - it falls straight out of the standard fund model.

At a $24M post, a $1.5M cheque buys 6.2%.

They’re short of their own minimum while writing the biggest cheque they have. Not because you’re asking too much. Because prices went up and the fund model didn’t move with them.

Run that across the whole range, and the line lands somewhere specific: below about $75M in fund size, a fund can’t hit its ownership target at today’s market price at all. A chunk of your target list is structurally unable to lead you. And none of them will say so. They’ll say it’s too early, or they need to see more traction.

The cost isn’t one lost deal. It’s six weeks in rooms where the arithmetic had already closed the door, and a founder walking out convinced the problem was his traction.

The three inputs and the one everyone prepares for aren’t among them

Three things set the number at seed:

• The fund’s arithmetic: The binding constraint, and the one founders never see.

• The partner’s personal comp set: Not the market. The twenty to forty deals that specific partner has looked at in your category over the last six months, and what those closed at. This is why quoting Carta’s median in a meeting rarely works.

• Whether a second term sheet exists: The only input you meaningfully control, and the only one that reliably moves the price.

What isn’t on the list: your TAM slide, your five-year projections, comparable exits in your space, or how much money you’ve calculated that you need.

Founders spend their prep on the second list.

What’s inside today’s deep dive

• The chain, worked end to end. Five steps from fund size to the highest post-money they can accept and the two heuristics that let you run it in your head.

• The 2026 lookup table. Fund size against check, ownership target, the price where their math works, and what their check actually buys at today’s median.

• The $75M line, and the three things funds below it do about it - each of which changes whether they can lead you.

• Where to find any fund’s size in four minutes, using a public filing most founders don’t know exists.

• The reframe: you set your valuation when you named your round size, not when you named your valuation.

• The pool shuffle that quietly costs more than the pre-money you’re negotiating.

• What to say when their number is below your ask, and the one question on the first call that saves six weeks.

Plus the pre-meeting worksheet, and why pre-seed works completely differently.

Let’s get into it.

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📜 DEEP DIVE

What are VCs really asking AI founders during due diligence? (And what you’re expected to show).

Over the last few weeks I’ve been talking to investors actively writing checks into AI companies. Not the growth people putting $50M into a Series C. The ones doing $400K to $4M into companies that are nine months old - four people, one paying customer, a demo that works about 80% of the time.

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

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

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

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

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

So they gave me the questions. All of them.

Why it changed this fast

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

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

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

The actual shift: they stopped wanting answers

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

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

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

What’s inside today’s deep dive -

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

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

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

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

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

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

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

Before you start

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

Read more

👋 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 :

• Which companies are producing next unicorn founders?

• Is geography becoming more important in the AI era?

• Why are so many 2021 venture funds underperforming?

Frameworks & insightful posts :

• Y-Combinator advice: What should founders build as AI moves into the real world?

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

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🧠 Big idea + report of the week

Which companies are producing next unicorn founders?

For years, the “PayPal Mafia” was considered the ultimate startup factory. Many of Silicon Valley’s most successful founders had worked at PayPal before launching billion-dollar companies. But that pipeline is changing fast.

Recently, Ilya Strebulaev from Stanford GSB analysed the backgrounds of 4,357 unicorn founders and found that a new generation of companies is now producing the next wave of billion-dollar entrepreneurs.

One of the biggest shifts is where future founders are coming from. The old leaders have lost ground:

• PayPal’s odds of producing a unicorn founder dropped from 12.0x before 2016 to 1.7x today.

• LinkedIn fell from 6.0x to 1.1x.

• Apache Software Foundation and Qualcomm also saw similar declines.

Meanwhile, newer technology companies have become the strongest founder pipelines:

• OpenAI now has the highest odds ratio, with alumni being 14.7x more likely to become unicorn founders than a random sample.

• Check Point and D.E. Shaw also rank among today’s strongest founder-producing organisations.

• Google, Facebook, and MIT have all become significantly stronger launchpads for new founders over the past decade.

Among 1,377 US unicorns with multiple founders, nearly 69% of founding teams had previously worked or studied together. Shared workplaces were even more common than shared universities, suggesting that strong professional relationships often become the foundation for successful startups.

The takeaway isn’t that working at one of these companies guarantees success. Rather, the best founder factories tend to give people three advantages: they solve difficult problems, work alongside exceptional talent, and build lasting relationships with future co-founders.

As the AI era unfolds, today’s high-growth AI companies may become tomorrow’s equivalent of the PayPal Mafia, producing the next generation of billion-dollar founders.

Is geography becoming more important in the AI era?

For years, many people predicted that remote work would make location irrelevant. The thinking was simple: if great companies can be built from anywhere, capital would eventually spread everywhere.

The latest data suggests the opposite is happening.

Carta analysed $124 billion invested into U.S. startups between July 2025 and June 2026, ranking startup ecosystems by where companies are headquartered. Instead of venture capital becoming more distributed, the AI cycle is pulling even more money into a handful of cities.

The Bay Area alone attracted 41.3% of all U.S. startup funding, or $51.3 billion, more than double New York’s 18% share.

But the real story appears when looking at AI and B2B.

The Bay Area captured 51.5% of every AI venture dollar and 53.2% of every SaaS/B2B dollar invested during the period. That means more than half of all capital flowing into the two sectors driving today’s venture market ended up in a single metro.

The concentration becomes even more striking when New York is added.

Together, the Bay Area and New York account for 67.5% of AI funding and 72.2% of B2B funding. In other words, nearly seven out of every ten venture dollars invested into B2B startups are flowing to companies headquartered in just two ecosystems.

The leaderboard also reveals that startup ecosystems are becoming increasingly specialised.

While the Bay Area dominates AI, SaaS, biotech, healthcare and hardware, New York overwhelmingly leads fintech, capturing 58.9% of funding in the category. Boston remains a strong biotech hub, while cities like Austin and Los Angeles appear in specific sectors but at much smaller shares.

The numbers reinforce an important pattern:

Venture Capital follows ecosystems where talent, customers, founders and experienced investors are already concentrated.

AI infrastructure talent continues to cluster around the Bay Area, while financial services expertise remains heavily concentrated in New York.

For founders, this doesn’t mean you must move to San Francisco to build a successful company. Great companies will continue to emerge everywhere.

But if you’re building an AI or B2B startup and planning to raise institutional capital, geography is still part of the fundraising equation. Being close to customers, investors, and other ambitious founders creates more opportunities for introductions, hiring and fundraising momentum than many founders assume.

The AI era isn’t making venture capital more evenly distributed. If anything, it’s making the biggest startup ecosystems even stronger.

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

Y-Combinator advice: What should founders build as AI moves into the real world?

AI is no longer limited to chatbots, copilots, and software tools. It is beginning to shape how children learn, how physical work gets managed, how companies stay compliant, how defence systems operate, and even where future data centres may be built.

The problem is that most startup activity still clusters around familiar AI use cases. Hundreds of teams are building similar agents, while some of the largest and hardest markets remain underserved because they require deeper domain knowledge, hardware, regulation, or new infrastructure.

Y Combinator recently shared its Fall 2026 Requests for Startups, outlining the areas where its partners believe ambitious founders should look next. The list is less about adding AI to existing products and more about rebuilding important systems around intelligence from day one.

Some of the strongest ideas include:

• The Primer: A deeply personalised AI tutor that grows with a child, beginning with reading, writing, and arithmetic before gradually helping them develop reasoning and independent thinking.

• Multiplayer AI: Shared agent workspaces where several employees can watch, guide, and collaborate with the same AI agent, instead of every employee working inside isolated chat threads.

• A cloud for small software: Infrastructure for the lightweight internal tools people can now create with coding agents. The ambition is to make custom software as easy to deploy and share as a Google Doc.

• New operating systems for physical work: Platforms designed to coordinate humans, robots, wearables, and AI agents across construction, maintenance, logistics, and field operations.

• AI for an aging population: Voice assistants, monitoring systems, care-coordination tools, and robotics that help older adults remain independent while reducing pressure on families and caregivers.

• Proving you are human: A new internet trust layer that verifies whether the person behind a call, message, review, or transaction is real without forcing everyone to surrender their privacy.

• AI-native compliance infrastructure: Systems that monitor regulatory changes, generate reports, maintain audit trails, and reduce the growing cost of operating across multiple jurisdictions.

• Self-maintaining APIs: Agents that detect when an API changes, identify affected customer code, and automatically open a pull request containing the required fix.

YC is also asking founders to explore defence technology, low-cost interceptors, offshore data centres, crypto rails for agentic commerce, consumer AI products for billions of people, and new systems for collecting physical-world data.

What stands out is that the most promising opportunities are not necessarily another AI interface.

They sit inside difficult workflows where information is fragmented, labour is expensive, infrastructure is outdated, and existing software was designed for a world run entirely by humans.

The next generation of large AI companies may therefore look less like chatbots and more like education systems, compliance networks, industrial operating systems, defence infrastructure, and shared workplaces where humans and agents operate together.

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 monetisation.

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.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Founders Fund, a San Francisco-based venture capital firm, hired former OpenAI VP of Product Policy Ryan Beiermeister as a partner. (Link)

• ReefHaven Ventures, a San Diego, CA-based early-stage venture capital firm, launched. (Link)

• Dimension Capital, a New York-based venture capital firm founded by Zavian Dar, Adam Goulburn, and Nan Li, raised $800 million for its third fund. (Link)

New Startup Deals

• Runta, a San Francisco, CA-based AI agent runtime infrastructure company, raised $20M in Seed funding. (Link)

• Sonata, a New York City-based AI-powered preventive healthcare platform, raised $7M in Seed funding. (Link)

• Apaluma, an Albuquerque, NM-based regulatory intelligence platform, raised $5.55M in Seed funding. (Link)

• Crystalys Therapeutics, a San Diego, CA-based clinical-stage biopharmaceutical company, raised $130M in Series B funding. (Link)

• Candid Health, a San Francisco, CA-based autonomous revenue cycle automation platform for healthcare providers, raised $120M in Series D funding. (Link)

• Assort Health, a San Francisco, CA-based AI agents platform for managing patient journeys, raised $120M in Series C funding. (Link)

TODAY’S JOB OPPORTUNITIES

💼 Venture capital & startup jobs

All-In-One VC Interview Preparation Guide: In partnership with a leading investor group, we have created an all-in-one guide for aspiring VCs. Don’t miss this. (Access Here)

• Visiting Engineer - AI fund | USA - Apply Here

• Investment Associate - Global Health Corporation | USA - Apply Here

• Investment Associate / Analyst - Flashpoint VC | USA - Apply Here

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

• Investment Associate - Antler | UK - Apply Here

• Investment Manager / Principal - Edenred Venture - Apply Here

• Executive Assistant - Playground Capital | USA - Apply Here

• Analyst - ICONIQUE Capital | USA - Apply Here

• Finance Director - AI Fund | USA - Apply Here

• Senior Investment Manager - Sagana | India - Apply Here

• Head of Fund Accounting - Stepstone Group | USA - Apply Here

• Associate or Senior Associate - Bessemer Partner | USA - Apply Here

• VC Fellow - V11 | Remote - Apply Here

• Investment Analyst - Forum Venture | Canada - Apply Here

• Portfolio Analyst Intern - Motion Venture | Singapore - Apply Here

• Investor - Superseed | UK - Apply Here

• Fund Controller - Alleygroup | USA - Apply Here

• Associate - Engine Venture | USA - Apply Here

• AI Engineering Lead - Palermo Advisor | USA - Apply Here

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📜 DEEP DIVE

How to get a warm intro to a VC without a network? (The ranking system most founders don't know exists.)

Most founders think a warm intro is a favour. Someone who likes you nudges a busy person to take a look. That’s the whole model in their head: informal, personal, a little bit of luck.

That’s not how the other side of the table works anymore -

Warmth used to be a judgment call - a partner trying to remember who might know you, weighing a favour by gut feel. That model is quietly gone.

It’s been replaced by something closer to a live score: every real interaction a fund has with its network - an email, a call, a calendar invite - gets tracked and turned into a measurable connection strength between each person at that firm and everyone in their extended world. When your name comes up, nobody has to rack their brain for who might know you. The strongest, most recent path is already sitting there, ranked.

This isn’t a niche habit at a handful of firms chasing new software. Roughly half of the top 300 VC firms globally now run tools built specifically to do this at scale - and the funds that don’t still do a rougher version of it by hand, through deal trackers and partner notes that log the same underlying thing: who talked to this founder, how recently, and through what channel.

The tool changes from firm to firm. The logic underneath it doesn’t. Intros are being scored now, formally or informally, whether or not a founder ever sees it happen.

This matters more than it sounds like it should, because of what it changes underneath. A warm intro isn’t evaluated as “did someone vouch for this person.” It’s evaluated against a live map the firm already has of its own network, and firms running this formally report closing deals roughly 25% faster off the back of it. Which means the real question isn’t “who can I get to introduce me.” It’s “who already shows up as a strong, recent connection to this fund” - and those aren’t always the same person.

Here’s the part most founders miss: the score isn’t how well someone knows you - it’s activity. How often, how recently, through what channel. A portfolio founder gone quiet for eight months can score weaker than a co-investor who called last week. An old college friend at the fund shows up instantly, years out of touch, because only the fund-side contact gets measured.

It’s not a trust map. It’s an activity map and that changes who you ask, and when.

So why doesn’t this change how founders approach it? Two reasons.

They don’t know the hierarchy exists.

Every “sure, happy to intro you” feels like the same win, so they treat it that way - and burn their one real relationship, the portfolio founder who actually knows their work, on a fund that was never a great fit. By the time the right fund comes along, that favour’s already spent.

They don’t know how to ask.

A vague “would love an intro if you’re open to it!” forces the connector to write the pitch themselves - figure out what to say, why it matters, how to frame it. Most people, even ones who genuinely like you, will let that sit in their drafts rather than do the work. The ask itself is usually the thing that kills it, not the relationship.

Today’s deep dive covers:

• How do you build real investor relationships months before you need them?

• Which type of “warm” actually moves a VC and which just feels like it does?

• Why does your best relationship keep landing at the wrong fund?

• What do you actually say to a portfolio founder, a co-investor, an advisor, or a scout?

• What’s the one rule message that keeps an intro from quietly backfiring?

• What do you send the second someone says yes?

• Which mistake kills a warm intro before it ever reaches the partner?

Read more

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

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FROM OUR PARTNER - FINLESS FOODS

Investors are backing Finless Foods, and the timing makes sense. 🐟

Wild bluefin tuna is one of the most prized ingredients in the world, and also one of the most at risk. Decades of overfishing have pushed populations to a fraction of their historic levels. Add contamination concerns and the hard limits of ocean-based supply chains, and you’ve got a $2B premium market that can’t sustain the demand being placed on it.

Finless Foods is building a new path: sushi-grade tuna, cultivated without the catch. They’re starting with bluefin for premium sushi restaurants, where quality standards are the highest, and the market is willing to pay for it, and expanding from there into the broader $360B+ global seafood market, plus adjacent categories like protein, collagen, and functional foods.

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📜 DEEP DIVE

How to get investors interested months before you raise (with the exact emails that get replies).

Two founders. Same sector, same stage, nearly identical metrics. Both start their seed raise the same month.

The first closes in nineteen days - two term sheets, clean terms, a lead who “already gets it.” The second spends four months in market, takes forty meetings, and signs the one offer that shows up: lower number, heavier liquidation preference, a board seat he didn’t want to give.

The second founder wasn’t worse. His numbers were arguably better the week they both started.

The difference had already happened, months before either of them pitched. When the first founder sent his “we’re raising” email, the partner had been watching the company grow for half a year - six updates, six data points, a slope. The second founder was a stranger with a good month.

That’s “lines, not dots” - Mark Suster’s line from 2010 (he’s a managing partner at Upfront Ventures). The first time an investor meets you, you’re a single point. They can’t tell if you were higher last quarter or lower, so they price that uncertainty into everything: the valuation, the terms, the speed of the yes. Give them six months of data first, and the bet stops feeling like a bet.

And the update isn’t just “keeping you top of mind.” A good one gets forwarded - dropped into the firm’s deal tracker, raised in a Monday meeting as “one to watch.” By the time you formally raise, you’re not cold inbound triaged against fifty others. You’re a name three people already recognise.

The data backs it: Visible.vc finds founders who send consistent updates are roughly twice as likely to raise follow-on funding - their own platform data, not a peer-reviewed study, so weight it accordingly. But it’s exactly what Suster described, and it costs about an hour a month.

So why doesn’t every founder do this? Two reasons, both quiet killers.

They wait until they need the money. They go silent until runway gets short, then show up cold - exactly when they have the least leverage and look the most desperate. Dig the well before you’re thirsty. One founder I know raised $1.2M, went dark for fourteen months, and when he resurfaced low on cash, nearly every investor he’d wanted ignored the email.

They don’t know what to send. Most founders know they should do this - so they Google “investor update template,” and they find the same recycled, generic, five-years-stale formats written by people who have never sat on the other side of the table. So they send fluff. “We’re excited and working hard!” and train the exact investors they’re courting to skim right past them.

The system below is the opposite: built from the investor’s side of the inbox - what gets read, what gets forwarded, what moves you into the “one to watch” pile, and the exact emails that do it.

Today’s deep dive covers:

• How to build a qualified pre-raise investor list - not a 500-row spreadsheet that wastes everyone’s time, but the 50–100 names actually worth warming

• The tiering and segmentation system that decides who gets a warm intro first and who never slips through the cracks

• Why the associate - not the partner - is usually your best entry point, and how to use that

• The Permission Email: the exact cold note that gets a “sure, go ahead” from an investor you’ve never met (full copy-paste template)

• The pre-raise update that gets forwarded: the section-by-section structure, plus a complete real-world example you can model line for line

• How to flip six months of updates into an actual raise = the transition-to-” ask” scripts that make the pitch feel inevitable instead of cold

• The five mistakes that quietly torch all of this, ranked by how often they kill founders

• The 6-Month Pre-Raise Runway - a week-by-week checklist you can start this week, whether your raise is next quarter or next year

The whole thing runs on about an hour a month. Here’s the system, starting with the list you build before you write a single email.

Read more

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FROM OUR PARTNER - SYNERGY FOOD GROUP

Better Nutrition. Better Outcomes. Built to Scale….

Healthcare is embracing a simple but powerful idea: better nutrition can lead to better outcomes. Medically Tailored Meals (MTMs) are dietitian-led, condition-specific meals designed to help people manage chronic illnesses through proper nutrition. Research suggests MTMs have the potential to save the U.S. healthcare system $32.1 billion annually while reducing hospitalisations by as many as 3.2 million each year.

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2 Centres for Disease Control and Prevention (CDC)

FROM OUR PARTNER - OSHI

Investors are backing Oshi, the plant-based salmon company backed by investors behind Oatly and Impossible Foods

Americans consume nearly 1 billion pounds of salmon every year, but wild fish populations are under pressure and farm-raised salmon comes with growing concerns around sustainability, antibiotics, pesticides, and contaminants.

Oshi is building the first whole-cut 100% plant-based salmon fillet in the U.S. market, designed to look, cook, flake, and taste like premium salmon - without relying on oceans or fish farming.

Oshi at a Glance:

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As consumers look for cleaner, more sustainable protein options, this is the time to back Oshi as we define a new category in premium whole-cut plant-based seafood.

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📜 DEEP DIVE

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

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

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

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

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

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

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

The deal didn’t stall. It went underground.

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

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

Today’s deep dive covers:

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

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

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

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

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

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

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

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

Let’s get into it.

The diligence you never see

Read more

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

Big idea + report of the week :

• The new startup stack is here. Most founders haven’t caught up.

• What 1,133 funding rounds reveal about startup dilution?

Frameworks & insightful posts :

• Are your “Contact Us” forms quietly killing high-intent leads?

• Claude can do far more than you think: The hidden features most founders never touch.

• Is your AI product actually a software business? The margin data says maybe not.

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🧠 Big idea + report of the week

The new startup stack is here. Most founders haven't caught up.

Supabase surveyed 2,000 startup builders worldwide to understand how startups changed between 2025 and 2026.

The report shows that AI has dramatically reduced the cost of building software, allowing more people to launch startups while shifting the real competitive advantage toward distribution, customer acquisition and product differentiation.

More founders are building alone than ever before.

Solo founders now make up 61% of startups (up from 53% last year), while only 29% have two founders.

At the same time, 22% of startups are founded by non-technical founders, showing that AI has significantly lowered the technical barrier to building software.

The founder demographic is also becoming more experienced, with founders aged 40+ increasing to 24% as seasoned operators use AI to build companies without needing large engineering teams.

AI-generated code is now the default way startups build products.

Around 62% of startups have the majority of their code generated by AI, while 40% say more than three-quarters of their codebase is AI-written.

Anthropic has become the preferred AI platform, with 64% using Claude models, 59% paying for Claude subscriptions, and 63% actively using Claude Code, overtaking OpenAI across multiple developer categories.

Building products has become easier, but getting customers has become much harder.

The report shows customer acquisition (32%) is now the biggest challenge for founders, replacing technical complexity, which has fallen to 11%.

Interestingly, startups with the highest percentage of AI-generated code are less likely to be monetising than companies writing little or no AI-generated code, suggesting that shipping products has become much easier, while distribution and finding paying customers remain the real bottlenecks.

AI agents are moving into production, but the tooling ecosystem is still immature.

• More than 52% of startups are building AI agents,

• 24% already have multi-agent systems in production, and

• 57% have adopted or are experimenting with MCP (Model Context Protocol).

• Yet 59% don’t monitor AI workloads, 47% have no formal prompt management system, and 36% have no structured evaluation process, highlighting a major opportunity for startups building AI infrastructure and developer tools.

The modern startup stack is rapidly consolidating.

Supabase (65%), Vercel (45%), Cloudflare (27%), and Postgres (80% combined) have become the dominant infrastructure choices among startups. On the frontend, React and Next.js remain the leaders, while newer frameworks like Expo and TanStack are seeing rapid adoption. The report suggests founders are increasingly standardising around a smaller set of AI-native development tools.

The broader implication is that AI is no longer the differentiator - it has become table stakes. As software becomes cheaper and faster to build, founders who win will likely be those who excel at distribution, community building, customer acquisition and creating products people genuinely want, rather than simply shipping features faster.

What 1,133 funding rounds reveal about startup dilution?

Startup fundraising has remained challenging over the past two years, but founders negotiating new rounds may finally be seeing better terms.

Fresh data from Carta, covering 1,133 U.S. software funding rounds raised over the last six months, suggests investors are asking for smaller ownership stakes while companies continue raising substantial amounts of capital.

Seed founders are giving up less equity.

The median seed round closed at a $24.3M post-money valuation, with $4.1M raised for 18% dilution. Carta notes that seed dilution has been gradually declining for several months, giving founders more ownership after fundraising.

Series A terms are improving as well.

The median Series A round came in at an $80M valuation with $14.4M raised, also at 18% dilution-the lowest median dilution Carta has recorded at this stage in several years. Later-stage companies are selling even less equity, with median dilution falling to 12% at Series B, 9.8% at Series C, and just 8.1% at Series D.

Valuations need context.

While higher valuations grab headlines, they’re often simply a result of raising more capital while targeting a specific dilution level. Carta also notes that many companies in this dataset are AI-native, meaning these benchmarks shouldn’t be treated as universal targets for every startup.

These numbers are useful as fundraising reference points - not fundraising goals. Every company raises under different market conditions, and the right round is the one that provides enough capital to reach the next meaningful milestone without unnecessary dilution. Founders should optimise for building leverage over time rather than chasing benchmark valuations.

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SOMETHING MORE

🧩 Frameworks & insightful posts

Are your "Contact Us" forms quietly killing high-intent leads?

Most B2B companies spend heavily to bring visitors to their website, only to slow everything down at the moment someone is finally ready to buy. A prospect fills out a contact form, waits for a salesperson to respond, and by the time someone reaches out, they’ve often moved on to another vendor.

Recently, Jason Lemkin shared how SaaStr replaced its traditional contact form with an AI-powered inbound agent - and the results were hard to ignore.

Instead of asking visitors to submit a form and wait, the AI immediately started conversations, answered questions, qualified prospects, and booked meetings in real time.

The numbers from a single event were impressive:

• Around 2.25 million website sessions

• Over 402,000 conversations handled by AI

• 614 qualified meetings booked

• More than $52M in sales pipeline generated (based on an average deal size of ~$85K)

• Almost zero complaints from users interacting with the AI agent

What made the agent effective wasn’t simply using AI - it was how deeply it was trained.

Instead of acting like a generic chatbot, it was continuously updated with the latest website content, event information, CRM data, different buyer personas, and separate playbooks for sponsors, attendees, and customers.

That meant it could give accurate answers instantly while understanding who it was talking to.

The AI also handled work that normally requires multiple sales operations processes:

• Routed meetings to the salesperson most likely to close the opportunity using historical CRM data.

• Automatically followed up with visitors who abandoned sponsorship or ticket purchases.

• Applied pre-approved discount rules without creating inconsistent pricing or relying on manual sales decisions.

• Stayed available 24/7 without adding headcount.

So - the biggest opportunity isn’t replacing salespeople - it’s eliminating waiting time.

Warm inbound leads are at their highest intent the moment they arrive. Every hour of delay reduces the chances of converting them. An AI agent responds instantly, qualifies the opportunity, and only brings humans into the conversation once the lead is ready.

The takeaway is simple: if your website still relies on a “Contact Us” form followed by manual follow-up, you’re introducing friction exactly when prospects are most interested.

AI agents won’t replace complex enterprise sales conversations, but they can remove the slow, repetitive work at the top of the funnel and help sales teams spend more time closing qualified opportunities instead of chasing inbound requests.

Claude can do far more than you think: The hidden features most founders never touch.

Most founders use Claude the same way: open a chat, type a question, copy the answer, close the tab.

That’s roughly 20% of what the product actually does.

A recent guide from Antoli mapped out 17 features hidden across Claude’s ecosystem - most of them off by default, buried in settings, or living in separate products people don’t know exist. Here are the ones that matter most if you’re running a company.

1. Stop re-explaining yourself: Projects + Memory

Every new chat starts from zero - Claude doesn’t know your company, your stage, or your investors. Most people accept this and re-type context every single conversation.

Two features fix it:

• Projects let you upload documents and write standing instructions once. Open the project next week and Claude picks up exactly where you left off - your deck, your metrics, your positioning, all held permanently.

• Memory builds a profile of you across all chats - your role, what you’re working on, how you like output formatted. It’s off by default (Settings → Capabilities), which is why most users have never seen it work.

The combination means Claude behaves less like a search box and more like a chief of staff who’s been in every meeting.

2. Turn off the validation machine

By default, Claude agrees with you. It supports your reasoning, finds the positives, adds to your ideas.

For a founder making capital allocation decisions, that’s actively dangerous.

The guide’s most useful insight isn’t a feature - it’s a usage pattern: give Claude an adversarial role before you commit to anything expensive. Three prompts worth stealing:

• The hard mentor: “Disagree with me when I’m wrong. Point out what I’m avoiding because I want my plan to work. If it’s a bad idea, say so - don’t balance it with ‘on the other hand.’”

• Devil’s advocate: Before committing to a decision you’ve already made, have Claude build the strongest case against it - the assumptions that could be wrong, the three most realistic failure modes, what you’d need to believe for it to be a genuinely bad call.

• Negotiation practice: Have Claude roleplay your investor, your co-founder, or the enterprise buyer you’re about to call - responding the way they would, not the way you’d like them to. Practice the hard conversation before the real one.

Also worth flipping on: Adaptive/Extended Thinking (in the model selector). For strategic decisions, it forces Claude to reason step-by-step instead of pattern-matching - and you can watch the entire process.

3. Claude that works without you in the loop

The biggest gap between casual users and power users isn’t prompting - it’s that power users have Claude running when they’re not there:

• Scheduled Tasks: Set a task once and it runs automatically - a competitor scan every Monday, a funding-news brief every morning at 7:30, saved to your folder before you wake up.

• Claude Cowork (desktop): Direct access to your file system. It reads your actual files, edits documents, organizes folders - no copy-pasting into a chat box.

• Claude in Chrome: A browser extension that sees your active tab and acts on it - reads pages, clicks through pagination, fills forms, extracts data into comparison tables.

• CLAUDE.md: A rules file in your project folder that Claude reads automatically at the start of every session. Your writing conventions, your terminology, your brand voice - write it once, and it applies forever.

• Claude Design (claude.ai/design): A separate tool for pitch decks, one-pagers, and landing page layouts. Exports to PPTX, Canva, PDF, or HTML - replaces a 3-hour Figma session for non-designers.

The takeaway: the gap between AI power users and everyone else isn’t intelligence or prompting skill. It’s simply knowing what exists. Most of these take under five minutes to set up - and the founders getting outsized leverage from AI aren’t using a different model than you. They’ve just turned more of it on.

Is your AI product actually a software business? The margin data says maybe not.

For twenty years, the deal with software was simple: build it once, sell it forever, enjoy 75-85% gross margins. Every SaaS playbook, every valuation multiple, every hiring plan was built on that math.

AI products don’t work that way. And a new dataset - one of the few honest looks inside the P&Ls of companies actually building AI - shows exactly how different the math has become.

ICONIQ just released its State of AI: Bi-Annual Snapshot - 44 slides, fully ungated, built on surveys of roughly 300 executives at software companies building AI products: CEOs, heads of engineering, heads of AI, CROs, and CFOs, from sub-$5M to $1B+ in ARR. It’s their second bi-annual survey, which means we finally have trendlines, not snapshots.

Three findings matter more than the rest.

1. AI margins are improving - but they may never look like software margins.

Average AI product gross margins are projected to hit 52% in 2026, up from 45% in 2025 and 41% in 2024. Real progress. But 52% is still a full 25-30 points below the SaaS standard and the cost-structure breakdown explains why the gap may be structural, not temporary.

In classic software, most costs are fixed: build the product, and every incremental customer is nearly pure margin. In AI products, talent - the main fixed cost - is only about 26-28% of the cost structure for companies at GA through scaling stage. Almost everything else is variable: inference, cloud, even model training, which never really ends. Every new customer brings new costs with them.

That’s not a software business with a temporary margin problem. That’s a different kind of business.

2. The model layer stopped being the moat - officially.

Only 14% of companies say proprietary model development is their primary differentiation. 49% now differentiate at the application layer - UX, workflows, integrations, data application and ~70% of builders are focused on vertical AI applications.

The margin data backs the strategy: companies routing most workloads to smaller or fine-tuned models and escalating only complex tasks to frontier models are the ones driving margins up.

Model usage tells the same story: OpenAI leads at 77%, but Gemini jumped from 43% to 55% and Anthropic sits at 51% - the standard pattern is now OpenAI plus one or two others.

Models are becoming interchangeable infrastructure. Nobody’s paying a premium for what everyone can rent.

3. Nobody has figured out pricing - including your competitors.

58% of companies still include a subscription component, but consumption-based (35%) and outcome-based (18%) pricing both grew meaningfully in just six months. And here’s the number to sit with: 37% of companies plan to change their AI pricing model within the next 12 months, driven by customer demand, competitive pressure, and margin concerns.

More than a third of the industry is about to reprice. If you’ve been treating your pricing model as settled, you’re likely the outlier.

One more warning shot buried in the deck: internal AI is increasingly funded from existing R&D budgets, not net-new spend. AI vendors are no longer selling into a separate “innovation” pool - they’re competing for the same IT dollar as every other line item. Budget season just got harder for everyone.

What this means for you:

Run your own margin stack the way ICONIQ breaks it down: what % of your COGS is fixed (people) vs variable (inference, cloud, training)? If variable costs dominate, your margins won’t improve with scale by default - they only improve with deliberate model routing, and the companies doing it are already 10+ points ahead.

And if your pricing hasn’t been revisited in the last two quarters, the market is moving without you: the fastest-growing companies in the survey are the ones aligning price to demonstrable value - cost saved or revenue generated - not seats.

The takeaway isn’t that AI products are bad businesses. It’s that they’re new businesses, and the founders who win will be the ones who stopped pretending the old math applies.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Greylock Ventures, a Silicon Valley-based venture firm, raised $1.5 billion for its 18th flagship fund. (Link)

• Isogon Ventures, a New York City and Madrid, Spain-based early-stage venture capital firm, is raising its first fund. (Link)

• Paradigm, a U.S.-based venture capital firm founded by Matt Huang and Fred Ehrsam, raised $1.2 billion for its latest fund. (Link)

New Startup Deals

• Spectro Cloud, a San Jose, CA-based AI infrastructure management software company, raised more than $100M in Series D funding. (Link)

• Pure, a San Francisco, CA-based financial infrastructure platform for peer-to-peer games, raised $8M in Seed funding. (Link)

• Valarian, a London, UK-based sovereign AI infrastructure company, raised $50M in Series A funding. (Link)

• Quadric, a Burlingame, CA-based AI inference processor company, extended its Series C to $46M. (Link)

• Auxilius, a Munich, Germany-based AI-native governance, risk and compliance solutions company, raised €1.3M in Pre-Seed funding. (Link)

• Reformed, a London, UK-based wellness beverages company, raised $22M in Series A funding. (Link)

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• Associate - Artha Capital | India - Apply Here

• Investment Associate - Alexandar Chapman | USA - Apply Here

• Marketing Associate - Turbostart | India - Apply Here

• Marketing Intern - TNB Aura | USA - Apply Here

• Finance Intern - 0g Labs | Remote - Apply Here

• Associate - Sowefund | France - Apply Here

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

• Investment Associate - Antler | UK - Apply Here

• Analyst - ICONIQUE Capital | USA - Apply Here

• Finance Director - AI Fund | USA - Apply Here

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• Associate or Senior Associate - Bessemer Partner | USA - Apply Here

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📜 DEEP DIVE

What really happens in the 48 hours after you pitch a VC? (Most founders never see it).

There’s a written version of your company out there, and you’ve never seen it.

It was written within hours of your pitch. While you were on the way home, replaying the meeting - all that nodding at the retention slide - that's good, right? Someone at the fund was already typing. Your team. Your numbers. Your market. Your weak spots. All of it squeezed into about two pages.

And here’s what most founders never learn: from that moment on, the fund isn’t evaluating your company anymore. It’s evaluating that document.

Think about how the decision actually gets made. The partner meeting where your fate is decided - you’re not in it. Your deck isn’t in it either.

Nobody re-watches a pitch on a Monday morning. So when a sceptical partner asks, “Okay, but what’s their wedge?” - nobody calls you. Someone pulls up the memo. If the answer isn’t in there, you don’t have an answer. Simple as that.

You spent three weeks preparing the pitch.

Your company gets judged on two pages you didn’t write, will never read, and didn’t know existed.

Most founders assume the decision happens in the meeting. It doesn’t. The meeting is just where they collect the raw material. The real decision starts the same evening, in a process you can’t see and once you understand how that process works, everything about how you fundraise changes. You stop trying to win the room. You start trying to win the write-up.

That’s what this issue is about: what actually gets written about you, how the Monday partner meeting really runs, the funnel math of where deals die, how to tell from a rejection email exactly which stage killed you and how to quietly write the memo for them.

The funnel nobody shows you

Let’s start with the honest math, because almost no founder has seen it.

The largest study ever run on how VCs actually make decisions - Gompers, Gornall, Kaplan, and Strebulaev surveyed 885 institutional venture investors for the Journal of Financial Economics - mapped the full internal pipeline. For every single investment a firm makes:

• It considers roughly 100 deals

• 28 get a meeting with the founders

• 10 survive to be discussed at a partner meeting

• 4.8 enter due diligence

• 1.7 reach term sheet negotiation

• 1 gets funded

Read that funnel from where you’re standing. You got the meeting - you’re one of 28. And the biggest cliff on the chart is the very next step: almost two-thirds of companies that pitch never reach the partner meeting at all.

They don’t get rejected by the partnership. They get filtered out at the memo stage - someone wrote you up, someone else skimmed it, and nobody fought for you. The founder experiences this as silence, or a warm-but-vague email ten days later.

Two more numbers to calibrate:

The same study found that the median deal takes ~83 days from first contact to close, and that a moving deal shows visible motion: follow-ups, data requests, reference calls (they make about 10). Silence is not a slow yes. Silence is the memo sitting in a channel with no sponsor pushing it.

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📜 DEEP DIVE

You’re benchmarking your AI spend against the wrong number (The data just proved it).

At some point in the next quarter, someone on your board is going to ask what you’re spending on AI.

You’ll pull the number. It’ll be a few thousand dollars a month - some ChatGPT and Claude seats, a Cursor bill, an API line that’s been creeping, a couple of tools someone on the team swears by. You’ll say the number.

Someone will nod and say it seems reasonable. And the conversation will move on, because nobody in the room - including you - has any idea what “reasonable” means.

Here’s the uncomfortable part: the question can’t be answered in the form it was asked. “What are we spending on AI?” produces a genuinely meaningless number, because it adds together three completely unrelated kinds of spending that happen to share a vendor category. It’s like asking “what are we spending on electricity?” and including the office lights, the product servers, and the salary of the person you didn’t hire because a machine does their job.

This issue is about fixing that - with the best spending data that exists, and a framework for reading your own statement that most founders have never applied. By the end, you’ll run a 30-minute audit on your last three months of card statements and know, with actual confidence, whether your AI spend is working. Most founders who run it find the same thing: they’re overspending on the bucket that returns nothing and underspending on the one that compounds.

The data: 70,000 companies, one cliff

Ramp sits in an unusual position. As a corporate card and bill-pay platform, it sees actual payments - not surveys but money leaving accounts. Its AI Index tracks this across more than 70,000 US businesses, and last month it published the number that should reframe every AI budget conversation you have this year.

The median company on Ramp spends $11.38 per employee per month on AI. That’s one enterprise seat.

• The top 10% of companies spend $611 per employee per month.

• And the top 1% - the companies Ramp’s economists call “AI-pilled” - spend $7,449 per employee per month, a figure growing 14.1% month over month.

Read that distribution again. It’s not a bell curve with a long tail. It’s a cliff - a roughly 650x gap between the median and the top. Adoption is no longer the story; Ramp itself has stopped treating “does this company use AI” as an interesting question and rebuilt its entire index around intensity, because nearly everyone now pays for something.

The story is that “using AI” has split into two economies: a small group treating it as a core input to production, and everyone else buying subscriptions.

Startups sit somewhere in the middle of this picture. Kruze Consulting’s data across 1,000+ venture-backed startups shows the average AI line item climbing from about $2,000 a month in early 2023 to $5,000-6,000 by 2024, and roughly 70% of startups paying for at least one AI tool. The line item nearly tripled in 18 months and almost nobody restructured how they account for it.

So far, this could just be an interesting distribution. Here’s where it becomes a warning.

Dabbling returns nothing. Literally nothing.

In June, Ramp’s economics team linked its payment data to Revelio Labs’ workforce records across 21,559 US companies - the first study to connect observed firm-level AI spending (not surveys) to what actually happened to those companies afterwards.

The finding:

Companies that adopted AI grew headcount 10.2% over the following two years, with entry-level hiring up around 12%.

Growth, not layoffs - the companies spending seriously on AI were expanding, and the gains showed up across sales, admin, finance, and customer service, not just engineering.

But that’s not the finding that matters for you. The finding that matters is in the split:

Those gains were entirely driven by high-intensity adopters. Low-intensity adopters - companies that technically “use AI” but spend little per employee - showed no statistically significant change at all.

The honest caveats: this is correlation, not causation - heavy adopters were already larger, more technical, and faster-growing, and the gains took 6-12 months to show up, because integrating AI into real workflows takes time. But even read conservatively, the data kills the most common startup AI posture stone dead.

The $200-a-month, few-seats, we’re-experimenting position — the one the median $11.38 represents - is indistinguishable from doing nothing. There is no measurable dividend for dabbling. The returns live entirely at intensity.

Which creates an obvious question: fine - spend more. On what? Cursor seats? API credits? That vertical sales tool the team wants? More Claude?

The rest of this issue:

• The denominator error - the number your AI spend should actually be judged against (it’s not your software budget).

• The three budgets - the sort that shows where you’re overspending and where the returns actually live.

• The kill-criteria - the cancellation signal from 70,000 companies that tells you a tool failed before you’ve admitted it.

• The 30-minute audit - run it on your own statements this week and find the $500–$2K/month you’re burning on nothing.

Read more

This Week at a glance -

Big idea + report of the week :

• AI agent reality check: What 16,000 companies reveal about AI agent adoption?

• The $212.9B venture paradox: Why most founders still can’t raise.

• Why are U.S. companies quietly switching to Chinese AI models?

Frameworks & insightful posts :

• The Task Economy: AI’s next $1 trillion market (And most founders are missing it).

• How to build AI agents as a non-technical founder (Anthropic’s guide).

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🧠 Big idea + report of the week

AI agent reality check: What 16,000 companies reveal about AI agent adoption?

Autonomous AI agents are one of the biggest technology trends of 2026, promising to handle complex workflows with minimal human involvement. But while adoption is accelerating, most companies are still trying to figure out how to deploy them successfully.

A new report by FirstPageSage combining data from 30+ industry studies and 16,000+ businesses offers one of the clearest snapshots yet of where agentic AI stands today.

• Adoption is growing, but deployment remains early

Enterprise companies lead adoption with 25% already using agentic AI in some form, while mid-market and SMBs are catching up quickly.

However, across every company size, the vast majority of deployments remain in the experimentation stage. Only 11% of enterprises, 6% of mid-market firms, and 2% of SMBs have deployed AI agents at scale.

• The biggest challenge isn’t the AI - it’s the business.

The leading reasons projects fail are unclear ROI (43%), poor data quality (38%), rising implementation costs (35%), cybersecurity concerns (32%) and a lack of internal AI expertise (29%).

The report suggests that many companies begin AI projects before building the data infrastructure and processes needed to support them.

• Customer service is leading real-world adoption.

Around 64% of enterprise and mid-market deployments focus on customer support, followed by supply chain coordination (58%), IT operations (53%) and software development (51%).

Highly regulated functions like finance remain much earlier in adoption due to governance and compliance requirements.

The bigger opportunity may not be building another AI agent, but helping businesses successfully deploy them.

Companies increasingly need better data infrastructure, integration tools, governance software, monitoring platforms and implementation services before AI agents can deliver measurable business value.

As adoption moves beyond experimentation, the businesses solving these operational challenges are likely to become just as valuable as the AI models themselves.

The $212.9B venture paradox: Why most founders still can't raise.

Here’s a strange thing to sit with: investors deployed $212.9B into startups last quarter - the second-highest total in the history of venture capital - and if you asked most founders raising right now, they’d tell you the market feels dead.

Both things are true. And the gap between them is the most important story in venture this year.

Fewer companies got funded last quarter than at any point in the past decade. New unicorns collapsed from 83 to 26 - the lowest since Q4 2024. Exits fell for the second straight quarter.

Deal count dropped double digits in every major market on earth: US down 31%, India down 37%, Germany down 33%.

Record money. Shrinking market. The money isn’t spreading - it’s stacking.

That’s the picture from CB Insights’ State of Venture Q2’26 report, released this week, and the mechanics underneath are worth understanding precisely:

• 263 mega-rounds ($100M+) captured 81% of all funding. Anthropic alone accounted for more than a third of it - closing three separate rounds this quarter: $50B, $10B, and $5B

• Add Project Prometheus ($12B) and DeepSeek ($7.5B), and five deals absorbed what used to be an entire quarter of global venture activity

• Total deal count: 7,086 - a decade low

For two straight quarters, one or two companies have carried the headline number, which makes total funding a weak proxy for the actual market.

The unicorn stat is the one to sit with.

OpenAI and Anthropic pulled in nearly $187B combined in the first half of 2026 - capital that, in any prior cycle, would have been spread across hundreds of new billion-dollar companies. Instead, unicorn formation cratered to 26, and 17 of those were AI companies. If you’re building anything else, the path to a $1B mark has rarely been narrower.

One quiet bright spot: median deal size rose 5% to $4.2M. Fewer companies are getting funded, but the ones that do are getting slightly more. Investors haven’t left - they’ve consolidated conviction.

What this means if you’re raising:

Benchmark your raise against the deal-count line, not the funding line.

A decade low in deals means fewer term sheets in circulation than at any point since 2016 - plan for more parallel conversations and longer timelines, because “the market is hot” only describes a market you’re probably not in.

Watch where deal count is actually clustering. The most active categories this quarter weren’t LLMs - they were industrial humanoid robots (20 deals) and robot foundation models (15 deals). Physical AI is where net-new investor attention is forming, and deal count is a cleaner read on appetite than funding totals right now.

And know who’s still writing checks: a16z led the quarter with 47 deals, General Catalyst right behind at 46 - with Hillhouse and Accel newly entering the top five. In a market this concentrated, the active-investor list is more useful than any funding chart.

Why are U.S. companies quietly switching to Chinese AI models?

For the past two years, the AI race has largely been dominated by OpenAI, Anthropic and Google. But as businesses scale AI across their products, many are running into an unexpected problem: cost.

A new CNBC report, based on data from OpenRouter and conversations with AI companies, shows that U.S. businesses are increasingly turning to Chinese AI models because they offer similar performance at a much lower price.

Adoption has surged in 2026.

The share of tokens from U.S. companies routed to Chinese AI models through OpenRouter has climbed from an average of 11% over the previous year to more than 30% every week since February, peaking at 46%.

Models from companies like DeepSeek, Z.ai and Alibaba’s Qwen are gaining traction across enterprise AI workloads.

Price is becoming the deciding factor.

Chinese open-source and open-weight models are estimated to be 60-90% cheaper than leading proprietary models from OpenAI and Anthropic.

AI startup Lindy recently shifted all of its AI traffic from Claude to DeepSeek, expecting to save millions of dollars while also improving performance on several workloads.

The performance gap continues to narrow.

Analysts estimate that the latest Chinese models are now only six to nine months behind the best U.S. frontier models.

For many business tasks, that gap is small enough that companies increasingly choose the lower-cost alternative, especially for internal tools, AI agents and customer-facing applications.

As AI becomes part of everyday software, model selection is becoming an economic decision as much as a technical one. For founders building AI products, this creates an opportunity to lower infrastructure costs by intelligently routing workloads across multiple models instead of relying on a single provider.

Companies that optimise for both performance and cost are likely to gain a meaningful advantage as AI usage continues to scale.

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SOMETHING MORE

🧩 Frameworks & insightful posts

The Task Economy: AI's next $1 trillion market (And most founders are missing it).

Whenever OpenAI, Anthropic, or Google talk about AI growth, they usually highlight one number: tokens (the small pieces of text AI reads and generates whenever you use ChatGPT, Claude, or Gemini).

The logic is simple. The more tokens AI processes, the more people are using AI and the more revenue AI companies generate.

That’s why investors closely track token growth as one of the biggest indicators of the industry’s expansion.

But Everett Randle (General Partner at Benchmark) argues the next trillion-dollar AI category won’t be tokens.

It’ll be tasks. The companies that own the best data for training AI may end up owning the biggest competitive advantage.

Tokens measure AI usage. Tasks improve AI.

Think of it this way.

• Tokens tell us how much AI is being used.

• Tasks determine how much AI gets better.

Early AI models improved by training on the public internet. That approach is now hitting limits.

The next generation of improvements will come from expert-generated tasks that teach models how real professionals work.

For example, if you want an AI to become a great lawyer, it’s not enough for it to read legal textbooks.

It needs thousands of real-world exercises:

• Reviewing contracts.

• Drafting legal arguments.

• Working inside legal software.

• Having experienced lawyers score and correct every response.

Every completed task becomes training data that improves future models.

Why task demand is exploding

The same forces that increased token usage are now increasing demand for tasks.

Every major AI breakthrough requires dramatically more high-quality training work.

• Better reasoning models → more complex expert tasks.

• AI agents → more real-world workflows to practice.

• Industry-specific AI → legal, healthcare, finance, engineering, and scientific datasets.

• Enterprise AI → companies creating proprietary data instead of relying on public models.

As AI becomes capable of handling longer and more sophisticated work, the amount and quality of training data required grows exponentially.

Data is becoming the real moat

For years, companies believed the model itself was the competitive advantage. That assumption is changing.

If every company can access frontier models, then the differentiator becomes the proprietary data used to improve them.

The businesses that build unique datasets, evaluation systems, and expert workflows will likely outperform competitors using the same foundation models.

The numbers are already moving fast

The market is growing much faster than most people realise. Some notable signals include:

• OpenAI and Anthropic are reportedly increasing spending on expert-generated training data by roughly 10x year over year.

• Many AI application companies expect $100M+ annual spending on data generation and evaluations.

• Mercor, which connects experts to AI training work, reportedly doubled from $1B to $2B ARR in just four months.

• Expert hours on Mercor’s platform have surged from just 2,000 hours in early 2024 to more than 2.5 million hours by mid-2026, showing how quickly demand for specialised human expertise is accelerating.

Why founders should pay attention

Most founders think about AI in terms of choosing the best model. The bigger opportunity may be asking a different question:

What unique data can only our company create?

Every customer interaction, workflow, approval process, expert review, and business decision can become proprietary training data that improves your AI faster than competitors relying on off-the-shelf models.

The next AI race may not be won by whoever has the smartest model.

It may be won by whoever owns the richest collection of expert tasks that continually make those models smarter.

How to build AI agents as a non-technical founder (Anthropic’s guide).

Most founders use AI every day, but they’re still using it like a search engine. They open ChatGPT or Claude, explain their business again, describe the task again, paste the same context again, and repeat the whole process the next day.

Recently, Anthropic shared a practical guide showing that the real opportunity isn’t just chatting with AI - it’s building simple AI agents that already understand your work, your business, and your goals.

The biggest misconception is that AI agents require coding. They don’t. For most founders, an agent is simply an AI assistant you’ve configured once so it remembers your context and performs a specific job whenever you ask.

Instead of starting from scratch every conversation, you create a workspace with four simple ingredients:

• Role: Tell the AI what job it has (executive assistant, sales researcher, customer support, etc.).

• Context: Give it information about you, your business, customers, and how you like to work.

• Tools: Connect services like Gmail, Google Drive, Calendar, or documents so it can access real information.

• Trigger: Create repeatable instructions like “Prepare today’s meeting brief” or “Draft support replies.”

Once these are in place, the AI stops behaving like a chatbot and starts behaving like an assistant that already knows your business.

Some of the highest-impact agents founders can build include:

• A personal assistant that summarises your inbox, prepares meeting briefs, drafts emails, and prioritises your day.

• A sales research agent that studies prospects before meetings and prepares customised talking points.

• A customer support assistant that drafts replies using your documentation while following company policies.

• A content repurposing agent that converts one long article into LinkedIn posts, tweets, newsletters, or emails in your writing style.

One insight stood out: most people think the AI is the problem when the real problem is poor setup.

If an agent produces generic answers, it’s usually because it wasn’t given enough context, examples, or clear instructions. Small improvements to its instructions make every future response better, turning it into a system that improves over time instead of starting from zero every conversation.

The guide also recommends creating multiple specialised agents instead of one giant assistant. A dedicated sales agent, content agent, and operations agent are easier to improve because each has a single responsibility.

The takeaway is simple: you don’t need autonomous AI running your entire company to save meaningful time. Start by building one agent for the task you repeat most often - whether that’s email, meeting preparation, customer support, or content creation. Get that one working well before expanding.

Over time, you’ll build a small team of AI assistants that quietly handle repetitive work, giving you back hours every week while letting you focus on decisions only a founder can make.

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NEWS RECAP

🗞️ This week in startups & VC

New In VC

• Paradigm, a U.S.-based venture capital firm founded by Matt Huang and Fred Ehrsam, raised $1.2 billion for its latest fund. (Link)

• Vermilion Cliffs Ventures, a U.S.-based venture firm founded by solo GP Ashley Smith, raised $25 million for its second fund. (Link)

• Chemistry Ventures, a U.S.-based early-stage venture firm founded by Mark Goldberg, Ethan Kurzweil, and Kristina Shen, is raising $500 million for its second fund. (Link)

• Omni Ventures, a San Jose, CA-based Manufacturing Tech VC firm, closed Fund I, at $33m. (Link)

• B Capital, a global multi-stage investment firm, closed B Capital Ascent Fund III, L.P., at $500m. (Link)

• Magnify Ventures, a Los Angeles, CA-based early-stage venture capital firm focused on transforming the care economy, announced the closing of Fund II, at $46.6m. (Link)

New Startup Deals

• AIsa, a San Francisco, CA-based provider of a transaction network for the AI agent economy, raised an undisclosed Seed funding round. (Link)

• Handspring Health, a NYC-based virtual mental health company, raised $19M in Seed funding. (Link)

• Norm AI, a NYC-based company building agentic legal AI, raised $120M in funding at a $1.2B valuation. (Link)

• Agave, a San Francisco, CA-based AI platform for construction financials, raised $15M in Series A funding. (Link)

• Acti, a Singapore-based developer of an AI-native agentic smartphone keyboard platform, raised $5.3M in Seed funding. (Link)

• geoSurge, a London, UK-based AI company helping brands inside generative AI systems, raised $12M in Seed funding. (Link)

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💼 Venture capital & startup jobs

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• Head of fund accounting - Stepstone Group | USA - Apply Here

• Associate or Senior Associate - Bessemer Partner | USA - Apply Here

• VC Fellow - V11 | Remote - Apply Here

• Portfolio & Ecosystem Analyst Intern - Motion Venture | Singapore - Apply Here

• Investor - Superseed | UK - Apply Here

• Principal - ICMG Group | India - Apply Here

• Ventures Analyst/ Associate - plug and Play tech center | USA - Apply Here

• Associate - Primary VC | USA - Apply Here

• Associate, Healthcare (SF based) - B Capital | USA - Apply Here

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

• Senior Associate - ICONIQUE CAPITAL | USA - Apply Here

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

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

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

• Associate - Engine Venture | USA - Apply Here

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

• Talent Investor - Associate - Entrepreneur First | India - Apply Here

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📜 DEEP DIVE

The Hidden VC Scorecard That Decides Your Next Round.

There’s a meeting your investors hold that you will never be invited to.

It usually happens quarterly. The partners sit down, pull up the portfolio, and go through the companies one by one. Not to review your board deck.

To answer one question: if this company raises again, do we put in more money?

Every name gets a mark. At some funds it’s a formal score. At others it’s shorthand in a reserves model. But the output is the same everywhere: your company gets sorted into one of three buckets.

• Back the truck up. The fund will defend its ownership at any reasonable price, take super pro-rata if it can get it, and pre-empt your round if you let them.

• Hold. The fund will do its pro-rata if a credible new lead shows up and sets the price. It won’t lead. It won’t stretch.

• Quiet zombie. The fund has mentally written you down. It will keep taking your board calls, keep saying encouraging things, and will not wire another dollar. You just haven’t been told.

Here’s what makes this uncomfortable: the sorting isn’t a reaction to your next fundraise. It happens before your next fundraise, continuously, whether you’re raising or not. By the time you send that “excited to share we’re opening our Series A” email, the most important investors on your cap table decided their answer months ago.

Most founders don’t know this system exists. They think of their investors as people who made a decision once, at the seed, and will make a fresh decision later, at the A. That’s not how a fund works. A fund is a portfolio machine that re-underwrites you every quarter, and the machine’s output determines more about your next round than your pitch will.

This issue is about that machine - how it works, the five observable signals that tell you which bucket you’re in, and the six-month playbook to change buckets before you raise.

Half the fund was never for you (well - not automatically)

Start with a number most founders have never thought about: the reserve ratio.

When a VC raises a $100M fund, they don’t have $100M to hand out to new startups. Management fees eat roughly 15-20% over the fund’s life.

Of what remains, funds typically hold 40-60% back as reserves - capital earmarked for follow-on investments into companies already in the portfolio. Fund construction guides from Kauffman Fellows and Tactyc both put the standard follow-on allocation at 40-60% of the fund, and analyses of fund performance consistently find that top-quartile managers reserve 40-50%.

Sit with that for a second. The check your investor wrote you at seed came from the smaller half of their fund. The bigger pool of money - the reserves - is sitting there right now, and the entire job of a venture partnership between fundraises is deciding which portfolio companies get it.

That decision is not sentimental, and it’s not evenly spread.

Sophisticated funds literally calculate a “follow-on MOIC” - the expected return on the next dollar into your company, modelled separately from the first dollar. If that number doesn’t clear the bar, the model says pass, no matter how much the partner likes you.

So when your investor says “we’re saving reserves for our best companies,” understand the precise meaning: there is a live, internal ranking, you are on it, and your position on it is the single most important fact about your next fundraise that you don’t know.

The market just made the buckets brutal

This sorting always existed. What’s changed is how much it now decides.

Three numbers from the current market, and they stack into one picture.

First: graduation rates have halved.

Carta’s cohort data shows that 30.6% of companies that raised a seed round in Q1 2018 reached a Series A within two years. For the Q1 2022 cohort, that figure was 15.4%. Early reads on the 2023 cohort land around 15% as well. The median time from seed to A has stretched past two years, up from roughly 1.7 years in 2019.

Second: capital has sorted, not spread.

In 2025, per Carta, the bottom 50% of US startups that successfully closed a round split just 14% of all capital raised between them. The top 10% took roughly half. That’s among companies that raised - the sorted-out companies aren’t even in the denominator.

Third: more companies are living on insider money.

Bridge rounds accounted for 16.6% of all cash raised on Carta in Q2 2025, up from 11.8% a year earlier. At the seed stage it’s starker - roughly 46% of seed-stage financings in early 2025 were bridge or extension rounds rather than new-priced rounds.

Put the three together, and the picture is this:

The gap between rounds has stretched to 2+ years, most companies won’t clear it, and the thing keeping companies alive across the gap is insider capital - which is to say, reserves - which is to say, the bucket you’re in.

In 2021, the buckets barely mattered. Growth-stage tourists would fund your A whether your seed investors showed up or not. In 2026, the bucket decision is the fundraise. If your insiders are in, you get a bridge that buys you 12 months and a warm co-sign that de-risks your round for new leads. If they’re out, you’re raising into the hardest Series A market in a decade with a silent cap table behind you, and every new investor you pitch will notice the silence.

Which raises the obvious question. Your investors know your bucket. New investors will infer it. The only person operating blind is you.

The rest of this issue fixes that. Below the line:

• How the reserve decision actually gets made (including the factor that has nothing to do with your metrics)

• The five observable signals that reveal your bucket with surprising precision

• A decoder for what investor language actually means, and

• The six-month playbook for changing buckets before you raise.

How the decision actually gets made

Read more

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📜 DEEP DIVE

What should founders never automate with AI? The Signal vs. Repetition framework.

Over the last couple of weeks, we’ve published two of the issues -

• How to automate your startup fundraising with Claude and

• How to build your personal and business AI agents,

and my inbox has looked the same ever since. Founders sending me screenshots of their setups. Founders asking which agent to build next. Founders proudly listing everything they’ve taken off their own plate.

Reading through those emails, a pattern started bothering me. Almost every founder was asking how to automate something. Almost nobody was asking whether they should. And the functions they were proudest of automating - support, outreach, onboarding - were consistently the ones that made me wince.

One email made the problem impossible to ignore.

A seed-stage founder wrote that his biggest ops win of last year was taking himself out of customer support entirely. He’d wired up an AI agent - deflection rate north of 70%, response time under a minute. Then, six months later, a churn number he couldn’t explain.

He ended up digging through old resolved tickets at midnight and found, buried in the transcripts, the same feature complaint phrased forty different ways. His agent had answered every single one politely. Nobody on the team had read any of them.

He didn’t lose those customers because the AI gave bad answers. He lost them because the answers were good enough that the signal never reached a human.

Here’s the thing: he didn’t pick the wrong tool. He automated the wrong function, and there was no way for him to know that, because nobody has given founders a way to tell the difference.

But first, the numbers - because this isn’t one founder’s mistake. It’s most of the market’s.

The money is going in. Almost nothing is coming back.

MIT’s NANDA initiative studied 300 enterprise AI deployments and put a number on what most operators already suspected: 95% of generative AI pilots deliver no measurable P&L impact. Not underwhelming returns. No measurable returns - against an estimated $30-40 billion in enterprise spend.

Companies are responding the way you’d expect. The share of companies abandoning most of their AI initiatives jumped from 17% to 42% in a single year, per S&P Global’s survey of 1,000+ organisations, and the average company now scraps 46% of its AI proofs of concept before they ever reach production.

The easy read is “AI is overhyped.” That’s not what the data says. MIT’s researchers were explicit that the failure rate has almost nothing to do with model quality. The tools work.

The 95% failed because of where companies pointed them.

And buried in the same report is the finding that matters most for founders: more than half of generative AI budgets went to sales and marketing tools, while the biggest measured ROI came from back-office automation. Operations. Process work. The unglamorous internal stuff.

The money went to the visible half of the company. The returns lived in the invisible half. At enterprise scale, that mismatch burns budget. At your stage, it does something much worse, and almost nobody is talking about it.

You don’t get to sit this out, either

Here’s the uncomfortable part: “just automate less” is not an available move anymore, because investors have started pricing automation into your round.

Carta’s data shows Series A teams have shrunk from an average of 25.9 employees in 2021 to 16.8 today - same milestones, a third fewer people. The median time to a startup’s first hire has stretched from 214 days to 284.

And VCs are now explicit that revenue per employee has become a core efficiency signal at Series A - CRV goes as far as saying that if you’re building with AI tools and not showing superior capital efficiency, “that now reads as a weakness.”

So this is the squeeze every early-stage founder is sitting in right now:

Automate aggressively, and you risk joining the 95% or worse, automating away the exact conversations that would have gotten you to product-market fit. Automate timidly, and you walk into your Series A with a 2021-shaped headcount and get benchmarked against companies running twice your revenue per head.

The founders getting this right aren’t the ones with better tools. They’re using the same models as everyone else. What they have is a different map - a simple way of sorting every function in the company before deciding whether AI touches it. Once you see it, the MIT budget mismatch, the churn story above, and the rev-per-head data all snap into the same picture.

Here’s what the rest of this deep dive covers:

• What actually separates the functions you should automate this week from the ones that will quietly cost you product-market fit if you touch them?

• Why does the same function flip from “never automate” to “automate immediately” as you grow, and what’s the specific trigger to watch for?

• Where does the first $500-$2,000/month of automation spend reliably pay for itself at seed stage?

• Which three automations can you build in Claude, with copy-paste prompts, and which popular one should you refuse to build?

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 :

• 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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🗞️ 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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