These Are a Few of My Favorite Things

After a decade at 500 Global — 50,000+ startups evaluated, 30+ accelerator batches — I've learned to pay attention when the pattern shifts before the narrative catches up. Walking around the Bay this week, the trees are blooming ahead of schedule. The market feels similar.

Within AI, there are two parallel stories that almost never get told in the same room. The loud story is about foundation models and frontier robotics — $168 billion into OpenAI, $67 billion into Anthropic, $42 billion into xAI, and billions more flowing into neo labs and embodied AI. In February alone, OpenAI closed $110 billion and Anthropic closed $30 billion.

The Scoreboard Most Investors Haven't Seen

There is now a real, growing scoreboard tracking AI-native companies that reach $5M, $10M, $100M+ in annual revenue with teams you could fit in a single conference room. Henry Shi's Lean AI Leaderboard. Ben Lang's Tiny Teams. Jeremiah Owyang's research on lean AI anatomy. Spencer Belsky's Lean AI Report. These aren't VC marketing projects — they're independent researchers documenting something that keeps showing up in the data no matter how you cut it.

The old "healthy" benchmark of $200K in revenue per employee now looks quaint. SaaStr recently argued that $500K is the new floor, with companies like Lovable and ElevenLabs pushing $1.5–2 million per employee.

These are not edge cases anymore. What makes them different isn't just that they're using AI. It's that they were built around AI from inception. The hiring plan, the org chart, the cost structure, the go-to-market — all designed for a world where intelligence is an API call, not a headcount line item.

The takeaway isn't that AI makes companies more efficient. It's that AI-native companies are a fundamentally different kind of business.

The Capital Stack Hasn't Caught Up

On the investor side, there's a growing chorus of voices naming the same problem from different vantage points. What struck me is how many of them arrived at the same conclusion from completely different starting points.

The thing I keep coming back to is that this isn't a niche trend. It's a structural shift that's already happened — the instruments just haven't caught up yet.

What Changes When Companies Don't Need You

Here's the uncomfortable question that most of the investor-side analysis stops short of answering: what do you actually offer a company doing $5 million in ARR with 8 people, healthy margins, and no intention of raising a Series A?

The traditional pitch — "we'll help you scale" — doesn't land when the company is already scaling faster than any VC-backed competitor in their space. "We'll introduce you to customers" is irrelevant when the product sells itself. "We'll give you governance experience" is actively unattractive to a founder who doesn't want a board.

The founders I'm talking to aren't anti-capital. They're anti-misalignment.

They want growth capital with a defined cost, not an open-ended equity claim. They want a partner who understands that giving up 20% or more of a company to take money it doesn't desperately need is a bad deal for both sides.

This is the founder profile that traditional VC was not designed for. And increasingly, it's the best founder profile — because the people who build $10M businesses with 12 people are exactly the people you want to bet on.

The obvious objection: if these companies don't need capital, aren't the ones who take it just the ones who couldn't command venture valuations? The adverse selection argument. I've heard it enough times to take it seriously. But it gets the causation backwards. The best companies in this category aren't raising because nobody offered — they're not raising because the terms don't match what they've built. That's not adverse selection. That's a market signal.

What I'm Paying Attention To

The capital innovation is real, but early. Revenue-based financing providers are deploying hundreds of millions into the space. Hybrid instruments are being discussed seriously at fund formation level, not just in blog posts. The structural precedent is already there — $30 billion+ across oil & gas royalties, biopharma milestone payments, and restaurant profit-share models. The question isn't whether these mechanics work. It's who adapts them for AI-native companies first.

The best companies in this category are invisible to traditional deal flow. They don't attend demo days. They don't apply to accelerators. They don't hire investment bankers. They're building in public on X and shipping product while the industry debates whether they exist. Finding them requires a different kind of infrastructure — signal intelligence, not pitch deck volume.

Three Calls I'm Making

Revenue per employee becomes a standard diligence metric within 12 months. Right now it's a curiosity that shows up in leaderboards and Twitter threads. By this time next year, Series A leads will be asking for it in data rooms alongside ARR growth and net retention. When a metric starts getting tracked by independent researchers, it gets adopted by operators, then by investors. We're in the second phase. The third is coming fast.

The first $100M+ fund explicitly built for capital-efficient AI will announce this year. The structural logic is too compelling and the ecosystem validation is too broad for this to remain a whitespace. Someone is going to build the vehicle that matches the asset class. The question is whether it's an existing firm retooling or a new entrant who sees it clearly. I have my bet.

At least one top-20 VC firm launches a dedicated capital-efficient AI vehicle or strategy by year-end. The thesis is too well-validated and the LP demand for DPI is too strong for every major firm to keep running the same playbook. Someone at the institutional level will retool. The question is whether they build it from scratch or acquire the positioning from someone who already has.

I could be wrong on timing. I don't think I'm wrong on direction.

The founders are ahead of the investors. The companies are ahead of the instruments. And the most important stories in early-stage right now are not the loudest ones — they're the small teams shipping, the disciplined operators stacking ARR, and the handful of investors quietly building capital structures that match what these companies actually need.

More soon.