In October 2023, four MIT students who had graduated the year before raised an $8M seed for a code editor. The OpenAI Startup Fund led the round and Nat Friedman came in alongside it. The founders had come through an OpenAI accelerator, so the people who wrote the first check had already watched them work. It was a seed priced like a seed, for a product competing with tools every developer already had. Two years later the company behind Cursor raised a $2.3B Series D at a $29.3B post. The investors who made money on that were not the ones who chased the Series D. They were the ones in the room when four young engineers asked for $8M.
I keep coming back to that round because it is the opposite of what most new investors in the United States think AI investing looks like. They read that OpenAI raised $122B in March 2026 and assume the game is about getting into the big round. It is not. The headline number is the least useful number in the market.
So I looked at the beginning instead. I took 38 American AI companies founded in 2020 or later that have since been valued at $1B or more, and asked who the founders were, how big the first round was, and who wrote the first checks. The list was built by hand and leans toward survivors, so it is not a census. It is enough to see the shape.
The shape is two games that barely touch. The first is the lab spinout. Thinking Machines Lab opened with a $2B seed. Safe Superintelligence opened with $1B. Not one of the lab spinouts I could trace started under $10M. Those rounds are assembled before they are announced, by firms that already know the researchers.
The second game is the one that matters. Fifteen of these companies started with a first round under $10M. Perplexity, Mercor, Harvey, Decagon, Modal, Distyl and Cursor all began that way. And when I looked at who those founders were, it was not who I expected. Most were not dorm room prodigies and not famous researchers. They were engineers and operators out of big companies: Palantir alumni at Distyl, a former Spotify engineer at Modal, Meta and Google alumni elsewhere. People who already knew how to ship and had not yet been priced as stars. That is the gap a new fund can live in.
Then the question is who is already standing in that gap. Here I distrust the usual answer. Count every first round in the set and a16z looks dominant and Sequoia looks strong. But volume flatters whoever writes the biggest checks. Cut it to the rounds under $10M and the picture changes. The OpenAI Startup Fund was in three of them. Nat Friedman and Daniel Gross were in three. a16z was in three, out of twelve first rounds overall. Sequoia was in none; its smallest first check in this set was $25M. The people worth standing next to are the ones who keep showing up when the round is small and the founder is still unknown, and that is a short list.
So here is what I would do if I were starting a new fund in the US today.
I would pick a lane and stay in it: technical operators leaving big companies, young technical founders, and repeat application founders, all raising seeds under $10M. I would write checks of $250K to $1M and be the useful second check a lead actually wants, not pretend to out brand a16z. I would be in San Francisco, because the Bay Area took $122B of the $159B of US AI funding in 2025, and you cannot see deals first from a distance when the concentration is that extreme. I would stay close to the feeder programs and operator networks, because Cursor came through an OpenAI accelerator, Mercor through the Thiel Fellowship, and Distyl out of Palantir. The deal usually starts years before the round. And I would track early signals with software, because a person reading the news is always late.
None of that is an edge, though. It is table stakes. A new fund needs a reason for a founder to pick its check over a dozen others, and the only reason that works is something the founder cannot get elsewhere.
From where I sit in Singapore, that reason is Asia. Think about what happens to a company like Cursor or Harvey a year after the seed. The product works, American customers are paying, and the next question from the board is where the second market comes from. Asia is large, but it is not one market. Buyers in Japan, Korea, Indonesia and Singapore procure differently, regulators ask different questions about data, and the large enterprises that would anchor a regional business mostly buy through relationships rather than inbound demos. A small team in San Francisco cannot learn that from a deck. An investor who can put the founder in front of the right three enterprise buyers, explain how a regional bank will run procurement, and help hire the first person on the ground is offering something concrete. It does not replace a great lead, but it is exactly the kind of help a seed stage founder remembers when the next round gets tight and allocation is being decided.
The big rounds are not wrong. They are just the wrong place for a new fund to look. Across these winners, the lab spinouts started big and closed, and the rest mostly started small and open, founded by people who had shipped before and were not yet priced as stars. Cursor was one $8M round with the right two investors in it. A new fund wins the same way those investors did: small checks into those founders, next to the handful of people who keep seeing them first, carrying an edge a founder will still value two rounds later.