Nvidia 6B 买 Poolside 模型工厂，种子轮 15x 不够

Harry Stebbings, Rory O’Driscoll and I went through Nvidia’s ecosystem spending spree, OpenAI’s move from number one to number two, and what happens to CFO budgets when your best people are addicted to tokens.

#1. Nvidia paid $6B for Poolside’s Model Factory, and the trigger was a $2B round that didn’t close

Nvidia is paying $6B for a non-exclusive license to Poolside’s Model Factory, the internal system Poolside used to build its models, and separately investing $1B at a $12B pre-money valuation. 109 engineers got offers to join Nvidia’s open-weight Nemotron effort. The three founders are staying. The investor letter says explicitly that this is not an acquisition and not an acquihire.

Poolside had a six-week window to raise $2B to pay for a 40,000 GB300 cluster coming online in January. They didn’t close it in time, and they lost the cluster.

My read: it read almost depressing. Not being critical, but it showed that infinite capitalism is not as infinite as it looks. Even in the age of AI, and even with Nvidia seemingly funding everyone on planet Earth, the VC gravy train only runs so long. There’s only so much money coming out of big funds.

Rory’s read: it was an excellent letter, and one line does the work. They said they had found themselves on the right side of prediction in a market that scaled exponentially in capital intensity. Translated: we were right three years ago that there was a market for a US open-weight model, we built it, and the capital required for the next turn of the crank is beyond us.

Harry’s read: neo labs are out of favor, and this is universal across every investor he talks to. Nobody was surprised Poolside found it hard to raise.

#2. A $9B outcome sometimes doesn’t clear the bar for seed investing in 2026. Welcome to the AI Era

Harry, a Poolside shareholder, said the outcome is roughly a 15x for them as a seed investor and that it’s pretty great. That’s where I pushed.

My read: 15x sounds good for a late-stage investor. For a true seed fund it isn’t enough. You don’t get a fund return out of a 15x. If you’re deploying a seed fund at today’s entry prices, you need your best deals doing 50x for the math to work, unless you’re running a hyper-concentrated book.

Rory’s math on the dilution: a 15x on a $9B outcome implies an effective entry of $600M, not $60M. The nominal seed price was much lower. The dilution across the capital-intensive rounds is what got you there. To hit 50x, that company needed to exit around $63B.

Rory’s defense of the bet: it was rational at the time. The two closed-source frontier winners are worth a trillion each. The two or three open-weight winners in China are worth $50B to $100B each. The upside case supported a 100x. The thesis broke on capital markets, not on execution, and the team still returned 15x on a bet that didn’t work. If you get 15x on your failures in venture, you’ll die a rich man.

#3. Only four or five companies on the planet can finance a frontier model

Rory’s frame: the VC money ran out on Anthropic and OpenAI a long time ago. That’s why no VC owns more than 1% or 2% of either of them. The only entities capable of financing a state-of-the-art frontier model in the United States are the hyperscalers. OpenAI and Anthropic exist because Microsoft, Google and Amazon wrote checks nobody else on earth could write. Nvidia is now the fifth member of that club, which is why it’s the one financing the open-weight side. VCs have been along for the ride, providing occasional pricing discipline.

The negative implication for everyone else: the next-smartest team going for it just hit the capital wall. Everyone behind them hits the same wall.

#4. Nvidia’s plan is to spend the entire free cash flow on the ecosystem

Nvidia is also in talks to back Mercor in a round at a $20B valuation led by General Catalyst, doubling the $10B mark from October. Harry, an investor, said Mercor is crossing $2.5B in run-rate revenue and that he never thought it would get this big this fast.

Rory’s question: the other Nvidia bets are all TAM expansion. Fund a neocloud, they buy more chips. Fund Poolside, more open-weight inference runs on Nvidia. Fund Mercor, and you get more training data, which doesn’t obviously sell more chips.

My read: there’s no thesis here. They have something on the order of $70B of free cash flow this year, the strat team and top VPs get in a room, everyone brings their best ideas, and the budget gets allocated. Somebody in that room thinks data labeling matters, and his best idea was Mercor. Adding cash to the balance sheet does nothing for a CEO of a profitable company beyond defense. If Wall Street lets you spend it, spend it.

Rory’s one caveat: four years ago Nvidia’s cash flow was a tenth of what it is today, and they only carry about $50B of cash and investments. There’s a version of him that would keep more for a rainy day. He agrees the highest-return use is buying ecosystem viability, especially when the OpenAI-style deals give you a customer, revenue and ecosystem insurance in one transaction.

#5. Kroll’s data: gross margins above 30% no longer earn you anything at exit

Kroll published a look at M&A and large transactions over the last six months.

My read of it: above 30% gross margin, you get no additional credit in M&A or other exits. There’s a penalty below 30. There is no premium above it. Value Mercor on classic multiples for 80% gross margins and growing, and $20B isn’t expensive.

Rory’s caution: don’t overfit on the sample of deals where negative gross margins resolved. Cursor started with negative gross margins, subsidized unlimited usage, had no way to defend it, capped it, built its own models, and got to a $60B outcome. He can also name plenty of deals that started with bad gross margins and ended with bad gross margins. The question is when it’s rational to underwrite massive improvement. His doubt on the training-data companies: Cursor had hundreds of thousands of customers to work margin against. The training companies have three to five.

#6. OpenAI’s reacceleration story is existential, not promotional

OpenAI’s CFO Sarah Friar told employees the company will be public by 2027.

Rory’s read on why the narrative shifted when it did: Q1 to Q2 was roughly $5B to $6B in GAAP revenue, about 18% quarter over quarter. That annualizes to just under 100% and would put them somewhere under $30B in GAAP revenue this year against about $12.5B last year. In isolation that’s a spectacular business. Against a competitor at a $60B run rate mid-year and growing faster, anyone can model it into irrelevance in two years.

Then there’s Broadcom, Nvidia and everyone else planning to sell $200B of chips to OpenAI. They run the same math and ask whether that demand is real. So sitting on the number for three months was never an option. A concerted “Q2 was an anomaly, Q3 is exploding” push was the only move available.

My read: I was stunned by the 18%. At almost any other company that number is a celebration. Relative to what everyone had priced in, it was a disappointment.

#7. Being number two is worse when there are seven candidates for number two

My read: at the start of the year there were effectively two choices, and everyone wanted multi-model anyway, so you got a clean oligopoly bake-off. You hire a few Salesforce people, they put up a slide, it’s us versus them, and that motion sells. Today there are eleven or twelve competitors running inference on open weights with performance close enough to matter. It’s hyper-competitive for number two, and especially hard if you’re the premium product, because you can’t compete on price and you’re left with brand and security.