20VC x SaaStr: Google Loses Two Generational Scientists in 48 Hours, the $725B Question Wall Street Is Finally Asking, and Why #3 in AI Is a Death Sentence

With Harry Stebbings, Jason Lemkin, and Rory O’Driscoll

Every week, Harry, Rory, and I sit down to argue about what’s actually happening in AI and B2B. This week I was back from two weeks in China, where it’s a parallel universe of DeepSeek and Gemini, and where I finally understood the sovereignty argument in a way I never did from San Francisco.

The throughline of the whole episode was the same force showing up in five different costumes. Capital and talent are flooding into AI so hard that everything else bends around it. DeepSeek raised $7.4B at a $50B price where only the Chinese government gets voting rights. Google lost two of the most talented researchers alive to Anthropic inside 48 hours. DRAM prices are up 90 to 95% in a single quarter, which means your next iPhone costs more because of a data center in Tennessee. And Wall Street is finally asking the only question that matters: who is actually going to pay for all of this?

Here’s what we got into.

Top Takeaways

1. Google Lost Two Generational Scientists in 48 Hours. The Whole Story Is Momentum.

Inside two days, DeepMind lost Noam Shazeer (co-author of the original attention paper, character.ai, brought back to Google in a clever multi-billion-dollar acquihire) and John Jumper (Nobel Prize winner, co-creator of AlphaFold). Both went to Anthropic.

Harry’s read was the simple one: the best researchers alive only want to work on exactly what they want to work on, and the new labs can promise them that. I think that’s true but one-dimensional. Rory caught the second dimension, which is the frustration of not being able to ship. Google had a ChatGPT alternative and the bureaucracy smothered it while OpenAI jammed theirs out the door and took the lead.

There was a rumor that Anthropic had a secret breakthrough that pulled Jumper over. Rory was skeptical, and the reason is the lag time. In core LLM work, the gap between invention and revenue is one to three years. In medical work, it’s ten to fifteen. Protein folding collected its Nobel and still has no drug in production. So this isn’t “join in the next 90 days or miss it.” It’s a multi-year science bet and a question of where you want to spend the next five years.

My honest aha was momentum. When you start winning, everything goes your way. The talent, the breaks, the projects. Winners win and they compound until something breaks the chain. Anthropic and OpenAI can let people work on whatever they were promised because other people spent $300B in capex on their behalf. They have a luxury the incumbents don’t.

And to be fair to Google: over the last 18 months, only two of the Mag 7 outperformed the S&P, Nvidia and Google. They are very much in the frame. But you don’t wake up and try the new Google harness or the new Google coding tool. You try Claude Code. You try Cowork. You try OpenAI. Being number three in innovation beats being Microsoft or Meta and saying “we spent $70B, we might have something next year.” It’s still number three.

2. Why #3 in AI Is the Most Dangerous Place to Be

Tech markets don’t end up with millions of players. They end up as tight oligopolies: a leader, a number two, and maybe a three and a four, with the vast majority of revenue going to one and two. Cloud went AWS, then Azure, then Google Cloud.

What makes number three uniquely exposed right now is routing. Three months ago it wasn’t clear how big a deal multi-model routing was. Now everything except the smallest startups is routing workloads to different models. And here’s the problem for a closed-source number three: number two is usually simpler, number three is usually cheaper, and the cheaper slot is exactly where open source attacks. Google Cloud a generation ago won by being cheapest and simplest. Now your number three closed-source model is competing for that same slot against six Chinese open-source models being subsidized into the ground.

Rory’s frame: when an industry gets ground down by a 5x-cheaper alternative, the number one guy makes a little less, the number two guy makes a lot less, and the number three guy goes bust. Google isn’t going bust because it has the balance sheet. But the downward pressure on profitability is real, and the implicit conclusion is brutal: it’s now almost impossible for a closed-source number four to emerge and catch up. The market is set.

This morning I got an email from Anthropic telling me my prompt cache hit rate was low. That’s not a Gemini shot. That’s Anthropic fighting open source directly, getting you to cache prompts so heavily that they can be cheaper than open source. That’s the actual battlefield.

3. DeepSeek’s $7.4B Round at $50B: Only the People Who Don’t Need Votes Get Them

The round itself is wild. $7.4B at roughly $50B. The founder is committing about 20B yuan himself, close to $3B, around 40% of the round. Fewer than ten investors including JD.com. None of them get rights. The only entity with governance control is the Chinese state.

Rory nailed the punchline: the only people getting voting rights are the only people who don’t need them. The Chinese government doesn’t need votes. They have sovereignty and an army, and as we saw with Meta, they can make you hand back your $2B after you’ve taken it. So the structure isn’t surprising at all.

Two weeks in China made the sovereignty argument real for me. Anthropic and OpenAI intentionally block access there, even in Hong Kong. So you live on DeepSeek and Gemini. DeepSeek is intentionally crippled inside China, can’t search the web, trained on different data. This is China deciding it will not be reliant on Anthropic and OpenAI to run the next-generation economy. Whatever it costs to subsidize that, $10B, $20B, $50B, it’s a rounding error next to an aircraft carrier.

And before anyone gets too smug: we’re doing a version of it too. Anthropic couldn’t ship its most recent model until it satisfied the U.S. government. Leopold’s situational awareness called this. Both governments now treat this as existential. China solves it their way, we solve it ours, Europe pays a tax to be first in a market where it would otherwise be fourth.

4. The $725B Question: Who’s Actually Going to Pay for AI?

Goldman projects $7.6 trillion in cumulative AI capex from 2026 to 2031. David Cahn’s Sequoia piece started at $600B. Now it’s the $725B question.

Here’s the plain-English version. The hyperscalers are spending roughly $700B a year in capex. You only do that if you expect revenue to exceed expenses. So at some point someone has to spend $700B a year in revenue for the industry to make a buck. Right now total AI revenue is well under $100B. So AI is pulling in maybe $100B and spending $700B a year. That’s not a great business yet.

And it got more aggressive, not less. A year ago capex was about 60% of Mag 7 free cash flow. Now it’s 120%, and they’re borrowing to fund it. That’s the classic bull market trap: you can be intellectually right that this is stretched, and the narrative keeps going for years.

Run the math forward. Round to $1 trillion of revenue needed, because you have to pay for electricity on top of capex. For companies to hand over $1 trillion, they need more than $1 trillion of value back. Total U.S. labor spend is under $20 trillion. So you’re talking about 7 to 8% of the labor force getting replaced by tokens for the math to work. That’s a dauntingly high bar, and the only way that last capex dollar earns a return is through enormous productivity gains and the labor displacement that comes with them.

There’s also a parity tax nobody wants to say out loud. If everyone deploys the same AI, the cost savings get competed away and show up as lower prices, not higher profits. Every bank adding ATMs at the same time didn’t make banking more profitable. But the company that doesn’t adopt is dead. That’s why you lean in even when the industry-wide return is questionable. Your team has to be 15% leaner next year, not because t