Invest Like the Best with Patrick O'Shaughnessy
· Gavin Baker, Patrick O'Shaughnessy
The first iteration of that was the Blackwell 200. A lot of different SKUs were canceled. And the reason for that is it was, by far, the most complex product transition we've ever gone through in technology. Going from Hopper to Blackwell, first you go from air-cooled to liquid-cooled. The rack goes from weighing round numbers, 1000 pounds to 3000 pounds, goes from round numbers 30 kilowatts, which was 30 American homes, to 130 kilowatts, which is 130 American homes. I analogize it to imagine if to get a new iPhone, you had to change all the outlets in your house to 220 volt, put in a Tesla power wall, put in a generator, put in solar panels, that's the power, put in a whole home humidification system, and then reinforce the floor, because the floor can't handle this. So, it was a huge product transition. And then just the rack was so dense it was really hard for them to get the heat out. So, Blackwells have only really started to be deployed and really scaled deployments over the last three or four months. [00:00:00] S0: Can you explain why it has been such an important thing that Blackwell was delayed?
· Gavin Baker
[00:00:00] S1: Because Blackwell was so complicated, and it was so hard for everyone to get these exquisitely complex racks working consistently, had reasoning not come along, there would have been no AI progress from mid-2024 through, essentially, Gemini 3. There would have been none. Everything would have stalled. And can you imagine what that would have meant to the markets? For sure, we would have lived in a very different environment. So, Reasoning bridged this 18 month gap, Reasoning saved AI, because it let AI make progress without Blackwell, or the next generation of TPU, which were necessary for the scaling laws for pre-training to continue. Google came out with a TPU v6 in 2024, and the TPU v7 in 2025. In semiconductor time, imagine Hopper. It's, like, a World War II era airplane. And it was, by far, the best World War II era airplane. It's a P-51 Mustang with a Merlin engine. And two years later in semiconductor time, that's, like, you're an F-4 Phantom, okay? Because Blackwell was such a complicated product and so hard to ramp, Google was training Gemini 3 on '24 and '25 era TPUs, which are, like, F-4 Phantoms. Blackwell, it's, like an F-35. It just took a really long time to get it going. So, I think Google for sure has this temporary advantage right now from a pre-training perspective. I think it's also important that they've been the lowest cost producer of tokens. And this is really important, because AI is the first time in my career as a tech investor that being the low-cost producer has ever mattered. Apple is not worth trillions because they're a low-cost producer of phones. Microsoft is not worth trillions, because they're a low-cost producer of software. Nvidia is not worth trillions because they're the low-cost producer of AI accelerators. It's never mattered. And this is really important, because what Google has been doing as the low-cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them. And for anyone who is a low-cost producer. Let's make life really hard for our competitors. So, what happens now? I think this has pretty profound implications. One, we will see the first models trained on Blackwell in early 2026.
· Gavin Baker
I think the first Blackwell model will come from xAI, and the reason for that is just according to Jensen, no one builds data centers faster than Elon. Jensen has said this on the record. And even once you have the Blackwells, it takes six to nine months to get them performing at the level of Hopper, because Hopper is finely tuned. Everybody knows how to use it. The software is perfect for it. The engineers know all its quirks. Everybody knows how to architect a Hopper data center at this point. And, by the way, when Hopper came out, it took six to 12 months for it to really out-perform Ampere, which was the generation before. So, if you're Jensen or Nvidia, you need to get as many GPUs deployed in one data center as fast as possible in a coherent cluster, so, you can work out the bugs. And so, this is what xAI, effectively, does for Nvidia, because they build the data centers the fastest, they can deploy Blackwells at scale the fastest, and they can help work with Nvidia to work out the bugs for everyone else. So, because they're the fastest, they'll have the first Blackwell model. We know that scaling laws for pre-training are intact, and this means the Blackwell models are going to be amazing. Blackwell is ... It's not an F-35 versus an F-4 Phantom, but from my perspective it is a better chip. Maybe it's, like, an F-35 versus a Rafale. And so, now that we know pre-scaling laws are holding we know that these Blackwell models are going to be really good. Based on the raw specs, they should probably be better. Then something even more important happens. So, the GB200 was really hard to get it going. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks. Now you're not going to replace the GB200s.
· Patrick O'Shaughnessy
[00:00:00] S0: Yeah. No new power laws. Yeah.
· Gavin Baker
[00:00:00] S1: Yeah. Just any data center that can handle those, you can slot in the GB300s, and now everybody is good at making those racks. You know how to get the heat out. You know how to cool them. You're going to put those GB300s in, and then the companies that use the GB300s, they're going to be the low-cost producer of tokens, particularly, if you're vertically integrated. If you're paying a margin to someone else to make those tokens, you're probably not going to be. This has pretty profound implications. I think it has to change Google's strategic calculus. If you have a decisive cost advantage, and you're Google, and you have search and all these other businesses, why not run AI at a negative 30% margin? It is, by far, the rational decision, take the economic oxygen out of the environment. You eventually make it hard for your competitors, who need funding, unlike you, to raise the capital they need. And then on the other side of that, maybe you have an extremely dominant share position. Well, all that calculus changes once Google is no longer the low-cost producer, which I think will be the case. The Blackwells are not being used for training. And then when that model is trained, you start shifting Blackwell clusters over to Inference. And then all these cost calculations and these dynamics change. It's very interesting. Like, during the strategic and economic calculations between the players, I've never seen anything like it. Everyone understands their position on the board, what the prize is, what play their opponents are running, and it's really interesting to watch. If Google changes its behavior, because it's going to be really painful for them as a higher-cost producer to run that negative 30% margin, it might start to impact their stock, that has pretty profound implications for the economics of AI. And then when Rubin comes out, the gap is going to expand significantly.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Versus TPUs? [00:00:00] S1: Versus TPUs and all other ASICs. Now I think Trainium3 is probably going to be pretty good, and Trainium4 are going to be good. [00:00:00] S0: Why is that the case? Why won't TPU v9 be every bit as good?