we produced our own conservative estimate, finding that the climate impact from 2025's use of NVIDIA'S AI products is on par with the state-owned Russian Coal company.
If the useful life of an NVIDIA H100-vintage cluster turns out to really be three years rather than six, the capex cycle's economics invert, and the leverage that looked prudent looks reckless.
We are at a point metallurgically where video games were at the start of the last console generation-upgrades aren't necessarily noticeable based on specs alone. And noticeable improvements require a huge increase in resources.
I can get to the emotion state from seeing a bear or smelling a bear or hearing a bear or hearing a twig snap, hearing somebody yell there's a bear. Zillions of different inputs completely unlike the rigid reflex which only has one input and that's it.
If you keep adding deadlines without also checking whether the workload itself is sustainable, you risk what I call Obliger-rebellion-the point where an Obliger who has met, met, met, and met expectations suddenly snaps and refuses to meet any more.
likely this will be one of the largest industries in the world and um, uh, it'll take longer than a couple two, three years. It'll take less than 10. And so this will this will be our next $100 billion business.
I don't think anyone found it useful enough to like maintain a system to keep the specs and the code in sync versus just using the code as the source of truth always.
The bottleneck has shifted from doing the work to writing clear specs and reviewing outputs fast enough to keep the pipeline moving-the middle is hollowing out.
Nor nor did I I would say my mistake is I didn't deeply internalize that they they really had no other options that that that a VC would never put in 510 billion of investment into an AI lab with the with the hopes of it turning out to be anthropic. And so that was my miss.
because our computers are designed to be operated by other people, anyone who's an operator could buy our systems. Most of these homebuilt systems you have to be your own operator because it was never designed to be flexible enough for other people to operate.
Nvidia would be the top of that list not to make it. You know, this is long before you, but Nvidia's graphics architecture was precisely wrong. It's not a little bit wrong. We created an architecture that was precisely wrong.
Nvidia's computing stack is the best performance per TCO in the world, bar none. Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company.
I was not impressed with the DGX Spark; it's described as an "AI supercomputer on your desk" but in reality it has lower tokens/sec than a good laptop GPU - and on top of that, you have to figure out the networking details of how to connect to it from your actual work device etc.
You can you can take the margin like Nvidia takes the margin. memory players are taking the margin, but ASML has never risen the price more than they've increased the capability of the tool. Um, and so in a sense, they've always provided net benefit to their customer.
As high-level programming cedes way to the prose compiler, making your goals and specs well understood to the ambiguity loop and showing good judgment is going to matter more than ever.
I think that the pathway that you would need (in terms of revenue growth and profitability) to justify Nvidia's and OpenAI's current pricing is improbable, but that is just my view
the thing that is the biggest competitor for any new accelerator is kind of even the previous generation of Nvidia right I mean in a fleet what I'm going to look at is the overall TCO
They do have flame graphs in Nsight Graphics for GPU workloads, although their flame graphs are currently shallow as it is GPU code only, and onerous to use as I believe it requires an interposer; on the plus side they have click-to-source.
The more progress that AI makes or the higher the derivative of AI progress is, especially because NVIDIA's in the best place, the higher the derivative is, the sooner the market's going to be bigger and expanding and NVIDIA's the only one that does everything reliably right now.
But Google has never had that DNA of like, "This is a product we should sell." The Google Cloud, which is a separate organization from the TPU team, which is a separate organization from the DeepMind team, which is a separate organization from the Search team. There's a lot of bureaucracy here.
And they're decent, their hardware is better in many ways than in NVIDIA's. The problem is their software is really bad and I think they're getting better, right? They're getting better, faster, but the gulf is so large and they don't spend enough resources on it or haven't historically, right?
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