The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 10 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Staniszewski: Skeptical of valuations for GPU and inference providers built on NVIDIA
“It's all the, like, GPU providers that are built on top of NVIDIA that, like, help you get Effectively inference capacity. Like I'm a little bit skeptic on some evaluations there”
Arora: Private Enterprise Domains Hold 10x More Data Than the Public Web
“I think there's 10 times more information in private domains, which is not available in the public domain. All the drug discovery data, which is sitting in every drug company that has made any drug in the past, it's all proprietary data, right? All the intelle…”
Aravind Srinivas: Google is the only company full-stack independent of Nvidia
“The only one who's managed to do this, I would say, is Google. They built their own chips, they built their own software around it called JAX, and then they built their own accelerated linear algebra library called XLA. And then, you know, they have their own …”
Hardware rivals struggle against NVIDIA because of its dominant software stack
“Training is dominated by NVIDIA at the moment. There's going to be other players, but they have a hard time competing because of the software stack, basically. Their hardware may be really good, but the software stack is is a challenge.”
Mayya: Nvidia will maintain a monopoly over AI compute
“Nvidia will own everything. That's the only monopoly in any market that I've ever seen.”
Kamath: AI upside is already fully priced into Nvidia and Microsoft
“Definitely Microsoft and Nvidia is priced in. They've corrected the least. They're sitting at lofty valuations. So you'll have to pick the next guy to make money.”
Aravind Srinivas: Nvidia GPUs succeeded because AI scaled through neural networks
“If AI was not neural nets, then GPUs wouldn't have mattered. But AI happened to be just basically neural nets at scale. And so all the primitives they built, all the software stack they built ended up being, like, The core foundational building blocks for neur…”
Srinivas: Perplexity built custom Nvidia runtimes and uses Cerebras chips
“A lot of the open source models that we serve ourselves with some fine tuning, ah, we, we've tried to serve it with extreme efficiency, like we wrote our own runtimes for NVIDIA chips, and we used other chips like Cerebras, and that helps us to, like, make the…”
Srinivas: Training neural networks solely on daily stock opening prices is useless
“If you're training it on the raw stock price, let's say you just have a bunch of numbers of the stock price of Nvidia opening price every single day. Sure, it's not going to be useful on its own, because there are so many other factors that influence the price…”