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 12 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
Morin: GPUs are a clever workaround, not natively built for AI
“GPUs are, you know, are a good trick for AI, but they're not built for AI.”
Morin: Nvidia won AI training via Mellanox interconnects, not raw compute
“The reason probably Nvidia won, at least in the training space, is because of Mellanox, right? Not because of the raw compute.”
Morin: Cloud compute hoarding creates fake AI GPU scarcity
“So in, in the case of, you know, Amazon or Google, that would be buying reserved compute, which you're not going to use because if you buy it on demand, you will get tremendously ripped off. So that creates this like face scarcity of compute because that peopl…”
Morin: Being 7x better on cost won't get customers off Nvidia
“I know for a fact that being seven times better and whatever, take whatever metric you want. Whether it's spend, whether it's whatever. It's not enough to get people to switch. People will choose nothing over something.”
Morin: Talent and energy are the primary bottlenecks in AI
“What is ultimately the number, the, probably the two limiting factor today is talent. And energy. That's it.”
Morin: AI startups must avoid reselling compute and verticalize on product
“Probably the number one thing I would say is do not resell compute if you can. A lot of, you know, AI startups That are building on top of AI are trying to make a margin, you know, on top of a very big cake. And ultimately what they sell is compute. If you loo…”
Morin: Scaling SRAM is a dead end for AI hardware
“No, SRAM, this will not deliver. It's a dead end in terms of scaling SRAM means scaling the surface mean you get, you know, depreciating problems.”
Morin: Bottom-up AI infrastructure strategies fail because developers do not care
“I think that if you are doing it bottom up, infra to applications, you will lose because nobody will care. As they don't today, right? If you look at TPUs, they're available, they're great. Nobody cares.”
Morin: Closed-source AI models are actually complex backend constellations
“At least if you look at close source model, they're not really models. They're more like backend, right? And there are a lot of tricks that you feel like you're talking to one model, but ultimately you're talking to a constellation, an assembly of backends tha…”
Morin: Interconnect dependency is the core difference between training and inference
“In terms of infra, probably the number one thing that is the number one difference between these two is the need for interconnect. So if you do, you know, production, you, if you can avoid to have interconnect between, you know, let's say a cluster of GPUs, of…”
Morin: Non-transformer models fundamentally alter LLM compute requirements
“In the case of LLMs, for instance, you have these, what's called non transformer models that changes fundamentally the compute requirements.”
Morin: AI developers will always choose smaller models if performance matches
“What really pushes model sizes are the efficiency rather than specializing. So meaning that if you can do the same performance with a smaller model that is fine tuned with rag or whatever, then you'll do it with a smaller because again, less is better.”