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 17 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
Feldman: Cerebras is 20 times faster than Nvidia B200 GPUs
“Really focused on performance, both for training and for inference. You think 20 times faster than Nvidia B 200 GPUs and it's been an amazing run.”
Feldman: Cerebras provides 2,625x more memory bandwidth than traditional GPUs
“And we have 2625 times more memory bandwidth than the GPU does.”
Cerebras leads all Artificial Analysis inference benchmarks by a large margin
“I think also just go up and look at artificial analysis. Wherever we are, we're the fastest not by a little bit, but by a lot.”
Feldman: AI startups are replacing closed-source models with fine-tuned open-source
“I think for sort of AI companies like Cognition, like all your competitors, like AlphaSense, like dozens of others, they are trying to replace closed source models with very, very fast open source models, and they're trying to drive the open source Accuracy dr…”
Feldman: Cerebras raised $1.1B at an $8.1B valuation
“So we announced a 1.1 billion dollar fundraise that we had completed. It was done at an 8.1 billion dollar post money valuation, and it was led by Fidelity and Atreides management.”
Feldman: Multi-chip SRAM architectures make speculative decoding extremely difficult
“It limits the things you can do. It makes all sorts of cool AI techniques like speculative decode extremely difficult. Whereas if you have a giant chip, you might only need a handful.”
Enterprises retrain 10B-30B open models from scratch for legal data compliance
“We see at the large enterprise level, particularly those who have large data assets, a desire to train their own models and to go a little smaller, say in the 10 to thirty billion parameter category. I think especially the very large companies, enterprises hav…”
Feldman: Transformer architecture has several more years of viability
“And finally, I think the transformer, the current architecture has a way still to run. I think we will see that for several years more.”
Feldman: Sam Altman and Ilya Sutskever invested in Cerebras' early rounds
“In 2016, we met with Sam Altman and Ilya Suskovard at OpenAI and they were an idea and we were PowerPoint, right? That's amazing. And what AI was doing was identifying cats in pictures. And I think they ended up investing in us, both of them and many of their …”
Feldman: Pre-IPO startups should target investors primarily focused on public markets
“I think in later stages, as you get close to IPO, you're looking for a very different type of investor. You're looking for an investor who Primarily does public markets.”
Feldman: Memory bandwidth is the primary bottleneck in AI inference performance
“Inference performance comes from memory bandwidth and the memory bandwidth is the limiting factor. In inference performance. Remember, in order to generate a token, to generate a word, all the weights have to move from memory to compute. If you're constrained …”
Feldman: Running dozens of AI agents expands security attack surfaces geometrically
“When you spin off dozens of agents, the sort of attack surface of the of the solution expands geometrically.”
Feldman: Chip architecture begins by deciding what not to be good at
“One of the hardest things in computer architecture, and one of the first things you do is you decide what you're not going to be good at. I'm going to build chip. What am I not going to be good at? We're not going to be good at general purpose compute. We're n…”
Feldman: Non-compute bottlenecks masked Cerebras' 20-25x speedup for a hyperscaler
“We were doing work with, we're doing work with one of the hyperscalers and for a product they have, and they said, look, we used your system and it didn't make us that much faster. And so we said, well, that's a surprise because we're 20, 25 times faster on th…”
Feldman: AI taking 10-15 minutes for answers are proofs-of-concept, not products
“Number two is that for AI to deliver on its promise, to be embedded in our lives, it must be fast. There aren't things that are embedded in your life that make you wait 10 or 15 minutes to get a good answer. Those are proof of concepts, but those aren't produc…”
Feldman: Cerebras trains models for Mayo Clinic, GSK, and US military
“We do a great deal of work with large enterprises in training, with Mayo Clinic, with GlaxoSmithKline, with the US military, with the Department of Energy, with our customers in the Middle East. We've trained leading models, language models in, in Arabic, in C…”