The Ledger, every show

“When Meta releases this behemoth model, I don't think anyone's really gonna run behemoth. For like their day to day stuff. It's just probably not gonna be worth it.”

“That's kind of like my bet is like, people are going to really want to train models to be agents. And I think you can get that to work well with a pretty small model.”

“Like Deep Seek is serving all of China on 2000 GPUs.”

“I mean, I do think that like the best source of evals going forward is probably going to be academia.”

“GRPO is, like, great for, like, leaning heavy on highly parallel inference compute. It's more memory efficient for the actual training process. It's much easier to do in a distributed fashion because you have less gradient syncing and less model weight copies.”

“Multi-agent RL's hard. I did five years of it in grad school. It's like not easy. And to the algorithms don't really even exist for the things you would really want to do.”

“Scaling to, like, million token contexts is, like, really, really hard. There, I don't think there are real, like, open source replications, open token context scaling, Beyond, like, tiny, like, academic model sizes.”

“I think in chat interfaces, it probably won't stick around. Like, I don't think we're always going to have the dropdown of like Oath for many and Oath for many high. That feels silly.”

“There really isn't currently an open source model that behaves in the way that these models do. We have things like R-one, which are great at kind of the single-term math and code reasoning problems. But they are not in the general purpose agents world yet.”

“Coherent to this very extensively in their latest flagship model, command A”

“Like, the day GPT-IV launched, Morgan Stanley had integrations, because we had been working on it, and these were, like, we had press releases for these, like, we are ready to go”