The Cognitive Revolution / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
13 published records
01 / belief
“There was, I would say, a remarkable amount of not just like cross lab camaraderie, because, I would say people are generally friendly to each other always, even if they're competing fiercely.”
02 / commitment
“Now, obviously there's some selection effect there, but you could just go to, the whole event was under Chatham House rule, so I will respect that and not attribute specific statements to specific people or organizations.”
03 / recommendation
“I'm a member, by the way, I use, I'm still a bunch of partner X, but I view the platform and it is, I think I was third of the cost that I was quoted from my account to use the same, to drive the same outcomes with a software-driven solution.”
04 / evaluation
“Obviously, like a lot of companies would want to build something for their patient, for their customers using APIs like opening an API or I forward API, but they simply cannot because they do not have an in-house expertise to steer those model towards working the way it would want them to be.”
05 / evaluation
“A happy path of I read a document and I put it in a database is not a happy path because the document can be in Spanish, in Chinese, a Colombian.”
06 / prediction
“It's where sort of our future focus is, on essentially being able to do what we do and do it on streaming tokens effectively so that we can kind of just be like the old days of TV and kind of just like run the conversation on a five second delay and bleep out anything that's bad basically. Because I just, what we see is that if we're asking too much from our customers of asking something of our customers that is going to significantly impact the user experience, then they are less likely to adopt the controls that they ultimately need.”
07 / evaluation
“They can't solve most of those, whereas you get those real human scientists and you get most of them. So the summary of that is it's really easy to be excited when they work well, but you got to pay attention to all the really simple ways that they break before you get too excited, I think.”
08 / preference
“I mean, the fact that they offer that for free, I mean, one of my favorite strategies in philanthropy or in general, in efforts to make the world a better place is the unilateral provision of public goods.”
09 / evaluation
“A company running thin margins on top of Opus is going to struggle to say, no, don't use that.”
10 / commitment
“The booking, the research, the clipping, those are AI skills we refine as we go, and we plan to publish them in all sorts of artifacts as this matures.”
11 / prediction
“One of the interesting ideas that I heard there that I had not heard before was that the model that you would want to have internally for AI research might have quite a different constitution from the one that you deploy publicly for kind of general purpose AI assistant use cases. And they seem to think that, in fact, you probably would want to have something even more focused on safety and more sort of restricted in some ways, but maybe also less inclined to refuse certain tasks, but basically a different behavioral profile, which I do think is interesting because if you're going to make this sort of chain of thought monitoring plan work, I do think you're probably going to need some meaningful diversity of the AIs.”
12 / evaluation
“If you believe that models have their own deep-seated goals and that those goals might diverge from ours, then this could be very bad, right? It could be like, it could be extremely bad because they would be using this reasoning to figure out how to please us while like still having their own goals.”
13 / recommendation
“This is thought to be dangerous because if you have a disconnect between what you really want and the signal that you are rewarding the AI for, then you can get into bad places. And so the obfuscated reward hacking paper that I think is still one of the most important papers of the last few years from OpenAI showed that if you have a hackable reward signal and your model learns to hack that, you can then put pressure on the chain of thought.”