Dwarkesh Podcast / 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
29 published records
01 / evaluation
“” Actually, most of the times where we make some decision that ends up seeming good is because we messed something up before and just didn't want to repeat the mistake.”
02 / prediction
“I think that that will shift from something that feels more like a chatbot, where you ask a question and it formulates an answer, to things where you're giving it more complicated tasks and then it goes away and does them.”
03 / evaluation
“A bunch of them were about the classics. Clearly that was important in some way.”
04 / belief
“We would hope that that'll be the case. But I think that at each point, when you're building software there's a ton of stuff that you can do with software but then at some level you're constrained by the chips that it's running on.”
05 / belief
“I think like a 70B model that we trained with a Llama-3 architecture can get better, it can keep going.”
06 / belief
“I think the intuition is that this stuff ends up being pretty important and valuable for both economic and security reasons and other things.”
07 / belief
“I just don't want to shortchange that. I think we have a responsibility to make sure we do a good job on that.”
08 / belief
“If you take the security one that I was talking about, I think someone with a weaker AI trying to hack into a system that is protected by a stronger AI will succeed less.”
09 / belief
“I think that at some point AI is probably going to surpass people at most of those things, depending on how powerful the models are.”
10 / belief
“I think that a world where AI is very widely deployed, in a way where it's gotten hardened progressively over time, is one where all the different systems will be in check in a way.”
11 / belief
“I guess our prediction going in was that it was going to asymptote more, but even by the end it was still learning.”
12 / uncertainty
“I don't know what that ratio is going to be but I consider the generation of synthetic data to be more inference than training today.”
13 / belief
“I think you'll get smaller versions. One thing is that I think 8B isn’t quite small enough for a bunch of use cases.”
14 / belief
“I think that there's a lot baked into that question. I'm not sure that we're replacing people as much as we’re giving people tools to do more stuff.”
15 / belief
“I think people do it but I don't think this is something that can be quite as magical as just getting to a level of AI, getting a bunch of capital and putting it in, and then all of a sudden the models are just going to… You do hit different bottlenecks along the way.”
16 / belief
“If it's like talking on Slack with a biology researcher… I think models are very far from this.”
17 / belief
“Just to put this in perspective, I think a gigawatt would be the size of a meaningful nuclear power plant only going towards training a model.”
18 / belief
“Regarding other open source dangers, I think you have genuine legitimate points about the balance of power stuff and potentially the harms you can get rid of because we have better alignment techniques or something.”
19 / belief
“I think we would probably build out bigger clusters than we currently can if we could get the energy to do it.”
20 / evaluation
“I think one lesson of technology is that you want to move things from the physical constraint realm into software as much as possible because software is so much easier to build and evolve.”
21 / prediction
“I assume that as we release bigger and bigger models, that will become a bigger thing. It's not the main thing that we're doing, but I think if those companies are going to be selling our models it just makes sense that we should share the upside of that somehow.”
22 / preference
“There's a case in which you don't want to open source the architecture because China can use it to catch up to America's AIs and there is an intelligence explosion and they win that.”
23 / evaluation
“Even when we were putting together the business case for investing a $100 billion in AI or some huge amount in the metaverse, we have plans that I think made it pretty clear that if our stuff works, it'll be a good investment.”
24 / evaluation
“I think that helps you then hone your intuition for what you want to try to train into the next version of the model itself. That makes it more general because obviously for anything that you're hand-coding you can unlock some use cases, but it's just inherently brittle and non-general.”
25 / commitment
“At that point we started a second group, the gen AI group, with the goal of bringing that stuff into our products and building leading foundation models that would power all these different products.”
26 / preference
“I like understanding what's going on with people and the dynamics between people. So I think if I sold this company, I'd just go build another company like this and I kind of like the one I have.”
27 / commitment
“We built two, I think 22,000 or 24,000 clusters that are the single clusters that we have for training the big models, obviously across a lot of the stuff that we do.”
28 / evaluation
“The current incarnation of all this stuff feels like it's going in a direction where intelligence can be pretty separated from consciousness, agency, and things like that, which I think just makes it a super valuable tool.”
29 / prediction
“For where it is right now in training, it is already at around 85 MMLU and we expect that it's going to have leading benchmarks on a bunch of the benchmarks.”