Speakers in the public record
Claim mix
belief 7evaluation 7prediction 6
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
The useful parts, with receipts.
20 published records
“It can be that it's automated a bunch of things and then those are being done in extreme profusion. A thing AI can do, you can have it done much more often because it's so cheap.”
- Publisher
- Dwarkesh Podcast
“I think that set of inputs probably would yield the kind of AI capabilities needed for intelligence explosion but if it doesn't, after we've exhausted this current scale up of increasing the share of our economy that is trying to make AI.”
- Publisher
- Dwarkesh Podcast
“I think that that is one of the ways in which partial automation can fail to really translate into a lot of economic value.”
- Publisher
- Dwarkesh Podcast
“There was an element of that where more and more investment has been thrown into the field and the market has rapidly expanded as the technology improved. But I think the closest analogy is actually the long run growth of human civilization itself and I know you had Holden Karnofsky from the open philanthropy project on earlier and discuss some of this research about the long run acceleration of human population and economic growth.”
- Publisher
- Dwarkesh Podcast
“Back then when I said, yeah, I expect this by the middle of the century-ish, that was a backstop if we found it absurdly difficult to get to the algorithms and then we would learn from neuroscience.”
- Publisher
- Dwarkesh Podcast
“If the current scale up doesn't work, all we're left with is just like the economy growing 2% a year, we have 2% a year more resources to spend on AI and at that scale you're talking about decades before just through sheer brute force you can train the 10 trillion dollar model or something.”
- Publisher
- Dwarkesh Podcast
“I think that that's enough to go to the 10 billion and then combine with stuff like the H100 to go up to the hundred billion.”
- Publisher
- Dwarkesh Podcast
“I think I saw an estimate that GPT-4 cost like 50 million dollars or around that range to train.”
- Publisher
- Dwarkesh Podcast
“I think some people might be skeptical that existing robots given their current hardware will have the dexterity and the maneuverability to do a lot of physical labor that an AI might want to do.”
- Publisher
- Dwarkesh Podcast
“I think that probably won't happen and later I can maybe give a more exclusive breakdown of why.”
- Publisher
- Dwarkesh Podcast
“[unclear] That's not to say that's the limit of the most that technology could do because biology is able to reproduce at faster rates and maybe we're talking about that in a moment, but if we're trying to restrict ourselves to robotic technology as we understand it and cost falls that are reasonable from eliminating all labor, massive industrial scale up, and historical kinds of technological improvements that lowered costs, I think you you can get into a robot population industry doubling in months.”
- Publisher
- Dwarkesh Podcast
“Because things like designing the custom curriculum maybe some humans put some work into that but you're not going to employ billions of humans to produce it at scale and so it winds up being a larger share of the progress than it was before.”
- Publisher
- Dwarkesh Podcast
“Some people have an intuition that what matters is time, that it's not how many people working on a problem at a given point.”
- Publisher
- Dwarkesh Podcast
“In the 2000s, I would say well, I'm gonna have a pretty uniformish prior I'm gonna put weight on it happening at the equivalent of 10^25 ops, 10^30, 10^35 and spreading out over that and then I can update another information.”
- Publisher
- Dwarkesh Podcast
“We're running through the orders of magnitude of possible resource inputs you could need for AI much much more quickly than we were for most of the history of AI. That's why this is a period with a very elevated chance of AI per year because we're moving through so much of the space of inputs per year and indeed it looks like this scale-up taken to its conclusion will cover another bunch of orders of magnitude and that's actually a large fraction of those that are left before you start running into saying well, this is going to have to be like evolution with the simple hacks we get to apply.”
- Publisher
- Dwarkesh Podcast
“There were Winograd schemas, catastrophic forgetting, quite a number and they have repeatedly gone away through scaling. So there's a picture that we're seeing supported from biology and from our experience with AI where you can explain — Yeah, in general, there are trade-offs where the extra fitness you get from a brain is not worth it and so creatures wind up mostly with small brains because they can save that biological energy and that time to reproduce, for digestion and so on.”
- Publisher
- Dwarkesh Podcast
“You see bits of tool use in some other primates who have an advantage compared to say whales who have quite large brains partly because they are so large themselves and they have some other things, but they don't have hands which means that reduces a bunch of ways in which brains can pay off and investments in the functioning of that brain.”
- Publisher
- Dwarkesh Podcast
“I think that's a bit lower now as we get towards the end of Moore's law although interestingly not as much lower as you might think because the growth of inputs has also slowed recently.”
- Publisher
- Dwarkesh Podcast
“The way to think about it is — we have a process now where humans are developing new computer chips, new software, running larger training runs, and it takes a lot of work to keep Moore's law chugging (while it was, it's slowing down now).”
- Publisher
- Dwarkesh Podcast
“You can also make improvements on the software side and when we think about an intelligence explosion that can include — AI is doing work on making hardware better, making better software, making more hardware.”
- Publisher
- Dwarkesh Podcast