Market incentives won't bring about robust alignment: the market tolerated OpenAI's 03 model that lied to users constantly for months and demanded the return of GPT-4.0 despite its misalignment, proving capability is prized over reliability.

99:08advantage of having better models to invest more in this safer related stuff while they're doing that. So that could have its advantages, but internal models are kind of what we're scared of the most right now. And the internal like automation of AR and D is what we're scared of. So that's kind of the thing we want to stop, right? Like you really don't want to have a situation in which

99:28like enveloping and open A six months ahead of their public releases and that six months like they have super intelligences before we have QBD7, right? which is entirely a plausible thing that can happen in the world they're afraid of and if anything like that causes lots of new pro it solves some problems and causes others right like I don't know if it's good or bad that's a huge conversation I think when

99:52I say pacing pacing the the frontier we're talking about actively restricting like levels of training runs and ability to like develop the models including internally if it comes to that I think it has to be talking about that kind of approach or it doesn't really work cuz like again If there's an if there's a singularity that's kind of internal to the top lab or to or maybe you know

100:15someone catches up three or four labs then it's not necessarily safer in many sense that people care about obviously if you are in favor of unipolar um singleton style solutions to this problem you have a different perspective on some of these questions but you know a lot of people these days have expressed strong opposition to that kind of on principle which solves some problems and makes some other problems

100:46so much harder, right? Like if you only have to worry about one AI in some important sense, then there are a lot of problems that get a lot easier to solve. Like you don't have to be perfectly efficient. You can afford to make a lot of trade-offs. You don't have to worry about like whether or not you know the more competitive, more ruthless, more misaligned one has competitive

101:14advantages. You don't have to worry about the person who moves faster. You don't have to worry about like the humans being forced to delegate to their AI somebody else's AIS because like if I don't do it, they will. If the AI like has can have princip have like guidelines as to what it will and will not do that are the same for everybody. And so like we can reserve areas of

101:33life, areas of action, areas of decision-m, you know, like we can do all these things potentially, but at the cost of potential centralization, at the cost of concentration of power, at the cost of somebody who's making those some some group of people is making those decisions about what this AI is going to do or they're not, which is worse, right? Like in some important sense because then nobody's making a good

101:54decision. But if there's a lot of AI, then importantly, no one is making the decisions and there's no good answers. the thing right nobody has a good solution to this and that's why to circle it back to David's comment right like to to unify it this idea of if we solve the technical problem and we're 95% succeed ignores the narrow path problems that we then have to walk