@brettcodes.bsky.social appreciated watching your video on why you stopped using AI. Recently talked with Casey Muratori who also doesn't use it. Could I message you somewhere? Or you can also send me DM.
I don't yet have numbers to back this up, but I gotta believe that someone connecting their agent to Kody in one agent and then switching their main driver to another somewhere else is suboptimal.
But keep those methods you use to investigate things and monitor things very separate from the methods you use to generate reward which is something that AI companies including open AAI have held up as a principle especially in the case of avoiding putting training pressure on the chain of thought. So you might have monitors that read the agents chain of thought in order to alert you if something is going wrong somewhere but you don't train the agents with the outputs of that monitor.
even in this incident we saw there was a lot of pressure um as a result of this incident to stop doing cyber security evaluations and I really don't think that stopping doing evaluations and like sort of blinding ourselves to the result of evaluations is the right reaction to this problem.
Like that just does not seem like the ideal artifact. We should be able to store them somewhere. We should be able to have architecture diagrams that we can review and discuss that generate that code to spec.
I would say that I expect like full automation of AR&D perhaps somewhere around like 2031 2030 and then getting to like the like beats all humans on the job milestone. Maybe I expect median around 2033
Second, I think ML is a very shallow domain relative to math. So I think in math there's much more of a you find some true deep abstraction um and then like that like if you really understand that thing which is hard to understand then you get somewhere
somewhere along the line this kind of HR ratio of like a pod popped into being. You know, every time you hire six engineers, you add a designer or you add a PM, you add an EM
if if we're if we're too ambitious and we're at the outset and too ambitious and visionary about the product we want to build, then we will probably miss product market fit because we won't start at a small enough humble enough place
it's not just like there's some factory somewhere that you can pay to produce the the data like you actually need to invent new novel scientific approaches
We made this something useful for adults and we had this user pay model that could monetize your engagement instead of trying to show you an ad and getting you to leave the game to go somewhere else. It actually the more engaged you were, the more likely you were to, you know, spend money.
But right now I think it's sitting somewhere between half million and five million lines of code, somewhere in there. Probably more on the half million side right now and with the next drop of an Anthropic model, we're probably going to see it jump up to a few million lines.
I think there's something going on that pre-training it's it's not like the process of humans learning. It's somewhere between the process of humans learning and the process of human evolution.
But I don't want to, like, pull that down because every time someone made the first pull request is a win for our society, you know? Like, it… Like, it doesn't matter how, how shitty it is, y- you gotta start somewhere.
I think some of the times they are but they're certainly involved and there are people and in some sense we haven't actually removed the person we've like moved them to somewhere where we can't see them.
the only thing that we know is that models will improve. Will it be incremental? Will it be exponential? I mean somewhere in between. Who knows? But uh what you have to believe is that you get better as models get better. Your organization gets better as models get better.
it's easy to um go on vibes for too long. Uh some folks, you know, just kind of like trust the vibes and you know that'll get you somewhere, but it's not rigorous.
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