Jeff Dean

Their wordsyou know, because sometimes if you just squint at a problem and you think about not necessarily being anchored on exactly how that problem is solved today, but how you would solve it from first principles, you can come up with really good ideas that are, you know, maybe not what other people are thinking about.

Their wordsAnd I think there's a lot of room in a lot of domains for much faster validation models, possibly learned valu validation models that can uh you know get you a a approximation to the true answer much much more rapidly. And that changes how those experimental loops can be thought of and how quickly you can go around those loops.

Their wordsUm but I think there's no uh you know real impediment to making that be a much more automated loop where the model itself decides it's going to explore or maybe with a nudge from some people uh at the various highest level like oh why don't you try some new ideas around model architectures that incorporate this and then it will go run lots of experiments uh see which ones work and then those will get incorporated at a much more rapid rate

Their wordsYeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs

Their wordsYeah, I mean I think uh sometimes it's uh a product that you build that might have access to particular kind of data that the underlying model might not the a general model. So it might be you're building something to help users organize all their own personal information and the model won't necessarily have access to that. And so there you can have a big advantage because all of a sudden your model has visibility or your product has visibility into important data.

Their wordsUm but I I would also caution that the general models are definitely getting better at a broader and broader range of things. So you have to figure out, you know, is that thing you're working on, is that going to be a durable thing or do you think the models uh at the forefront are going to get better at that in the next six months or 12 months or is it something they're not going to be able to do for a couple years or three years? And you know, you you want to weigh that as you're as you're deciding what to work on.

Their wordsYeah, I mean I think it's really having incredibly good taste in what you ask your agents to work on, right? That is the the crux of you know from my background uh a research problem. You know, a researcher can have all the tools and all the techniques, but often most of the battle is what problem are you gonna spend your time on?