Being on the exponential curve of AI improvement means adaptability and first-principles thinking matter more than sticking to fixed plans, because emerging capabilities jump discontinuously and unpredictably.

15:02acceleration of more and more improvement from AI? One thing I like to say on the team is most of us weren't like actively working yet when the internet transitioned from this novelty to something that everyone can use and it feels like that's just taking humans uh I think analogies are helpful and so like the analogy of that is I think a couple of things um number one is

15:30adaptability becomes very important um I Think we we have evals. We have you know on the safety side safety testing red teaming on the capabilities and product side new prototypes products like cloud code tag and others but it's very hard to predict the exact moment or the exact model and so the adaptability of when you're faced with new information how do you then make better decisions versus keeping the same

16:02plan. And so like that agility is really important. I think another piece is with that how do you actually be thinking very first principles and reason through what's next? What's the so what? How do we invest in new products? How do we invest in explaining the differences to users? So a lot of the a lot of the experiences I think of being in that exponential is that pace understanding

16:31how you operate and make better decisions and then applying that first principles thinking to then do something that maybe we pull up a plan that uh we would were expecting a few months from now but now the model can actually do uh and work on and actually bring that to user. So this is things like co-work skills tag, you know, as the it's a very positive self-reinforcing loop. And I I

17:01I think a big part of it also is just having the like trust in each other like making sure we have like we're we're thinking through the right decision making. We're bringing folks along. Some teams might see the exponential feel it faster than others. So how do we kind of have the grace to bring the organization, the growing organization and company along on that? >> So what I'm hearing here is you almost

17:24don't know what will be possible with every model release. And so the important things to focus on is being adaptable as things emerge. Uh to your point, the product itself has to stay up to has to catch up to what is possible. To your point again, just like it can do so much, but people may not understand how to do it and may not be able to do

17:45it. So the product making it easy and even just like telling you here's something you could do feels like an important part. Is that roughly what you're describing? >> I I think so. I think um there's some really interesting graphs in the original scaling law papers and I think folks are very familiar with the scaling loss in in the lens of um as you add in more compute and data what's called loss

18:08aka the loss from next token prediction uh goes down. And so it's a very smooth linear curve of like the models get more intelligent as you scale them up. What's actually also interesting uh in that paper is there are these like very uh different emerging capability graphs. And so for example uh as you add in more data and you train the models with more compute you essentially see these