AI demands empirical learning over analytical planning

I think because we don't exactly know what capabilities will even come up soon and we don't know what's going to work technically, and then we also don't know what's going to land even if it works technically, it's much more important for us to be very humble and learn a lot more empirically and just try things quickly.

Everywhere I've ever worked before this, you kind of know what technology you're building on, but that's not true at all with AI. Every two months, computers can do something they've never been able to do before and you need to completely think differently about what you're doing.

"That change" refers to rapid shifts in AI capabilities, and "when capabilities drop" means when new AI features are released by companies like Anthropic.

You want to be resilient to that change. When capabilities drop, you want to be able to jump on it really quickly. So being agile, not being stuck with roadmaps, being able to just say, oh, we're just going to switch priorities right away, is going to be super important.

Let go of what you've built. Go back to the objectives you were trying to solve and now with this technology, how can you do that objective better?

We think about it as what season are we in? Season one might've been prototyping of AI and then it was all around models and reasoning models, and now it's the advent of agents. That can last a year, that can last six months, that can last three months.

Don't build for today, build for six months from now, build for a year from now. The things that aren't quite working that are working 20% of the time, will start working 100% of the time.