Sundar Pichai: evaluation

Transcript context

…A lot of companies that I'm involved with, even ones that were started reasonably recently, have had to dramatically shift their workflows relative to product development, engineering practices. Who they even think of it should be on the design team and the capabilities of that. Are you revisiting all that at Google? Are you rethinking it? Has there been big shifts in workflow or other aspects? The way I would say it is, you can think of it as concentric circles. There are some groups within Google who are shifting more profoundly. For me, a big task is how do you diffuse that to more and more groups, particularly in 2026? Some of it, we couldn't do it early because it breaks so often that, it's almost like you see this promising new world, but it's semi-broken. But this year, I feel like the curve is shifting pretty dramatically. I can see groups, and particularly, I would say GDM and some of the SWE groups really change their workflows. They are using, we call this for some strange reason, we have a different name internally than externally of the same product, but it's Jet Ski internally, which is Antigravity. You're living on it, you're living in an agent manager world, you have workflows, and you're working in this new way. But just last week, we rolled it out to the Search team. We're constantly pushing that. In a large organization, I think change management is a hard aspect of this technology diffusing, which may be easy for a small company. You can quickly switch over. Can I add a few problems I see when it comes to actual diffusion of AI in industry? I'm curious how and when you think we'll solve them. Because as I see it, we have a big intelligence overhang. The AIs are now amazing in terms of what they can do in the abstract. If you look at how AI-native a company is or just how much it uses that intelligence, there'll probably be a shortfall. The problems that I see are something like, one: it actually takes a while to get good as an engineer at prompting your AI well. You can prompt AI better or worse to write code. Then there's a lot of, say, Stripe-specific prompting in our case to know which tools to use. There's the general being good at prompting, and then there's the Stripe being good at prompting. Then, of course, you have the fact that it's hard to share an AI-generated code base because you have a blast radius, and you're just changing so much and the turnover of the code is high enough where maybe you're rewriting it several times before you ship, that it's hard for many people to collaborate on the code base versus before when the code velocity was slower. Then as you go outside of engineering, the big one I see is access to data where you'd like to have your agent go, "How many times a day do people at companies around the world say, 'Hey, what's the status of this deal?'" That is like information that the company knows and should be agentically answerable. We actually have some cool stuff at Stripe where I was seeing where you can actually answer that pretty well. But with both habits and access to data, and as you get into a bigger company, the permissions engine of who can actually get access to this data, that all needs to be rewritten. Then you get into role definition where, like you were saying, Eng, PM, design stems a little bit from a prior year. You may want to, at least in some cases, merge those roles a little bit as AI gets better at all those since you've got a product... Anyway, that's my characterization of—in 2026—the models are capable of this, but we're only using them so much. What do you think that adoption of the intelligence looks like?…

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