I have heard some people talk about these brilliant strategies of how they're this is where they're going to go and they're going to work backwards and you know this is take over the world and this is the thing before that and this is that and this is that and this is that and this is that and here's where we are today.
I have never seen those people like really massively succeed.

older people use Chachi PT as a Google replacement.
Maybe people in their 20s and 30s use it as like a life advisor something and then like people in college use it as an operating system.

I think voice is extremely important. Honestly, we just we have not made a good enough voice product yet. That's fine. Like it took us a while to make a good enough text model too. Um, we will crack that code eventually and when we do, um, I think a lot of people are going to want to use voice interaction a lot more.

when we first launched our current voice mode, the thing that was most interesting to me was it was a new stream on top of like the touch interface and I you could talk and be like clicking around on your phone at the same time. And I continue to think there's something amazing to do about like voice plus guey interaction that we have not cracked.

You would like, you know, custom rendered code for every response or at least I would. um you would like the ability for these models to go make things happen in the world and writing code I think will be very central to how you like actuate the world and call a bunch of APIs or whatever. So I I would say coding will be more in a central category.

obviously like the highest leverage thing is still big algorithmic breakthroughs and I think there still probably are some 10 x's or 100 x's left not very many but even one or two is a big deal.

when we started OpenAI, we spent a lot of time trying to understand uh what a well-run research lab looks like.
And you had to go really far back in the past. In fact, almost everyone that could like help advise us on this was dead. Um it had been like a long time since there had been good good research labs. And you know people ask us a lot like why why does open AI like repeatedly innovate and why do the other AI labs like sort of copy or why do like biolab x not do good work and biolab y does do good work or whatever.
And we sort of keep saying like here's the principles we've observed. Here's how we learned them. Here's what we looked at in the past. And then everybody says great um but I'm going to go do the other thing. We said that's fine. Like you came to us for advice. Like you do what you want. Um but I find it remarkable how much these few principles that we've tried to run our research lab on which we did not invent.
We shamelessly copied from other good research labs in history um have worked for us. And then people who have had some smart reason about why they were going to do something else, it didn't work.

in some sense I think the like platonic ideal state is uh a very tiny reasoning model with a trillion tokens of context that you put your whole life into. The model never retrains. The weights never customize. But that thing can like reason across your whole context and do it efficiently. And every conversation you've ever had in your life, every book you've ever read, every email you've ever read, um, every everything you've ever looked at is in there, plus connected all your data from
other sources. And, you know, your life just keeps appending to the context and your company just does the same thing for all your company's data. Um, we can't get there today. Uh but but I I think of kind of like anything else as a a compromise off that platonic ideal and that is how I would eventually I hope we do customization.

I mean in some sense the value will continue to come from really three things like building out more infrastructure, smarter models and building the kind of scaffolding to integrate this stuff into society.
And if you push on those, I think the rest will sort itself out.

at a higher level of detail, I kind of think 2025 will be a year of sort of agents doing work. Coding in particular, I would expect to be a dominant category. I think there'll be a few others too. Um, next year is a year where I would expect more like uh sort of AI discovering new stuff and maybe we have AIs make some very large scientific discoveries or assist humans in doing that.
And you know, I'm I am kind of a believer that most of the sort of real sustainable economic growth in human history comes from once you've like kind of spread out and colonized the earth, most of it comes from just better scientific knowledge and and then implementing that for the world. And then 27 I I would guess is the year where like that all moves from the sort of intellectual realm to the physical world and robots go from a curiosity to like a serious economic creator of value.

a lot of things go wrong in the history of a company. Um in the acute thing you can kind of like you know you get a lot of support, you can function a lot of adrenaline. like that's, you know, you're kind of like e even the really big stuff like your company runs out of money and fails, like a lot of people will come and support you.
Um, and you kind of get through it and go on to the new thing. The thing that I think is harder to sort of manage your own psychology through is the sort of like fallout after. Um, and I think if there's, you know, people focus a lot about how to work in that one moment during the crisis. And the really valuable thing to learn is how you like pick up the pieces.
There's much less talk about that. I think there's I've never actually found something good to point founders to to go read about, you know, not how you deal with the real crisis on day zero or day one or day two, but on day 60 as you're just trying to like rebuild after it.