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If you could go back to that moment and show someone chat GPT today, to say nothing of Codeex or anything else, but just chat GPT, uh, I think most people would say that's AGI for sure.
Just when you think you have a handle on the state of AI, the goalposts move again. The person moving them faster than anyone is OpenAI CEO Sam Altman. At the recent Snowflake Summit, he gave one of his clearest interviews yet, mapping out the immediate future of AI for businesses, what AI agents will be capable of in the next year, and he even put some surprisingly concrete dates on the arrival of AGI and super intelligence. This is the road map for the next few years directly from the source. Let's break it down.
First, the host Sarah asks a critical question: What has changed for big businesses using AI over the last 12 months? Sam's answer reveals we've passed a major tipping point, moving from AI as a novelty to a core business tool. Interestingly, I, I, I wouldn't have quite said the same thing last year. Um, I would have said the same thing to a startup last year, but to like a big enterprise, I would have said like, I, I would say like, uh, you know, you can experiment a little bit, but this is maybe not totally ready for production use in most cases. And that has really changed. Our enterprise business has gone like this. And we talk to big companies who are now like really using us for a lot of stuff and say like, what's so different? And, and, and we're like, did it just take you a while to figure it out? And they say that was part of it, but it just works so much more reliably. It works. You know, it can do all these things that I just didn't think were going to be possible. And it does, it does seem like sometime over the last year we hit a real inflection point for the usability of these models.
Now, an interesting question is what will we say differently next year? Um, and I think we'll be at the point next year where you can not only use a system to sort of automate some business processes or build these new products and services, but you can really say, I have this hugely important problem in my business. I will throw a ton of compute at it if you can solve it. And the models will be able to go figure out things that teams of people on their own can't do. And the companies that are have gotten experience with these models are well positioned for a world where they can say, okay, you know, AI system, whatever, go, you know, like, redo my most critical project, and here's a ton of compute, think really hard, just figure out the answer. People who are ready for that, I think we'll have another big step change next year. The key word there is reliability. For the first time, large companies trust these models enough to run critical operations at scale. But notice how he immediately pivots to next year. He's signaling a move from AI automating processes to AI solving core problems that even human teams can't crack.
So what does that problem-solving AI actually look like? The answer is agents. These aren't just chatbots; they are autonomous systems that can take on complex multi-step tasks. Here's Altman describing what they can do today and what's coming.
Sam, is there a framework you can give every leader here to think about like what can agents do today and next year? Um, I mean, the the coding agent we just launched called Codeex has been one of my like, feel-the-AGI moments. You like watch this thing. You can give it a bunch of tasks. It goes and works in the background. It, it's really quite smart. It can do these long-horizon things, and then you get to just sit there and say yes to this one, no to that one, try again. And it is able to just kind of like connect to your GitHub, and you know, at some point it'll be able to also watch your meetings if you want and look at your Slack and read all your internal documents, and it's just doing incredibly impressive stuff. And you know, maybe today it is like a sort of intern that can work for a couple of hours, but at some point it'll be like an experienced software engineer that can work for days. And then we'll see this for many other categories of work. And so you see you hear from companies that are building agents to automate most of their customer support or their outbound sales or any number of other things. And you hear people that talk about their job now is to assign work to a bunch of agents. Um, look at the quality, figure out how it fits together, give feedback, and it sounds a lot like how they'd work with a team of, you know, still relatively junior employees. And that's here. It's not evenly distributed yet, but that's happening. Um, I would bet next year that in some limited cases, at least in some small ways, we start to see agents that can help us discover new knowledge or can figure out solutions to business problems that are kind of very non-trivial. Um, right now it's, it's very much in the category of okay, if you got some like repetitive cognitive work, we can automate it at a kind of a low level on a short time horizon. And as that expands to longer time horizons and higher and higher levels, you know, at some point you get an AI scientist, uh, an AI agent that can go discover new science, and that will be kind of a significant moment in the world.
He's describing a new kind of management where humans act as directors for a team of AI agents. But the most important part is the progression from a junior intern that works for hours to an experienced pro that works for days and eventually to an AI scientist capable of discovering new knowledge. This idea of AI moving beyond simple productivity is a recurring theme. He believes we are very close to a world where businesses can hand off their most ambitious and complex innovation challenges to an AI.
All right, this is where the conversation gets into territory that most CEOs avoid. With AI capable of designing chips and curing diseases, the question of AGI becomes unavoidable. Sarah asks him directly for his timeline. His answer is surprisingly candid.
And if you could go back to that moment and show someone chat GPT today, to say nothing of Codeex or anything else, but just chat GPT, uh, I think most people would say that's AGI for sure. Mhm. And you know, so we're great at adjusting our uh expectations, which I think is like a wonderful thing about humanity. Um, I think mostly the question of what AGI is doesn't matter. It is a term that people define differently. The same person often will define it differently. Um, the the thing that matters is the rate of progress that we have seen year-over-year over the last 5 years should continue for at least the next five, probably well beyond that, but hard to say. And whether you declare the AGI victory in 24 or 26 or 28, um, and whether you declare the super intelligence victory in 28 or 30 or 32 is way less important than this one long beautiful, shockingly smooth exponential. Um, all of that said, to me, a system that can either autonomously discover new science or be such an incredible tool to people that our rate of scientific discovery in the world like quadruples or something. Um, that would, that would satisfy any test I could imagine for an AGI. Some other people would say it's got to be a system capable of self-improvement. Plenty of people would say like chatGPT with memory today, very AGI-like.
Let's quickly recap those dates. He throws out 2026 to 2028 for AGI and 2030 to 2032 for super intelligence. But his bigger point is that we're so focused on the label that we miss the trend line: a smooth exponential curve of progress. For him, the real AGI moment is when AI can quadruple the rate of human scientific discovery.
So what is the final form of this technology? In the last clip, Altman shares his long-term vision, and it's not about making models bigger, but making them fundamentally different.
The, the framework that I like to think about, this is not something we're about to ship, but like the Platonic ideal, is a very tiny model that has superhuman reasoning capabilities. It can run ridiculously fast and one trillion tokens of context and access to every tool you can possibly imagine. And so, it doesn't kind of matter what the problem is; doesn't matter whether the model has the knowledge or the data in it or not. Like, the model using these models as databases is sort of ridiculous. It's a very slow, expensive, very broken database. But the amazing thing is they can reason. And if, and if you think of it as this reasoning engine that we can then throw like all of the possible context of a business or a person's life into and any tool that they need, for that physics simulator or whatever else. That's like quite amazing what people can. And I think, you know, directionally we're headed there.
That is a profound vision. He's saying the future isn't a giant model that knows everything. The future is a small, incredibly fast reasoning engine. It doesn't store the knowledge; it uses its reasoning power to access all of your knowledge, your data, and your tools in real time. It's less of a chatbot and more of a universal co-processor for human thought. The implications of that are hard to even comprehend, but it's clear this is the direction we're headed.
So that's the road map: from enterprise tool to scientific discoverer to a personal reasoning engine. I'm curious what you think. Is this vision of an all-access, all-knowing AI assistant exciting, or does the idea of giving one company access to every part of your digital life feel like a step too far? Let me know your take in the comments below. If you enjoyed this breakdown, a like is always appreciated, and subscribe so you don't miss the next one. Thanks for watching.