Azeem Azhar's Exponential View
The method of invention, AI's new clock speed and why capital markets are confused
05 Dec 2025
Full Episode
So ChatGPT has its third anniversary this week. Let's look at ChatGPT through the lens of my exponential age framework. I guess the first point is that ChatGPT and the large language model it's built on, they're just one segment of a larger exponential transition, even if we just think about computing. But ChatGPT tends to drown things out because
It's becoming the verb for AI and with some justification. According to a recent counter, a message on X from one of the investors in OpenAI, nearly 900 million users. That means people seem to like it. Data from SimilarWeb, which monitors web usage and app usage, shows that it's a really, really sticky business. app, about a third of people who use ChatGPT in a month use it every day.
Now that's not as much as Instagram for sake of argument, but it's about the same level as YouTube and higher than Snapchat, both really sticky, well-loved apps. So there's scale there that is creating noise and occupying headspace.
But of course, you can't say scale and think about chat GPT without thinking about the grammar of scale that is involved, those large language models, those scaling laws, the increasingly large, voracious demand for compute and for chips, the bigger and bigger data centers. The numbers are so big, hundreds of billions of dollars, they seem to tower over the debate like a skyscraper.
But in a way, that hides what else is going on. As some of you may remember, OpenAI launched GPT 4.5. It was a new foundation model replacing GPT-4 that they put out soon after ChatGPT, and 4.5 was kind of a flop. It was an attempt to do a big model. We didn't really like it. It sort of fell a bit flat. But what OpenAI did and what the researchers did is they found a new approach.
They found that approach of reasoning, that's thinking at inference time, the point at which you or I might put a query into the chatbot. And those reasoning models, 01, 03, 04, performed really, really well. I think it was a real milestone moment in how an emerging technology starts to improve.
Now, if you've used Gemini Pro, which Google released an update to a few days ago, it really feels like there's something going on beyond either the reasoning model approach or the large language model. Of course, Gemini Pro is using both of those techniques.
But it feels like there's a new technology sitting behind that because Gemini 3 is really well grounded in the complexities of the real world. And perhaps that's a hint to the kind of world model that Demis Hassabis has alluded to. So what we often see with these exponential technologies is that from a distance, they look like, you know, one planet.
single smooth curve, but in fact, there are a series of overlapping curves of different technologies and different approaches that ultimately give you that exponential. Now, it's not to say that the foundation models companies are not pursuing scale. And what Google proved with Gemini was that scale still works at each stage of building these models.
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