So Chat GPD has its third anniversary this week. Let's look at Chat GPD through the lens of

my exponential age framework. I guess the first point is that the chat GPT and the large

language model it's built on, there are just one segment of a larger exponential transition,

even if we just think about computing. But chat GPT

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 similar web, which monitors web usage and app usage, shows that it's a really, really

sticky app.

About a third of people who use Chat Jupy 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

centres. 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 GPT4 that they put out soon after chat GPT. 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 opening I 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.

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