Dylan Patel

And anyways, the cost of these things tanks rapidly with algorithmic improvement, not necessarily model getting bigger.

But at X level of intelligence, you can only serve so much demand.

The flip side is it takes time for people to realize how to use it.

So when GPT-3 launched, no one cared.

When GPT-3.5 launched, it was like still most people didn't care.

ChatGPT launched with GPT-3.5, people cared a little bit.

GPT-4 launched on ChatGPT, then people cared a lot.

But...

A model tier of GPT 3.5 or 3 still can be very useful in a lot of the world.

Now, it's not useful for like a lot of use cases, right?

Like for coding, it was terrible.

For copywriting, it's okay.

There's some level of use case.

And that happens to 4, but it takes time for that adoption to happen.

And so you've kind of got this challenge of like,

If I pause on a model capability, then I end up taking way too long for adoption.

And also, how can I get people to adopt it if I don't let people use it?

So OpenAI had this tremendous problem with GPD 4.0.

4.0 Turbo was smaller than 4.0, and 4.0 was smaller than 4.0 Turbo.

What OpenAI basically did was they made the model as much smaller as possible while keeping roughly the same quality or slightly better.