Azeem Azhar

And so I think on the consumer side of the business, the fact that they own the word for it will continue to help them for many, many years to come.

On the business side, I think it's a really fair question that

For many use cases, models will end up super serving that use case.

If you need to do something really simple, like get a summary of a meeting, you were probably already at the point where the models are just generally good enough to do that in the same way that vision models a few years ago approached 99% accuracy.

Actually, I've made that number up, but they exceeded human accuracy.

Let's think about what that means for models.

I mean, the truth is I can tell the difference between

a ChatGPT output and a Gemini output and a Claude output.

Maybe it's because of the custom prompt I've put in ChatGPT.

It's given a prompt to be unduly difficult and to not answer my questions directly and to leave with open questions or critical perspectives.

and also to push harder than it thinks I might understand.

So I'm constantly confused by the answers and I have to put them into another LLM to make sense of them.

But my experience there is designed to push me as a cognitive partner.

So I can tell the difference.

I would also say that in our experience with an exponential view, where we run a bunch of AI-based workflows in the background, helping us with research and analysis, we can also

not necessarily tell the difference, but different models perform differently on a cost and latency basis.

And does it work for this particular use case?

And so, you know, I have a particular workflow which still uses Gemini 2.5 Flash, which is the quick

Google Gemini model.

If I put it into 2.5 Pro, it doesn't work as well.