Azeem Azhar

So complex that you have to be a trained tax expert to use it.

And I want you to just hold those two use cases in mind and think about the range, the incredible range that this technology is actually delivering on today.

For all its shortcomings, growing hallucinations, inability to be deterministic, to be a bit sycophantic, sometimes lumbering and slow, it's got that range.

And so when we think about will OpenAI be able to compete in the enterprise space, even though it's got this very strong consumer proposition,

I think we need to look at that and say, well, maybe the past view of serving two masters being too difficult doesn't hold as much.

So there's another question, which is, can we really tell the difference between any of these models in a double blind?

And if you can't, aren't they already commodities, in which case they'll compete on price?

I think that's a great question.

On the consumer side, it doesn't matter.

People are locked in, right?

They are using ChatGPT in the same way that they used iPhones rather than the Android device.

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.