Azeem Azhar

People say, hey, that's just computation.

And that's what's happening.

We're moving these goalposts.

I think that if you took the capabilities of GPT-5 and dropped them into that 2014 Turing test challenge at the Royal Institution, people would have had their minds absolutely blown.

But now it's just seen as a small improvement from something like 03.

Now, the second paradox is the negative space paradox.

Now, that sounds all fancy, but it is more subtle.

You've probably lived through it, experienced it yourself.

I mean, consider flying.

When people first got access to transatlantic flights in the 1930s, it was really remarkable.

No more five-day steaming across the Atlantic.

But within a few years, passengers were complaining about the time it took, the comforts on board and the food that they were being served.

And so the paradox of negative space is that progress makes the gaps stand out much more.

And I think for many people who are using large language models today through these chatbots, there are these concrete contrasts.

Models have got faster.

They've become more reliable, better at using tools.

They are hallucinating less.

You can more reliably get them to search the web and extract information for you.

But they still lack a whole range of capabilities, whether it is long-term memory about you, whether it's actually actively learning from your experiences.

And you also get a sense that maybe they don't generalize as well as a real intelligence would.