Today I want to talk about GPT-5. This model was met with mixed emotions. I called it evolutionary

rather than revolutionary. Many people were a bit underwhelmed by it. Some were positively cross.

Here's the thing. GPT-5 could never have impressed us. It's because it falls between two

paradoxes of progress. Paradoxes that have

played out time and time again through history, and they do give us a clue to how we're going to react

to ever improving artificial intelligence. To understand this first paradox, we have to go back to the early days of computing.

Actually, back to 1950 before the term artificial intelligence had been coined.

We'll go to Alan Turing, he was doing all that breakthrough work in cryptography and the theory of

computation. And he came up with this test for machine intelligence that later on got known as

the Turing test. The test was reasonably simple, right? If the output of a computer system and

machine was indistinguishable to other humans from the outputs from other humans,

you've got a machine that is exhibiting some type of thinking, and the Turing test became

the thing people measured towards artificial intelligences. So we have this test, and it's pretty

explicit. But here's the thing. Back in 2014, Eugene Gooseman

at Computer Program won the Turing test. It persuaded judges at Britain's Royal Institution that it was

human years before ChatGPT. And so we end up in this world where we say, well, it used deception,

it's a parlor trick, this isn't a good

test. Today's LLMs easily pass the cheering test, and we've already started to see some media

outlets having to retract stories that they now realize weren't written by freelancers,

but were actually written by people using AI systems end to end.

So today we don't use the Turing test as a test for machine intelligence.

We have shifted the goalposts.

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