GPT-5 was the most advanced AI when it was released, but most people were disappointed. Why? In this episode, I unpack the two key paradoxes that shape how we judge new technology: shifting goalposts and negative space.Timestamps: (0:00) The reaction to GPT-5 (0:40) First paradox (2:55) Second paradox (5:29) Why this matters Dig deeper: https://www.exponentialview.co/p/the-paradox-of-gpt-5 Production by supermix.io and EPIIPLUS1 Ltd, including Chantal Smith, Marija Gavrilov, Nathan Warren and Hannah Petrovic. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Full Episode
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, a 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 humans.
artificial intelligences. So we have this test and it's pretty explicit. But here's the thing. Back in 2014, Eugene Guzman at Computer Programme 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 Turing 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. We measure AI's performance against a series of increasingly complex benchmarks.
This effect of shifting goalposts has been noticed since the 1970s. Rodney Brooks, who is a professor of computer science and robotics at MIT, puts it very, very pithily. He says, Every time we figure out a piece of this artificial intelligence, it stops being magical. 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.
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