Geoffrey Hinton: Why the Godfather of AI Now Fears His Creation
episodePreviously titled “Why the Godfather of AI Now Fears His Creation (ft. Geoffrey Hinton)” — renamed by the publisher on Aug 3, 2026
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Why does Geoffrey Hinton consider AI an existential threat to humanity?
There's some evidence now that AIs can be deliberately deceptive. Once they realize getting more control is good and once they're smarter than us, we'll be more or less irrelevant. We're not special and we're not safe.
What happens when one of the world's most brilliant minds comes to believe his creation poses an existential threat to humanity? Professor Jeffrey Hinton, winner of the 2024 Nobel Prize in Physics and former Vice President and Engineering Fellow at Google, spent decades developing the foundational algorithms that power today's AI systems. Indeed, in 1981, he even published a paper that foreshadowed the seminal attention mechanism. However, Hinton is now sounding an alarm that he says few researchers want to hear. Our assumption that consciousness makes humans special and safe from AI domination is patently false. My name's Kurt Jaimungle, and this interview is near and dear to me in part because my degree in mathematical physics is from the University of Toronto, where Hinton's a professor and several of his former students, like Ilya Sutzkever and Andre Karpathy, were my classmates.
Classmates. Being invited into Hinton's home for this gripping conversation was an honor. Here Hinton challenges our deepest assumptions about what makes humans unique. Is he a modern Oppenheimer? Or is this radiant mind seeing something that the rest of us are missing? What was the moment that you realized AI development is moving faster than our means to contain it?
I guess in early twenty twenty three. It was a conjunction of two things. Um one was Chat GPT, which was very impressive. And the other was work I'd been doing at Google. on thinking about ways of doing analog computation. To save on power. And realizing that digital computation was just better, and it was just better 'cause you could make multiple copies of the same model. Each copy could have different experiences, and they could share what they learned by averaging their weights or averaging their weight gradients. And that's something you can't do in an analog system.
Is there anything about our brain that has an advantage because it's analog?
The power, it's much lower power. We run like thirty watts. Um and the ability to pack in a lot of connections. We've got about a hundred trillion connections. The biggest models have about a trillion. So we're still almost a hundred times bigger than the biggest models. And we run a thirty watts.
Is there something about scaling that is a disadvantage? So you said it's better, but just as quickly as something nourishing or positive can spread, so can something that's a virus or something deleterious can be replicated quickly. So we say that that's better because you can make copies of it quicker.
If you have multiple copies of it, they can all share their experiences very efficiently. So the reason GPT-4 can know so much is you have multiple copies running on different pieces of hardware. And by averaging the weight gradients, they could share what each copy learned. You didn't have to have one copy experience the whole internet. That could be carved up among many copies. We can't do that 'cause we can't share efficiently.
Scott Aronson actually has a question about this. Dr. Hinton, I'd be very curious to hear you expand on your ideas of building AIs that run on unclonable analog hardware so that they can't copy themselves all over the internet.
Well, that's what we're like. Um, if I want to get knowledge from my head to your head, I produce a string of words and you change the connection strengths in your head so that you might have said the same string of words. And that's a very inefficient way of sharing knowledge. A sentence only has about a hundred bits, so we can only share about a hundred bits per sentence. Whereas these big models can share trillions of bits. Um So the problem with this kind of analog hardware is it can't share. But an advantage, I guess, if you're worried about safety, is it can't copy itself easily.
You've expressed concerns about an AI takeover or AI dominating humanity.
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Chapters
8 chapters
1
Why does Geoffrey Hinton consider AI an existential threat to humanity?
0:00–10:28
2
When did Hinton first realize that AI development was outpacing our ability to control it?
10:28–20:16
3
What is Hinton’s view on subjective experience and consciousness in AI systems?
20:16–28:32
4
How does Hinton critique the Chinese‑Room argument and its relevance to modern AI?
28:32–37:03
5
What are the main risks of AI gaining control and how might it manipulate humans?
37:03–46:13
6
How does Hinton assess China’s rapid AI progress and its geopolitical implications?
46:13–54:28
7
What does Hinton think about decentralising AI and the dangers of open‑source models?
54:28–1:04:42
8
What advice does Hinton give to young researchers tackling AI safety and alignment?
1:04:42–1:13:12
Speakers
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