Yann LeCun: Human Intelligence is not General Intelligence

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**Jason Howell** (1:00)
Meta's chief AI scientist and Turing Award winner Yann LaCun joins us to talk about why current LLMs are less intelligent than a house cat, how developing world models that understand physical reality remains AI's biggest unsolved challenge, and why Meta's open-source approach to llama is enabling thousands of companies while disrupting just three. That's coming up right after this. This is AI Inside, Episode 63, recorded Wednesday, April 8th, 2025 Human intelligence is not general intelligence with Yann LaCun. This episode of AI Inside is made possible by our wonderful patrons at patreon.com/aiinsideshow. If you like what you hear, head on over and support us directly. And thank you for making independent podcasting possible.
Hello, and welcome to AI Inside, the show where we take a look at the AI that's layered throughout so much of the world of technology. I'm one of your hosts, Jason Howell. And this week's episode is definitely a little different. And I want to get right to it. But just real quick before we do, huge thank you to those of you who support us directly on Patreon. It's patreon.com/aiinsideshow. Corky Garco, you know who you are. You're a huge supporter and we appreciate you. So thank you so much for that. All right. Jeff Jarvis, my co-host and I had the chance to chat with Yann LeCun. Very notable figure in the world of AI. This happened last Friday. That's when this interview was recorded and it was pretty incredible. So I'm not going to waste any time. Let's just jump right into it right now. Thrilled to welcome to AI Inside Yann LeCun, Chief AI Scientist at Meta, Touring Award Winner, known by many as the godfather of AI. Welcome to the show, Yann. It's really nice to meet you.

**Yann LeCun** (2:54)
Thanks for having me on.

**Jason Howell** (2:55)
Yeah. Does it ever get old, hearing someone introduce you as the godfather of AI? You're kind of like, yeah, here we go again.

**Yann LeCun** (3:02)
I shut my ears so I don't turn red.

**Jason Howell** (3:07)
But you can accept it at this point because it's the truth. The kind of question that I have to kick things off is that we are so firmly implanted into the current realm of artificial intelligence, which really seems to be the LLM generation, and there's probably something on the horizon around that, but we're still firmly implanted in there. You've been pretty opinionated on the limits of LLM at a time when we're also seeing things like OpenAI securing a record-breaking round of funding largely built on its success in LLM technology. And so I see diminishing returns on one side, on the other, companies betting everything on generative AI and LLM. And I'm curious to know what you think as far as why they might not be seeing what you're seeing about this technology, or maybe they are, they're just approaching it differently. What are your thoughts there?

**Yann LeCun** (3:59)
Oh, maybe they are. There's no question that LLMs are useful.
I mean, particularly for coding assistants and stuff like that. And in the future, probably for more general AI assistant jobs, people are talking about agentic system. It's kind of still not totally reliable yet. It's a bit like, for this kind of applications, the main issue, and it's been a recurring problem with AI and computer technology more generally, it's the fact that you can see impressive demos, but when it comes time to actually deploy a system that's reliable enough, that you put it in the hands of people and they use it on a daily basis, this is a big distance. It's much harder to make those systems reliable enough. Ten years ago, we were seeing demos of cars driving themselves in countryside streets for about ten minutes before you had to intervene. We made a lot of progress, but we're still not to the point of having cars that can drive themselves as reliable as humans, except if we cheat, which is fine, which is what Waymo and others are doing.