Episode Summary
Executive Summary: Kara Swisher interviews Meta chief AI scientist Jan LeCun on AI’s future, Meta’s open-source strategy, and the regulation debate. LeCun argues current fears of existential AI risk are overblown, says AGI is still years away, and contends open systems are safer and more democratic than closed models. He also explains Meta’s long-term bet on AI assistants, smart glasses, and next-gen systems that understand the physical world.
Main Topics: Meta’s AI strategy and open-source Llama: LeCun explains why Meta keeps Llama open and why he believes open platforms accelerate innovation, improve safety through more eyeballs, and support global adoption across languages and use cases. AGI timelines and what current models can’t do: He argues that LLMs are hitting limits because they are trained mainly on text, not sensory experience, and says human-level AI is still several years away, likely closer to a decade than the near term. Regulation, SB 1047, and the role of government: LeCun supports regulating AI products and ensuring governments are informed, but strongly rejects regulating AI R&D or computation thresholds, calling such limits counterproductive and potentially harmful to openness. Disagreement with Hinton and Bengio on existential risk: Swisher presses him on his public dispute with his Turing Award co-winners. LeCun says they are wrong about current systems having subjective experience and says catastrophe timelines have been overstated. Meta’s future: assistants, smart glasses, and search: He frames Meta AI as part of a coming computing platform centered on always-available assistants in smart glasses, with search as one component of a broader intelligent assistant system. Safety, misuse, and open-source responsibility: LeCun says AI misuse is a real concern, but argues open models have not caused the feared harms and that red-teaming, product-level rules, and better AI defenses are more effective than R&D bans. Training AI on human knowledge and culture: He advocates distributed, globally sourced training data to preserve minority languages and cultural knowledge, arguing future AI should function as a shared repository of human knowledge.
Key Arguments: Open-source AI platforms like Llama are essential for innovation, broad access, and democratic control, especially if AI becomes a universal knowledge platform. Regulating AI research by compute thresholds would stifle progress and likely entrench a few powerful private companies. Current LLMs are fundamentally limited because they mostly learn from text; real progress requires systems that learn from video, sensory input, and interaction with the physical world. AGI is not imminent; the field is overconfident about timelines, and useful human-level capabilities in robots remain unsolved. AI safety should be addressed through product-level rules, guardrails, and architecture design—not by stopping foundational research. Open-source systems are safer in practice because more people can inspect, fine-tune, and stress-test them. Meta’s strategy is to build ubiquitous AI assistants integrated into smart glasses and other devices, not just better chatbots. The biggest AI danger is not rogue sentience but concentration of power among a few West Coast companies controlling digital experiences and information flows.
Data Points: Meta AI monthly active users: nearly 600 million - LeCun cites this as evidence that Meta’s AI assistant is already widely used. Llama downloads: 650 million - He says the open-source Llama engine has been downloaded this many times. Public projects derived from Llama: 85,000 - He cites the number of publicly available or open-source projects built from Llama. Meta FAIR team size: about 500 people - LeCun describes Meta’s Fundamental AI Research organization. AI spending (Meta forecast): $38 billion to $40 billion - He references Meta’s planned infrastructure spend. AI spending (Google): more than $51 billion - Used in comparison to Meta’s spending. AI spending (Microsoft): close to $90 billion - Used in comparison to major industry AI investment. Regulatory compute threshold: 10^24 to 10^25 FLOPs - He criticizes proposed limits in the EU AI Act and Biden AI executive order. Open-source adoption timeline: early 2023 - He notes Meta’s decision to fully open-source Llama 2 occurred around summer 2023. Personal timeline at Meta: 11 years ago - He says Mark Zuckerberg approached him almost exactly 11 years prior. Training data scale: about 20 trillion words - He estimates the scale of text used to train large language models. AI progress window: several years; possibly 5–10 years - He says human-level systems are not decades away but not near-term either. Video-data comparison: 16,000 hours of video ≈ 30 minutes of YouTube uploads - He uses this comparison to argue there is far more video data than needed for future learning.
Pivotal Quotes: "Regulating R&D would have apocalyptic consequences on the AI system." — Jan LeCun: He explains why he opposed SB 1047 and similar proposals that would require government licensing for large-scale AI training. "The best protection we have against this is AI systems." — Jan LeCun: He argues AI is more effective as a countermeasure to hate speech and disinformation than as a source of those harms. "We’re actually very far from it. I mean, when I say very far, it’s not centuries. It may not be decades, but it’s several years." — Jan LeCun: He responds to AGI hype and argues that human-level AI remains several years away.
Implications: The interview highlights a major split in AI governance: LeCun’s view favors open systems, product-level safeguards, and continued research, while rejecting alarmist regulation. For industry, it reinforces the push toward AI assistants, multimodal learning, and open ecosystems.
From the Episode
You've called their warnings complete BS. I don't think you mince words there. Talk to me about why that's complete BS. And one of the things you disagreed was one of the first attempts at AI regulation here in the U.S., California Bill SB 1047. Hinton and Benjio both endorsed it. You lobbied against it. You wrote: Regulating RD would have apocalyptic consequences on the AI system. Very dramatic of you, sir. You said the illusion of existential risk is being pushed by a handful of, quote, delusional things. Tanks. These two aren't delusional, I don't believe. Hinton just won the Nobel Prize for his work. Talk about that in particular. And by the way, Governor Newsom vetoed the bill, but is working with people like Stanford Professor Faif Ailey to overhaul it. Talk about why you called it complete BS. You're very strong on this. I'm very vocal about that. Yes. So, Jeff and Yoshua are both good friends. We've been friends for decades. I did my postdoc in 1980.
We look at the evidence from you know the people who said that AI was going to destroy society because we're going to be inundated with disinformation or generated hate speech or things like this. We we're just not seeing this at all. We're not seeing it. We we've not seen it I mean people produce hate speech, people produce disinformation and they try to disseminate it or you know every way they can. A lot of people are trying to disseminate hate speech on Facebook and it's against the content policy. At Facebook to do this. Now, the best protection we have against this is AI systems. We couldn't do this in twenty seventeen, for example. 2017 AI technology was not good enough to allow Facebook and Instagram to detect hate speech in every language in the world. And what happened in between is progress in AI. Okay? So AI is not the tool that people use to produce hate speech or disinformation or whatever. It's actually the best countermeasure against it. So what you need is
Human-like intelligence, human-level intelligence, or perhaps even superhuman intelligence in many ways. And now, how do we get to that point? And we're very far from that point. Some people are kind of making us believe that we're really close to what they call AGI, artificial general intelligence. We're actually very far from it. I mean, when I say very far, it's not centuries, it may not be decades, but it's several years. And the way you can tell is that. The type of task, right? We have LLMs that can pass the bar exam or, you know, pass some college exam or whatever. But, you know, where is our domestic robot that cleans the house and clears up the dinner table and fills up the dishwasher? We don't have that. And it's not because we can't build the robots. We just cannot make them smart enough. We can't get them to understand the physical world. Turns out the physical world is much harder for AI systems.
About Pivot
With great power, comes great scrutiny. Every Tuesday and Friday, journalist Kara Swisher and NYU Professor Scott Galloway offer sharp, unfiltered insights into the biggest stories in tech, business, and politics. They make bold predictions, pick winners and losers, and bicker and banter like no one else. From New York Magazine and the Vox Media Podcast Network.