The idea of like restricting a technology like AI based on risks is just like, for example, you would say, okay, some people can punch other people.

So let's tie down everybody's hands, right? Because it's too dangerous. Some people can punch. Right.

But you really don't want to do that because your hands are so useful. The way you want to control it is untie everyone and then regulate or fight the bad actors.

So, for example, if hacking that creates cybersecurity risks, it's illegal, right?

So you have to fight it, but not by preventing everyone from getting these capabilities.

Otherwise, you blow down progress, you create massive gaps in terms of controls, in terms of

capabilities, and you create actually additional risks.

This episode originally aired on MTS.

Open source software built much of the modern internet.

Linux, Apache, Kubernetes, and even the transformer architecture behind ChachyPT, all spread

because researchers and developers could study, modify, and improve them in public.

But AI is increasingly moving in the opposite direction, with the most powerful models

distributed behind closed APIs controlled by a small number of companies.

At the same time, China has emerged as one of the biggest contributors to open source AI,

while debates around safety, regulation, and access are

becoming more politically charged. And now those same tensions are extending into robotics, where

AI is beginning to move off the screen and into the physical world. Theo Jaffe and Sophia Puccini

speak with Clem DeLong, CEO at Hugging Face.

We are live here on MTS with Clement DeLong, who is the CEO of Hugging Face, which has been

really an incredible resource for anyone who's interested in large language models and especially

open weight large language models. I've been a hugging face

user for a while now. So it's great to have you here. Clem, thanks so much for coming on MTS.

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