🎙️Hugging Face’s Clem Delangue: Stop Comparing Engines to Cars

Why open models, local AI, and coding agents are changing who gets to build.

In conversation with Clem Delangue, co-founder and CEO at Hugging Face about the state of Open Source AI (and the myth of Sisyphus)

One of Clem’s clearest points is that comparing open weights to closed APIs is like comparing an engine to a car. And that’s why the question “are open-source models catching up?” doesn’t even matter in the same way. Behind an API, there are tools, harnesses, routing, and sometimes several models. So when people say open models are “behind,” the real question is: behind what system, for what task, and at what cost?

Opening it up means making it possible for many more people to build. Clem is specific about where this goes. He expects AI builders to grow from a few million today to tens of millions, maybe even 100 million: people who train, fine-tune, optimize, and run models themselves. Hugging Face is already preparing for that world, where agents pull models, use datasets, read docs, and may become a larger user base than humans by the end of 2026.

We also talk about Reachy Mini, Hugging Face’s open-source desktop robot, which has sold close to 10,000 units. Clem’s point is simple: people change their view of AI when they build with it, assemble it, break it, fix it, and make something small work. When it’s something physical – like a cute robot – it works even better! I loved that idea.

And we get into the arguments open source conversations often avoid: why the cybersecurity case is more complicated than “closed is safer,” why safety can be used as cover for business interests, and why local models sometimes look weaker because the surrounding agent harnesses were built for proprietary APIs.

This is a conversation about choice, control, and the next class of AI builders. Watch it! (I recommend 1.2x or 1.5x speed)→

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We prepared a transcript for reference, but the full experience is in the video. And as always: like and comment. It helps us grow on YouTube and bring you more insights.

Clem Delangue on why AI builders may multiply

Ksenia:
Thank you, Clem, for agreeing to this interview. I’m a big fan of Hugging Face and what you’ve been doing for the open-source community. It’s been amazing to know you for many years and finally meet you in person.

Clem:
Yes – thanks for having me.

Ksenia:
Let’s start with your recent post about ML Intern and how you’ve been playing with it on Hugging Face. What’s the most surprising – and maybe funniest – thing you’ve learned about how agents work on real machine learning tasks right now?

Clem:
What’s interesting is that default coding agents are still pretty bad at building AI. You saw that when Andrej Karpathy released Auto Research – or maybe it was something before that – and said he barely used agents to build it, because either it was too out-of-distribution or it just didn’t work yet for building AI.

But with a couple of tweaks to the harnesses, the model connections, and the tools – like the Hugging Face Hub – you can actually make a lot of progress. We were surprised that ML InTern is now managing to fine-tune small models, create datasets, convert models into different formats. Today the team got it to pass the interview test they had for researchers. In half an hour, it aces the test.

We’ve been really excited about that. If agents can lower the barrier to entry for building AI, it’s going to be very valuable for the world. It will enable more people to build open-source models, create open datasets, and maybe play with local models – which historically has been a bit hard to do, but is getting easier now.

Ksenia:
How do you see this developing over the coming months? Where is the acceleration?

Clem:
I think the number of people who can become AI builders is going to explode. We’ll go from maybe a few hundred thousand – or low millions – of people who have the skills to do this kind of work, to tens of millions, maybe fifty million, maybe a hundred million at some point.

Maybe eventually every software engineer will be able to optimize models, train models, fine-tune models themselves. That would be amazing, because it would mean they’re not only relying on closed APIs and third-party vendors that can dictate terms, raise prices whenever they want, deprecate models whenever they want, or change them behind the scenes so you’re not even sure why the quality has gone down on your workloads.

It gives some control back to builders, which is nice.

Ksenia:
A couple of months ago I did a little interview with Steve Yegge, and he said non-technical people will definitely come into this coding world. How do you feel about that? Are we ready?

Clem:
The beauty of AI is that a lot of it is driven by datasets and text in general. Compared to software engineering, where you had to learn a programming language, AI has the potential to have a much wider base of users – people who can contribute to it.

So I hope it happens. I think it would be good, too, because the more diversity of builders you have, the wider the perspectives. And what’s good for the field is that it pushes it toward actual challenges and things that are important to people.

If more people could build AI, maybe we’d have a little less video-AI slop and a little more biology, chemistry, medicine, climate, things that a couple of Silicon Valley guys may not care that much about, but that other people do care about. It brings more perspective, and hopefully more real problems get solved.

Ksenia:
Do you think more creation with AI eventually gets to some quality threshold – where people stop creating slop and start actually solving problems?

Clem:
I think there’s a lot more than slop to build. The more you empower people to build, the more they’ll build things other than slop.

And empowering more people to become AI builders will also change the public perception of AI. Right now, the perception is terrible. If you look at the studies, it’s crazy – people are either very scared, or they hate AI, or they don’t want to hear about it.

Whereas if you help them understand how to build these systems, and let them build them, they start seeing AI as something empowering – something they can use to solve problems that matter to them. I think that’s one of the best ways to change public perception. If AI stays only in the hands of a few companies and a few builders, those companies can do marketing, sure, but I don’t think they’ll convince people that AI is good.

Opening AI to More People

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