Skip to content
No PriorsNo Priors

No Priors Ep. 5 | With Huggingface’s Clem Delangue

After starting as a talking emoji companion, Hugging Face is now an organizing force for the open source AI research ecosystem. Its models are used by companies such as Apple, Salesforce and Microsoft, and it's working to become the GitHub for ML. This week on the podcast, Sarah Guo and Elad Gil talk to Clem Delangue, co-founder and CEO of Hugging Face. Clem shares how they shifted away from their original product, why every employee at Hugging Face is responsible for community-building, the modalities he's most interested in, and what role open source has in the AI race. 00:00 - Introduction 01:53 - how Clem first became interested in ML, being shouted at by eBay sellers, and the foretelling of the end of barcode scanning 03:34 - early iterations of Hugging Face, trying to make a less boring AI tamagotchi, and switching directions towards open source tools 05:36 - advice for founders considering a change in direction, 30%+ experimentation 07:39 - 1st users, MLTwitter, approach to community 10:47 - enterprise ML maturity, days to production 12:54 - open source vs. proprietary models 15:56 - main model tasks, architectures and sizes 19:12 - decentralized infrastructure, data opt out 24:16 - Hugging Face’s business model, GitHub 28:09 - What Clem is excited about in AI

Elad GilhostClem DelangueguestSarah Guohost
May 19, 202337mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Hugging Face’s Clem Delangue on Open-Source AI, Community, and Infrastructure

  1. Clem Delangue traces his path from top eBay seller to machine learning entrepreneur, explaining how a chance encounter at a trade show led him into computer vision and ultimately co-founding Hugging Face.
  2. He recounts Hugging Face’s pivot from a playful ‘AI Tamagotchi’ chatbot to becoming the leading open-source hub for ML models, driven by organic traction around their Transformers library and a strong community-first culture.
  3. Delangue discusses the balance between exploration and exploitation inside startups, the strategic importance of open-source in countering AI power concentration and bias, and the role Hugging Face plays as infrastructure and distribution layer for ML.
  4. Looking ahead, he highlights infrastructure efficiency, decentralized training, consent-aware datasets, and full-stack ML companies—especially in biology and chemistry—as the most exciting frontiers.

IDEAS WORTH REMEMBERING

5 ideas

Leave room for structured experimentation alongside core execution.

Delangue argues startups should consistently devote ~30–40% of effort to exploration, both before and after product-market fit, to avoid getting stuck in local optima and to surface new directions like Hugging Face’s pivot to open source.

Make community engagement everyone’s job, not a separate function.

Instead of hiring dedicated community managers, Hugging Face expects all employees—including researchers—to interact with users, tweet, and support open source, which creates authenticity and deeper feedback loops.

Follow organic pull from open-source usage to guide product strategy.

The company doubled down on Transformers and the model hub only after seeing explosive, organic adoption by researchers and enterprises, using that traction to justify a full strategic shift and to raise their Series A.

Expect a multi-model ecosystem, not a single dominant AI model.

With over 250,000 models from ~15,000 companies on Hugging Face, Delangue notes that specialized models often outperform giant general models on cost, latency, and accuracy for specific tasks, ensuring a diverse model landscape.

Invest early in infrastructure and cost-aware ML operations.

He emphasizes that the field has under-focused on the real cost and speed of running models and calls out infrastructure optimization as a major enabler, hinting that platforms like Hugging Face should become gateways to compute.

WORDS WORTH SAVING

5 quotes

It's funny how a single small encounter like that can completely change your trajectory.

— Clem Delangue

We were like, 'Okay, we're gonna build some sort of an AI Tamagotchi.'

— Clem Delangue

We make sure to spend at least like 30 or 40% of the company's efforts on explorating new things.

— Clem Delangue

If there wouldn't have been as much open source as there's been in the past five years, we would be decades away from where we are now.

— Clem Delangue

I don't really believe in this scenario of one model, one company to rule them all.

— Clem Delangue

Clem Delangue’s personal journey into machine learning and early career at eBayHugging Face’s origin as a conversational ‘AI Tamagotchi’ and its strategic pivotBuilding and scaling an open-source ML community and model hubOpen-source vs proprietary AI models, specialization, and industry adoptionML model modalities, sizes, and emerging application domainsInfrastructure costs, decentralization, consent in datasets, and ethical AIHugging Face’s business model, GitHub analogy, and future directions in AI

High quality AI-generated summary created from speaker-labeled transcript.

Get more out of YouTube videos.

High quality summaries for YouTube videos. Accurate transcripts to search & find moments. Powered by ChatGPT & Claude AI.

Add to Chrome