Guillaume Lample
person on 6 shows · 6 statements across 1 episodes · said 23 times in 13 episodes since 2023
the a16z Podcast 10 
Latent Space 6 
the MAD Podcast 4 
No Priors 1 
TBPN 1 
20VC 1
On the record as a speaker too: Guillaume Lample across every show, appearances and statements → this page counts the times other people say the name.
Mentions by year, every show
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the a16z Podcast 10Latent Space 6the MAD Podcast 420VC 1No Priors 1TBPN 1
2026 5 mentions in 4 episodes 1 per episode
- Mistral AI vs. Silicon Valley: The Rise of Sovereign AI
- Why AI Labs With Unlimited GPUs Still Fail — Anjney Midha, AMP
- FULL INTERVIEW: Anjney Midha on Fixing AI’s Biggest Bottleneck
- AI That Can Prove It’s Right: Verification as the Missing Layer in AI — Carina Hong
- every mention in 2026, scene by scene →
2025 2 mentions in 2 episodes 1 per episode
2024 6 mentions in 4 episodes 2 per episode
- Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI
- Agents @ Work: Dust.tt — with Stanislas Polu
- The $4.5B Platform Driving the Open Source AI Revolution | Clem Delangue, CEO, Hugging Face
- Training Llama 2, 3 & 4: The Path to Open Source AGI — with Thomas Scialom of Meta AI
- every mention in 2024, scene by scene →
every mention on every show, scene by scene, with the transcript →
6 statements about Guillaume Lample, every show
Lample: Closed models prevent enterprises from leveraging proprietary domain data
“When customers use this off-the-shelf closed model, what's very sad is that they are not leveraging, you know, these data that they have been collecting for four years, or sometimes for decades. So much data, sometimes it's trillions of tokens or data in a ver…”
Lample: Mistral develops specialized single-capability models before merging them
“The way we kind of do things internally, that we have like one team, focus on one capability, build one model, and then when it's mature enough, we decide to merge this into the Mixture. So, but hey, here's, it was the first time we basically merged all of thi…”
Lample: 1B to 3B parameter models are optimal for speech transcription
“For instance, for audio here, if you want to do transcription, I think it makes no sense to use a model as this large. If you just want to transcribe tech, it would be very inefficient. Like if you want to do audio, you probably just want to do the one B or a …”
Lample: Frontier models underperform in legal and CAD due to missing benchmarks
“Things around, like, legal, finance, computer-aided design, all of these things that it's, these models out of the box are never too good at that, because people really don't prioritize this, there is no, like, too many benchmark on that but it's not hard to m…”
Lample: Post-training breakthroughs like DPO were impossible without open LLaMA
“And if you look at many of the techniques that were developed after, for instance, Temma was open source, like all these post-training approaches like even DPOD, like performance optimization, all of this were done by people that had access to this model, and …”