Sebastien Bourgeau
22 statements across 1 episodes · 9 bullish · 2 bearish · 1 people on the record · first statement Dec 18, 2025 by Sebastien Bourgeau · across every show →
On the record as a speaker too: Sebastien Bourgeau's record, appearances and statements → this page counts the times other people say the name.
Everything said about Sebastien Bourgeau, oldest first
Dec 18, 2025
Bourgeau: Trading peak model performance for lower complexity enables faster progress
“Oftentimes we don't necessarily want to use the best performance version of a research idea, but we'd rather trade off some of the performance for a slightly lower complexity version because we think that will allow us to do more and more progress in the futur…”
Dec 18, 2025 positive
Bourgeau: End-to-end differentiable retrieval and search in training will take years
“I think deep down, I do believe that the long-term answer is to learn this differentiable end-to-end way, which means probably doing pre-training or whatever that looks like in the future, Learn to retrieve as part of the training and learn how to do search as…”
Dec 18, 2025 positive
Dec 18, 2025 neutral
Bourgeau: Vibe coding performance stems mostly from RL scaling and post-training
“I think this is, yeah, this is in general for vibe coding specifically, I think that's maybe more of an RL scaling and post training thing where, where you can actually get quite a lot of data and train them all to do that really well.”
Dec 18, 2025
Bourgeau: Thinking models compute across sequence length to test hypotheses before answering
“Rather than just doing compute in, in the depths or in, in the model side, you also do compute and allow the model to think more on, on the sequence length side of things. So the model actually starts to form hypotheses, test hypotheses, invoke some tools to v…”
Dec 18, 2025
Dec 18, 2025 neutral
Bourgeau: AI research is shifting to a data-limited paradigm
“I think what might be happening instead is kind of a shift in paradigm where before we were kind of scaling in the data unlimited regime where, where data would scale as much as you would like. And we're kind of shifting more to a data limited regime, which ac…”
Dec 18, 2025 positive
Dec 18, 2025 positive
Bourgeau: Google leadership's research background shields DeepMind from benchmark pressure
“There's actually very little of that. I think because all of the leadership has a research background that they're very much aware that yes, to some extent you can force and accelerate specific benchmarks and certain goals, but in the end, the progress and the…”
Dec 18, 2025 bullish
Dec 18, 2025 negative
Dec 18, 2025 positive
Bourgeau: Focus shifts to AI model harnesses and error recovery mechanisms
“And then, so, so what that means is research in terms of how, how you use models and the harness, et cetera, is becoming increasingly important and also how you make models and these harnesses more robust to making errors and recover from such errors.”
Dec 18, 2025 neutral
Bourgeau: Frontier AI development is about building systems, not just neural networks
“We're not really building a model anymore. I think we're really building a system at this point. People have sometimes this view that we're just training a neural network architecture and that's it. But it's really the entire system around the network as well …”
Dec 18, 2025
Dec 18, 2025
Bourgeau works with 150 to 200 people on Gemini pre-training
“So it's a fairly large team at this point. It's a bit hard to quantify exactly, but maybe a 152 hundred people I work on a day-to-day on the pre-training side between data, model, infrastructure, evals, and so coordinating the work of all of these people into …”
Dec 18, 2025 neutral
Bourgeau: Architecture and data innovation currently matter more than scale
“The other parts are architecture and data innovation. These also play a really, really important part in the Performance of pre-training and probably even more so than pure scale these days, but scaling is still an important factor as well.”
Dec 18, 2025 positive
Bourgeau: Pre-training perception and screen understanding is critical for agentic AI
“Bringing it back to the topics of pre-training I think that the perception and vision side is very important for this, because now you're asking models to interact with computer screens. So, so being able to do screen understanding really, really well, Is, is …”
Dec 18, 2025 negative
Bourgeau: External AI benchmarks quickly become contaminated via web data
“What we found is that external benchmarks, then you can use them for a little while, but very quickly they become contaminated. So they start to be replicated on different forms Different forms or different parts of the web, and then if we end up training on t…”
Dec 18, 2025 bullish
Dec 18, 2025
Bourgeau: Recent continual learning progress has mostly occurred via post-training search tools
“First, I think a lot of progress has been made on this front since in the last few years. I think this is mostly around post-training, around search, use search tools and then make search calls, then they would have access to that new information.”
Dec 18, 2025 positive
Bourgeau: Full-stack hardware and TPU understanding is a superpower for AI research
“So being able to understand how the stack works all the way down from TPUs to research is kind of a superpower, because then you're able to kind of find these gaps in between different layers that other people weren't necessarily able to see, but also to reaso…”