The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 15 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
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.”
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…”
Bourgeau: AI models must be trained on harmful data to avoid it
“So at a fundamental level, you did, you do need the model to know about those things. So you have to train a bit at least on those so that it knows what those things are and knows to stay away from those, right?”
Bourgeau: Gemini 3 progress came from small contributions, not one breakthrough
“In my experience, there's maybe one or two of those things that make a larger difference than other things, but it's really a combination of many, many changes and many, many things from a very large team that actually makes Gemini three so much better than th…”
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 …”
Bourgeau: AI scale has blurred the line between research and engineering
“I think over time that boundary has blurred quite a lot because we're working on these very large systems now. Research really looks like engineering and vice versa.”
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…”
Bourgeau: In deep learning, negative results often mean unoptimized techniques
“Especially in deep learning, a negative results doesn't mean something doesn't work. It means you haven't made it work yet often.”
Bourgeau: Pre-training scaling lessons apply directly to RL scaling
“On the RL and RL scaling side, I think we're seeing a lot of the same things we're seeing in pre-training or we saw in pre-training. What's interesting here is because we have the experience of pre-training, a lot of the lessons apply, and we can reapply some …”
Bourgeau: Internal held-out evals are the only way to prevent benchmark self-deception
“The only way you really have to protect against cheating yourself and thinking you're doing better than you are is by actually creating held out evals and not really keeping them held out.”
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 …”
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.”
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.”
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…”
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.”