The Ledger
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Dubois: AI progress feels discontinuous because OpenAI crossed a reliability threshold
“Even though the, in my mind, everything, the progress is actually pretty continuous, you need to reach this level of reliability. To really make any of these AI tools very useful, and I think we just crossed that probably December last year, at least at OpenAI…”
Dubois: Test-time compute scaling exhibits logarithmic, diminishing returns
“We, we've seen again and again, the longer the model think for the better answers we will get. The problem is that this, these curves that we're talking about are not, are definitely not linear, and like they, there's some plateauing effect, and they kind of l…”
Dubois: Larger AI models achieve higher efficiency by thinking through weights
“If you have larger models the amount of thinking time, so the amount of tokens they will think for will usually decrease. And the way that you can think about it is that metaphorically, the model already thinks through its weights when it generates a certain t…”
Dubois: AI frontier labs have successfully bypassed internet data walls
“There were a lot of conversation about hitting data walls, and it seems like we did not quite hit it. So the larger the model is, the more data it needs to ingest to be trained. And it seems like different companies kind of found different ways to overcome the…”
Dubois: Simulations will never fully eliminate the need for real-world AI training
“The problem is simulations are always going to be really hard and are not going to be truthful. So I think there will always need to be a certain, a little bit of training that will need to happen in the real world to make sure that the model realizes kind of …”
Dubois: RL becomes effective once base models possess strong world priors
“It seems that after crossing a certain scale of models that know basically everything about the world, and what we call, like, good priors about the world, It seems that reinforcement learning just started to work, and this is not only with LMS. Robotics seems…”
Dubois: AI model knowledge calibration generalizes across all domains
“When you have hallucination of LMs, if a model is really bad at saying that it doesn't know, that usually happens in every single domain. You won't have, like, one domain where the model is extremely calibrated about its knowledge, and another domain where it'…”
Dubois: Effective reinforcement learning pipelines prevent AI hallucinations caused by SFT
“So, so hallucination at least the intuition that people have is that it can come, for example, from SFT, and it can come from this, like, pursuing pipeline, but if you have good reinforcement in pipeline, that shouldn't happen too often.”
Dubois: Model capacity does not limit AI performance in legal or medical fields
“But there's nothing, I would say, in the capacity of the model That is constraining the model to be as good at legal and like medical and like other domains.”
Dubois: AI's coding discontinuity will permeate other verticals within two years
“Now the feeling of discontinuity will happen. It did happen three months ago with coding or four months ago with coding, and I think that will happen now in every other domains. Like most people are not feeling the same way Like the, like kind of the capabilit…”
Dubois: AI models outperform new employees initially but lack continual learning
“Right now, actually most models at day zero, if you just drop them in a company arguably they are more useful than most new employees. So they start higher at T zero. But then across time they are mostly constant because they don't really learn kind of company…”
Dubois: General AI agent harnesses designed to endure will not work
“If you try to have, like, a general harness to, that will, like, sustain over time I don't think that will work.”
Dubois: Current models with optimized harnesses would feel like AGI
“If we froze the models that we have right now, and you really worked on the harness, and, like, maybe, like, we also spend more time, like, training with, like, a great harness I think people would really feel the AGI in every single domain, or could already f…”
Dubois: Last-mile integration is the main AI bottleneck, not raw intelligence
“I think most of the time, the bottleneck is the last mile.”
Dubois: Horizontal AI model progress will not stop anytime soon
“Maybe one day when we stop making horizontal progress, which I don't think is anytime soon, maybe we will start focusing on that, but yeah, that's not what we're doing now.”
Dubois: Better AI models accelerate AI research by building tools and training models
“Once you start having models that are really good, you accelerate yourself. Especially in terms of coding, given that we all code internally yeah, you accelerate yourself both for having these models, like train the other models, but also like build like the t…”
Dubois: OpenAI expanded RL training from math competitions to real-world coding
“We were able to take many of the tools that we built for these, like, verifiable reward cases, and we were able to use them more generally in on, for reinforcement on, like, real use cases, and I think that's, like, really why we're feeling that right now in, …”
Dubois: OpenAI internal sentiment goes through waves of hype and doubt
“It's kind of funny because in general with every model that is looking really good early on we have a model, we all get really excited about it. And then there's like tons of doubts. That start coming up because it's like, oh, like everyone is so high, is like…”
Dubois: OpenAI model iteration speed spans months upstream to days downstream
“So we really have like different sub teams including pre-training and you have like the mid training stage and like you have some post training and usually the closer you get to products like pushing being the last one, the faster the iteration cycle is. And i…”
Dubois: GPT-5.5 performs most tasks roughly two times faster
“Most of the tasks can be basically performed, I would say like two X faster now with this model.”
Dubois: GPT-5.5 succeeded by combining inference efficiency and latency optimizations
“And the final thing that people care about is latency on x-axis, performance on y-axis, and this is where everything comes together, and this is really what happened with 5.5.”
OpenAI's Yann Dubois rarely uses GPT-5.5 Pro due to high latency
“I personally don't use Pro that much because I really don't like waiting. I'm pretty impatient, so I don't like waiting for that long, and I know that the probability of being correct definitely improves, but it doesn't improve, like, enough for me to use it.”
Dubois: Multimodal data is not strictly necessary for strong AI reasoning
“I always thought That it would really help, ah, kind of your reasoning abilities if you have a lot of multimodal data And I still think this, but for example, like if you look at entropic models, they tend to not be that good on multimodal, and they are still …”
Dubois: Models like Kimi and DeepSeek use ~1M RL data points
“Now when you look at reinforcement learning from models like Kimi or from DeepSeq models, it seems that they are closer to one million data points.”
Dubois: Agentic RL training suffers from sparse reward credit assignment
“When we are training more agentic systems, you only know whether you're correct at the end of your very long rollout. So you get very little information per token of whether you were correct or not. And it's hard to say it's hard to basically do attribution. I…”
Dubois: AI outperforming humans in specific domains creates evaluation bottlenecks
“Models in specific axes are becoming better than the majority of humans, and so we have fewer and fewer humans that can actually evaluate these models in particular axes.”
Dubois: AI models create a capability flywheel as better models become better teachers
“As we get, like, better models we have this self-reinforcing loop, and we have this, like, capability flywheel, where better models become better teachers for other models.”
Dubois: Final rehearsal for OpenAI's GPT-5 live demo failed
“Right before we did that, like the last rehearsal, it did not work.”
Dubois: Embodied AI will advance general intelligence through world interaction
“I still believe that once we go to embodied agents, embodied AI, you will learn a lot about the world. And you will kind of improve general intelligence and usefulness to users by learning how, how the world interacts with itself.”
Dubois: AI community is still pretty far from real-world embodied interaction
“So I do feel like we will improve the common sense of our model by having them interact in the real world. But we are, we're still pretty far from that, I think. And by we, I mean just generally the academic community and the AI community seems pretty far from…”
Dubois: Supervised fine-tuning cannot exceed human demonstrator capabilities
“The problem with this is that you will never get better than what your ground truth gives you. And humans are actually pretty limited in many sense, so you will never, like, overcome the human labelers that you, you're working with.”
Dubois: Starting post-training with RL without SFT is extremely inefficient
“Because if you just started from reinforcement learning, it would be very inefficient. Because the problem with reinforcement learning is that you have to stumble across the right answer, basically.”
Dubois: AI models good at math competitions will probably excel at coding
“If you are really good at, like, math competitions, your model will probably be pretty good at, like, coding competitions.”
Dubois: AI performance in new verticals depends on domain expert focus
“And in general, I would say the performance of the model really depends on, like, the number of people who care about the final output of the model and who are looking at that model. So if they start looking more on specific verticals, like, these verticals wi…”
Dubois: Horizontal AI capabilities can conflict with one another
“You will have cases where basically these horizontal capabilities go against each other.”
Dubois: Quantifying AI improvements is as important as training models
“Finding issues and, like, making sure that we can quantify improvements is just as important, if not more important, but there's always this, like, cultural gap.”
Dubois: Creating AI evaluations inherently creates methods for building training datasets
“Every time you build an eval, you actually build a way to build training data sets.”
Dubois: Longer-running agentic AI tasks inherently carry higher overall error probabilities
“Given that these are agentic models the longer, if you just think about it as like every two minutes, there's like a certain probability that they are wrong. The longer that they run, The higher the probability that, like, the final answer is going to be wrong…”
OpenAI focuses on reducing AI agent error probability per time interval
“What we've been pushing a lot on is, like, making sure that the model, like, we decrease this probability of being wrong every, like, two minutes.”
Dubois: AI research aims to shift test-time scaling curves left
“The usual plot that you should be looking at is X axis, the number of tokens that you think for and Y axis the performance. So this is the, these test time scaling curves that we look at. And research basically tries to move this curve to the left. So think le…”
Dubois: RL allows AI reasoning models to backtrack wrong paths earlier
“Part of it is the model knowing when it's going down the wrong path. But this is also something that we can that the model can be trained for with reinforcement learning is like knowing, okay, like that seems like not a great path. Let me backtrack and let me …”