Julian Schrittwieser
AI Researcher, Anthropic. On 1 show, 1 appearance. The Shows tab opens the full record on each.
scientistengineerauthor@mononofu ↗julian.ac ↗
Julian Schrittwieser spent a decade at Google DeepMind, where he was a primary contributor to landmark reinforcement learning systems including AlphaGo, AlphaGo Zero, AlphaZero, MuZero, and AlphaProof. Today at Anthropic, he focuses on reinforcement learning, autonomous agent development, and frontier model capabilities.
1shows
1appearances
27statements
2resolved
1supported
0contradicted
50%fully supported
Everything Julian Schrittwieser said on any show that made the record, most notable first. Each card names its show and opens the statement there.
AI will make Nobel Prize-level scientific discoveries by 2027 or 2028
“I think my guess for that level of capability might be maybe 2027. I think we're probably not going to find out for quite some time afterwards because of the delay in getting prices. But I think by 20, 27, 20, 28, I think extremely likely that the models will …”
Top AI models will work autonomously for full days within two years
“In a year from now, maybe two years from now, it's the top models are going to be able to work completely on their own for like a whole day or more”
Valuations for OpenAI, Anthropic, and Google are fairly conservative
“If you look at OpenAI, if you look at Anthropic, if you look at Google, those evaluations, those revenue numbers are actually fairly conservative.”
A sudden AI singularity or intelligence explosion is extremely unlikely
“Yeah, I think a true discontinuity is extremely unlikely from, you know, obviously AI researchers are already using AI to accelerate themselves. And so what's, what's already happening and like what is likely to continue to happening is that we see like a smoo…”
AI autonomous task duration doubles every three to four months
“We are seeing this very consistent improvement over many, many years where every say like, you know, three, four months is able to like do a task that is twice as long as before completely on its own.”
Schrittwieser: Wider AI ecosystem may face bubble while frontier labs thrive
“There may simultaneously be like some sort of bubble in, you know, the wider ecosystem, while at the same time, the frontier labs on a very solid trajectory, having a lot of revenue, making a lot of money.”
Schrittwieser: Current AI paradigm likely to achieve human-level performance in productivity tasks
“I think if you're thinking of, oh, we want some kind of system that can perform at roughly human level in basically all tasks that we care about. Productivity wise. Then I think, yeah, it's extremely likely that the current approach, pre-training RL, you know,…”
Pre-training aids AI alignment by implicitly instilling human values
“I definitely think we would keep using pre-training data, not just from an efficiency point of view as well, but also I think there is interesting safety angles, because by pre-training and, you know, all this human knowledge, we're implicitly creating an agen…”
Using chain-of-thought as an RL reward destroys model interpretability
“If you're not careful with RL, you can make interpretability harder. For example, one Common thing with modern models is they do reasoning with the chain of thought. You could look at the chain of thoughts to, you know, see what are the model internal thoughts…”
Schrittwieser: Task duration dictates how much work can be delegated to AI
“The reason I think why task length specifically is interesting is because that's What allows you to delegate more and more work to language models, to agents. Now, even if you have a very clever model, but if it needs feedback or the interaction with you very …”
Schrittwieser: AI language models are clearly capable of novel output
“For me, as somebody who has been doing research a long time, I think it's pretty clear that these models can do novel things.”
Future AI models will continue to rely on pre-training data
“Personally, I think that's unlikely. Not, not because pre-training is strictly necessary. I think we may well be able to train something completely from scratch, as we've been able to do in other domains, but more because pre-training on this vast data sets th…”
AlphaGo would have probably lost to Lee Sedol if played earlier
“And I think if we had done it a few months earlier, we would have probably lost.”
Schrittwieser: Language models possess an implicit world model
“So I think, yes, I would say that language models have an, not an explicit world model, but they do have an implicit model of the world.”
Reinforcement learning scaling yields returns on compute similar to pre-training
“If you look at all the RL literature over time, we see very similar returns on compute in pre-training and in RL, where we can invest exponentially more compute in RL and keep getting benefits.”
Schrittwieser: Adding model reasoning improves RL training stability and scaling
“One direction of scaling RL and making it more stable is by improving this by, for example, putting more reasoning into your language model to generate much more high quality training data. That can then give us training that is much more stable, and then we c…”
AI app developers do not need custom fine-tuning for top models
“I think nowadays, with the capabilities of, like, you know, top, probably cloud models, top OpenAI, GPT models, You don't need to do any fine tuning. You can take the model as is, ride your own tools, your own harness, and benefit from that agentic training. B…”
Schrittwieser: AI progress will remain smooth and incremental without a single bottleneck
“There's probably, yeah, not one individual blocker. And that's why we will continue to see sort of smooth incremental progress over model releases”
Schrittwieser: Distributing AI productivity gains is a political problem, not technological
“I think it's much more like a political, social problem of, like, figuring out how do we actually benefit from all these improvements, and, like, you know, bring the increases in wealth and productivity to everybody, and it's much less a technological problem.…”
Schrittwieser: OpenAI's GDPval is a strong benchmark for economic impact
“I think that GDP is like a super cool evaluation from OpenAI where they collected a lot of, like, you know, real world tasks from real domain experts to make sure that it is actually representative of what you might do in the economy.”
Schrittwieser: AI pre-training risks over-restricting an agent's exploration search space
“I think the main, you know, the main challenge or the main thing you need to watch out for is that you don't over encode or you don't restrict your search space too much. If your pre-training, if your prior knowledge prevents you from exploring something that …”
Schrittwieser: Reliable reward sources will be key to scaling reinforcement learning
“Figuring out what are the best reward sources, and how do we scale it up, and how do we, you know, get more rewards, more reliable rewards. That will be one of the key ingredients in scaling up RL further.”
AI research still lacks scaling laws for training data quality
“I think we don't have any good scaling laws yet. That tell us the trade off, especially I think because it's very hard to measure what is the quality of a data point, right? Like how good is this example compared to this other example without being able to mea…”
Schrittwieser: Raw pre-trained AI models make poor agents without RL
“Our pre-training data is not very agent-like. If you think of the pre-training data, right, there is like websites and books and, you know, all kinds of recent text that has a lot of information, but it doesn't have a lot of actions. It doesn't really capture …”
One line per show, most statements first. The link opens Julian's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 MADLEDGER | AI Researcher, Anthropic | 1 | 27 | 50% 1/2 | full record on the MAD Podcast → |