Prediction Not checkable as stated
Fedus: Physical sciences and engineering will follow machine learning scaling laws
“And I think the physical sciences, physical engineering, Will have a very similar property where we establish these scaling properties and Bring that mindset.”
Insight
Fedus: Intelligence is not scalar; AI shows extreme, non-intuitive spikiness
“I think one fallacy is thinking about intelligence as a scalar. We've consistently seen these systems have a very odd spikiness, and it's actually possible to architect a system that is world-class on some math domain, but then you could do some perturbations …”
Prediction Not checkable as stated
Fedus: Self-improving software AI will not automatically generalize to domains like biology
“So rolling forward that software engineering self-improvement, I think you're going to have a system that can write complete repositories, identify bugs, refactor code, But it doesn't suddenly understand biology. Right? It's just like there's a domain gap ther…”
Prediction Not checkable as stated
Fedus: Recursive self-improvement is happening in software and entering AI research
“So, In that domain, I think it's happening now-ish. And I think we'll see the same thing too for AI research. That's a slower outer loop because now the experiment isn't just checking some unit tests passing, but it's checking what was the scaling property? Di…”
Assertion Not checkable as stated
Fedus: John Schulman steered OpenAI toward a general chatbot over narrow tools
“And we're all spitballing ideas, like writing bot coding bot. You know, very natural at the time. Some of our least interesting ideas were a meeting bot, so it would just sit in a Google Meet, take notes, and then send out, like, to-dos after. But John Schulma…”
Opinion
Fedus: AI must connect to the physical world to accelerate science
“The opinion that I and others held as periodic was, you're not going to see the same kind of acceleration in science and technology unless you start connecting these things to the physical world.”
Insight
Fedus: Training materials AI on academic literature fails to find ground truth
“One of the engineers on our team was looking at a reported material property and It was just sort of extracted values from literature, and it was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on th…”
Prediction Not checkable as stated
Fedus: AI Matter Synthesis Will Profoundly Impact Semiconductors, Aerospace, and Energy
“As you're pointing out, you're going from systems that aren't just writing essays, not just writing software, but to literally generating matter. And I think it has pretty profound implications to semiconductors, airspace, energy.”
Insight
Fedus: Post-Higgs experimental bottlenecks drove physicists to transition into AI
“After the discovery of the Higgs I think a lot of high energy physicists were sort of looking for what's next. Ultimately it becomes bottlenecked on the new apparatus for, you know, pushing the next energy frontier. And I think a lot of physicists were looking…”
Opinion
Fedus: Reasoning and coding agents connect software AI to physical domains
“And I think those were foundational technologies necessary To then connect these systems to the physical world. Like it was just not impossible, not possible with like the AI technology of.”
Insight
Fedus: Quantum AI models do not generalize across abstraction levels to fluids
“But like if you produce a system that has modeled quantum mechanical objects really accurately, it's not really helping much on like, you know, fluid dynamics or, you know, like another kind of like level of abstraction.”
Disclosure
Fedus: Periodic Labs launches as a software intelligence layer, not discovery model
“We're thinking about us ourselves as an intelligence layer for these companies. So you can think about system of record control plane for different Experiments and getting to solutions. But like you're saying, there is a very interesting aspect of some breakth…”
Assertion Not checkable as stated
Fedus: Compute costs outweigh physical infrastructure in materials AI labs
“What's interesting is just the compute costs relative to physical infrastructure is actually surprising where, you know, so much money is spent on the compute that the physical infrastructure sometimes is actually lower, but, you know, has very large lead time…”
Assertion Not checkable as stated
Fedus: Hybrid automated lab systems already produce large amounts of reliable data
“Already the reliability of the sort of like hybrid systems is sufficient to produce Huge amounts of reliable data”
Disclosure
Fedus: Periodic Labs uses off-the-shelf robotics instead of building custom hardware
“Right now we're using almost like more like off the shelf robotics. It's like very simple, very commoditized not doing like a huge amount of innovation on, on that front.”
Disclosure
Periodic Labs spends zero effort building or fine-tuning coding models
“Periodic spends zero effort on improving coding models. We're, you know, incredibly impressed by codex, cloud code. And so that's been a huge accelerator for the company. But focus our machine learning efforts where You know, the existing frontiers is not suff…”
Disclosure
Periodic Labs uses LLMs to orchestrate symmetry-aware atomic neural networks
“We think about them almost as like an orchestration layer. So that's sort of a co-pilot assistant, but also like a system that can direct experiments. And it's almost, it's orchestrating other specialized models as well. So we do construct neural nets that are…”
