Liam Fedus

Periodic Labs

@LiamFedus

Building industrial-scale science at @periodiclabs

Past: VP of Post-Training @OpenAI; Google Brain

San Francisco, CA periodic.com Joined October 2012

1.3K Following 42K Followers 1.3K Posts

Latest posts 5

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Liam Fedus @LiamFedus Sep 15

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.

Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.

This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.

Read our blog posts below.

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Liam Fedus @LiamFedus 5h

This is a fun era to build a startup

Xander Dunn Verified account @xanderai 9h

Our spend on DataDog is too high ->

Hey Opus 5.5 switch everything to Grafana and VictoriaMetrics ->

1 day later we spend 38x less on all logs, alerts, and graphs. No loss of functionality.
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Liam Fedus @LiamFedus 2d

PageRank for startups, @periodiclabs coming in at #5

Deedy Verified account @deedydas 3d

Top 30 Startups and BigTech companies based purely on ~50,000 engineer job changes in the last 3 years.

If engineers leave a company to join another, it ranks higher especially if the company they come from ranks highly.

Here are the final results:

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Liam Fedus @LiamFedus 4d

In the long run, put optimization pressure on AI for unbounded opportunities, e.g., science and tech (which build upon prior art) rather than accounting (bounded once an AI successfully follows all tax laws and regulations).

TBPN Verified account @tbpn 4d

.@dylan522p explains his position that the first trillion dollars of AI spend won't be used to replace white-collar work:

"Replacing white-collar work is something that will happen, but that's not going to drive the first trillion dollars of AI spend. Those are not industries that are going to diffuse AI capabilities quickly. It's going to take them quite some time to reorder how their OpEx is spent. Because companies have a lot of inertia with regard to people's jobs, changing how their compensation works, how many people work on what, etc."

"There are areas where we think that intelligence is very, very important, though. These include areas like chip design, cybersecurity, financial services, life sciences, materials science, software engineering. You can absorb unlimited intelligence [in these areas]. And there are two reasons for that."

"One is that it's a domain where humans aren't anywhere close to the frontier and there's just so much to discover. Think materials science, chip design, anything in the scientific realm. We figure out new stuff, we deploy it, we make everyone's lives better."

"And there's the other world which is adversarial. Specifically, finance and cybersecurity. It's human vs. human, and we're constantly going to be leveling up, either trying to arb each other like quants do, or trying to hack each other and then play defense. So there's this constant demand for intelligence because when you have more intelligence than the other parties in the market, you win."

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Liam Fedus @LiamFedus 5d

Catch a high-alpha talk by @hsu_byron on our collaboration between Periodic and RadixArk on building our inference stack

Byron Hsu Verified account @hsu_byron 5d

Periodic Labs is heading to #SGLangSummit 2026!

We’re building AI scientists and autonomous labs that learn from real-world experiments. This requires powerful models, robust infrastructure, and a tight feedback loop between ideas and experimental results.

I’ll be talking about how we’re building RL infrastructure for physical experiments—from developing custom trainers and sandboxes from scratch to adapting SGLang to optimize RL workloads.

Excited to see you all there! @sgl_project @radixark

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