Will Brown
Research Lead, Prime Intellect. On 2 shows, 2 appearances, plus 1 compilation re-air not counted. The Shows tab opens the full record on each.
scientistengineer@willccbb ↗LinkedIn ↗willcb.com ↗
Will Brown leads open-source research and infrastructure for agentic reinforcement learning at Prime Intellect, creating tools such as the verifiers library and the Environments Hub. He earned a PhD in algorithmic game theory from Columbia University and previously conducted machine learning research on large language models at Morgan Stanley.
2shows
2appearances
46statements
2resolved
2supported
0contradicted
100%fully supported
7said about them ↓ Everything Will Brown said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Current multi-turn RL research ignores discarded thinking tokens, breaking the math
“The existing paper people are writing about multi-turn RL are not actually incorporating this, and it kind of, like, breaks all the math.”
Averaging weights of models trained on separate domains works effectively
“You can have a model trained on code, and a model trained on math, and a model trained on Spanish, and you can literally average the weights, and it works.”
Weight updates across specialized tasks are orthogonal enough to merge asynchronously
“The updates made to model weights are orthogonal enough for specialized tasks that this is actually like totally fine. Things are nice and linear in most cases, things are nice and orthogonal, and you can get away with a lot of async updates to models that are…”
Brown: GPT-4.5 Likely Has Trillions of Parameters Enabling Sparse Connections
“GPT, 4.5 is like ginormous model, trillions of parameters, most likely. And that like, there's more room in the model to have these like little sparse connections materialize as you go through layers of the transformer. And I just haven't seen anything like th…”
Will Brown: AI scaling faces diminishing returns on capital investment
“The quality bump over things that are much smaller is just like the, we're hitting diminishing returns on capital investment is a lot of it. Like they're taking out of the API because like the, they can sell other things with the same GPUs and make more money …”
Will Brown: Nobody will run Meta's giant model for daily tasks
“When Meta releases this behemoth model, I don't think anyone's really gonna run behemoth. For like their day to day stuff. It's just probably not gonna be worth it.”
Will Brown: Effective agentic AI will work well on small models
“That's kind of like my bet is like, people are going to really want to train models to be agents. And I think you can get that to work well with a pretty small model.”
Brown: OpenAI's o3 qualifies as 10-minute AGI
“I'm happy to call O three, 10 minute AGI. And I think like framing AGI in terms of like length of time, it takes a human to do a task is like more reasonable than like a global framing. Like, sure. There's a bar of like drop and replace for a human that we are…”
Brown: Most Enterprise AI Pilots Are Structurally Designed to Churn
“Most of these pilots are very much intended to be churned. Like, they're not, they're very much in a, they're not being rolled out broadly. They are coming through in a kind of walled off environment for people who are, like, gonna be the beta testers.”
Brown: DeepSeek serves all of China on 2,000 GPUs
“Like Deep Seek is serving all of China on 2000 GPUs.”
Brown: Anthropic treats extended thinking as tool use, not distinct model class
“And it seemed like Anthropik's kind of attitude has been that extended thinking is an instance of tool use and that it's the kind of thing you want to equip the model with the ability to do. But it's not like, oh, it's a thinking model. It's just a sync for th…”
Brown: Anthropic safety issues stem from conflicting model objectives
“A lot of the kind of headline anthropic like safety results, especially related to reward hacking and kind of deviation and alignment faking, Are all things to me that seem like a rock and a hard play situation where the model has two objectives it's given tha…”
Will Brown: Selling to AI Labs Compromises Model Evaluation Integrity
“I think being an eval company puts you in a really hard spot. Some people are talking about this on Twitter, like just that to be an ed-all company, you kind of have to sell to the labs, but selling to the labs doesn't really, like kind of wrecks the revals.”
Will Brown: Academia Will Likely Be the Best Source of AI Evals
“I mean, I do think that like the best source of evals going forward is probably going to be academia.”
Brown: GRPO is more memory efficient and easier to distribute
“GRPO is, like, great for, like, leaning heavy on highly parallel inference compute. It's more memory efficient for the actual training process. It's much easier to do in a distributed fashion because you have less gradient syncing and less model weight copies.”
Brown: RL training is shifting toward model-based LLM judges
“So it, like, feels like people are moving in the direction of model-based rewards, where you, either LLM is a judge where the judge sees the correct answer, or it has questions it's supposed to verify as properties of the response, just because that's much mor…”
OpenAI's o3 acts as a '10-minute AGI' for human tasks
“Whether or not you want to call this, like, end minute AGI, I kind of like the phrase, 10 minute AGI, for just, like, how to think about O-three is that anything that you can do as a human in 10 minutes, O-three is usually going to be able to do reasonably wel…”
Programmatic tools beat end-to-end image generation for multimodal reasoning
“Where I think for a while some people were, like, speculating, like, oh, what if you have the model, like, generate images in its chain of thought reasoning where everything is, like end-to-end multimodal input and output, and it seems like you don't really ne…”
Group-chat multi-agent systems like CrewAI do not work well
“I think people were, like, excited about multi-agent systems for a while, like, the crew AI sort of thing of, like, oh, I'm gonna put my coder agent, my finance agent in group chat, and, like, a lot of these are just kind of silly. They don't actually work ver…”
Algorithms for practical multi-agent RL do not yet exist
“Multi-agent RL's hard. I did five years of it in grad school. It's like not easy. And to the algorithms don't really even exist for the things you would really want to do.”
No legitimate open-source million-token context models exist at scale
“Scaling to, like, million token contexts is, like, really, really hard. There, I don't think there are real, like, open source replications, open token context scaling, Beyond, like, tiny, like, academic model sizes.”
Reasoning models acting as reward models are key to agent RL
“And the most, one of the most promising ways, I think, towards doing this is having the reward models also be able to answer harder questions by themselves being reasoning models.”
Will Brown: OpenAI o3 succeeds because reinforcement learning enables tool use
“The reason O-three is good is because it's trained to use tools. The way you train a model to use the right tool for the job is reinforcement learning. And they've said as much, like, deep research, reinforcement learning.”
The other half of the tape: Will Brown's own voice is left out of every number here. Other people bring the name up 7 times in 7 episodes across the shows. every mention, with the transcript →
Who brings them up most John Coogan 2Tyler Cosgrove 1Shawn Wang 1Jordi Hays 1
Every mention by year
tap a year for its mentions
TBPN 6
Latent Space 1
2026 5 mentions in 5 episodes 1 per episode
- SpaceX-Cursor Deal, ChatGPT Images 2.0, Fake Bear Scam | Diet TBPN
- AI Is Coming for Your Memes, Crypto’s Quantum Clock, Axios Hack| Diet TBPN
- The Axios Supply Chain Attack Explained
- The AI lab market map, Robinhood brings startups to retail, GLPs & hedge funds | Diet TBPN
- The Cournot Equation, Micron’s $200B Bet, Hollywood vs. Seedance 2.0 | Diet TBPN
- every mention in 2026, scene by scene →
One line per show, most statements first. The link opens Will's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| LATENT SPACELEDGER | Research Lead, Prime Intellect | 1 +1 | 31 | 100% 1/1 | full record on Latent Space → |
| TBPNLEDGER | Research Lead, Prime Intellect | 1 | 15 | 100% 1/1 | full record on TBPN → |