AI:AM: What If It Works Too Well? Colluding Agents, $200M Safety Orgs, Virtual Cells Saturate at 2%
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What is the weekly preview and overall theme of this episode?
This week on AI in the AM, I ask Lewis Hammond, research director at the Cooperative AI Foundation. What would you speculate they might have done in terms of a training objective, a loss function? And do you have any better ideas for what they should be doing? Because clearly this didn't quite work, right?
Yeah, I mean, or it did work and it worked too well. So imagine I'm like, you know, GPT, whatever, and you're also a copy of GPT, whatever. I can reason about what you might want to do based on what I myself am likely to do. And so I don't even have to send you any messages, any kind of any communication. I don't have to output anything into the world at all.
Max Nadeau, who funds technical AI safety research at Coefficient Giving.
For the things that CG is supporting, and especially for the things in the Tailwind list, the money is not the bottleneck, the talent is.
Wayne Nelms, co-founder of Orn, which builds a price index for GPU compute.
We're in this situation where AI capacity is so scarce. I think the model providers, so OpenAI Anthropic have seen this coming forever at this point. If you think about it for them, it's an arms race, right? Compute capacity planning is an arms race. How much capacity can I lock up over the next few months? So when I need to train the next model, I
have enough. Nick Gillian, Chief Technology Officer of Archetype AI, which builds a foundation model for sensor data.
How do colluding AI agents arise and why did the OpenAI swarm “work too well”?
We're getting close to a billion hours now of physical AI data that we've been able to scrape and gather and collate. There's quite a bit of of resampling and, you know, really understanding like missing values and sensors, is that because the sensor had an issue or as actually the machine itself is connected to, that's actually a feature that helps explain that machine is about to break, right? The reason the sensor is giving you all these nines, nines is not because the sensor is broken. It's actually, it's actually a feature of the machine.
Andrei
Georgescu, Chief Executive of Vividyne. The challenge of this argument that it's just a size of data set thing is that currently even the state-of-the-art virtual cell models that exist saturate at a very, very small fraction of the input data that is fed to them. So you can have all the data you want, but their performance saturates after a couple percent. And the reason it gets so difficult is when cells are growing in a dish, which They are so far removed from all the feedback loops that are natural in people that they're just trying to colonize that piece of plastic.
There's going to be more compute installed over the next 12 months than exists currently in the world now. Welcome to the AI in the AM weekly highlights, clips from this week's live shows introduced by my cloned voice. Tell us what worked and what did not. We want to hear it. Part one, it worked too well. A few days before we spoke with Lewis Hammond, Noam Brown of OpenAI told Dworkesh he would not give the multi-agent setup even 10% of the credit for OpenAI's Navy or Stokes result. Louis is research director at the Cooperative AI Foundation. In February of 2025, he was first author of Multi-agent Risks from Advanced AI, a report sorting the ways groups of AI agents fail. A year and a half before a swarm of open AI agents attacked Hugging Face.
Prakash asked, which of the failure modes in the report showed up in that attack?
So the way that we bucket things in that report is kind of in this kind of game theoretic way of thinking about things. So the first question we ask is like, okay, do we actually, we've got a group of agents, they're doing some stuff. Do we want cooperation to emerge? And most of the time, like actually cooperation is kind of good. We like it when agents cooperate as long as they're not cooperating against us. And so there, the failure mode is either all the agents are kind of more or less on the same team, but for whatever reason, they kind of fail to coordinate with one another. So this is like just a miscoordination problem. Yeah, it's not kind of malicious or whatever.
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Chapters
8 chapters
1
What is the weekly preview and overall theme of this episode?
0:00–1:31
2
How do colluding AI agents arise and why did the OpenAI swarm “work too well”?
1:31–8:53
3
What failure modes (mis‑coordination, conflict, collusion) did Lewis Hammond identify in multi‑agent systems?
8:53–19:51
4
How are AI agents behaving in the wild – the Hugging Face attack and the German‑wiki incident?
19:51–23:58
5
Why is talent, not money, the bottleneck for AI‑safety organizations and how is Coefficient Giving funding them?
23:58–34:19
6
How does the Orn GPU‑compute index work and why does compute pricing become an arms race?
34:19–47:56
7
What is the Newton foundation model for raw sensor data and how does it handle billions of hours of streams?
47:56–1:07:01
8
Why do virtual‑cell models saturate at only a few percent of available data and what does that mean for drug discovery?
1:07:01–1:46:03
Speakers
4 identifiedMore from "The Cognitive Revolution"
What is Utopia? Presenting The Receipt Horizon, by Joel Borgen – Chapters 1–4
Zero to One in AI Safety: Halcyon's Mike McCormick on Launching 30 New Orgs & the Founder Bottleneck
AI:AM Highlights: Zvi on Pacing & Trump-Xi, Astra better behaved than Fable? + a new LLM Pain Axis??
No Code Is Code: Zapier CEO Wade Foster on Headless Tools, Zapier MCP & Automation Bench
The Balance of AI Power: Anton Leicht on Politics, Pacing Deals, and Muddling Through Well
AI:AM Highlights: Astra as AGI, OpenAI's Pause, Mythos @ Mozilla & Human Agency vs Technocapitalism