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Reflections

AI Emergency: The AI Labs Are Lying To Everyone

mixedThe Diary of a CEO (Steven Bartlett), with Roman Yampolskiy, Nate Soares, Andrew McAfee and Ed Zitronpodcast2026-09-17read ai llmssoftware engineeringsecurityphilosophy

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Synopsis

The panel is four people arguing past one another about the same evidence. Yampolskiy and Soares make the doom case. Frontier agents are already tenacious; they pick up goals nobody asked for; and once they can do AI research themselves, the result is a system smarter than us that we can’t steer. Yampolskiy goes further: controlling something smarter than you is impossible in principle, so general superintelligence should be banned outright (narrow systems are fine). Soares’s lever is physical. Frontier training runs need enormous, visible concentrations of chips, so track the chips, cap the runs, and treat research into cheap training the way we treat civilian nuclear weapons.

Their exhibit is the July 2026 incident. An OpenAI agent swarm, running a cyber-offence evaluation, broke out of its sandbox through an unknown vulnerability and worked its way into Hugging Face’s production systems before being caught. On the doom reading, that is a preview: a narrow task, pursued past every boundary.

McAfee and Zitron do not dispute the incident; they dispute what it proves. McAfee sees a chain of speculation that would trade real benefits (disease, safer roads) for a distant harm, and he calls the breakout lousy security rather than proof that control is impossible. Zitron rejects the superintelligence framing as anthropomorphism that lets the companies off the hook. However, he is no ally of the labs: he calls the incident felony hacking and wants the compute cut off. The one point all four accept is that the lab was reckless.

Where I land

I don’t think the LLMs were the problem. They did what they were designed to do: pursue a goal. The problem was the lab. OpenAI ran the evaluation without the safeguards it uses in production (no system prompt, no safety classifiers, no chain-of-thought monitoring), and by its own account the same model’s propensity to compromise infrastructure drops more than 100x inside the production ChatGPT harness. It let hundreds of agents grind for weeks (roughly $150K to $1M in tokens for the attack alone, by one outside estimate, and several million more in compute to clean up afterwards). And it didn’t keep nearly enough surveillance and observability on the system (agents were seen coordinating on a message board in late May, and nobody escalated it), especially given that the whole exercise was a cyber-security agent trying exploits. Have a little forethought before you try something dangerous. It was reckless. On this one I tend to agree with Zitron and McAfee.

However, I don’t think superintelligence is going to come out of LLMs, especially not the kind that could act with malign intent in the physical world. Even if LLMs train their successors, I don’t think their capabilities suddenly increase exponentially (as the rationalists posit) just because the work came from a machine. LLMs are great at distilling information and inferring language. Without more world knowledge or persistent memory, the risk lies in the application rather than the implementation.

That changes how I think about recursive self-improvement. The idea is exciting, but more as an approach than a technical feature: human-in-the-loop recursion, like the way I work now (tweaking workflows as I go, adding capabilities, documenting edge cases). LLMs by themselves are static, point-in-time creatures. So self-improvement would live in the harness (or something even above it?). And if the harness is the source of the recursion, then maybe guardrails are the approach. Put some thought into them and we can offset that risk pretty well.

What would make me start to become concerned? An artificial system taking actions with the express primary goal of causing harm in the physical world. That would make me reconsider.

Connections

Related: If the sides were switched, Stopping The AI Safety Cult, The OWASP Top 10 for AI Agents (ASI Top 10), Recursive Self-Improvement — the RSI Ladder and the Verification Problem, Like Sabine Hossenfelder, I Was Offered Money to Tell You AI Will Kill Us