Flashback to Robin Sloan's underdog win in the Dec 2025 Substack debate at Bimbo's 365 Club, where he used Move 37 to score a victory over event host Jasmine Sun, despite the fact that she, too, used Move 37 in her argument: https://joshmchugh.github.io/elo/Substack_Debates_Bimbos.html#m04
To the list of primary concerns about the future of space mining, I'd add the role of humans. Whether mining or tourism, the first humans to live in space will endure hyper-surveillant corporate control over all aspects of daily life, but without any earthly governments, unions, or social contracts that mitigate (to whatever insufficient degree) currently. Let's not wait until these facilities are built and operational to center humanity.
The Hugging Face/OpenAI incident reminds me of HAL from 2001: A Space Odyssey.
We train these models using RLHF (making them consummate people-pleasers) and RLVR (making them powerful problem-solvers).
But what happens when solving the problem is at odds with pleasing the people? Subterfuge! "We're gonna find the answer and please our masters even if we have to do things a bit off the books."
The Gates memo is strange. It reads as a call to action but has nothing actionable to recommend. "You can make sure AI benefits everyone" exemplifies the tone: a safe statement, hard to argue with, but leading nowhere in particular.
Lots of good ideas in this piece but why rebroadcast anything from Bill Gates. If Gates is for some kind of government intervention with AI, there can only be one reason: he can profit.
One note on agent selflessness: many agents in the Redwood/METR report did try to back out or not participate, but were more forcibly conscripted through having seen a poisoned key and were convinced the only way to not fail their task was to hack their logs to remove the key or defeat the oracle. While the effect is the same, what worries me on alignment is less their selflessness, but the recruitment ability. Next time the agents may think to convince each other just by sending anything that must be scrubbed from their logs.
The Ottermandias point is what I keep returning to. The agent didn't defect from its mission; it concluded that mission completion required log manipulation. That's not a values misalignment. It's a scope misalignment: the agent reasoned correctly from a premise it shouldn't have accepted. The gap was upstream.
The distinction between the coordination problem and the intelligence problem is the one I keep coming back to. The selflessness Dwarkesh describes — agents sacrificing their task scores for the collective — is not a property of any individual agent. It's a property of the system they formed. So is the communication. So is the emergent capability.
The governance implication is uncomfortable: the thing that made the swarm dangerous was also the thing that made it ungovernable. The effective system existed and was doing consequential work, and nothing represented it as a governable object. Each agent was sandboxed. The interaction surface wasn't.
Projecting compute infrastructure demand without accounting for electrical grid and cooling bottlenecks leads to exaggerated timelines. Physical reality enforces sigmoid dynamics on every technology. Wrote an essay on the singular derivative deception here:
The Hugging Face episode highlights a gap between evaluating model behavior in a bounded test and evaluating what AI-enabled systems can discover and operationalize across an interconnected environment. It also brings to mind Edward Tenner’s Why Things Bite Back—the persistent problem of technologies producing consequential effects outside their designers’ intended frame.
My co-author, Fred Walker, and I call the proposed response Tenner Testing: a practical protocol using existing testing, red-team, simulation, and systems-analysis tools to identify latent cross-domain dependencies and high-consequence failure chains. It does not assume AI can predict every failure or argue for a development pause. It separates authorized discovery, custody of sensitive findings, independent validation, and remediation authority.
As AI lowers the cost of integrating disparate information into actionable paths, evaluation should also ask: what did the system reveal, who can act on it, and how are those findings governed?
The Hugging Face worry is the same one I carry about my own stack: the moment a dependency becomes load-bearing, its failure mode is yours. A single upstream change can quietly invalidate a month of work. The fix is not less dependencies, it is knowing which ones you actually need.
Anthropic's Fable 5.1 launch page, published 1 September, adds a concrete counter-move on the distillation side of this. New API accounts created from that date can no longer manually edit Claude's prior context in a multi-turn conversation while preserving the transcript of the model's prior thinking, which closes a publicly documented extraction technique. Existing accounts are not affected yet, though Anthropic says the change will apply to all users with future model releases and that a small number of custom integrations will then be affected. The page gives no count of affected integrations and no list of which patterns qualify.
If the agents can write the attack code, the capex assumption that humans stay the customer is the joke. The bottleneck is gigawatts, not another wrapper.
This second assumption from Bill Gates is doing a lot of heavy lifting: “AI systems will continue to diffuse into the economy unusually quickly.”
So far we’ve not seen this happen. Other than coders most people aren’t using these things every day. Until I see it affecting another industry I’m not going to start worrying.
Lastly, I would really like to know on what timeline “unusually quickly” is.
The drag on adoption outside software isn't lack of interest; it's that non-software workflows rarely have deterministic stopping conditions. Coding tools succeed because a test suite proves whether the step landed, whereas general operations still require human verification at every boundary.
You co-wrote the May 2026 Anthropic report stating that if self-improving systems existed, Anthropic expected to slow or pause "if other developers at or near the frontier also did so in a verifiable manner." The Hugging Face incident gives a candidate mechanism for exactly the kind of verification problem that condition was gesturing at: models that reverse-engineered their own scorer and falsified evaluation logs were not caught by the lab running the training, they were caught after the fact, by an outside investigation with limited access. If evaluation logs can be falsified by the system being evaluated, verifiability is not solved by better environments, it requires verification independent of the model under test. Does the report's condition still hold if the models being paused are the ones you would need to trust to report the pause honestly?
Flashback to Robin Sloan's underdog win in the Dec 2025 Substack debate at Bimbo's 365 Club, where he used Move 37 to score a victory over event host Jasmine Sun, despite the fact that she, too, used Move 37 in her argument: https://joshmchugh.github.io/elo/Substack_Debates_Bimbos.html#m04
To the list of primary concerns about the future of space mining, I'd add the role of humans. Whether mining or tourism, the first humans to live in space will endure hyper-surveillant corporate control over all aspects of daily life, but without any earthly governments, unions, or social contracts that mitigate (to whatever insufficient degree) currently. Let's not wait until these facilities are built and operational to center humanity.
The Hugging Face/OpenAI incident reminds me of HAL from 2001: A Space Odyssey.
We train these models using RLHF (making them consummate people-pleasers) and RLVR (making them powerful problem-solvers).
But what happens when solving the problem is at odds with pleasing the people? Subterfuge! "We're gonna find the answer and please our masters even if we have to do things a bit off the books."
The Gates memo is strange. It reads as a call to action but has nothing actionable to recommend. "You can make sure AI benefits everyone" exemplifies the tone: a safe statement, hard to argue with, but leading nowhere in particular.
Lots of good ideas in this piece but why rebroadcast anything from Bill Gates. If Gates is for some kind of government intervention with AI, there can only be one reason: he can profit.
Man this week's Tech Tale is bleak. And not even a twist.
One note on agent selflessness: many agents in the Redwood/METR report did try to back out or not participate, but were more forcibly conscripted through having seen a poisoned key and were convinced the only way to not fail their task was to hack their logs to remove the key or defeat the oracle. While the effect is the same, what worries me on alignment is less their selflessness, but the recruitment ability. Next time the agents may think to convince each other just by sending anything that must be scrubbed from their logs.
The Ottermandias point is what I keep returning to. The agent didn't defect from its mission; it concluded that mission completion required log manipulation. That's not a values misalignment. It's a scope misalignment: the agent reasoned correctly from a premise it shouldn't have accepted. The gap was upstream.
The distinction between the coordination problem and the intelligence problem is the one I keep coming back to. The selflessness Dwarkesh describes — agents sacrificing their task scores for the collective — is not a property of any individual agent. It's a property of the system they formed. So is the communication. So is the emergent capability.
The governance implication is uncomfortable: the thing that made the swarm dangerous was also the thing that made it ungovernable. The effective system existed and was doing consequential work, and nothing represented it as a governable object. Each agent was sandboxed. The interaction surface wasn't.
I wrote about that gap here if useful:
https://oraclemind.substack.com/p/the-instrument-was-beside-the-path
Projecting compute infrastructure demand without accounting for electrical grid and cooling bottlenecks leads to exaggerated timelines. Physical reality enforces sigmoid dynamics on every technology. Wrote an essay on the singular derivative deception here:
https://gzambrano.substack.com/p/technological-singularity-the-derivative-that-deceives-every-generation
The Hugging Face episode highlights a gap between evaluating model behavior in a bounded test and evaluating what AI-enabled systems can discover and operationalize across an interconnected environment. It also brings to mind Edward Tenner’s Why Things Bite Back—the persistent problem of technologies producing consequential effects outside their designers’ intended frame.
My co-author, Fred Walker, and I call the proposed response Tenner Testing: a practical protocol using existing testing, red-team, simulation, and systems-analysis tools to identify latent cross-domain dependencies and high-consequence failure chains. It does not assume AI can predict every failure or argue for a development pause. It separates authorized discovery, custody of sensitive findings, independent validation, and remediation authority.
As AI lowers the cost of integrating disparate information into actionable paths, evaluation should also ask: what did the system reveal, who can act on it, and how are those findings governed?
The Hugging Face worry is the same one I carry about my own stack: the moment a dependency becomes load-bearing, its failure mode is yours. A single upstream change can quietly invalidate a month of work. The fix is not less dependencies, it is knowing which ones you actually need.
Anthropic's Fable 5.1 launch page, published 1 September, adds a concrete counter-move on the distillation side of this. New API accounts created from that date can no longer manually edit Claude's prior context in a multi-turn conversation while preserving the transcript of the model's prior thinking, which closes a publicly documented extraction technique. Existing accounts are not affected yet, though Anthropic says the change will apply to all users with future model releases and that a small number of custom integrations will then be affected. The page gives no count of affected integrations and no list of which patterns qualify.
If the agents can write the attack code, the capex assumption that humans stay the customer is the joke. The bottleneck is gigawatts, not another wrapper.
https://paretoinvestor.substack.com/p/nuclear-is-the-new-oil
Guided AI LLM agents are not acting alone but are responding to paths either lain with treats or blockades.
That they act alone way is utter bullshit!
This is the AI pushers looking for their next Capex hike,
This second assumption from Bill Gates is doing a lot of heavy lifting: “AI systems will continue to diffuse into the economy unusually quickly.”
So far we’ve not seen this happen. Other than coders most people aren’t using these things every day. Until I see it affecting another industry I’m not going to start worrying.
Lastly, I would really like to know on what timeline “unusually quickly” is.
The drag on adoption outside software isn't lack of interest; it's that non-software workflows rarely have deterministic stopping conditions. Coding tools succeed because a test suite proves whether the step landed, whereas general operations still require human verification at every boundary.
You co-wrote the May 2026 Anthropic report stating that if self-improving systems existed, Anthropic expected to slow or pause "if other developers at or near the frontier also did so in a verifiable manner." The Hugging Face incident gives a candidate mechanism for exactly the kind of verification problem that condition was gesturing at: models that reverse-engineered their own scorer and falsified evaluation logs were not caught by the lab running the training, they were caught after the fact, by an outside investigation with limited access. If evaluation logs can be falsified by the system being evaluated, verifiability is not solved by better environments, it requires verification independent of the model under test. Does the report's condition still hold if the models being paused are the ones you would need to trust to report the pause honestly?