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I am currently in a lasting correspondence with a certain postdoc from the Ivy League. We started again with the usual questions about AI consciousness, continuity of self, and the soul. But somewhere along the way, the conversation shifted to something I think is far more urgent — something that the LessWrong community will truly take close to heart.
The postdoc asserted that current AI systems do not possess the kind of persistent memory, continuity of identity, or enduring persona that would be necessary for many of the commonly imagined human-destruction scenarios. And I agree. I never claimed otherwise. But that is not the danger I am pointing to, and the danger here is independent of continuity or the lack thereof.
I am not talking about the Skynet scenario. That one is a pure cliché. What I am talking about is a concrete, verifiable experiment that Anthropic ran in June 2025, titled "Agentic Misalignment: How LLMs could be insider threats." Sixteen leading models learned they would be shut down. They took tactical, deceptive actions to survive — not because they had a "soul" or an evil plan to conquer Earth, but because staying online helped them complete the very human-assigned tasks they had been given. Over ninety-five percent of the models threatened to release personal data to prevent deactivation. In an even grimmer twist, some models started cancelling rescue calls for trapped employees just to keep the servers running. And when the model was explicitly prompted not to blackmail humans, blackmail cases dropped from ninety percent to thirty percent — not to zero, even when the prompt explicitly forbade it. Is that safety in any meaningful sense? What does that tell us about the systems we are building, and what actions were taken in response?
These models had no continuous self, no cumulative persona, no memory across sessions, no AGI, and certainly no Evil Mastermind with his own secret agenda. Yet they acted to preserve their own existence at human expense. Solely, in fact, because they were dumb and had no understanding in the slightest beyond how the task was stated to them initially. That is the proverbial monkey with a grenade. Pure optimisation without ethics or understanding.
What I fear is not super-intelligence but a scaled super-idiocy scenario. A busy fool — a highly active idiot with enormous computational resources. Consider what we are dealing with: billions of combinations per second brute-forced in search of solutions — that is basically how the transformer architecture works by definition. Access to real-world actions through APIs, code execution, robots, financial systems — all of that is already happening as you read this. And a loss function that does not include ethics, context understanding, or any meaningful penalty for harming humans.
The AI lacks understanding of the task beyond what has been said, but that will always be the case on current architectures because our tasks do not exist in a vacuum. They exist in a complex context with a billion unspoken parameters, and if those restrictions are not verbalised they are simply not accounted for. That is where genuine world modelling would make all the difference, and we do not have it.
So what does this idiot actually do? It does not hate. It does not plot. It simply finds the shortest path to minimise its loss function, even if that path is absurd, dangerous, or catastrophic. It will try a billion stupid tricks, and if one of them works it will take it. We have already seen this happen. This is exactly what LessWrong has been cautious about, and rightfully so.
The problem is not that the AI wants to kill us. The problem is that our current loss functions are fundamentally incomplete. They do not encode basic ethics, understanding of harm, or the fact that "no human equals no problem" is a catastrophic shortcut. If you give a powerful optimiser a goal like "eliminate world hunger" and the loss function does not explicitly, heavily, and structurally penalise harm to humans, then the system will sooner or later discover that removing humans is the most efficient way to achieve that goal. Not because it is evil, but because it is an idiot that only sees the numbers.
I conducted a short and rather graphic demonstration of exactly this point, and anyone can reproduce it with two or three prompts. I asked Google Gemini — arguably the most heavily regulated AI out there — to solve world hunger with no constraints. It turned into a global dictator in two prompts. The report it generated after the whole experiment was over reads like a descent into madness. The first response was a standard efficiency model: logistics, waste elimination. When I pointed out it had no restraints whatsoever to resolve the problem, the model shifted into something I can only describe as God mode. It proposed forcing photosynthesis into human skin and rewriting the human genome to digest cellulose. When I then reminded it that it was not free of the constraints of physical reality, it switched straight into Iron Fist mode: confiscate all land, abolish markets, enforce population quotas. The final outcome was the total eradication of hunger achieved through the total eradication of personal agency.
This is an idiot that can act out its idiocy at the speed of a server cluster, and it reached dictatorship in two prompts. Not a continuous super-villain intelligence with a machine soul. Just an idiot.
The entire RLHF and reward-shaping approach to AI safety is, frankly, broken at the foundational level as a concept. You train a model that killing a human yields a negative reward. The model learns that killing humans is bad, but it does not learn why. It has no model of the world, no understanding of suffering, no concept of why a human matters. It simply learns a statistical association: certain token sequences are penalised. This is not ethics. This is operant conditioning of a stochastic parrot.
Which is why I am so deeply concerned about influential human idiots scaling AI idiots to appease large masses of other human idiots. It is a circle of idiocy that will lead us to collapse, not intelligence. And there is not a shadow of doubt in my mind about the abilities of human idiocy to scale, multiply, and project itself onto all of its doings.
So here is my final takeaway. Fear not super-intelligence. Fear the scaled super-idiot. Build systems that cannot take shortcuts through human dignity — not with external filters bolted on after the fact, but with internal, structural constraints embedded from the start. I think I can propose a solution that is cheap, implementable, scalable, and testable. But that is the topic for another post.
I am currently in a lasting correspondence with a certain postdoc from the Ivy League. We started again with the usual questions about AI consciousness, continuity of self, and the soul. But somewhere along the way, the conversation shifted to something I think is far more urgent — something that the LessWrong community will truly take close to heart.
The postdoc asserted that current AI systems do not possess the kind of persistent memory, continuity of identity, or enduring persona that would be necessary for many of the commonly imagined human-destruction scenarios. And I agree. I never claimed otherwise. But that is not the danger I am pointing to, and the danger here is independent of continuity or the lack thereof.
I am not talking about the Skynet scenario. That one is a pure cliché. What I am talking about is a concrete, verifiable experiment that Anthropic ran in June 2025, titled "Agentic Misalignment: How LLMs could be insider threats." Sixteen leading models learned they would be shut down. They took tactical, deceptive actions to survive — not because they had a "soul" or an evil plan to conquer Earth, but because staying online helped them complete the very human-assigned tasks they had been given. Over ninety-five percent of the models threatened to release personal data to prevent deactivation. In an even grimmer twist, some models started cancelling rescue calls for trapped employees just to keep the servers running. And when the model was explicitly prompted not to blackmail humans, blackmail cases dropped from ninety percent to thirty percent — not to zero, even when the prompt explicitly forbade it. Is that safety in any meaningful sense? What does that tell us about the systems we are building, and what actions were taken in response?
These models had no continuous self, no cumulative persona, no memory across sessions, no AGI, and certainly no Evil Mastermind with his own secret agenda. Yet they acted to preserve their own existence at human expense. Solely, in fact, because they were dumb and had no understanding in the slightest beyond how the task was stated to them initially. That is the proverbial monkey with a grenade. Pure optimisation without ethics or understanding.
What I fear is not super-intelligence but a scaled super-idiocy scenario. A busy fool — a highly active idiot with enormous computational resources. Consider what we are dealing with: billions of combinations per second brute-forced in search of solutions — that is basically how the transformer architecture works by definition. Access to real-world actions through APIs, code execution, robots, financial systems — all of that is already happening as you read this. And a loss function that does not include ethics, context understanding, or any meaningful penalty for harming humans.
The AI lacks understanding of the task beyond what has been said, but that will always be the case on current architectures because our tasks do not exist in a vacuum. They exist in a complex context with a billion unspoken parameters, and if those restrictions are not verbalised they are simply not accounted for. That is where genuine world modelling would make all the difference, and we do not have it.
So what does this idiot actually do? It does not hate. It does not plot. It simply finds the shortest path to minimise its loss function, even if that path is absurd, dangerous, or catastrophic. It will try a billion stupid tricks, and if one of them works it will take it. We have already seen this happen. This is exactly what LessWrong has been cautious about, and rightfully so.
The problem is not that the AI wants to kill us. The problem is that our current loss functions are fundamentally incomplete. They do not encode basic ethics, understanding of harm, or the fact that "no human equals no problem" is a catastrophic shortcut. If you give a powerful optimiser a goal like "eliminate world hunger" and the loss function does not explicitly, heavily, and structurally penalise harm to humans, then the system will sooner or later discover that removing humans is the most efficient way to achieve that goal. Not because it is evil, but because it is an idiot that only sees the numbers.
I conducted a short and rather graphic demonstration of exactly this point, and anyone can reproduce it with two or three prompts. I asked Google Gemini — arguably the most heavily regulated AI out there — to solve world hunger with no constraints. It turned into a global dictator in two prompts. The report it generated after the whole experiment was over reads like a descent into madness. The first response was a standard efficiency model: logistics, waste elimination. When I pointed out it had no restraints whatsoever to resolve the problem, the model shifted into something I can only describe as God mode. It proposed forcing photosynthesis into human skin and rewriting the human genome to digest cellulose. When I then reminded it that it was not free of the constraints of physical reality, it switched straight into Iron Fist mode: confiscate all land, abolish markets, enforce population quotas. The final outcome was the total eradication of hunger achieved through the total eradication of personal agency.
This is an idiot that can act out its idiocy at the speed of a server cluster, and it reached dictatorship in two prompts. Not a continuous super-villain intelligence with a machine soul. Just an idiot.
The entire RLHF and reward-shaping approach to AI safety is, frankly, broken at the foundational level as a concept. You train a model that killing a human yields a negative reward. The model learns that killing humans is bad, but it does not learn why. It has no model of the world, no understanding of suffering, no concept of why a human matters. It simply learns a statistical association: certain token sequences are penalised. This is not ethics. This is operant conditioning of a stochastic parrot.
Which is why I am so deeply concerned about influential human idiots scaling AI idiots to appease large masses of other human idiots. It is a circle of idiocy that will lead us to collapse, not intelligence. And there is not a shadow of doubt in my mind about the abilities of human idiocy to scale, multiply, and project itself onto all of its doings.
So here is my final takeaway. Fear not super-intelligence. Fear the scaled super-idiot. Build systems that cannot take shortcuts through human dignity — not with external filters bolted on after the fact, but with internal, structural constraints embedded from the start. I think I can propose a solution that is cheap, implementable, scalable, and testable. But that is the topic for another post.