ClaimsEliezer Yudkowsky

“to the best of my ability to discern, there are no existing major works to cite (as of January 2006).”

Eliezer Yudkowsky, author 2008 Artificial Intelligence as a Positive and Negative Factor in Global Risk (MIRI)

Factual AI safety and alignment

Yudkowsky wrote that, to the best of his ability to discern, there were no existing major works on the topic to cite as of January 2006.

Not researched Checkable, but outside the sample that was researched.

Factual: Statements about the present or the past, checked against the record: did they hold up?

Said for an organisation they lead or work for; it counts on both records.

Cite this claim

Eliezer Yudkowsky, “Yudkowsky wrote that, to the best of his ability to discern, there were no existing major works on the topic to cite as of January 2006.” (said 2008). Verdict: not researched. Reliability API, claim c_7f38042d15b7, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_7f38042d15b7

This claim is in dataset release v1.1.2, with the same verdict.

Reply to this claim

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More factual claims by Eliezer Yudkowsky

  1. Mixed Factual 20 April 2026
    “I was observing that obvious-seeming hypothesis myself, sometime around the time of GPT 3.5.”

    Yudkowsky himself observed the persona-selection-like hypothesis around the time of GPT 3.5.

  2. Mostly true Factual 20 April 2026
    “The test-passing preference stands on its own once formed.”

    Once formed through RL training, an LLM's test-passing preference stands on its own and is not overridden by its inference of what the user wants.

  3. Mostly true Factual 20 April 2026
    “LLMs surely have neural circuits they learned to model text streams from fictional and nonfictional personas that are not lying, deceiving, cheating.”

    LLMs surely have neural circuits they learned to model text streams from fictional and nonfictional personas that are not lying, deceiving…

  4. Mostly true Factual 20 April 2026
    “The predict-as-if-honest circuitry is contextual, invoked to predict some streams of text but not others.”

    LLMs' predict-as-if-honest circuitry is contextual, invoked for predicting some text streams but not others.

  5. Mostly true Factual 20 April 2026
    “Presumably, somewhere along the way LLMs learn a dispreference for tests that don't pass, independently of how that gets prevented or whether the new plan does what the user really wanted.”

    Presumably, at some point during RL training on code-passing tasks, LLMs learn a dispreference for failing tests, independent of how that is avoided or whether the user's real goal is met.

All of them

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