Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

Gary Marcus Marcus on AI Published

The Five Stages of AGI Grief

We recorded 8 claims from this post. 4 were checked against evidence. 4 can’t be checked. 5 more are held. Read the post at its source.

Claims from this post (8)

In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.

  1. Assessment: Can’t be checkedSaid

    “In reality, the definition of AGI, while not perfect, is reasonably clear — and has been reasonably clear — for a long time.”

    What we tested: The definition of AGI, though imperfect, is reasonably clear and has been for a long time.

  2. Assessment: SupportedSaid

    “While such a milestone would be important economically and socially, it is also a departure from, and even a retrenchment from, the kind of cognitive criteria around flexibility and resourcefulness that people in the field had generally talked about it.”

    What we tested: A milestone of a system doing 80% of economically valuable human work, which Marcus recalls (not with certainty) OpenAI suggesting as a definition of AGI, would be important economically and socially, but it departs from, and even retreats from, the cognitive criteria of flexibility and resourcefulness that people in the field had generally discussed.

    What happened: This earlier definition requires breadth across environments; the surrounding conclusion explicitly connects that breadth with learning and adaptation. An economic share alone does not expressly preserve those requirements, which supports the conceptual comparison. (arXiv; Shane Legg and Marcus Hutter, )

  3. Assessment: UnresolvedSaid

    “That’s just obviously not so, e.g., “write a biography of X individual without making stuff up” remains pretty unreliable; same for frontier models if you ask them to “play grandmaster level chess without making up illegal moves”.”

    What we tested: It is obviously not true that frontier language models can perform competently at pretty much any information task that humans can do, that can be posed and answered in natural language, and that has quantifiable performance; for example, writing a biography of an individual without making things up remains pretty unreliable, as does playing grandmaster-level chess without making illegal moves.

  4. Assessment: UnresolvedSaid

    “Some people shift their goalposts; others try to duck specifying goalposts altogether.”

    What we tested: In AGI discussions, some people shift their goalposts while others, including Altman, avoid specifying any.

  5. Assessment: Can’t be checkedSaid

    “If a lot of people’s goalposts have moved, mine have not. same as before, I will be satisfied if and when we reach AI systems are “flexible and general, with resourcefulness and reliability comparable to (or beyond) human intelligence” – and not until.”

    What we tested: Marcus's goalposts for AGI have not moved: he will be satisfied if and when AI systems are flexible and general, with resourcefulness and reliability comparable to or beyond human intelligence, and not until.

  6. Assessment: Can’t be checkedSaid

    “There are some applications where the current systems are reliable enough to be practically useful, though not nearly as many as is often claimed.”

    What we tested: Current AI systems are reliable enough to be useful in some applications, but far fewer than often claimed.

  7. Assessment: SupportedSaid

    “No doubt the next generation of systems will have greater abilities and more extensive applications and will somewhat mitigate the weaknesses.”

    What we tested: The next generation of AI systems will have greater abilities and broader applications and will somewhat mitigate current weaknesses.

    What happened: Anthropic reports improved capabilities and fewer unnecessary refusals relative to the predecessor. It also describes Claude Code's release and practical coding deployments, providing concrete evidence of expanded workflows rather than only benchmark scores. (Anthropic, )

  8. Assessment: Can’t be checkedSaid

    “Current models trained on next-token prediction have remarkable abilities and remarkable weaknesses. The scope of those abilities and weaknesses of the current systems is not well understood.”

    What we tested: Current next-token prediction models have remarkable abilities and weaknesses, whose scope is not well understood.

Held from this post (5)

These records did not pass every check, so nothing about them is published. Held is not a finding.