ClaimsGary Marcus

“The problem of hallucinations is not solved; reliability is not solved; planning on complex tasks is (as the authors themselves acknowledge) not solved.”

Gary Marcus, Substack author 24 March 2023 The Sparks of AGI? Or the End of Science? (Marcus on AI) archived copy

Factual AI capabilities

GPT-4 has not solved hallucinations, reliability or planning on complex tasks, as the paper's authors acknowledge for planning.

Resolved true The record shows it happened or was so.

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

What would count as true or false

Judged as of 2023-03-24. The claim has three parts: GPT-4 had not solved (1) hallucinations, (2) reliability, and (3) planning on complex tasks. It also says the paper's authors acknowledged (3). True: evidence from that time shows GPT-4 still produced hallucinations and unreliable outputs and failed at complex planning, and the paper discussed (e.g. Bubeck et al.) acknowledges limits in planning. OpenAI's own GPT-4 report admitting hallucination and reliability limits counts toward (1) and (2). Mostly true: all three limits are confirmed but the attribution is slightly off, e.g. the authors acknowledged the planning limit only in qualified or different terms. Mixed: one of the three limits is shown to have been substantially solved at that time, or the attribution is plainly wrong while the substantive limits hold. Mostly false: evidence from that time shows two of the three were substantially solved. False: evidence shows GPT-4 had effectively eliminated hallucinations and unreliability and could plan complex tasks reliably.

Set before the research began.

Evidence (4)

  1. E1 GPT-4 OpenAI · 14 March 2023

    “Most importantly, it still is not fully reliable (it “hallucinates” facts and makes reasoning errors).”
  2. E2 GPT-4 Technical Report arXiv (OpenAI authors)

    “Most importantly, it still is not fully reliable (it “hallucinates” facts and makes reasoning errors).”
  3. E3 Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv (ar5iv rendering)

    “Throughout the paper we emphasize limitations whenever we found one, but we also dedicate Section to an in-depth analysis of the lack of planning, likely a direct consequence of the autoregressive nature of GPT-4’s architecture.”
  4. E4 GPT-4 Technical Report arXiv (OpenAI authors)

    “GPT-4 significantly reduces hallucinations relative to previous GPT-3.5 models (which have themselves been improving with continued iteration).”

Reasoning

OpenAI's GPT-4 announcement and technical report (E1, E2) say GPT-4 "still is not fully reliable" and that it "hallucinates" facts and makes reasoning errors, which supports parts (1) and (2). The report also says GPT-4 significantly reduces hallucinations compared with GPT-3.5 (E4), but that is a reduction, not a solution. The Sparks of AGI paper by Bubeck et al. (E3) gives "an in-depth analysis of the lack of planning" in GPT-4, which supports part (3) and the claim that the authors acknowledged it.

Cite this claim

Gary Marcus, “GPT-4 has not solved hallucinations, reliability or planning on complex tasks, as the paper's authors acknowledge for planning.” (said 24 March 2023). Verdict: resolved true. Reliability API, claim c_65baa675ff5a, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_65baa675ff5a

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

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More factual claims by Gary Marcus

  1. Mostly true Factual January 2026
    “Reviews by Joanna Stern and Marques Brownle of one early prototype were damning;”

    Reviews by Joanna Stern and Marques Brownlee of one early humanoid robot prototype were damning.

  2. Mostly true Factual January 2026
    “Many would agree we have reached a point of diminishing returns for scaling; faith in scaling as a route to AGI has dissipated.”

    Many would agree the AI field has reached a point of diminishing returns from scaling, and faith in scaling as a route to AGI has dissipated.

  3. Mostly true Factual January 2026
    “Despite all the hype, agents didn’t turn out to be reliable.”

    AI agents did not turn out to be reliable in 2025.

  4. Mixed Factual 2025
    “but the systems don’t have the knowledge graph to do that.”

    Current AI systems lack the knowledge graph needed to handle easy facts like those on the first page of Wikipedia.

  5. Resolved true Factual 2025
    “Hasn’t worked so far. That’s for sure.”

    Bolting extra components onto an LLM and scaling it up has so far not produced human-level AGI.

All of them

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