ClaimsGary Marcus

“Despite all the hype, agents didn’t turn out to be reliable.”

Gary Marcus, author January 2026 Six (or seven) predictions for AI 2026 from a Generative AI realist (Marcus on AI)

Factual AI capabilities

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

Mostly true True in substance, wrong in a detail.

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

The second judge said resolved true; the verdict shown is the first judge's.

What would count as true or false

Judge as of January 2026, covering AI agents during 2025. True: 2025 evaluations and reports (agent benchmarks such as task-completion rates, industry surveys, documented deployment failures) show that general-purpose AI agents commonly failed at multi-step tasks and were not dependable enough to run unsupervised for broad use. Mostly true: agents were broadly unreliable, but some narrow categories (e.g., coding agents) achieved fairly dependable performance. Mixed: the evidence is evenly divided, with substantial reliable deployments alongside widespread failures. Mostly false: agents were largely reliable in mainstream use, and the remaining unreliability was limited. False: the evidence shows agents in 2025 were broadly reliable.

Set before the research began.

Evidence (4)

  1. E1 Year in review: The highs (and lows) of AI in 2025 The Indian Express · 31 December 2025

    “AI agents, widely seen as a step toward making AI companies more commercially viable, struggled to live up to expectations in 2025. While they might have shown some promise in tightly controlled enterprise settings, most AI agent-focused tools rolled out this year proved unreliable and inefficient for everyday tasks.”
  2. E2 Why AI agents failed to take over in 2025 - it's 'a story as old as time,' says Deloitte - ZDNET ZDNET · 10 December 2025

    “Deloitte’s 2025 Emerging Technology Trends study, which surveys 500 US Tech Leaders, found that 30% of the surveyed organizations are exploring agentic options, with 38% piloting solutions and only 14% having solutions ready to deploy. The number of organizations actively using the systems in production is even lower, at 11%.”
  3. E3 Gartner Survey Finds Just 15% of IT Application Leaders Are ... Gartner, Inc. · 30 September 2025

    “Seventy-five percent of survey respondents said they were piloting, were deploying or had already deployed some form of AI agents into their organization; however, concerns around governance, maturity and agent sprawl continue to hamper the deployment of truly agentic AI.”
  4. E4 Developers remain willing but reluctant to use AI: The 2025 Developer Survey results are here Stack Overflow · 29 December 2025

    “We asked if the AI agent revolution was here, and the answer is a definitive "not yet." While 52% of developers say agents have affected how they complete their work, the primary benefit is personal productivity: 69% agree they've seen an increase.”

Reasoning

A year-end review [E1] states that most AI agent tools released in 2025 proved unreliable and inefficient for everyday tasks, while showing some promise in tightly controlled enterprise settings. Survey evidence fits limited maturity: Deloitte found only 11% of organisations using agents in production [E2]; Gartner reports that concerns about maturity and governance were holding back truly agentic deployment [E3]; and Stack Overflow's developer survey answered "not yet" to whether the agent revolution had arrived, while reporting that 52% of developers said agents had affected their work and 69% agreed they had seen a productivity increase [E4]. No task-completion benchmarks were provided. The evidence points to broad unreliability, with narrow pockets of usefulness, which fits mostly true.

Cite this claim

Gary Marcus, “AI agents did not turn out to be reliable in 2025.” (said January 2026). Verdict: mostly true. Reliability API, claim c_ffe59f328691, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_ffe59f328691

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

Reply to this claim

For Gary Marcus, or someone speaking for them: a correction or counter-evidence. Replies are read before anything is shown; an approved reply shows your name, role, date, statement and link. Your email address is never shown and is deleted 30 days after the decision (privacy).

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. 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.

  4. 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.

  5. Mostly true Factual 31 March 2024
    “They are inherent to how the system makes predictions”

    Hallucinations are inherent to how LLMs make predictions.

All of them

Verdicts are made automatically; how verdicts are made.