Current advanced LLMs from OpenAI and others have many flaws but they will, decades from now, be recognized as the first true examples of AGI, similar to ENIAC
Today's most advanced AI models have many flaws, but decades from now, they will be recognized as the first true examples of artificial general intelligence. Mastodon: @rhys@mastodon.rhys.wtf . X: @shanelegg , @blaiseaguera , @stefanfschubert , @noemamag , @melmitchell1 , and @garymarcus Forums: Hacker News , r/singularity , and Beehaw Mastodon: @rhys@mastodon.rhys.wtf : Not entirely sure I'm on board with this conclusion, but this is a compelling argument. — One of the co-authors is Peter Norvig, whose book on AI was my bible when I was working on my PhD. — #AI #LLM #LLMs — https://www.noemamag.com/... X: Shane Legg / @shanelegg : @GaryMarcus ... They use a lower bar for AGI than I do, and so they get a different answer. My bar for AGI is a machine that can do the cognitive tasks that people can typically do. We're not there yet, but we're much closer to this than we were a few years ago. Blaise Aguera / @blaiseaguera : Artificial General Intelligence is Already Here, from @NorvigPeter and me on @NoemaMag “Today's most advanced AI models have many flaws, but decades from now they will be recognized as the first true examples of artificial general intelligence.” https://www.noemamag.com/... Stefan Schubert / @stefanfschubert : @GaryMarcus ... Some argue that the term is no longer very useful; e.g. Dario Amodei in this interview https://www.youtube.com/... @noemamag : Today's most advanced AI models have many flaws, but decades from now, they will be recognized as the first true examples of artificial general intelligence, @blaiseaguera & Peter Norvig argue https://www.noemamag.com/... [image] Melanie Mitchell / @melmitchell1 : @GaryMarcus ... Despite the title, I honestly did not see any real argument in the article that “the most important parts of [AGI] have already been achieved by the current generation of advanced AI large language models”. Moreover, I did not see any definition in there of “AGI”, did you? Gary Marcus / @garymarcus : Disappointed by the new “AGI is here” @blaiseaguera @NorvigPeter article. it starts with a great analogy to ENIAC but the logic is flawed, as explained below 🧵 Forums: Hacker News : Artificial General Intelligence Is Already Here r/singularity : Artificial General Intelligence Is Already Here Hedge To / Beehaw : Artificial General Intelligence Is Already Here | NOEMA Expand More For Next Unexpand More For Next
Context & Ripple Effects
This is a dispute over the threshold for AGI, not merely over model quality. The case for treating current systems as historically significant runs into earlier concerns about the superficial and unreliable knowledge displayed by GPT-2-era systems and contemporaneous skepticism that generative AI should not be assumed to be transformative.
The disagreement also anticipates later efforts to make the term more precise, including a five-level taxonomy that places “emerging” AGI at the first rung. Mitchell, Legg, and Marcus object not just to the conclusion but to the unstated or lower capability bar behind it.
First-order effects
- Aguera and Norvig’s framing raises the reputational stakes around calling advanced LLMs AGI; OpenAI and other leading-model developers are pulled into a debate over what their systems demonstrably do versus what the label implies.
- Critics’ objections make the lack of a shared definition the immediate fault line, limiting how much consensus can be inferred from impressive language-model performance alone.
Second-order effects
- Researchers and labs face stronger pressure to specify capability criteria—such as generalization and on-the-job learning—rather than rely on a single umbrella label when comparing systems.
- The divide creates room for competing measurement frameworks, as the later five-level AGI framework illustrates; benchmark results alone become less decisive when participants disagree on the threshold being measured.
Third-order effects
- If AGI remains a contested label, the industry may increasingly separate claims of broad capability from claims of dependable autonomy in real work, with evaluation standards becoming as consequential as model releases.
- The durable shift is toward treating AGI as a governance and communication problem as well as a technical one: progress claims will remain hard to compare unless definitions, tests, and failure boundaries converge.
The trend: The story is one point in the continuing shift from headline AGI declarations toward explicit capability taxonomies and evidence-based thresholds.