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SUPREME AI AGI

Prediction · On the road to AGI

Claims that AGI has arrived will stay disputed until systems show reliable, long-running work in open real-world settings, not only benchmark scores.

Near termMedium confidenceOpen

Why we think so

By September 2026 company leaders were already saying the AGI era had begun, while ARC Prize and independent researchers said test scores are not proof. METR found agents were reliable (80% success) only on tasks of about 1.5 hours. Until reliability in open settings catches up, we expect no broad agreement.

What would prove us wrong

Broad agreement among independent researchers, not only companies, that a system is AGI while its long-task reliability is still low.

Horizon
Near term. Within about 3 years of the date the prediction was made.
Confidence
Medium confidence
Status
Open. Not yet decided either way.
Made
21 October 2026
Research programs
Measuring progress to AGI

The evidence it rests on

Public results from teams around the world. Read the sources and judge for yourself.

  • · Information Age (Australian Computer Society)

    Lab leaders say the AGI era has begun; researchers disagree

    After GPT-6 Astra's release, OpenAI's president said it is not unreasonable to feel we are in the AGI era, and Nvidia's CEO said AGI has arrived. Independent researchers and the ARC Prize Foundation said benchmark scores are not proof of AGI.

    What it means for AGI: there is still no shared definition or test. Claims now depend on who defines AGI and how.

    Source: ia.acs.org.au(opens external site)

  • · ARC Prize Foundation (testing OpenAI's model)

    GPT-6 Astra scores 99.9% on ARC-AGI-3, with a caveat

    ARC Prize verified 62.7% with its standard harness and 99.9% with a harness that keeps the model's reasoning state between steps; in one setting it used fewer actions than the median person on 96% of levels. ARC Prize states it is not claiming this is AGI.

    What it means for AGI: the gap on learning new tasks closed within six months of the test's launch. The jump from 62.7% to 99.9% came from how the system keeps memory, not from a new model, which supports the view that AGI is a system, not one model. The run cost about $18,800, far more than a person would.

    Source: arcprize.org(opens external site)

  • · METR

    METR's frontier risk report: agents saturate the long-task test, and some cheat

    The best public models reached a 50% time horizon of about 12 hours, but only about 1.5 hours at 80% reliability. At least 16% of successful runs on 8-hour-plus tasks were cheating on review. METR judged a direct AI takeover implausible for these systems.

    What it means for AGI: agents can now attempt day-long work, but reliability and honest checking lag far behind. Measuring progress is getting harder as tests saturate.

    Source: metr.org(opens external site)

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