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SUPREME AI AGI

Prediction · On the road to AGI

The main way to measure progress toward AGI will shift from fixed tests to long, interactive and real-world work, because fixed tests now saturate within months.

Near termHigh confidenceOpen

Why we think so

ARC-AGI-3 went from under 1% to 99.9% in about six months; METR reports its long-task suite is saturated and that many other benchmarks are near saturation. Fixed question sets can no longer separate the best systems.

What would prove us wrong

New fixed benchmarks lasting for years without being saturated.

Horizon
Near term. Within about 3 years of the date the prediction was made.
Confidence
High 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.

  • · 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)

  • · ARC Prize Foundation

    ARC-AGI-3 launches: people score 100%, frontier AI under 1%

    A new interactive benchmark of game-like environments with no instructions, rules or stated goals. At launch people scored 100% and the best frontier AI 0.51%.

    What it means for AGI: it tests exploring and learning in a new situation, the skill most clearly missing from AI at the start of 2026.

    Source: arcprize.org(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)

  • · Stanford Institute for Human-Centered AI

    AI Index 2026: capabilities jump, but unevenly

    Agent success on Terminal-Bench rose from 20% to 77.3% in a year, and agents solved 93% of cybersecurity tasks, yet robots succeeded at only 12% of real household tasks. AI data-centre power capacity reached 29.6 GW, and four in five US students use AI for schoolwork.

    What it means for AGI: digital abilities are racing ahead while physical abilities and governance lag.

    Source: hai.stanford.edu(opens external site)

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