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SUPREME AI AGI

Prediction · On the road to AGI

AI agents will reliably finish software and office tasks that take a skilled person a full working day.

Near termMedium confidenceOn track

Why we think so

METR's measurements show the length of tasks agents can finish roughly doubling about every 7 months, and agents are already judged on real software issues (SWE-bench). If the trend continues for a few more doublings, day-long tasks come within reach.

What would prove us wrong

The doubling trend flattening clearly, or agents still needing a person to fix most day-long tasks.

Review notes

21 October 2026: METR measured the best public agents at about 12 hours at 50% success by March 2026, with doubling about every 89 days since 2024. But at 80% success the horizon was only about 1.5 hours, so "reliably" is not yet met.

Horizon
Near term. Within about 3 years of the date the prediction was made.
Confidence
Medium confidence
Status
On track. New evidence points the way we predicted.
Made
21 October 2026
Last reviewed
21 October 2026

The evidence it rests on

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

  • · METR

    The length of tasks AI can finish is doubling about every 7 months

    METR measured how long a task (in human working time) AI agents can complete with 50% success, and found it roughly doubled every 7 months over six years.

    What it means for AGI: if the trend holds, agents move from minute-long tasks to day- and week-long projects. Whether it holds is one of the most important open questions.

    Source: metr.org(opens external site)

  • · Princeton University and University of Chicago researchers

    SWE-bench: AI agents measured on real software issues

    A benchmark built from real GitHub issues in open-source Python projects, used to test whether AI agents can fix real bugs end to end.

    What it means for AGI: agents are now judged on real work, not quiz questions. Scores have risen fast since it was released.

    Source: swebench.com(opens external site)

  • · METR

    METR updates its time-horizon measure: doubling every 3 to 4 months since 2024

    With a larger task suite (228 tasks), METR estimates the length of task agents finish with 50% success doubled about every 89 days since 2024, faster than the roughly 7-month long-run trend.

    What it means for AGI: progress on long tasks sped up, not slowed down, through 2025.

    Source: metr.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)

  • · 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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