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SUPREME AI AGI

Prediction · What changes

Most routine software maintenance, such as fixing bugs and updating code, will be done by agents and reviewed by people.

Near termMedium confidenceOn track

Why we think so

Agents are tested on real repository issues and scores keep rising. Software has automatic tests, so agents' work can be checked, which makes it the first kind of work to be handed over.

What would prove us wrong

Teams finding that reviewing agent work costs more time than doing it themselves.

Review notes

21 October 2026: The AI Index 2026 reports agent success on Terminal-Bench rising from 20% to 77.3% in a year and SWE-bench Verified scores near the human baseline.

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
Research programs
Long-horizon autonomy

The evidence it rests on

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

  • · 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)

  • · Princeton University and Google researchers (arXiv)

    ReAct: language models that reason and act with tools

    Showed that letting a model alternate between reasoning steps and tool actions improves results on question answering and decision tasks.

    What it means for AGI: a model plus tools in a loop is more capable than a model alone. Most agent systems today build on this idea.

    Source: arxiv.org(opens external site)

  • · Microsoft Research and collaborators (arXiv)

    AutoGen: multi-agent conversation for complex tasks

    A framework where several agents with different roles talk to each other, use tools and involve people to solve tasks.

    What it means for AGI: building with groups of agents has become a mainstream engineering pattern.

    Source: arxiv.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)

  • · 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)

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