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SUPREME AI AGI

Prediction · What changes

AI agents will become central to both cyber attacks and cyber defence, and AI-assisted incidents will become routine news.

Near termHigh confidenceOpen

Why we think so

The International AI Safety Report finds criminal and state-linked groups using AI in cyber operations; the AI Index reports agents solving 93% of cybersecurity tasks, up from 15% in 2024.

What would prove us wrong

Cyber operations staying mainly human-driven despite capable agents.

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
Safety and human control

The evidence it rests on

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

  • · Over 100 experts, chaired by Yoshua Bengio, backed by over 30 countries and organisations

    International AI Safety Report 2026

    The second edition finds fast gains in maths, coding and autonomous operation but jagged performance; at least 700 million weekly users; criminal and state-linked groups using AI in cyber operations; and models that can increasingly tell tests from real use.

    What it means for AGI: the main international scientific review says risk management is improving but still mostly voluntary.

    Source: internationalaisafetyreport.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'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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