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SUPREME AI AGI

Prediction · What changes

AI systems will run whole cycles of research in narrow fields, from hypothesis to experiment design to analysis, with scientists approving each step.

Medium termMedium confidenceOn track

Why we think so

AI has already made Nobel-recognised progress in protein structure, proposed millions of candidate materials and improved basic algorithms. These were single steps. Connecting them into a loop with automated labs is the next step.

What would prove us wrong

AI staying a tool for single steps, with no published discovery where an AI system led the full cycle.

Review notes

21 October 2026: A multi-agent system proposed drug-repurposing candidates that were validated in the lab, and the AI Index 2026 reports a full weather-forecasting pipeline run end to end by AI. Scientists still choose and approve the steps.

Horizon
Medium term. About 3 to 10 years out.
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.

  • · Google DeepMind (published in Nature)

    AlphaFold predicts protein structures at near-experimental accuracy

    A deep-learning system predicted the 3D shape of proteins with accuracy close to lab experiments, solving a problem biology had worked on for about 50 years.

    What it means for AGI: AI can make real scientific progress in a narrow field. The open question is whether the same can happen across many fields at once.

    Source: nature.com(opens external site)

  • · Google DeepMind (published in Nature)

    AI finds 2.2 million new crystal structures

    Graph networks for materials exploration (GNoME) proposed about 2.2 million new crystals, of which about 380,000 were predicted to be stable.

    What it means for AGI: AI can search spaces far larger than people can. Lab confirmation is still the slow step.

    Source: nature.com(opens external site)

  • · Google DeepMind (published in Nature)

    AI discovers faster sorting algorithms used in a standard C++ library

    A reinforcement-learning agent found sorting routines faster than human-written ones for short sequences; they were added to the LLVM libc++ library.

    What it means for AGI: AI can improve the basic tools computing is built on, a small step toward systems that improve themselves.

    Source: nature.com(opens external site)

  • · The Nobel Prize

    Nobel Prize in Chemistry awarded for computational protein design and AI structure prediction

    Half of the 2024 Chemistry prize went to protein design, half to the AlphaFold work on protein structure prediction.

    What it means for AGI: AI-driven discovery is now accepted at the highest level of science.

    Source: nobelprize.org(opens external site)

  • · Google researchers (arXiv)

    Co-Scientist: a multi-agent system that proposes research hypotheses

    Specialised agents generate, critique and rank hypotheses in a tournament. Drug-repurposing candidates it proposed for acute myeloid leukaemia were validated in lab (in vitro) experiments.

    What it means for AGI: a team of agents, not a single model, did useful scientific work that was checked in a real lab.

    Source: arxiv.org(opens external site)

  • · Google DeepMind

    AlphaEvolve: a coding agent that discovers better algorithms

    An evolutionary agent built on Gemini models found a way to multiply 4×4 complex matrices with 48 multiplications, improving on Strassen's 1969 result, and improved on known answers for a share of over 50 open maths problems.

    What it means for AGI: a loop of models proposing, testing and keeping the best ideas can produce new knowledge, an early form of self-improvement.

    Source: deepmind.google(opens external site)

  • · Google DeepMind (graded by IMO coordinators)

    AI reaches officially graded gold-medal level at the Mathematical Olympiad

    An advanced Gemini Deep Think model solved five of six problems for 35 of 42 points, working in natural language within the 4.5-hour limit. IMO coordinators graded it with the same criteria as students.

    What it means for AGI: hard, creative reasoning in plain language, once thought far off, reached top human level.

    Source: deepmind.google(opens external site)

  • · Google DeepMind (published in Nature)

    AI weather model beats the leading physics-based ensemble forecast

    GenCast, a machine-learning ensemble model, was more accurate than ECMWF's ENS on 97.2% of 1,320 targets tested, including extreme weather and cyclone tracks, and produces a 15-day forecast in minutes.

    What it means for AGI: learned models can outperform decades of hand-built simulation in a complex physical system.

    Source: nature.com(opens external site)

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