Sector
Science and discovery
Science is where AI has already changed a field: protein structure prediction is the clearest example. We expect AGI to speed up the full cycle of science, from hypothesis to experiment to result.
Our predictions
Our view, with how sure we are. Each one links to the evidence it rests on.
What changes
A medicine whose target and molecule were found mainly with AI will be approved for patients by a major regulator.
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.
Progress around the world
Public results from other teams that shape our view.
· 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.
· 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.
· Insilico Medicine and collaborators (published in Nature Medicine)
An AI-discovered drug shows results in a randomised phase 2a trial
Rentosertib, a drug whose target and molecule were found with generative AI, was tested in 71 patients with idiopathic pulmonary fibrosis. Safety was similar to placebo, and the highest dose showed a lung-function gain, though the trial was small.
What it means for AGI: AI-designed medicines have reached patient trials. Approval still requires larger trials.
Source: nature.com(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.
· 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 (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)
· 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.
· 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)
· 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)
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