Intelligence · Updated July 2026Momentum · accelerating ↗
How close are we to artificial general intelligence?When will AI learn almost any thinking task?
or, simply: When will AI learn almost any thinking task?or, precisely: How close are we to artificial general intelligence?
Frontier models saturate most static benchmarks, yet reliable long-horizon autonomy, calibrated reasoning and a shared definition of general intelligence all remain open — so any single 'percent to AGI' is a guess dressed as a number.AI can already help with many kinds of thinking, but it still makes confident mistakes and struggles to carry a big job through on its own.
We are here
Benchmarks saturate while autonomy lags — Frontier models top out static benchmarks like MMLU and ARC-AGI-1, yet still struggle with long-horizon autonomy, exposing a gap between test scores and real agency. Next up — Forecast: "weakly general" AI (expected 2028).
01 · Where we stand
Four tests between here and the goal
Each threshold is a falsifiable claim with a named next test. We move the meter only when a result is public.
Broad cognitive competenceLearn many different kinds of workIn progress
62%
Next testARC-AGI-2 and held-out expert evals resistant to training-data leakage
Long-horizon autonomyFinish long jobs without hand-holdingEarly
28%
Next testMETR 50%-task time horizon extending from hours to multi-day tasks
Calibrated reliabilityStay reliable when things get weirdEarly
18%
Next testPre-registered external safety and calibration evaluations on frontier deployments
THRESHOLDS — Thresholds for AGI.
02 · How we got here
The record behind the verdict
Major events set large; context events set small but never hidden. Everything below the TODAY rule is a schedule, not a result.
The symbolic dream moved the field from turing asks "can machines think?" to the perceptron learns from data. The results narrowed the next question without closing it.
1950
Turing asks "Can machines think?"
Alan Turing's paper proposes the Imitation Game (Turing Test) as an operational bar for machine intelligence.
Winters and the wilderness moved the field from the first ai winter sets in to the second ai winter. The results narrowed the next question without closing it.
1974
The first AI winter sets in
After the critique of perceptrons and the pessimistic Lighthill report, funding and optimism collapse for roughly a decade.
Machines that learn moved the field from deep blue beats kasparov to alexnet ignites the deep-learning era. The results narrowed the next question without closing it.
1997
Deep Blue beats KasparovExperiment
IBM's Deep Blue defeats reigning world chess champion Garry Kasparov, the first computer to beat a champion under tournament conditions.
Foundation models and agents moved the field from the transformer architecture to forecast: "weakly general" ai. The results narrowed the next question without closing it.
2017
The transformer architectureTheory
"Attention Is All You Need" introduces the transformer, the attention-based architecture that underpins virtually every modern large language model.
Benchmarks saturate while autonomy lagsWe are here
Frontier models top out static benchmarks like MMLU and ARC-AGI-1, yet still struggle with long-horizon autonomy, exposing a gap between test scores and real agency.
Events outside the declared eras moved the field from alphago defeats lee sedol to projected: ai handles week-long tasks. The results narrowed the next question without closing it.
2016
AlphaGo defeats Lee SedolExperiment
DeepMind's AlphaGo beats top Go professional Lee Sedol 4-1, mastering a game long considered a landmark for intuition and search.
Projected: AI handles week-long tasksExperimentTarget
Extrapolating METR's seven-month doubling implies agents could autonomously complete software tasks that take humans days or weeks by the end of the decade.
The learning curves and comparisons that justify each threshold's percentage. Every series is measured, with the source event linked in the timeline above.
B
The contribution flow
Hardware on the left, software on the right, both feeding the capability frontier in the middle. Line thickness = share of today's progress; colour = momentum. Labels sit outboard so nothing crosses the flows. Select a lever for its explanation →
acceleratingactivematuringbottleneckemerging
Tap a node or label · thicker line = more of today's push
04 · What it unlocks
If the remaining tests pass
Downstream capabilities, drawn dashed because they depend on results not yet in.