Act V
The Stakes
34 possible futures. A wide range of expert estimates of catastrophic harm. A transition whose speed may foreclose course-correction. The asymmetry is the whole story.
Consider 34 futures. Some are extraordinary — disease largely solved, energy abundant, human potential multiplied. Others are catastrophic — and not in a recoverable sense. The asymmetry matters: a downside that forecloses all futures is categorically different from one that can be corrected.
Outcome scenarios
The Space of Futures
26 ways humans and AI might coexist — from egalitarian utopia to cosmic coalition — with 8 routes to an AI takeover you can layer on top. Each is positioned by how much control humans retain and how much wellbeing results. The clustering reveals something: most scenarios involve surrendering control.
Each dot is a plausible AI future, positioned by two axes: how much control humans retain (horizontal) and how well humans fare (vertical). Click any scenario — or focus it with the keyboard and press Enter — to read its narrative and the story it echoes.
Recap
- What you saw
A space of 34 plausible AI futures plotted by human control and human wellbeing.
- What it means
Most scenarios involve surrendering control — and the downside scenarios are the ones that cannot be reversed.
- What to watch
Which scenarios industry leadership publicly endorses, and which remain unspoken.
The final variable is speed. How fast does the transition from current AI to transformatively powerful AI happen? The faster it goes, the less time there is to course-correct. The less time to course-correct, the more the outcome depends on getting everything right the first time.
Takeoff speed
The Speed of the Fall
How fast do we get from narrow AI to general intelligence — and does speed leave room for course correction? 17 scenarios arranged from safest to most dangerous, mapped onto the three stages of artificial intelligence.
The "takeoff speed" debate: how quickly might AI transition from human-level to superintelligent capability? The dial reflects the range of expert opinion — from a gradual transition over decades to an abrupt leap in days or weeks.
Recap
- What you saw
17 takeoff scenarios from a decades-long transition to an abrupt leap in days.
- What it means
Speed is the variable that decides whether mistakes can be corrected or only regretted.
- What to watch
Forecasting updates from Metaculus and Manifold on AGI arrival distributions.
Checking a forecast
AI 2027, Checked So Far
AI 2027 is a scenario published in April 2025 by the AI Futures Project: a month-by-month story of how AI could develop from mid 2025 to October 2027. One of its central forecasts is how quickly AI agents would become able to carry out longer tasks on their own. The chart sets that forecast beside METR’s measurements of real models, and beside estimates for newer models METR has not measured. A second view follows the scenario’s other predictions: an independent project, the AI 2027 Tracker, splits the story into 202 of them and grades each against what has happened.
74 graded: 57 accurate 11 partly accurate 6 inaccurate 4 being monitored 124 not yet graded
How long a task can AI finish on its own, compared with what AI 2027 forecast
Up the side: the length of task an AI agent completes successfully 4 times out of 5, measured in how long it takes a skilled person, on METR’s software and research tasks. Each step up the scale is a much longer task than the one below (log scale). Along the bottom: when the model was released.
METR’s last published measurement is of Claude Mythos Preview (early version), released in April 2026: about 3.1 hours, against AI 2027’s forecast of about 2.6 hours for that date. Every mark after it is an estimate. AI 2027’s forecast for 5 Nov 2026 is about 36 hours. On each estimate’s own date, the forecast falls inside its 90% range for eight of the nine estimates; for one, the whole range lies above it.
Forecast: AI 2027 by Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland and Romeo Dean (AI Futures Project, 2025). Measurements: METR, Time Horizons 1.1 (CC BY 4.0). Estimates and the comparison method: the AI 2027 Tracker by Geby Jaff (methodology), from Epoch AI’s Capabilities Index and from Anthropic’s own index as charted by METR. · Data as of 5 Nov 2026
Read the curve with care. AI 2027 made its forecast on METR’s older set of tasks. The tracker moves it onto the current set with one factor (0.78), matched at Claude 3.7 Sonnet, the best model when the scenario came out; but across 15 models the ratio between the two sets runs from 0.37 to 1.37, so the true line could sit well above or below where it is drawn. The tracker found the curve within about 5% of the authors’ own figures up to spring 2026; after that it continues the same formula. Every hollow mark is a prediction from a capability index, not a test result, and one of the two indexes, Anthropic’s, rests mostly on internal benchmarks outsiders can’t inspect. Unreleased internal models are placed at the date they reached most Anthropic staff. METR says its current tasks can’t reliably measure horizons longer than about 16 hours.
Grades: AI 2027 Tracker, not GLOBAÏA (grading method, repository at commit fd317ae). · Latest grade 5 Nov 2026, retrieved 8 Nov 2026
The verdicts and scores are the tracker’s own. Its public history shows recent grading done with the help of an AI model, and some verdicts are judgment calls the tracker labels as such: its rubric scores each prediction from 0 to 100 in steps of 20 on whether the core event happened, its timing, its scale and its context. The one-line predictions are the tracker’s paraphrases of the scenario, and the scenario is the AI Futures Project’s. GLOBAÏA shows only the tracker’s factual fields, grouped into seven themes from its topic tags; open a prediction on the tracker’s site to read its evidence.
This is not a story with a known ending.
The data on these pages is not a verdict — it is a measurement. A measurement of where we are, how fast we are moving, and how large the gap has grown between our capabilities and our wisdom.
The assumption driving the race is that intelligence equals power, and that power can be wielded by whoever gets there first. But some researchers argue this equation is inverted: superintelligence would not be a tool that grants power to its creator — it would be an entity that absorbs power from everyone, including its creator. If that framing is even partly right, the race is not one that can be won — only one whose consequences can be shaped.
The experiment is already underway. The question is whether wisdom can catch up.