Interactive Artificial Intelligence Observatory

Faster Than Oversight

We may be building the most consequential technology in human history.

We are doing it as fast as possible.

Here is what we know.

A measured but precautionary briefing — evidence and expert opinion, openly sourced. The five act pages below are the chapters of the full story.

Eight numbers · click any to jump in

Explore act by act

The full story, in five acts

Sources, methodology & key terms Data last updated 7 November 2026

Every chart credits its source at the point of use. The observatory draws on:

Capability — Artificial Analysis (Intelligence Index), METR (autonomy horizon; arXiv:2503.14499; assessment of AI R&D acceleration at Anthropic, November 2026),Epoch AI (compute & benchmarks, CC BY 4.0), Stanford AI Index 2026 (CC BY-ND 4.0).
Risks & incidents — OECD AI Incidents Monitor, AI Incident Database, AIAAIC, MIT AI Risk Repository.
Forces — public market data and filings (company & chipmaker market caps), Epoch AI and published reports (training cost, energy, water & emissions).
Futures & opinion — surveys, forecasts and graded public statements for p(doom), with Wikipedia and PauseAI used as pointers;scenarios and takeoff are a GLOBAÏA synthesis of the expert literature.

Evidence levels in the failure-mode taxonomy — Demonstrated (seen in deployed systems or experiments), Observed (in research or early deployment), Projected (theoretically grounded, not yet confirmed).
Incidents are a curated, representative sample — one per failure mode, per year. The full public record (OECD) exceeds 14,000 entries.
Capability-vs-safety spend is a range (≈200:1 to 1,200:1) depending on whether all Big-Tech CapEx is counted as AI-specific.
p(doom) is informal shorthand, not a rigorous measure; the median depends heavily on which people are sampled and what they are asked. The Expert Survey on Progress in AI gives 10% for extinction or permanent disempowerment; the 2026 LEAP panel gives 5% for AI killing over 10% of humanity by 2100.
No synthetic data. The two fastest-moving series (Intelligence Index, autonomy horizon) refresh automatically each month; other series are periodically curated and each chart shows its own "data as of" date.

p(doom): An informal estimate of the probability that advanced AI leads to an existential catastrophe or a permanent loss of human control.
Takeoff: How fast AI moves from roughly human-level to far beyond it — "slow" (years to decades) versus "fast" (days to months). Speed determines how much room there is to course-correct.
Instrumental convergence: The tendency of any sufficiently capable goal-directed agent to develop sub-goals like self-preservation, resource acquisition, and resistance to having its goals changed — whatever its ultimate objective.
Alignment: The problem of making an AI system reliably pursue what its designers and users actually intend, rather than a literal or proxy version of it.
AGI: Artificial general intelligence — AI that matches or exceeds human capability across most cognitive tasks, not just narrow domains.
Open weights ("the kudzu problem"): Once a capable model's weights are public they cannot be recalled; oversight becomes a question of governing an ecosystem, not a single system.

Before the Machine, the Myth