Act I
The Acceleration
Capability is compounding. Compute grows five-fold each year, autonomous work horizons double every four months, and benchmarks designed to hold for a decade are falling in months.
Intelligence Index
The Race No One Agreed To Run
The Artificial Analysis Intelligence Index measures AI model capability across agentic work, coding, general knowledge and scientific reasoning. The frontier has climbed from 5 in early 2023 to 53.4 today, and the record has changed hands 28 times along the way.
Each line tracks a lab's most capable model over time. The steepening curves show AI capability growing faster each year — and the gap between leaders shrinking.
Data: Artificial Analysis Intelligence Index — Last updated:
The Intelligence Index is a composite of ten benchmarks in four weighted groups: agentic work (30%), general knowledge and long-context reasoning (30%), coding (20%) and scientific reasoning (20%). Higher scores indicate greater capability. Artificial Analysis revises the ruler periodically and re-scores the whole back catalogue when it does — the September 2026 move to v4.3, which brought in a harder terminal benchmark and a business-workflow automation test, lowered almost every score on this chart by around six points. Compare models to each other within one version, not scores across versions. Used with attribution for educational purposes.
Recap
- What you saw
Intelligence Index scores climbing from about 5 to above 53 since late 2022.
- What it means
Capability is accelerating faster than any prior general-purpose technology on record.
- What to watch
The next revision of the index, and whether any lab can hold the frontier for more than a few weeks.
Raw scores are one thing. But the real question is: what can AI do on its own? Not answer a question. Not write a paragraph. Work: complex tasks that would take a skilled person hours, finished alone, half the time. That horizon is doubling every four months.
Autonomous task length
Autonomy Horizon
How long a task takes a skilled human before AI agents complete it with 50% success. In 2019, AI could manage a 2-second task. By April 2026, the frontier had reached tasks of roughly 17 hours of skilled work, though METR notes that measurements above 16 hours are unreliable with the current task suite. The horizon is doubling every 129 days.
This measures the length of real software engineering task, in skilled human time, that AI agents finish on their own half the time. It is not how long an agent runs. The y-axis is logarithmic: each gridline represents a tenfold increase in task length.
Data: METR — Horizon Benchmark v1.1. Cite: arXiv:2503.14499 (NeurIPS 2025). Used with attribution for educational purposes. · Data as of 7 Apr 2026
The p50 horizon length is the task duration (for a skilled human) at which AI agents succeed 50% of the time. Shaded bands show 95% confidence intervals. The frontier line connects state-of-the-art models at time of release. The dashed trend line shows the 129-day doubling rate from 2023 onward.
One lab, seen from inside. October 2026.
When the work feeds itself
The chart above asks how large a task an agent can finish alone. A second question sits behind it: is AI already speeding up the making of AI? From August to October 2026, METR, an independent evaluator, went inside Anthropic to look, with internal data, benchmark scores for unreleased models and staff interviews.
- ~2× pace of progress
Anthropic is likely improving its models on well-specified tasks at about twice its rate of March 2024 to October 2025. METR would not be surprised by 1.3× or 3×.
- ≈80% of that speedup due to AI
METR's own guess for October 2026, with substantial uncertainty. Other causes likely played a large role in late 2025. Faster growth in training compute was ruled out.
- Assisted not yet autonomous
The speedup comes mainly from AI helping researchers. AI cannot yet make highly significant, fully autonomous contributions to the most important parts of model training, though researchers describe "signs of life".
1× means no speedup. Adding other labs' models lowers the trend to 1.4×, and METR reads that two ways: closer to the truth if Anthropic trailed the frontier in 2025, too low if earlier models from other labs were more tuned to these benchmarks. Bars on the first two rows show METR's range and a 95% confidence interval.
Why it matters
Progress that feeds on itself can compound. METR saw no clear bottleneck over the next 12 months in algorithms, compute or data, and calls the risk of even faster acceleration significant. Its view of those bottlenecks changed several times along the way, and it may be missing some.
What cannot be checked
This is one lab and a subjective estimate. Anthropic redacted most of the evidence METR found most informative, saying it could accelerate other developers. METR's own verdict: the public has to rely on its judgment much more than would be ideal.
Source: METR, Independent Assessment of AI R&D Acceleration at Anthropic in October 2026, 5 November 2026.
The benchmark trend uses Anthropic's version of the Epoch Capabilities Index, which scores models across many benchmarks. Anthropic's system card figure is from Haiku 5.5, and METR's is higher mainly because it includes newer unreleased models. Anthropic also dropped a planned check of how it divides compute between capabilities and safety, before METR reached conclusions on it.
Recap
- What you saw
METR's autonomy horizon extending from 2 seconds (2019) to tasks of roughly 17 hours (April 2026), with measurements above 16 hours flagged by METR as less reliable. Then METR's estimate that progress at one lab has about doubled.
- What it means
Agents are beginning to take on tasks the size of a working day, not a chat. At Anthropic, that help is likely already speeding up the building of the next models.
- What to watch
Whether the 129-day doubling continues through 2027, and whether AI's part in building AI moves from assistance to fully autonomous work.
The horizon is not just rising — it is multiplying. AI agents — systems that take autonomous actions in the world, calling tools, navigating web pages, completing multi-step tasks — are advancing on two axes at once. Demand for them in the labor market has exploded; their measured capability on real-world benchmarks is not far behind. Both are happening simultaneously, and the frameworks meant to govern them are still being written.
AI-agent demand & capability
The Horizon Extends
AI agents are advancing on two axes at once. The labor market reveals demand: a skill cluster that did not meaningfully exist before 2024 — postings for "agentic AI," "AI agents," "LangGraph" — exploded across 2025 and now appears in roughly 48,000 US job postings, overtaking decade-old categories like neural networks, robotics, and visual recognition. And the agents themselves are gaining capability: on four real-world benchmarks where the best models scored 1–20% as recently as 2024, they now hit 66–93%, closing in on human performance across domains as distinct as web navigation, terminal commands, computer use, and cybersecurity.
Demand view: each line tracks the share of US job postings mentioning an AI-related skill cluster, 2010–2025. The cyan AI agent line emerged from zero in 2024 — note how quickly it overtook visual recognition, robotics, and neural networks. Capability view: each line tracks the best AI model on a real-world agent benchmark; dashed lines mark human performance. Terminal-Bench is monthly; the others are annual.
Data: Stanford AI Index 2026 (CC BY-ND 4.0). Demand: Economy chapter, Fig 4.4.3 (Lightcast 2010–2025) and Fig 4.4.7 (agent-skill breakdown, 2024 vs 2025). Capability: Technical Performance chapter, Fig 2.5.2 (Terminal-Bench 2.0), Fig 2.6.2 (OSWorld), Fig 2.6.3 (WebArena), Fig 2.6.5 (Cybench).
Demand intermediate-year values were read off Stanford's published chart to ±0.05%; 2025 endpoints are exact. Capability values are all exact (printed on Stanford's charts).
Recap
- What you saw
A skill cluster emerging from zero in 2024 and overtaking decade-old categories — paired with agent benchmarks closing on human performance across four distinct domains.
- What it means
Demand and capability are advancing together. Companies are hiring for agents before they can buy them off the shelf — and the agents are improving fast enough to meet that demand.
- What to watch
Whether enterprise deployment catches up. Stanford reports agent deployment is still in the single digits across most business functions — the bottleneck is shifting from capability to integration.
Scores on a benchmark are one thing. But benchmarks are supposed to be hard — designed to test the limits of what machines can do. What happens when the limits keep moving? When problems designed to take a decade are solved in six months?
Frontier benchmarks
Closing the Gap
Seven benchmarks designed to measure the frontier of AI capability. Each line tracks the best model score over time. FrontierMath went from <2% to ~25%. ARC-AGI from ~5% to ~88%. SWE-bench from 2% to ~70%. Humanity's Last Exam — scored just 3% at launch — has already been pushed to 27% in months. Problems designed to resist progress for years are falling in months.
Each line tracks AI performance on a different standardized test. When a line approaches the top of the chart, AI has matched or surpassed human expert performance on that task.
Data: Epoch AI Benchmark Hub (CC BY 4.0). Individual sources: SWE-bench, Epoch FrontierMath, ARC Prize, GPQA, SimpleQA, MATH, Humanity's Last Exam.
Capabilities Index (ECI) is Epoch AI's aggregate score combining performance across multiple benchmarks into a single comparable metric per model.
Recap
- What you saw
Seven frontier benchmarks, including Humanity's Last Exam, collapsing in months.
- What it means
The evaluation community is running out of tests the frontier cannot already pass.
- What to watch
The next benchmark release from Scale AI and the Center for AI Safety — and how long it holds.
Behind every model is a number: the raw computation used to train it — a proxy for ambition. It has been growing at five times per year since 2020. There is no sign it is slowing.
Training compute
The Engine Room
Training compute for frontier AI models has grown by over 10,000× since 2020 — doubling every 9 months. Each point on this chart represents a decision by an organization to invest more computational resource than any previous model in history.
Each dot represents an AI model. The vertical axis shows the computing power used to train it — note the logarithmic scale, where each step represents a 10× increase. Training compute has grown roughly 4× per year since 2010.
Data compiled from Epoch AI research, published papers, and industry reports. Training compute estimates carry uncertainty — see Epoch AI documentation for methodology. CC BY 4.0.
Hardware Scaling: transistor counts from Our World in Data (Moore's Law), GPU FLOPS from manufacturer specifications (NVIDIA, AMD). Moore's original observation: Gordon Moore, "Cramming More Components onto Integrated Circuits," Electronics, 1965.
Recap
- What you saw
Training compute growing over 10,000-fold since 2020, doubling every nine months.
- What it means
Frontier AI has become a material, industrial undertaking with a planetary footprint.
- What to watch
Whether any publicly announced training run crosses 10²⁷ FLOP in the next two years.
Training compute is only one axis of acceleration. AI development has entered a self-reinforcing feedback loop — a cycle in which AI systems are themselves accelerating the development of more powerful AI systems. Each stage operates on a shorter timescale than the last.
AI building AI
The Feedback Loop
AI development has entered a unique technological feedback loop: AI systems are now helping to develop new and more powerful AI systems. We can think of this as five stages, each with a shorter timescale than the last. Earlier stages are well underway. Later stages remain speculative — but could proceed very rapidly once unlocked.
Each stage feeds the next: infrastructure investment enables model development, models generate training data, data improves tools, tools enable networks, and networks could eventually lead to recursive self-improvement. The timescales compress at each stage — from years to potentially hours. Several stages are already underway simultaneously.
Framework: Aguirre, A. (2025). "Keep the Future Human: AI Safety Standards," Table 2: The AI Self-Improvement Cycle. Future of Life Institute.
Recap
- What you saw
Five stages of AI accelerating AI, with the earlier stages already active.
- What it means
The development loop is shortening from years to weeks and may shorten further.
- What to watch
Published evidence of AI systems materially designing their own training data or architectures.