While more data and compute yield linear improvements, true step-function advances in AI come from unpredictable algorithmic breakthroughs like Transformers. These creative ideas are the most difficult to innovate on and represent the highest-leverage, yet riskiest, area for investment and research focus.

A 10x increase in compute may only yield a one-tier improvement in model performance. This appears inefficient but can be the difference between a useless "6-year-old" intelligence and a highly valuable "16-year-old" intelligence, unlocking entirely new economic applications.

AI adoption isn't linear. A small, 1% improvement in model capability can be the critical step that clears a usability hurdle, transforming a "toy" into a production-ready tool. This creates sudden, discontinuous leaps in market adoption that are hard to predict from capability trend lines alone.

METR's research reveals a consistent, exponential trend in AI capabilities over the last five years. When measured by the length of tasks an AI can complete (based on human completion time), this 'time horizon' has been doubling approximately every seven months, providing a single, robust metric for tracking progress.

AI agents can now reliably complete tasks that take a human several hours. With a seven-month doubling time for task complexity, these agents are on track to autonomously handle a full eight-hour workday by the end of 2026, signaling a dramatic shift in the future of work.

A key metric for AI progress is the size of a task (measured in human-hours) it can complete. This metric is currently doubling every four to seven months. At this exponential rate, an AI that handles a two-hour task today will be able to manage a two-week project autonomously within two years.

The future of AI is hard to predict because increasing a model's scale often produces 'emergent properties'—new capabilities that were not designed or anticipated. This means even experts are often surprised by what new, larger models can do, making the development path non-linear.

The viral Meter chart showing exponential AI agent improvement is becoming unreliable. Models like Anthropic's Opus 4.6 are 'saturating' the benchmark's task set, meaning the tool used to measure progress can no longer keep up. The dramatic acceleration may be more a sign of the benchmark's limitations than a pure reflection of capability leaps.

While the long-term trend for AI capability shows a seven-month doubling time, data since 2024 suggests an acceleration to a four-month doubling time. This faster pace has been a much better predictor of recent model performance, indicating a potential shift to a super-exponential trajectory.