The biggest opportunity for AI isn't just automating existing human work, but tackling the vast number of valuable tasks that were never done because they were economically inviable. AI and agents thrive on low-cost, high-consistency tasks that were too tedious or expensive for humans, creating entirely new value.

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.

Leading LLMs can now replicate a two-hour human software engineering task with 50% accuracy. This capability is doubling every seven months, signaling an urgent need for organizations to adapt their data infrastructure, security, and governance to leverage this exponential growth.

Julian Schrittwieser, a key researcher from Anthropic and formerly Google DeepMind, forecasts that extrapolating current AI progress suggests models will achieve full-day autonomy and match human experts across many industries by mid-2026. This timeline is much shorter than many anticipate.

AI's growing ability to perform long-horizon tasks, like building software for hours without human intervention, means leaders must proactively rethink strategy, staffing, and budgeting. A responsible approach accounts for this increasing autonomy and its impact on knowledge work.

The evolution of Tesla's Full Self-Driving offers a clear parallel for enterprise AI adoption. Initially, human oversight and frequent "disengagements" (interventions) will be necessary. As AI agents learn, the rate of disengagement will drop, signaling a shift from a co-pilot tool to a fully autonomous worker in specific professional domains.

Elias Torres argues that the current AI paradigm, which focuses on tools that assist humans (e.g., summarizers, drafters), is fundamentally limited. He believes true value is unlocked when you can instruct an AI to perform a task *infinitely* on its own, without requiring a human to type into a chat box for every action.