A slowdown in compute growth may have a squared negative effect on AI progress. It not only reduces resources for training larger models but also stifles the discovery of new algorithms, as breakthroughs like the Transformer required immense compute for experimentation. This double impact could significantly delay major capabilities milestones.

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.

The relationship between computing power and AI model capability is not linear. According to established 'scaling laws,' a tenfold increase in the compute used for training large language models (LLMs) results in roughly a doubling of the model's capabilities, highlighting the immense resources required for incremental progress.

Data from research organization METR shows that the time it takes for AI task capabilities to double is itself decreasing—from seven months to four. This indicates a "super-exponential" growth curve, where the rate of acceleration is itself accelerating.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

AI progress was expected to stall in 2024-2025 due to hardware limitations on pre-training scaling laws. However, breakthroughs in post-training techniques like reasoning and test-time compute provided a new vector for improvement, bridging the gap until next-generation chips like NVIDIA's Blackwell arrived.

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.

The market often misinterprets AI progress as linear. However, a clear 'scaling law' dictates that a tenfold increase in the computing power used to train LLMs results in a twofold capability improvement. This exponential relationship means future advancements will be far more disruptive and surprising than incremental projections suggest.

Meter's researchers initially projected AI capabilities would double every seven months. However, recent data from 2024 models shows the trend has sped up significantly, with a new doubling time of just four months, indicating an accelerating pace of progress that has outstripped previous forecasts.