When AI models achieve superhuman performance on specific benchmarks like coding challenges, it doesn't solve real-world problems. This is because we implicitly optimize for the benchmark itself, creating "peaky" performance rather than broad, generalizable intelligence.

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.

A benchmark like SWE-Bench is valuable when models score 20%, but becomes meaningless noise once models achieve 80%+ scores. At that point, improvements reflect guessing arbitrary details (like function names) rather than genuine capability. This demonstrates that benchmarks have a natural lifecycle and must be retired once saturated to avoid misleading progress metrics.

AI struggles with long-horizon tasks not just due to technical limits, but because we lack good ways to measure performance. Once effective evaluations (evals) for these capabilities exist, researchers can rapidly optimize models against them, accelerating progress significantly.

As benchmarks become standard, AI labs optimize models to excel at them, leading to score inflation without necessarily improving generalized intelligence. The solution isn't a single perfect test, but continuously creating new evals that measure capabilities relevant to real-world user needs.

The gap between benchmark scores and real-world performance suggests labs achieve high scores by distilling superior models or training for specific evals. This makes benchmarks a poor proxy for genuine capability, a skepticism that should be applied to all new model releases.

Don't trust academic benchmarks. Labs often "hill climb" or game them for marketing purposes, which doesn't translate to real-world capability. Furthermore, many of these benchmarks contain incorrect answers and messy data, making them an unreliable measure of true AI advancement.

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.

Obsessing over linear model benchmarks is becoming obsolete, akin to comparing dial-up speeds. The real value and locus of competition is moving to the "agentic layer." Future performance will be measured by the ability to orchestrate tools, memory, and sub-agents to create complex outcomes, not just generate high-quality token responses.