Every Benchmark Launched 2023-2024 Has Fallen — The METR / SWE-Bench / CORE-Bench / MLE-Bench / PostTrainBench Sequence

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All six major benchmarks designed to measure AI research and development capability, launched between 2023 and 2024, have either saturated or are nearing saturation within a timeframe of months, according to recent analyses.

Research by Thorsten Meyer, based on Jack Clark’s analysis, shows that each of these benchmarks—covering software engineering, model training, research reproduction, and AI fine-tuning—has experienced rapid progress, reaching or approaching maximum performance levels in a short period. For example, SWE-Bench improved from 2% to 93.9% in 30 months, while the METR time horizon task expanded from 30 seconds to 12 hours over four years. The CORE-Bench, measuring research reproduction, was declared solved by its authors after reaching 95.5% in 15 months. This consistent pattern across diverse metrics indicates a structural shift in AI research capabilities, with all benchmarks tracking toward or having achieved saturation.

Implications of Uniform Benchmark Saturation for AI Trajectory

The rapid saturation across all six benchmarks suggests that AI research capabilities are advancing faster than many anticipated, potentially reaching a point where further improvements may be limited by current methods. This pattern influences forecasts of AI development timelines, policy considerations, and workforce planning, as it indicates a possible acceleration toward more autonomous AI research and deployment. Stakeholders should consider these findings when assessing future AI risks and opportunities.

Background on Benchmark Development and Progress

These six benchmarks were specifically designed to challenge AI systems across different facets of research and engineering, including software development, model training, and research reproduction. Launched from late 2023 through early 2024, each was intended as a measure of progress in AI R&D capability. Prior to this, progress was more incremental, but recent data shows a sharp acceleration. The benchmarks include SWE-Bench, METR time horizons, CORE-Bench, MLE-Bench, PostTrainBench, and CPU speedup tasks. The saturation of these benchmarks within months underscores a significant shift in AI research dynamics, aligning with forecasts of rapid capability growth.

“Every benchmark launched in 2023-2024 has saturated or is nearing saturation within months, indicating a structural acceleration in AI research capabilities.”

— Thorsten Meyer

Unclear Long-term Impact of Benchmark Saturation

While the rapid saturation suggests accelerated AI capabilities, it remains uncertain whether this trend will continue or plateau as benchmarks reach their limits. The implications for real-world AI deployment and safety are still being evaluated, and some experts question whether saturation in benchmarks translates directly to general AI progress.

Monitoring Future Benchmark Developments and AI Capabilities

Researchers and policymakers will closely watch upcoming benchmark results to determine if saturation persists or if new challenges emerge. Additionally, efforts are underway to develop more comprehensive measures to assess AI’s broader capabilities beyond current benchmarks. The next milestones include assessing whether AI systems can generalize improvements or if new limitations will slow progress.

Key Questions

What does benchmark saturation mean for AI development?

Saturation indicates that AI systems are reaching peak performance on specific tests, which may suggest limits to current methods and could signal a slowdown or shift in how AI capabilities evolve.

Are these benchmarks predictive of real-world AI capabilities?

While these benchmarks are designed to challenge AI systems, it remains uncertain whether saturation in tests directly correlates with broader, real-world AI performance or safety.

What are the implications for AI policy and regulation?

The rapid progress and saturation suggest policymakers may need to reconsider timelines for regulation, safety measures, and ethical guidelines as AI approaches higher levels of autonomy and capability.

Will new benchmarks be developed to measure further progress?

Yes, researchers are likely to develop more advanced benchmarks to push beyond current limits and better assess AI’s general intelligence and adaptability.

How soon could AI systems surpass human-level performance across all benchmarks?

Based on current trajectories, some forecasts suggest significant advancements by 2028, but reaching human-level performance across all tasks remains uncertain and depends on future innovations.

Source: ThorstenMeyerAI.com