📊 Full opportunity report: Every Benchmark Launched 2023-2024 Has Fallen — The METR / SWE-Bench / CORE-Bench / MLE-Bench / PostTrainBench Sequence on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
All six key AI benchmarks introduced between 2023 and 2024 have now saturated or are close to saturation. This pattern suggests a significant acceleration in AI research and development, with implications for industry and policy.
All six major AI research benchmarks introduced in 2023 and 2024 have now saturated or are nearing saturation, confirming a rapid advancement in AI capabilities within a short timeframe. This pattern underscores a significant acceleration in AI research progress, with broad implications for industry, policy, and future development trajectories.
According to Thorsten Meyer, as of May 2026, every benchmark designed to measure AI research and engineering capability launched during 2023-2024 has either been declared solved or is tracking toward saturation within a span of months. These benchmarks include SWE-Bench, METR Time Horizons, CORE-Bench, MLE-Bench, PostTrainBench, and CPU Speedup, each measuring different aspects of AI development.
For example, SWE-Bench, which assesses software engineering skills, improved from 2% to 93.9% in 30 months, reaching saturation. Similarly, METR Time Horizons, measuring task durations, expanded from 30 seconds to 12 hours over four years, indicating exponential growth in AI’s ability to perform research tasks. The CORE-Bench, used for reproducing research papers, was declared solved in December 2025 after improving from 21.5% to 95.5% in 15 months.
These findings suggest a consistent pattern: all six benchmarks, designed to challenge AI systems, are saturating or have saturated on a similar timescale, pointing to a rapid, overarching trend of capability escalation across multiple facets of AI research and development.
Implications of Rapid Benchmark Saturation
The saturation of all major AI benchmarks launched in this period indicates a swift and broad acceleration in AI capabilities, challenging previous assumptions about the timeline of AI progress. This trend suggests that AI systems are rapidly reaching or surpassing human-level performance across key research tasks, which could reshape industry practices, influence policy discussions on AI regulation, and accelerate deployment timelines. Stakeholders should reassess strategies and expectations in light of these developments, as the pace of AI advancement appears to be faster than many anticipated.
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Background on AI Benchmark Development and Progress
Throughout 2023 and 2024, researchers and industry leaders introduced several benchmarks aimed at measuring the progress of AI in research, engineering, and deployment tasks. These benchmarks were explicitly designed to be challenging, pushing AI systems toward their limits. The pattern of rapid saturation across these benchmarks, as observed by Thorsten Meyer, aligns with broader signals of exponential growth in AI capabilities, consistent with previous trends but now occurring on a compressed timeline.
Prior to this period, progress was more gradual, with benchmarks taking years to approach saturation. The current pattern suggests a structural shift, driven by advancements in model architectures, training techniques, and compute efficiency, which have collectively accelerated the pace of AI development.
“Every benchmark launched in 2023-2024 has saturated or is nearing saturation within months, indicating a rapid acceleration in AI capabilities.”
— Thorsten Meyer
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Uncertainties About Long-Term Impact and Future Trajectories
While the rapid saturation of benchmarks indicates significant progress, it remains unclear how this will translate into real-world deployment, safety, and policy challenges. It is also uncertain whether new benchmarks will emerge that can challenge AI further or if saturation signals approaching limits of current architectures. The long-term impact on AI safety, regulation, and societal integration requires further observation and analysis.
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Next Steps for Monitoring AI Capability Growth
Researchers and industry analysts will need to closely monitor ongoing developments, including the introduction of new benchmarks and real-world deployment metrics. Further studies are expected to assess whether saturation continues across emerging tasks and whether AI systems can sustain or surpass current performance levels. Policymakers should prepare for accelerated AI capabilities that may influence regulation and safety protocols in the coming months.
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Key Questions
What does benchmark saturation mean for AI development?
Benchmark saturation indicates that AI systems have achieved or exceeded the performance levels set by these tests, suggesting rapid progress and potential approaching limits of current architectures.
Are these benchmarks representative of real-world AI capabilities?
While these benchmarks measure specific research and engineering skills, their saturation suggests that AI systems are becoming more capable across a range of tasks, though real-world deployment may involve additional challenges.
What are the implications for AI safety and regulation?
Faster-than-expected capability growth could accelerate deployment but also raises concerns about safety, control, and ethical considerations. Regulators may need to adapt quickly to keep pace with AI advancements.
Will new benchmarks challenge AI further?
This remains uncertain. As current benchmarks saturate, the development of more challenging tests may be necessary to measure next-level capabilities and push AI systems further.
How soon might AI systems reach human-level performance across all tasks?
Based on current trends, some experts predict AI could match or surpass human performance in research and engineering tasks within the next few years, but broader societal impacts depend on deployment and safety measures.
Source: ThorstenMeyerAI.com
