📊 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
Six key benchmarks measuring AI research and development capabilities launched between 2023 and 2024 have all reached saturation or are close to it. This pattern suggests AI progress is accelerating faster than previously thought. The implications impact AI deployment, investment, and policy planning.
All six major AI research and development benchmarks launched between 2023 and 2024 have now been saturated or are on the verge of saturation, according to recent analysis by Thorsten Meyer. This pattern indicates that AI capabilities are progressing at a notable rate, with potential implications for AI deployment, policy, and investment strategies.
Thorsten Meyer’s analysis, based on data from Jack Clark’s Import AI #455, reveals that each of the six benchmarks measuring different facets of AI research—such as software engineering, model training, and AI fine-tuning—has either been declared solved or is tracking toward saturation within months. For example, the SWE-Bench, which measures real-world software engineering tasks, improved from 2% to 93.9% in 30 months, reaching saturation in late 2023. Similarly, the METR time horizon benchmark, which measures AI’s ability to complete research tasks within specified durations, expanded from 30 seconds to 12 hours over four years, demonstrating significant growth. The CORE-Bench, assessing research reproducibility, was declared solved in December 2025 after reaching 95.5% accuracy, while other benchmarks like MLE-Bench and PostTrainBench show ongoing rapid progress towards saturation.
According to Meyer, this pattern across all six benchmarks suggests a consistent trend rather than isolated occurrences. The timeline—spanning months rather than years—indicates that AI research capabilities are approaching or reaching a plateau in many areas, with some tasks effectively achieved. This development may influence expectations regarding AI system performance and deployment timelines.
Implications of Rapid Benchmark Saturation for AI Progress
The saturation of all six major benchmarks within a short timeframe indicates that AI research capabilities are advancing rapidly, with many tasks nearing completion. This trend could influence the development of autonomous AI systems capable of performing complex research, engineering, and development tasks. For industries, policymakers, and investors, this may lead to earlier adoption of advanced AI solutions and prompt considerations regarding regulation, safety, and economic impacts. The current growth in AI capabilities appears to follow an exponential pattern, which warrants ongoing assessment and strategic planning.
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Background on Benchmark Development and Progress
Since 2023, researchers and industry leaders have introduced several benchmarks designed to evaluate AI systems across various domains, including software engineering, research reproducibility, and model training efficiency. These benchmarks aimed to measure progress in AI capabilities related to real-world and research-specific tasks. Over the past two years, improvements have been documented, culminating in the recent saturation of all six benchmarks by May 2026. Notably, the SWE-Bench, which started at 2% in late 2023, reached near-complete performance in 30 months. The METR Horizon, measuring task duration, expanded from 30 seconds to 12 hours over four years, indicating substantial growth. These benchmarks were designed to be challenging, and their saturation indicates a significant milestone in AI development.
“The pattern across all six benchmarks indicates a structural trend. Their simultaneous saturation suggests consistent progress rather than isolated events.”
— Thorsten Meyer
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Uncertainties About Long-Term AI Capabilities
While the saturation of these benchmarks indicates notable progress, questions remain about how these results translate into broader, real-world AI deployment and whether new benchmarks will be developed to challenge AI further. Additionally, the extent to which saturation reflects general intelligence versus specialized competence is still under discussion among experts. There is also uncertainty about how these rapid advancements will influence safety, regulation, and societal impacts in the future.
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Next Steps for Monitoring AI Capability Trajectories
Researchers and industry stakeholders are expected to develop new, more challenging benchmarks to evaluate further progress. Continued monitoring of saturation patterns across current and emerging benchmarks will be important to determine whether progress continues at an exponential rate or stabilizes. Policy discussions around AI safety, regulation, and deployment are likely to intensify as the pace of AI development accelerates. Organizations may also reassess investment and strategic planning based on the rapid achievement of these benchmarks.
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Key Questions
What do benchmark saturations mean for AI development?
Saturation indicates that AI systems have achieved performance levels comparable to or exceeding human capabilities in specific tasks measured by the benchmarks, reflecting significant progress in AI capabilities.
Are these benchmarks representative of general AI intelligence?
No, these benchmarks evaluate performance on specific tasks within research and engineering domains. Saturation does not necessarily imply the achievement of general AI or broad intelligence.
What are the implications for AI policy and regulation?
The rapid saturation of benchmarks may influence considerations around AI safety, deployment, and societal impact, prompting policymakers to evaluate appropriate regulatory measures.
Will new benchmarks be developed after saturation?
Yes, researchers are likely to create more advanced benchmarks to continue measuring progress and to challenge AI systems further.
How reliable are these saturation assessments?
The assessments are based on publicly reported results and expert analysis; however, ongoing developments and future benchmarks may influence the understanding of AI progress.
Source: ThorstenMeyerAI.com
