Once an evaluation becomes an industry standard, AI labs focus research on improving scores for that specific task. This can lead to models excelling at narrow capabilities, like competition math, without a corresponding increase in general intelligence or real-world usefulness, a classic example of Goodhart's Law.

The proliferation of AI leaderboards incentivizes companies to optimize models for specific benchmarks. This creates a risk of "acing the SATs" where models excel on tests but don't necessarily make progress on solving real-world problems. This focus on gaming metrics could diverge from creating genuine user value.

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.

Public leaderboards like LM Arena are becoming unreliable proxies for model performance. Teams implicitly or explicitly "benchmark" by optimizing for specific test sets. The superior strategy is to focus on internal, proprietary evaluation metrics and use public benchmarks only as a final, confirmatory check, not as a primary development target.

There's a significant gap between AI performance on structured benchmarks and its real-world utility. A randomized controlled trial (RCT) found that open-source software developers were actually slowed down by 20% when using AI assistants, despite being miscalibrated to believe the tools were helping. This highlights the limitations of current evaluation methods.

Once a benchmark becomes a standard, research efforts naturally shift to optimizing for that specific metric. This can lead to models that excel on the test but don't necessarily improve in general, real-world capabilities—a classic example of Goodhart's Law in AI.

Just as standardized tests fail to capture a student's full potential, AI benchmarks often don't reflect real-world performance. The true value comes from the 'last mile' ingenuity of productization and workflow integration, not just raw model scores, which can be misleading.

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.

Benchmarking reasoning models revealed no clear correlation between the level of reasoning and an LLM's performance. In fact, even when there is a slight accuracy gain (1-2%), it often comes with a significant cost increase, making it an inefficient trade-off.