Major AI labs are releasing new models simultaneously, but user trust has shifted away from traditional benchmarks, which are seen as unreliable due to 'bench hacking.' Instead, evaluation now relies more on practical demos (e.g., 'pelican on a bicycle') and analysis from trusted industry voices.

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.

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.

Current AI benchmarks have become targets for competition, an example of Goodhart's Law. Models are optimized to top leaderboards rather than develop the general capabilities the benchmarks were designed to measure, creating a false sense of progress and failing to predict real-world performance.

Companies like Meta are engaging in "chart crimes" to frame new models in the best possible light. By selectively highlighting winning benchmarks (e.g., in blue), they create a visual impression of superiority, even when the model underperforms in other key areas. This signals that benchmarks are becoming marketing tools rather than objective measures.

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.

Leading AI companies like OpenAI are publicly discrediting established benchmarks (SuiteBench Pro) and creating their own. This signals a shift where companies use custom benchmarks to highlight their model's strengths, making direct comparisons difficult and forcing users to rely on subjective "vibes" rather than objective standards.

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.

When Meta released Llama 4, it excelled on public benchmarks where questions were open source. However, on VALS' private, held-out benchmarks, it significantly underperformed, revealing a major disconnect and showing that self-reported, public scores can be misleading indicators of true capability.