Everything on Evaluation Limitations

Current benchmarks often exclude messy, open-ended, or vision-heavy tasks, leading to an overestimation of model autonomy. Real-world deployment requires evaluating models on complex, multi-step workflows that standard tests do not capture.

Impact: Enterprises must develop internal evaluation frameworks that test AI models on real-world, complex tasks to avoid overestimating their capabilities and potential risks.

— from METR AI Capability Metrics and Market Implications · Latent Space: The AI Engineer Podcast· Feb 27, 2026