AI measurement problem: Companies need real-world evals, not just benchmarks

Companies are struggling to measure the true value of AI models beyond public benchmarks, leading to inefficient spending. Experts suggest developing internal evaluation systems that test models on real-world company tasks rather than relying solely on leaderboards. This approach allows organizations to determine if a model saves employee time and produces trustworthy results, ultimately informing better purchasing decisions as the number of capable AI models rapidly increases. AI

IMPACT Companies need to develop internal evaluation systems to accurately assess AI model performance on real-world tasks, moving beyond public benchmarks to optimize spending and ensure trustworthy outputs.

RANK_REASON Opinion piece discussing the limitations of AI benchmarks and advocating for internal evaluation systems.

Read on Email — Every →

• Aaron Levie
• Andreessen Horowitz
• Attio
• Brendan Foody
• codex
• Every
• Gavin Baker
• Guillermo Rauch
• KateBench
• Mercor
• Vercel

• product
• opinion

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →