Investing in Vals · a16z News
Science, Technology & Innovation · Aug 13, 2026
The article argues that benchmarks should be retired once models “benchmax” them, with independent evaluators continuously introducing harder, decision-relevant tests to preserve meaningful differentiation and buyer trust.
Investing in Vals · a16z News
Science, Technology & Innovation · Aug 13, 2026
Private, fast, reproducible, and anti-gaming evaluation infrastructure is becoming essential for trustworthy comparisons of frontier AI models, especially as they take on high-stakes operational and enterprise tasks.
Investing in Vals · a16z News
Science, Technology & Innovation · Aug 13, 2026
AI evaluation is moving from saturated academic benchmarks to expert-designed, workflow-specific tests that measure whether models can complete economically meaningful tasks, especially in long-running autonomous settings where poor model selection is costly.