An independent evaluation company for foundation models and agents
Create standardized evaluations that measure models and agent systems consistently, prevent benchmark cheating, and allow competing foundation-model companies to be compared under the same execution conditions.
From Latent Space — The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI at 01:07:36
Problem: Self-reported benchmarks can be difficult to compare because companies may run evaluations differently, use different infrastructure or tools, or inadvertently optimize for the benchmark rather than real-world capability.
For: Foundation-model companies, model users, investors, and developers who need trustworthy comparisons of models and agent systems.
Examples
- Artificial Analysis: Poolside identified it as a third party that could run independent evaluations of models, including checking the relevant infrastructure and tools.
Behind this: 8 build steps · 1 tool and how each is used · how to validate demand · 5 things the video never answers.