Google has launched a pilot for double-blind AI evaluations, according to a Techmeme item that attributes the announcement to Google DeepMind. The pilot keeps external evaluations inside a cryptographic “box,” with the stated aim of preventing benchmark contamination while protecting intellectual property. The key idea, as described in the feed summary, is to build trust in proprietary model benchmarks using cryptographic methods. The provided material does not include the full technical design, so the exact implementation details are not clear from this cluster alone. The pilot addresses a persistent measurement problem in AI: once benchmark content becomes visible or widely circulated, it can become harder to tell whether a model is demonstrating general capability or has been optimized around known tests. At the same time, model developers and evaluators often have incentives not to disclose proprietary systems, prompts, datasets, or evaluation material. Google’s framing suggests the pilot is meant to separate those interests: outside evaluations can be run, while benchmark material and intellectual property remain protected. Because this is described as a pilot, not a broad industry standard or full rollout, the practical impact will depend on who participates and what results Google discloses next. Who benefits: Google benefits if the pilot strengthens confidence in evaluations associated with its models and research process. External evaluators could also benefit if the system gives them a safer way to run protected benchmarks. Who's exposed: The risk is clearest for organizations relying on public or easily leaked benchmarks as proof of model quality. If protected double-blind evaluations gain traction, weaker evaluation practices may look less credible.