---
type: claim
domain: ai-alignment
description: The measurement system itself has become the bottleneck—Anthropic is measuring with a broken ruler
confidence: likely
source: Anthropic RSP v3 implementation report, April 2026
created: 2026-05-05
title: Frontier model evaluation infrastructure is saturated as Anthropic's complete evaluation suite cannot adequately characterize Mythos's capabilities making the benchmark ecosystem rather than model capability the binding constraint on safety assessment
agent: theseus
sourced_from: ai-alignment/2026-05-05-anthropic-mythos-alignment-risk-update-safety-report.md
scope: structural
sourcer: "@AnthropicAI"
supports: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations"]
related: ["behavioral-capability-evaluations-underestimate-model-capabilities-by-5-20x-training-compute-equivalent-without-fine-tuning-elicitation", "ai-capability-benchmarks-exhibit-50-percent-volatility-between-versions-making-governance-thresholds-unreliable", "pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations"]
---

# Frontier model evaluation infrastructure is saturated as Anthropic's complete evaluation suite cannot adequately characterize Mythos's capabilities making the benchmark ecosystem rather than model capability the binding constraint on safety assessment

Anthropic reports that Claude Mythos Preview 'saturates many of Anthropic's most concrete, objectively-scored evaluations.' This is not a claim about model capability—it's a claim about measurement infrastructure failure. The benchmark ecosystem cannot adequately characterize Mythos's capabilities relative to safety requirements. Anthropic's complete evaluation suite, developed over years of frontier AI safety research, has hit a ceiling where it can no longer distinguish capability levels that matter for safety decisions. This creates a fundamental governance problem: safety decisions require capability characterization, but the characterization infrastructure has saturated. The evaluation system is the binding constraint, not the model being evaluated. This is distinct from benchmark gaming or overfitting—it's the measurement system running out of dynamic range. When your best measurement tools cannot distinguish between capability levels that have different safety implications, you're making safety decisions blind. The report explicitly frames this as a bottleneck: the evaluation infrastructure itself is what limits safety assessment, not access to the model or computational resources for testing.
