The data suggests a paradox. Anthropic scores C+. OpenAI scores C. The AI safety index, much like a flawed smart contract audit, reveals governance gaps but not technical competence. Yet the market reacts with indifference. The bull market euphoria in AI, much like in crypto, masks a fundamental truth: security is not a score, it is a process. And the process is broken.
Context: The Safety Index as a Governance Proxy
The AI safety index in question measures public commitments, transparency, red teaming, external audits, and accountability mechanisms. It does not measure model reasoning, code generation, or multimodal accuracy. It is a governance proxy, not a technical capability benchmark. Anthropic and OpenAI, two industry leaders, received C+ and C respectively. The entire industry is in the "C" range. This is not a failure of technology; it is a failure of governance.
The military ties concern adds another layer. The article notes that AI companies are deepening ties with defense agencies. This is not inherently unethical, but it shifts the risk profile. Public trust, already fragile, erodes further when the line between civilian and military AI blurs. The safety index does not account for this, but the market does—or should.
Core: Tracing the Safety Score Anomaly Back to the Governance Model
Based on my experience auditing Layer2 fraud proofs, I see a parallel. In DeFi, security audits often measure code coverage, not economic resilience. Similarly, the AI safety index measures disclosures, not actual safety outcomes. The anomaly is not the low score; it is the assumption that the score correlates with real-world risk.
Let me break down the governance model. The index likely uses a weighted rubric: (1) public policy commitments, (2) red teaming frequency, (3) external audit independence, (4) transparency of training data, (5) incident reporting. Each metric is a proxy. But proxies can be gamed. A company can publish a policy document without enforcing it. An external audit can be a compliance checkbox rather than a deep dive. The index does not differentiate between theater and substance.
Tracing the safety score anomaly back to the governance model reveals a systemic issue: the industry lacks standardized, verifiable metrics. In blockchain, we solved this with on-chain proofs and decentralized verification. AI has no equivalent. The index is a centralized scorecard, subject to the same biases and limitations as any single source of truth.
The economic incentive behind AI safety theater is clear. Companies want to signal safety to regulators and enterprise customers without incurring the cost of actual safety. The cost of real safety—comprehensive red teaming, continuous monitoring, external audits with code-level access, provable alignment—is high. The cost of a safety report is low. The market rewards the report, not the substance.
From EVM to LLM: A security skeptic's framework applies the same first principles. In EVM, we trace gas costs to understand execution efficiency. In LLM, we trace governance costs to understand safety credibility. The transaction cost of a safety commitment is zero; the verification cost is non-zero. The index does not capture verification costs.
Contrarian: The Low Score Might Be a Feature, Not a Bug
Counter-intuitive angle: the low scores could be a sign of honesty. A company that scores itself a C+ might be more transparent than one that scores an A through obfuscation. The index does not adjust for self-reporting bias. If the scoring methodology is based on self-disclosed data, the lower scores from Anthropic and OpenAI might reflect a willingness to reveal weaknesses, not a lack of capability.
Furthermore, the military ties concern might actually force better security. Defense contracts often require rigorous security standards, independent audits, and supply chain verification. If AI companies are forced to meet military-grade security requirements, the overall safety posture could improve. The index does not account for this potential catalyst.
The real blind spot is not the score itself, but the lack of a standardized, auditable, and time-based measurement. In blockchain, we have block explorers, Merkle proofs, and real-time fraud detection. AI safety lacks such infrastructure. The index is a static snapshot, not a dynamic system.
Takeaway: The Future of Trust Requires Verifiable Oracles
The AI safety index is a symptom of a deeper problem: the industry needs a new form of trust—verifiable, transparent, and resilient. Just as DeFi moved from trust-based to trust-minimized systems, AI must move from governance reports to on-chain attestations, open-source red teaming, and cryptographic proofs of alignment.
The military ties will accelerate this shift. Defense agencies will demand provable safety, not just promises. The next generation of AI safety will be built on cryptographic primitives, not PDFs. The question is not whether Anthropic or OpenAI has a better score today. The question is whether the market will demand verifiable proofs before the next systemic failure.
