When the AI Safety Index recently assigned Anthropic a C+ and OpenAI a C, the industry's governance deficit became quantifiable. As someone who has spent years auditing smart contract security at the protocol level, I recognize the pattern: a low score not on capability, but on trust infrastructure. In blockchain, we've seen how a missing audit trail can erode billions in liquidity overnight. The same principle applies to AI safety—only the stakes are broader, and the metrics are still opaque.
Context: What the Index Actually Measures
The AI Safety Index, as reported, evaluates companies on governance mechanisms, transparency, red-teaming commitments, and external audit practices. It does not measure model performance, reasoning ability, or code quality. This is a critical distinction. The scores reflect how well a company has built the institutional scaffolding for safety, not whether its models are technically secure. Anthropic’s C+ and OpenAI’s C place both in the “inadequate” range, suggesting that even the industry leaders are failing to meet basic expectations for accountability.
What makes this report particularly relevant for blockchain observers is the parallel to early DeFi security scoring. In 2020, when DeFiLlama and other aggregators began publishing security ratings for protocols, many projects initially scored poorly. Over time, those ratings forced structural changes: audits became a prerequisite for listing, and insurance protocols emerged to cover residual risk. The AI industry is now at a similar inflection point, but with an additional layer of complexity—military partnerships.
The article notes growing concerns about AI companies deepening ties with the military. This introduces a geopolitical dimension that blockchain-native governance tools, such as decentralized autonomous organizations (DAOs) and on-chain voting, were designed to address. By contrast, the centralized governance of AI labs leaves them vulnerable to conflicts of interest that are hard to audit externally.
Core: From Code to Governance—A Technical Parallel
Tracing the hidden vulnerabilities in the code, I’ve learned that security is not a feature—it’s a foundation. In my Layer2 research, I routinely evaluate rollup architectures for economic finality and data availability. The same due diligence should apply to AI safety disclosures. For instance, Anthropic’s C+ may reflect its stated commitment to Constitutional AI, but without a public, verifiable audit trail of red-team results, the score remains a reputation signal rather than a technical guarantee.
Consider the blockchain analogy: a smart contract with a “low risk” audit rating but no public proof of invariant testing is still a black box. Similarly, an AI company that claims to perform red-teaming but does not publish the methodology or results is offering trust without transparency. The core insight here is that governance scores are only as valuable as the underlying data that supports them. The article does not reveal whether the index is based on self-reported surveys, independent audits, or a combination. Without that context, the scores are best interpreted as directional warnings, not fixed truths.
One of the hidden vulnerabilities in the current AI safety discourse is the conflation of “safety governance” with “safety outcomes.” A company can have excellent governance documents and still suffer catastrophic failures if its models are deployed in adversarial environments. This is analogous to a blockchain protocol that passes all audits but fails under extreme market conditions due to oracle manipulation. Quietly securing the layers beneath the hype requires more than policies—it requires continuous monitoring, circuit breakers, and incentive alignment.
Contrarian: Why Low Scores Might Be a Feature, Not a Bug
The contrarian angle is that the low scores, rather than indicating failure, might actually reflect a healthy skepticism toward simplistic metrics. The AI Safety Index itself is a product of a specific methodology—one that may prioritize certain governance dimensions over others. A company that scores higher might simply be better at PR, not at safety. For example, a firm could invest heavily in public transparency reports while neglecting model-level robustness. Conversely, a company that scores lower might be engaging in more complex, proprietary safety research that is not easily captured by a standardized checklist.
Building trust through rigorous, unseen diligence often means that the most effective safety measures are invisible to external scoring. In blockchain, we see this with off-chain computation and zero-knowledge proofs: the most secure systems are those that minimize trust surfaces, not those that maximize disclosure. The same principle applies to AI. A company that quietly embeds safety constraints into its training pipeline—without publishing a flashy report—may be more resilient than one that ranks high on governance metrics but has brittle adversarial defenses.
Moreover, the concern about military ties is not uniformly negative. In some contexts, defense contracts impose stricter security requirements than civilian ones, potentially driving better internal safety practices. The real risk is not the partnership itself, but the lack of transparent oversight. Here, blockchain’s immutable ledger could offer a solution: a public audit trail of model deployment, usage, and incident response, tied to on-chain identity. This would allow external stakeholders to verify safety claims without relying on self-reported scores.
Takeaway: The Future of Trust Infrastructure
Looking ahead, I expect the AI Safety Index to become a key metric for enterprise AI procurement, similar to how blockchain security ratings now inform DeFi investment decisions. However, the industry must move beyond annual surveys and toward continuous, verifiable governance. Decentralized audit networks, like those emerging for smart contracts, could be adapted to AI safety—enabling real-time red-teaming results, model versioning, and compliance checks on public blockchains.
Redefining what ownership means in the digital age includes owning the responsibility for safe deployment. The current C-grade is a wake-up call, but it is also an opportunity. By adopting blockchain-inspired transparency tools—on-chain governance, immutable logs, and decentralized verification—AI labs can turn governance from a checkbox into a competitive advantage. The question is not whether they will, but whether the market will demand it before the next crisis.