Anthropic released its second Responsible Scaling Policy (RSP) risk report in June 2025. The document is dense. It is structured. It is also, from a blockchain security architect's perspective, a fascinating case study in self-governance that the crypto industry should watch closely.
Hook
Here is the anomaly: the report contains no independent third-party audit. Zero. The same entity that trained the models, evaluated their risks, and deployed the safeguards is the one signing off on the assessment. In smart contract security, we call this a single point of failure. In AI safety, it is the norm.
Context
Anthropic's RSP is a framework that maps model capabilities to safety levels (ASL-1 to ASL-4), borrowing from biosafety classification. The second report continues this system, focusing on Claude 3/3.5 series models. It evaluates risks in CBRN (chemical, biological, radiological, nuclear), cyber attack capability, and autonomous replication. The report claims to operationalize safety from a static document into a dynamic assessment mechanism.
But the crypto industry has seen this play before. Projects launch with governance tokens, promise decentralization, and then retain admin keys. The RSP is the AI equivalent of a multisig where all signers are employees of the same company.
Core
Let me decompose the technical architecture of the RSP framework as if it were a smart contract audit.
First, the ASL classification system resembles a risk-level enum in Solidity. ASL-2 is safe for public deployment. ASL-3 triggers deployment restrictions — KYC, access controls, weight protection. ASL-4 is near-AGI, effectively a kill switch. The framework is programmable. It defines thresholds. But the oracle that determines whether a threshold is hit is internal. There is no external validator.
Second, the report's methodology for capability assessment is vague. It mentions red-teaming and benchmark tests. But what are the exact test sets? Are they reproducible? In blockchain, we demand that audit reports include pass/fail criteria, gas usage, and edge case coverage. The RSP report does not provide a comparable level of transparency.
Third, the coverage gap is structural. The RSP focuses on catastrophic risks — bioweapons, cyberattacks. It ignores everyday harms: bias, discrimination, privacy violations. This is like a DeFi protocol that audits only its flash loan functions but ignores its price oracle. The tail risk is sexy. The systemic risk is boring. But the boring risk is what breaks the system.
From my experience auditing the Ethereum Classic hard fork, I learned that the smallest gas calculation discrepancy can corrupt state. The RSP's blind spot on social risks is that discrepancy. It may not crash the model today, but it will erode trust over time.
Contrarian
Here is the counter-intuitive angle: the RSP's self-assessment model is not a bug. It is a feature designed to centralize regulatory power.
Consider the incentives. Anthropic defines what "dangerous capability" means. It decides the ASL-3 threshold. It controls the narrative. In a world where AI regulation is still nascent, this self-regulation gives Anthropic a seat at the table — it writes the rules that competitors must follow. The second report cements this position.
But there is a trap. The RSP's credibility is a fragile asset. If a future model triggers ASL-4 restrictions and Anthropic chooses to prioritize commercial deployment over safety, the entire framework becomes a liability. "Execution is final; intention is merely metadata." The same applies to governance promises.
Furthermore, the report's timing — released just before a new model launch — suggests a competitive orchestration. The report is not just a safety update. It is a marketing asset. Anthropic signals to enterprise clients: "We are the safe choice." This is analogous to a blockchain project releasing a security audit right before a token sale. The audit may be genuine, but its function is to build trust for monetization.
Another blind spot: the RSP's requirement for weight protection implies that model weights must be stored on centralized cloud infrastructure (AWS, Google Cloud). This creates a multi-party trust dependency. Anthropic trusts AWS. AWS trusts its security engineers. The model's safety relies on the weakest link in that chain. In crypto, we call this custodial risk.
Takeaway
The RSP second report is a signal. It tells the industry that safety governance can be institutionalized. But it is not a solution. It is a starting point — a minimum viable framework that still lacks independent verification, comprehensive coverage, and transparent metrics.
For the crypto industry, which is building AI-crypto hybrids — autonomous agents, decentralized trading bots, on-chain governance models — the RSP serves as both a reference and a warning. Reference: it shows how to structure risk levels. Warning: do not copy the self-audit model. Smart contract security learned this lesson the hard way. The DAO hack, the Parity wallet freeze, the Wormhole exploit — all stemmed from systems that were audited by the same teams that built them.
"Inheritance is a feature until it becomes a trap." Anthropic is inheriting the responsibility of defining AI safety. The trap is that without external checks, the framework becomes a tool for consolidating power rather than managing risk.
If I were to design a security standard for AI-crypto hybrids, I would require the following: a publicly verifiable risk registry, third-party red-teaming with full disclosure, and a decentralized appeals process for threshold disputes. The RSP has none of these.
The second report is progress. But progress is not safety. In the blockchain world, we know that trust is earned through transparency, not through declarations.
