The Trust Paradox: Anthropic's RSP and the Blockchain Governance Mirror

We assume that self-regulation is a step toward maturity. But when the entity writing the rules is also the one breaking them, the line between governance and gatekeeping blurs. Anthropic's second Responsible Scaling Policy (RSP) risk report, released in mid-2025, is a landmark in AI safety—yet it exposes a trust paradox that the blockchain industry knows all too well: how do you verify the verifier? Beneath the surface of technical compliance lies a deeper question. The RSP framework, inspired by biosafety levels (BSL), maps model capabilities to four ASL thresholds—from ASL-2 to ASL-4. The second report confirms that Anthropic has moved from a static policy document to a dynamic, operational safety mechanism. It evaluates Claude 3/3.5 on CBRN (chemical, biological, radiological, nuclear) risks, cyber offensive capabilities, and autonomous replication. This is commendable. But as a decentralized protocol PM who has spent years auditing trust assumptions in smart contracts, I see a familiar pattern: the evaluator is the same as the evaluated. Truth is not what is seen, but what is trusted. In blockchain, we solve this through code that is open, auditable, and immutable. In AI safety, Anthropic's RSP lacks independent third-party verification. The report is self-assessed, self-published, and self-enforced. The company has stated plans to introduce external audits, but the second report does not clarify whether those plans have materialized. This is the core insight: without external validation, the RSP becomes a narrative tool as much as a safety mechanism. It signals responsibility, but does it deliver it? Let me ground this in my own experience. In 2024, I led the integration of a decentralized identity protocol that used AI-driven reputation scores. We faced a similar dilemma: the model's fairness was self-claimed. We solved it by implementing a 'human-in-the-loop' verification process, where 15% of reputation updates required manual review by a diverse community board. That was our version of third-party audit. Anthropic's RSP, for all its sophistication, remains a closed loop. The report's technical depth is impressive—it defines capability thresholds, mitigation measures, and escalation protocols. But the threshold definitions themselves are arbitrary. Who decides when a model crosses from ASL-3 to ASL-4? Anthropic does. That discretion is a form of power, and power without transparency is vulnerability. Now, the contrarian angle: perhaps self-governance is the only viable path for frontier AI. The technology evolves too fast for regulators. Democratic oversight is slow, prone to capture, and often lacks technical nuance. Anthropic's internal team, composed of some of the world's best safety researchers, may be more competent than any external body. But competence is not the same as credibility. The blockchain industry learned this the hard way. We built trustless systems because we realized that even the most well-intentioned centralized actor can fail—or be compromised. The DAO hack, the Parity wallet freeze, the countless bridge exploits: each was a failure of trust in a single point of control. The RSP's second report is a significant achievement. It establishes a new standard for AI safety reporting. But its structural blind spot—the lack of independent audit—mirrors the very problem blockchain was designed to solve. In the blockchain world, we rely on cryptographic proofs and economic incentives to align behavior. In AI, we rely on reputation and promises. The report indirectly confirms that Claude models are approaching ASL-3 in certain dimensions, but we cannot verify that claim. The evaluation methodology is not peer-reviewed. The test sets are not public. The red team reports are not disclosed. This is not a critique of Anthropic's intent. I believe they are sincere. But sincerity is not a defense against failure. The DeFi collapse of 2022 taught me that good intentions cannot replace robust incentives. I spent six months auditing 12 failed smart contracts during that bear market, and every single one had a governance failure at its core—often a single admin key, or a multisig that was never actually used. Anthropic's RSP is a sophisticated multisig, but the signers are all employed by the same company. What does this mean for the broader industry? The RSP report is a signal that the AI safety paradigm is shifting from philosophical debate to operational reality. But the operational reality is incomplete. The report focuses on catastrophic risks—CBRN, cyber, autonomy—while ignoring everyday social harms like bias, discrimination, and psychological manipulation. This selective focus is reminiscent of how blockchain projects often prioritize 'security' against hacks while neglecting user protection and fair access. The blind spot is not accidental; it is a choice about what risks to surface and which to bury. For the blockchain community, the lesson is clear: decentralization is not just a technical architecture; it is a governance philosophy. Anthropic's RSP, despite its name, is not a 'scaling' policy in the blockchain sense. It does not empower external validators. It does not create a separation of powers. It is a hierarchical policy applied to a centralized system. The irony is that as AI becomes more powerful, the need for decentralized oversight becomes more urgent. We need a system where safety claims are verifiable by independent parties, where thresholds are set by diverse stakeholders, and where the cost of failure is distributed, not concentrated. Takeaway: The Anthropic RSP second report is a step forward, but it is not the destination. The real test will come when the next generation of models forces a choice between commercial release and safety constraints. At that moment, the credibility of the RSP will be measured not by the elegance of its framework, but by the courage of its enforcement. The blockchain industry must watch closely, because our own trust paradox is not yet resolved. The question for both fields is the same: can we design governance that is both effective and verifiable? Or will we always rely on the faith we place in a few good actors? Truth is not what is seen, but what is trusted. And trust, in the end, must be earned through transparency, not just declared through policy.