Leading AI labs OpenAI and Anthropic came close to a formal agreement to perform safety tests on each other's models but the deal was ultimately abandoned. This failure of industry self-regulation indicates that despite public calls for accountability, internal competition and complexity are preventing proactive measures, likely forcing government to step in.

The order's voluntary framework for pre-release model testing mirrors agreements major AI labs already have with the Department of Commerce. It reflects current industry behavior rather than imposing new, substantive regulations, failing to meet public calls for stronger government oversight.

The accord's requirement for external audits necessitates new compliance infrastructure for AI companies. This includes end-to-end traceability of AI actions, mapping AI policies to business risks, and creating auditable evidence for boards and regulators.

The FTC's investigation into OpenAI and Anthropic may not be purely adversarial. Some analysts suggest it could function as a "protection racket," where the current administration clears the companies to create hurdles for future probes, or as a way to give teeth to self-regulatory pledges via existing law.

High-profile government accords—such as the White House Accord on Super Intelligence or the 2023 voluntary commitments—consist of non-binding pledges that replicate existing industry promises without legal enforcement mechanisms. Rather than instituting actionable regulatory guardrails, these events serve primarily as political loyalty tests and photo opportunities where tech leaders publicly defer to administration narratives to protect regulatory leverage.

Federal and state governments are massive customers of technology. Instead of relying solely on legislation, they can use their procurement power to enforce AI safety and ethical standards. By setting strict purchasing requirements, they can compel companies to build more responsible products.

The White House has centralized AI oversight without enacting statutory regulations or making its testing framework public. This informal approach leaves emerging labs in a liminal zone, uncertain whether their models require review or how security testing is divided among agencies.

A significant but overlooked barrier to AI safety is the threat of antitrust litigation. The US government could view industry coordination on pacing development or safety standards as anti-competitive collusion. This risk of legal action, especially from a hostile administration, disincentivizes labs from pursuing crucial, voluntary safety agreements.

Instead of direct regulation, the government could act as a reluctant mediator for AI safety. By setting a deadline for labs to form their own collaborative safety pact, it creates a powerful incentive: if they fail, the government will impose 'heavy-handed' and likely suboptimal regulations, an outcome all parties want to avoid.