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state pre-emption

The most consequential and contested element of any federal push is whether it would pre-empt the growing number of state AI laws, several of which already impose transparency, bias-testing, or safety obligations that exceed anything under federal discussion. Industry has broadly favoured a single federal standard over a fifty-state patchwork, arguing that compliance complexity slows deployment without improving safety outcomes. State officials and some consumer advocates counter that pre-emption risks locking in a weaker baseline nationally, effectively overriding stronger protections that states adopted precisely because federal action had been slow to arrive.

• Mandatory reporting to a federal body once training runs exceed a defined computing-power threshold, mirroring an approach already floated in earlier federal AI guidance.
• Required disclosure of safety-testing results for frontier models before public release, though the scope of what counts as a qualifying model remains contested.
• An incident-reporting mechanism for serious AI failures, modelled loosely on existing frameworks in aviation and pharmaceuticals.
• Possible export-control coordination tying chip and compute access to safety-compliance status, linking the domestic framework to broader national-security policy.