Q: Why are bitcoin and crypto firms asking for early access to top AI models? A: They argue safety guardrails on frontier models block legitimate security audits and push defenders onto weaker open-weight models, while attackers face fewer constraints and can access new capabilities earlier. Defenders also point out they protect about a trillion dollars of infrastructure and need the strongest tools to find and fix bugs before criminals do. Q: What specific requests did the signatories make to AI labs? A: They asked for five things: early access to the strongest cyber-capable models (including pre-release), sufficient compute budget to run meaningful reviews, secure sandboxes for private code analysis, eligibility that includes small and independent maintainers, and a direct reporting channel to lab security teams. These requests reflect the push for AI model access for crypto security to level the playing field between defenders and attackers. Q: How should teams structure an AI-assisted code review workflow safely? A: Teams should use the strongest available model for reasoning and code understanding when policies allow, pair it with an open-weight model for offline triage if needed, and run scans in an isolated workspace with secrets removed. They should scope reviews to critical-path components, prompt models for defensive findings (not exploit code), triage and reproduce issues in a test harness, and add human review before disclosure or patching. Q: What operational safeguards should be in place before sending code to hosted models? A: Scan code in an isolated environment with no live keys or production data, limit network egress, and mask or remove secrets before sending any snippets. Never send user data, private keys, or production secrets to a model and follow model provider terms rather than attempting to bypass safety systems. Q: What does the 30-day checklist recommend to move fast and safely? A: Week 1: define scope, set up an isolated review environment, and draft a responsible disclosure policy; Week 2: pilot one hosted model and one local model on a small code slice and build prompt templates for defensive review. Week 3 triages results, verifies top findings and ships at least one patch, while Week 4 expands coverage to critical dependencies, engages a community red team, and requests lab access aligned to the five needs. Q: How can small maintainers and community defenders get support to run deeper reviews? A: The article recommends labs and funders provide compute credits and make eligibility open to small and independent maintainers rather than limiting access to large firms. It also suggests funding stipends for triage, testing and release engineering and encouraging participation in structured community red teams and audit sprints. Q: How should vulnerabilities found with AI be disclosed responsibly? A: Notify maintainers privately with minimal proof, an impact assessment and a suggested fix path, and publish an advisory only after a patch ships and users have time to update. Labs should also offer direct reporting channels so community researchers can reach lab security teams quickly for responsible disclosure. Q: Do attackers currently have access to the same AI capabilities as defenders? A: The article says attackers face none of the safety constraints defenders encounter and that labs and a handful of partners can see new offensive capabilities months before others, while those capabilities spread via public models, stolen access, or purpose-built tools. That dynamic is why defenders are pushing for improved AI model access for crypto security and better cooperation with labs.