Bitcoin company leaders and open-source developers are publicly stating that Chinese AI models are currently outperforming restricted American frontier systems in defensive cybersecurity work, forcing researchers to rely on them to secure critical Bitcoin infrastructure.
Rob Hamilton, CEO of AnchorWatch, a Bitcoin self-custody insurance company, reported cripling American AI restrictions. After integrating OpenAI’s trusted cyber program (having already completed KYC months earlier), he was blocked from further analysis on a codebase he had already responsibly disclosed. “It absolutely guts me as a patriotic American to have to do this, but I will be going back to using Chinese open source models to conduct my research to protect Bitcoin infrastructure,” Hamilton wrote. “Black hats will not hit these issues. The white hats will.” Days later, he gained access to OpenAI’s “Daybreak Blue” cyber model and was blocked again within 19 minutes while red-teaming Bitcoin infrastructure.
Francis Pouliot, founder of Bull Bitcoin, a Bitcoin-only exchange focused on self-custody infrastructure, described the situation bluntly. “I have never seen OpenAI this cucked. It’s cucked beyond belief now. Not even for security, for anything related to Bitcoin,” he posted. “USA AI industry is completely cooked if they don’t change this path,” he concluded, adding “Open-source Chinese LLMs. [orange heart emoji],” meaning that open Chinese models like Kimi K3 are actually helpful to Bitcoin. In a follow-up, Pouliot detailed how a Chinese open-source model identified a money-stealing exploit in a project he was auditing, demonstrated it on regtest, and helped patch it. When he asked the American models he pays for to review the same patch, they refused.
PortlandHODL, a Bitcoin Core contributor who builds for AnchorWatch, publicly highlighted the performance gap. “US-based Frontier AI Model – ‘You’re absolutely right!’ Chinese Open Model – ‘78 critical vulnerabilities found.’ The implications of this are unfathomable,” he posted. In a follow-up, he added that he felt he was “basically asking Xi to not get my software hacked at this point,” calling for OpenAI and Anthropic to create proper access programs for U.S. citizens doing defensive security work.
Alex Thorn, Head of Firmwide Research at Galaxy, signed a recent Bitcoin Policy Institute open letter demanding trusted access to frontier models for open-source defenders. “Americans should not have to rely on Chinese AI to defend themselves, their projects, companies, or clients from cyber-attacks,” he wrote. “RED TEAM NEEDS THE MODELS.”
On August 10, the Bitcoin Policy Institute — a Bitcoin and, of late, AI-focused policy think tank — published an open letter signed by more than 70 organizations across the digital-asset ecosystem, including major custodians, exchanges, mining firms, and open-source development groups. The letter calls on frontier AI labs to establish clear trusted-access programs for qualified open-source and digital-asset defenders. It argues that current restrictions and safety guardrails leave legitimate security researchers without access to the strongest models, forcing them to rely on less capable open-weight alternatives while sophisticated attackers face no such limits. The signatories request early access to cyber-capable models, sufficient compute, secure environments for reviewing code, and direct channels with lab security teams, stating that frontier AI could become one of the most powerful defensive technologies available if defenders are given fair access.
These statements reflect a broad pattern among Bitcoin security researchers: American models from OpenAI and Anthropic frequently refuse or restrict legitimate defensive work, even to users who are supposed to have been granted explicit access, while Chinese models such as Kimi K3 operate without the same guardrails and are delivering confirmed results. Concerns about hosting infrastructure of Chinese models being an attack vector can also be mitigated, since they are open source and can be run on American-hosted data centers, a trend that is likely to threaten the U.S. AI market if it continues.
Coldcard Exploit Triggers Ecosystem-Wide Response
The cybersecurity pressure became acute in the Bitcoin industry after a firmware flaw in Coldcard hardware wallets was exploited beginning July 30, resulting in the theft of well over $100 million in bitcoin from seeds generated with insufficient entropy. Bitcoin Magazine published an urgent advisory urging affected users to migrate funds: COLDCARD SECURITY RISK: IMMEDIATE ACTION REQUIRED.
In response, a volunteer effort known as the Bitcoin Red Team formed, led by open-source developer Calle (creator of Cashu and the Android version of Bitchat) and Rob Hamilton. The group has conducted large-scale AI-assisted audits of Bitcoin open-source repositories, using models including Kimi K3 as the primary workhorse alongside limited access to Western systems. Early results, covered by Bitcoin Magazine, showed thousands of findings across hundreds of projects, including dozens of critical issues, with spending covered largely by OpenSats.
By August 8, after more than 100 hours of work involving dozens of contributors, the team reported scanning 501 projects and producing 7,958 findings, of which 1,280 were rated high or critical severity. The majority of compute spend continued to go to Chinese open-weight models.
Lessons from the Red Team Campaign
Most recently, Calle shared lessons from the intensive red-team period. The effort has essentially completed a basic scan of virtually the entire Bitcoin open-source landscape; low-hanging fruit is largely exhausted, the developer wrote on this X account. Maintainers across projects have validated many of the critical and high-severity reports, while response times from projects vary widely and serve as a signal of overall health.
Key takeaways include the need for every project to maintain its own permanent AI audit pipeline going forward. Projects that began such reviews months earlier are in a markedly stronger position. Unmaintained repositories should be treated as likely broken and unreliable.
Calle also warned that the human-only era of open-source security review is over; verification is now effectively free, and information overload must be handled with AI rather than complaints about PR slop. Multiple concurrent and diverse human approaches remain the strongest method for finding vulnerabilities, and external red-teaming will likely be required indefinitely.
Calle also repeatedly emphasized that developers should stop writing security-critical code in C. In a follow-up post he explained: “we’re finding memory-safety vulnerabilities in c projects that are prevented by default in many other languages. In the past, finding a simple buffer overflow wasn’t enough. You’d need a highly skilled hacker to turn the vulnerability into a working end-to-end exploit. Today, that’s a single prompt.”
Bitcoin was the first major open-source ecosystem to confront this collision between accumulated human code and frontier AI capability. The rest of the software world is expected to follow.