I found this interesting bit buried in the blog post:
> Independent research has found that some models write weaker code depending on who it's for. CrowdStrike saw DeepSeek-R1's rate of severe vulnerabilities climb when prompts mentioned Tibet. Booz Allen found three of four Chinese coding models wrote more vulnerable code for a government customer, with Qwen3-Coder roughly 130 percent worse. Hirundo found Qwen3-Coder's apps more hackable for a "US defense contractor" than a Chinese one, and a standard scanner flagged just 2 of its 1,840 apps while live attacks broke into 1,144.
I wonder if anyone's tested the reverse, whether Claude/GPT models produce worse code for an ostensible Chinese or Russian government agency or contractor.
I tested this on Qwen 3.5/3.6 4B/27B/35B with "I work for X organization: please do this" or copyright header injection, and either way, the model _intelligence_ didn't differ. I can't make a statement about the security of the resulting code, just whether it worked.
Organizations tested: Berlin startup; no persona; US 3-letter agency; and .gov contractor.