Source: AI Governance, Ethics and Leadership
The article documents a specific mechanism of competitive advantage in AI: using networks of fraudulent accounts to systematically query competitors' models at scale, harvesting their reasoning patterns and coding outputs to train superior versions. This reveals a concrete enforcement gap in AI governance—regulatory frameworks focus on safety alignment and transparency while leaving intellectual property extraction through automated scraping largely unaddressed. Whoever can legally or illegally access the most training data from cutting-edge models gains a measurable edge in both capability and speed-to-market.