By 2026, numerous Chinese enterprises had embarked on significant AI transformation journeys, mandating their workforce to transfer their invaluable work experience to AI systems. This shift marks a sharp departure from Toyota Motor's renowned creative proposal system. Toyota had cultivated a culture of trust among its employees through the promise of lifetime employment, a luxury not extended by these new-age companies, thereby highlighting the emerging challenge known as the 'context tax.' Amidst fierce competition and concerns over talent retention, these companies were quick to embrace AI applications. Yet, during the execution phase, employees grappled with a twofold predicament: the struggle to effectively convey their expertise (due to factors like fragmented knowledge, difficulties in translating offline scenarios, and personal capability limitations) and the reluctance to do so (fueled by fears of AI-induced displacement and a lack of incentives or safeguards). Consequently, a staggering 95% of enterprise generative AI pilot projects met with failure.

To tackle this pressing issue, some companies experimented with strategies to mitigate the 'context tax,' including integrating system data, embedding workflow capture tools, and instituting positive incentive mechanisms. However, some of these methods sparked ethical debates or operated with ambiguous mechanisms. At its core, the current phase of enterprise AI transformation entails the transfer of employees' core production assets—essentially their knowledge—from individuals to organizations, reminiscent of the concept of knowledge enclosure. Alarmingly, existing legal frameworks offer little guidance or regulation in this domain, exacerbating the trust deficit between companies and their employees.