Topic: AI Economics

We examine the recent IPO filings of Zhipu AI and MiniMax, two leading Chinese AI unicorns preparing to list in Hong Kong. Zhipu AI focuses on the B/G sector with over 8,000 institutional users, aiming to be the foundational infrastructure for domestic enterprises through localized deployments. Conversely, MiniMax targets the global consumer market, generating 70% of its revenue from overseas through products like Talkie and reaching a massive scale of 27 million MAUs. While both companies show explosive revenue growth, they face significant financial pressure due to massive R&D and compute costs, with current cash reserves estimated to last only one to two years. This analysis highlights a critical juncture for AI startups transitioning to public market scrutiny amidst fierce domestic price competition.

Today we highlight a profound observation from Aaron Levie regarding the application of the Jevons paradox to the evolving landscape of knowledge work. As AI significantly reduces the marginal cost of performing complex tasks, we anticipate a massive surge in total activity rather than a simple replacement of human labor. We believe the vast majority of future AI tokens will be directed toward initiatives that are currently impractical, such as unstarted software projects, exhaustive contract reviews, and accelerated medical research. This shift suggests that developers and knowledge workers will manage a far greater volume of output, fundamentally expanding the scope of what is possible in enterprise environments. By making it cheaper to execute any imaginable task, the industry is poised to launch marketing campaigns and research endeavors that would have otherwise never existed. This perspective offers a critical lens for understanding how generative AI will scale human productivity beyond current constraints.