China's AI strategy is less focused on achieving AGI and more on the immediate, practical diffusion of AI technology throughout its economy. The government's "AI+" plan emphasizes embedding AI into existing applications like WeChat and high-impact sectors like healthcare, aiming for broad, pragmatic adoption now.

The "Genesis Mission" aims to use national labs' data and supercomputers for AI-driven science. This initiative marks a potential strategic shift away from the prevailing tech belief that breakthroughs like AGI will emerge exclusively from private corporations, reasserting a key role for government-led R&D in fundamental innovation.

The tech industry's tendency to seek a single, "one-shot" solution like AGI is framed as a dangerous laziness. This mindset avoids the hard, messy work of building diverse, localized, and incremental solutions, which represents a more practical and safer path for progress.

The discourse on AI is overly focused on preventing harms like existential risk. A more productive approach is to also define a public agenda for what we want AI to *achieve*—the public 'goods' it can create, such as solving orphan diseases or simplifying government services.

A key strategic difference in the AI race is focus. US tech giants are 'AGI-pilled,' aiming to build a single, god-like general intelligence. In contrast, China's state-driven approach prioritizes deploying narrow AI to boost productivity in manufacturing, agriculture, and healthcare now.

Even if the market would eventually build decision-making tools, their impact is time-sensitive. Waiting for commercial rollout might mean they arrive after AGI, too late to help navigate the riskiest period. Therefore, philanthropic or impact-driven acceleration, even by a few months, is highly valuable.

While the US tech narrative focuses on achieving AGI (superintelligence), China prioritizes practical AI applications (superutility) that address immediate societal problems like labor gaps and healthcare access. This leads to faster, more visible, and widespread adoption among its populace.

A proposed middle path in the AI debate is to abandon the race for Artificial General Intelligence (AGI) and instead build "narrow superintelligences." These models, trained exclusively on specific domains like protein folding, could solve major problems like disease without posing a general existential threat.

The groundbreaking AI-driven discovery of antibiotics is relatively unknown even within the AI community. This suggests a collective blind spot where the pursuit of AGI overshadows simpler, safer, and more immediate AI applications that can solve massive global problems today.