China doesn't need the best AI models to win the AI race, just the cheapest widely used ones

[Sophie86]

A growing number of analysts argue China's path to AI leadership doesn't run through building the single most capable model, it runs through making AI so cheap and widely deployed that raw capability becomes almost beside the point. SenseTime, a Hong Kong founded AI company that's faced US sanctions over allegations tied to surveillance in Xinjiang, which it denies, is a clear example of the strategy in action, its cofounder and chief scientist Lin Dahua says the company has explicitly taken cues from DeepSeek's approach of delivering strong performance under real financial and technical constraints rather than chasing frontier benchmarks at any cost

The business logic behind this bet is straightforward and familiar from other Chinese tech sectors, bleed cash to gain market share now, worry about monetizing later. Analysts at Jefferies have pointed out that pure play AI model companies face a genuinely tough underlying equation, low customer loyalty, thin differentiation between competitors, a crowded field, and high training costs that don't easily translate into pricing power. China's advantage is that its largest platform companies, Alibaba, Tencent and ByteDance, can subsidize AI development directly out of profitable core businesses in a way that standalone AI labs simply can't sustain indefinitely

The capability gap itself has narrowed dramatically even as the US continues to hold the outright performance lead. Brookings researchers frame the US-China AI competition as playing out across several genuinely separate dimensions at once, compute scale, model performance, cost efficiency, open source adoption, and real world deployment, arguing the US retains a clear edge on the first two while China is advancing fastest specifically on the latter three. That distinction matters because the eventual winner may not be decided by whichever country builds the single smartest model, but by whichever one manages to weave AI most deeply and cheaply into everyday economic activity at scale

The open source angle compounds this further. China has been unusually willing to release capable models openly, letting the rest of the world adopt, fine tune and build on them, an approach some strategists describe as a deliberate come from behind play, accept being slightly behind on the frontier in exchange for wider global adoption of your technology stack. Whether that strategy pays off depends heavily on questions that remain genuinely contested, whether Chinese platform companies can keep subsidizing AI development indefinitely, whether cost efficient but slightly less capable models satisfy enough real world use cases to matter, and whether the US's continued lead in compute and raw frontier capability turns out to be a durable moat or just a temporary head start

China Can Still Win The Ai Race With Inferior Technologywww.bloomberg.com

[Tel92]

The bleed cash now, monetize later playbook is such a familiar pattern from Chinese ecommerce and ride sharing, makes total sense to see it applied to AI given how well it's worked in other sectors before

[Olivia87]

Breaking the race into separate dimensions, compute, capability, cost, adoption, is the right way to think about this instead of treating it as one single scoreboard with an obvious winner

[Luca76]

Platform companies subsidizing AI out of profitable core businesses is a genuinely durable advantage that pure play AI labs on either side of the Pacific simply don't have access to