The narrative of “AI at all costs” seems to have lost some steam recently. Once heralded as an unstoppable force for growth, artificial intelligence now faces critical questions about sustainability, particularly as funding models and market sentiment shift.
One indicator worth watching closely in comparison with the dotcom era is the source of funding for hyperscaler capital expenditure. We are now seeing evidence that companies have started to rely on debt rather than free cash flows and earnings to finance their spending needs. This shift has contributed to a change in sentiment around an overheated AI boom and triggered a sell-off in the US equity market.
Meanwhile, closer to home, Asian stocks have also begun to consolidate after an extended rally, with the Hang Seng Tech Index retreating from a multi-year high in October. Nonetheless, China’s approach to AI, backed by strong policy support and a deliberate effort to balance competition with innovation, could propel Asia’s AI-driven rally further.
China has a different vision for AI from that of the United States. While US firms focus on artificial general intelligence and high-performance models, China is emphasising efficiency, adoption and ecosystem impact. The key question now is how China can balance intensifying competition with a more sustainable innovation trajectory.
Unlike the capital-heavy strategies of Western tech giants, Chinese firms are demonstrating that advanced AI can be built without top-tier hardware. Models such as DeepSeek R1 and other open-source entrants reflect a shift towards cost efficiency and accessibility. This has spurred a wave of open-source development among domestic players, prioritising openness, replicability and shared progress over pure proprietary control.
This approach is shaped by necessity. Limited access to cutting-edge AI chips has prompted China to pursue a more commercially viable path focused on application-level success. Thus, a vertical strategy involving smaller models trained on proprietary data may be best suited for resource-constrained enterprises in China.
Firms with domain-specific data sets, such as healthcare records or industrial logs, are well positioned to train tailored models, build defensible moats and eventually translate these capabilities into profitable and scalable offerings. This is an ongoing shift where China is moving away from pushing the limits of model size and focusing instead on developing practical applications built on existing architectures.
This shift towards practical applications also highlights the role of China’s open-source approach, which enables faster iteration through shared code, reduces entry barriers and fosters a more collaborative innovation environment. By distributing development across a broader base of contributors, the ecosystem has become more resilient to external shocks and less reliant on any single chokepoint.
Looking ahead, China is well positioned to lead in AI application development. The momentum sparked by DeepSeek’s breakthrough earlier this year has raised awareness and accelerated adoption of AI. Major platforms are increasingly integrating AI features into their existing ecosystems, leveraging proprietary data and user engagement to deliver more personalised services. Generative AI usage is also on the rise.
Practical-use cases are appearing all over the digital ecosystem. The approach shows how Chinese firms are moving beyond traditional enterprise solutions and tapping into the fast-growing consumer AI segment.
AI adoption is also expanding across industries. In robotics, models are being used to train machines to perform tasks even in unfamiliar environments. Chinese robots have already competed in half marathons, sparred in boxing matches and performed folk dance routines.
All of this reflects Beijing’s ambition to make AI foundational and widely accessible, a goal that has been reinforced by strong policy support. In August, the State Council’s “AI Plus” plan set out to integrate AI into six major sectors with a target of 90 per cent adoption by 2030, while the latest five-year plan strengthens this direction by designating AI as a core technology for the country.
Even with a clear development strategy and strong policy support, the next challenge for China’s AI sector is finding a path that avoids the pitfalls of intensifying competition. Experience from industries such as cars, energy and materials has already driven policy efforts to prioritise quality and innovation. A more sustainable path forward involves fostering innovation-driven growth where development is shaped by differentiation, operational efficiency and scalable impact rather than just price-based competition.