- OpenAI is ceding enterprise share to Anthropic/Alphabet, raising funding risk that could delay fulfilment of >USD1.4tn infrastructure commitments
- US data-centre growth is constrained by grid congestion where some key hubs reported 2–5-year of wait time, unlike China’s remote, power-abundant hyperscalers’ bases
- While DRAM suppliers’ shift toward high margin HBM is driving memory price inflation and weighing on PCs and smartphones, the impact on AI players remains limited
- China’s domestic AI chips are moving from inference to training – supporting a multi-year substitution away from US AI chips
- We prefer full-stack AI plays in China as their vertical integration support cost-performance for clients
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We see two challenges in the AI space specific to the US. First, the world’s biggest large language model (LLM) player, OpenAI, has been losing market share in the enterprise space to Anthropic and Alphabet. This casts doubt on OpenAI’s ability to raise funds. Meanwhile, OpenAI has made several significant deals with a total estimated value of over USD1.4tn in future commitments for GPUs and cloud infrastructure. If OpenAI continues to lose market share and is unable to raise funds, there is a risk of it not being able to fulfil orders with Oracle, Broadcom etc. On the other hand, key LLM players in China such as Alibaba, TikTok, and Baidu are not reliant on external funding to fulfill their capex commitments. Second, we see challenges in the US data-centre space where power crunch is causing significant delays for new DC projects. Northern Virginia – the largest DC market in the US – is facing severe grid congestion while cities like Phoenix, Dallas, and Atlanta are reporting 2–5 year wait times for new capacity. Meanwhile, we don’t see any power crunch in China as many hyperscalers’ DCs are already operating in remote regions with abundant cheap power. The third challenge is the recent surge in memory prices due to manufacturers diverting their production towards high-margin high bandwidth memory (HBM) for AI. This has caused a memory supply shortage for consumer electronics (PCs, smartphones) which are forced to pass down the costs to consumers.
China is also accelerating the build-out of a domestic AI chip ecosystem. There were four AI chip IPOs completed across China onshore and in Hong Kong since 4Q25, boosting sentiment. Meanwhile, Baidu has proposed a spin-off to separately list its AI chip arm Kunlunxin, and Alibaba is reportedly preparing an IPO for its chip unit T-Head. Equally important, domestic accelerators are being paired with maturing software stacks that reduce developer switching costs. This is done either via CUDA alternatives (e.g., Moore Threads’ MUSA) or other CUDA compatibility architecture for model training. Domestic stacks are progressing from inference to training-grade workloads and are gradually reducing reliance on imported chips especially from Nvidia over time. As such, we see China’s domestic chip developers are pushing for a multi-year substitution cycle rather than a short-term policy trade.
We prefer full-stack AI players such as Alibaba, Baidu in China, and Alphabet in the US. These companies span the AI value chain from chips and cloud to LLM and end-user applications. This matters because vertical integration offers immediate monetisation via chips and cloud in the near term, and applications in the medium term, while other carve-outs (Kunlunxin; T-Head) can act as valuation catalysts. Alibaba’s open-source LLM Qwen has surpassed 700mn cumulative downloads on Hugging Face, while Baidu’s overseas cloud app TeraBox (c.400mn global users), and other Chinese AI applications, such as Kuaishou’s Kling with 60mn+ creators worldwide, are increasingly signing up global users. Among the US players, we prefer Alphabet for its ability to break the monopoly of OpenAI in LLM and Nvidia in GPUs for training.
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- GDP Nowcast: India’s GDP growth likely to moderate in 1Q 04 Mar 2026