China AI Strategy Evolving: CAPEX Vanished? 2B & 2C AI Demand High
Driven by continued innovation in business models and top-down encouragement, AI demand is high and opportunities continue to grow
Hi All,
Today’s post is an update snapshot of China’s 2B and 2C AI adoption and where the major players are all at with their AI game. Given that Tencent and Alibaba just announced their earnings last week, I will give them a little special attention, and then we’ll take a look at how the BBATs are all doing on the AI front.
Where’s the CAPEX?
While Tencent highlighted “Agent AI” as its next race track, as written in its earnings coverage, it aims not only to build general-purpose intelligent assistants, such as Yuanbao and Ima, but also to create “unique-type Agents” that are deeply integrated into the WeChat ecosystem. We’re seeing more and more that the existing ecosystem control is critical to the adoption of AI at scale, similar to Google and Microsoft (which we’ll explore in another piece).
Leveraging its exclusive network of social relationships, the mini-program system, walled-garden content, and chatbox interface will really propel AI adoption at speed at later stages when technology and interaction become more seamless. On the other hand, deploying “Game AI” in large-scale competitive games remains its biggest opportunity at the current stage.
That largely sums up what we wrote about last week - the so-called “consumer winning formula”.
But hold up, just a day after Tencent announced its earnings, its arch nemesis, Alibaba, also announced its 2025 March Quarter earnings results.
One noteworthy thing is that Tencent seems to be clocking in ~some~ AI ROI, or at least has a more straightforward strategy on that front. Meanwhile, its arch nemesis, Alibaba, while seeing high double-digit growth in its cloud business this quarter, was largely expected in many ways given its positioning as Asia’s leading cloud provider. However, it has still not given a clear AI go-to-market strategy…?
The biggest puzzle this quarter is that both companies' capital expenditures have decreased significantly quarter over quarter. Has that loud tout of $$ seemed to have been hushed this quarter? (I mean yes, yes, we’ve moved on from the capital discipline talk from 6- 12 months ago, but where is da monehh?)
Alas, it seems like the pre-planned spending was largely on GPUs, but as Nvidia’s H20 ban goes into effect, the gold is now sitting there without a newfound way to deploy for both BABA and Tencent.
Based on 信息平权 Information Democratized, a trusted WeChat blog’s calculation, “that the $5.5 billion impairment provision by Nvidia, when converted into the terminal sales price of H20, would result in an income impact of approximately $15 billion, which is about 100 billion yuan. Deducing the impairment provision by Nvidia is essentially the expenditure saved by BAT (Baidu, Alibaba, and Tencent). And it further estimated that if 40% of it belongs to Tencent and Alibaba, about 40 billion yuan would have occurred between January 31 and April 30 (Nvidia’s fiscal quarter),” which basically offsets the missing capex questioned above.
And on that note, most recently, at Computex 2025, Nvidia CEO Jensen Huang cautioned against writing off the entire Chinese market in Taiwan. He noted that “50% of the world’s AI researchers are Chinese.” If U.S. tech is absent, “they will build on Chinese platforms—and they will build excellent things.”
He estimates China’s AI market could reach $50bn as early as next year, underscoring its importance from both a demand and innovation standpoint.
But until China comes out with better domestic solutions, big tech is looking at software solutions. [Huawei Ascend explainer coming next week]. To DeepSeek’s role in all of this, it seems like it could potentially lead to finding an innovative solution in model + chip + system optimization.
Strategic Disruption to China’s Internet BBAT
From 2023-2024, the competitive logic of Chinese AI labs, from big tech to the startups, was based on several assumptions: 1) The stronger the self-research and development capability, the more solid the moat; 2) The number of parameters is related to capability, and performance is won by piling up large models; 3) It is necessary to build a "self-controlled" closed loop (ecosytem) of model + applications.
However, that has primarily been challenged by DeepSeek’s R1 release early this year, as its open-weight model has allowed everyone to start expecting models to be accessible and reshaped the landscape.
Tencent was the first to integrate DeepSeek into its applications and recognize that the battlefield wasn't in LLMs for them. The “four tigers” all somewhat pivoted into vertical use cases.
But not all disruptions were bad. They simply changed how the game was going to be played. Probably comforting to the startups is that a recent Jefferies China Software report highlighted that startups with deep vertical know-how, industry-specific data, and knowledge of foundation models are likely to succeed given the strong 2B demand, so the pivots made by startups like Zhipu, Baichuan, and so on seems to be now proven wise.
Despite concerns that we have written about China’s enterprise adoption rate and willingness to pay, Jefferies wrote in the same report that China AI’s 2B demand and willingness to pay are actually - surprisingly high. After the success of DeepSeek and Manus on a global stage, both 2B and 2C-end appetite in China has increased. That is based on the caveat that the applications are high-performance and low-cost solutions.
In one of our previous articles, we wrote that sometimes China’s SOEs do not prioritize efficiency gains, and that partially explained the low SaaS adoption rate, but it seems like that can be scrapped for AI. I guess I was wrong.
The current AI 2B willingness to pay is driven by fear of losing competitiveness, rising budgets in tech spending, reduced AI costs, and SOE/government top-down KPIs. The top-down encouragement of AI adoption is rallying SOEs in finance, telecom, and healthcare to want to be AI-empowered, particularly, and SOEs in these industries, well, their ability to pay (their pockets are deep) is high (was just a matter of willingness).
And in 2C apps, whether in search, office, travel booking, or companionship, have shown early signs of good demand, wrote Jefferies. I believe these are areas where the big internet players can leverage their ecosystems and existing mega-numbered MAUs.
AGI and Applications
The BBATs are now racing toward AGI, with some saying that ByteDance’s Seed Edge team is low-key pushing boundaries in research and development. But not a lot of public information on this so far.
For Alibaba, the recent results showcased that customers include not only early adopters such as internet, fintech, education, and EV but also other traditional sectors such as manufacturing. Customers' need for AI continues to rise, thus the demand to migrate to the cloud. The services this bundle needs to include are not just APl calls, but also post-training using specific internal data. On top of that, we’re seeing the need to lease GPUs and laaS services increase to satisfy various demands. At its core business, Al has been a positive booster for its commerce business, especially for online shopping with enhanced user experience, including search and ad recommendation, increased merchant productivity, and new forms of interaction (check out Alibaba.com’s 2B Accio).
As the company pushes ahead with its “AI-first strategy”, Alibaba CEO Eddie Wu predicted that 50% of global GDP will be based on or reliant on AI in the future. This bold and aggressive claim has become a guiding belief internally to push forward its flywheel: 1) cloud + model; 2) AGI.
My understanding is that Wu has repeatedly said that AI will become infrastructure, and the company is there to be the provider of that demand.
Because in many ways, models are now commoditized, and focusing on improving the models solely will be like how the internet went from 3G to 4G to 5G. As in, the experience can get better, but it shouldn't be a bottleneck to application innovation. So what is happening now is that the R&D can continue, but the adoption and innovation CAN now be applied. So, whether it’s Tencent or Baidu, they’ve fully embraced that point and have made it clear that they’ll integrate whichever model is the best into their applications.
Open-Source Era
Baidu’s pragmatic approach shows that the company is quite self-aware, given that their models really haven't made any major splash since their initiation. Now, embracing open source seems like a pivot that Robin Li reluctantly had to make, as prior to the February announcement of opening up its models, he repeatedly said that “closed source ensures technical control and business models; open-source is simply a fluke/ gimmick.”
Alibaba seems to be softening its tone of only using its Qwen series as its base infrastructure as well, but so far, no major moves have been made. However, Alibaba has the advantage of continuing to be loyal to Qwen. AI Proem recently wrote about its Qwen2.5-Max’s popularity amongst open-source developers.
ByteDance seems to be the most conflicted internally. On one hand, it’s spent massively on its LLM Doubao and the namesake application, hoping to continue its closed-loop ecosystem dominance in algo-driven applications like its Douyin, Jinritoutiao, and so on. However, to be the leader in AGI, it currently doesn’t have the frontier LLM capabilities. According to internal discussions, whether to integrate DeepSeek or not has been broadly discussed, but the attitude so far is “what’s the rush?”
Note that since DeepSeek’s release, ByteDance has stayed relatively low-key. Given that it’s the only non-public company in the BBAT, it has the luxury of not disclosing and addressing investor concerns quarterly.
All in all, open-source/ weight seems to be the trend forward. Under the impact of DeepSeek-R1, major companies have been re-examining their relationship with AI, and subtle changes in AI strategies are coming to life. This change will not stop: in the rapidly evolving technological wave of AI, to be honest, no one should be bogged down by legacy ways.
Until Next Earnings Season, Y’all
Do the BBAT still have an advantage in ecosystem moats? It seems like yes. And are startups even competing in the same tracks as the incumbents? It seems like no.
On the superhighway of AI technology advancing at breakneck speed, the most dangerous thing is not falling behind by one step, but it may be more important to be open-minded to continue to iterate and adapt quickly.
Great coverage and look forward to the Ascend explainer! I’m especially curious what your thoughts are going forward as well. Huawei doesn’t have as easy access to TSMC capacity anymore, but will the Ascend help bootstrap the ecosystem on Huawei’s platform?