EVs taking on AI OS and the Delivery War. The Chinese Tech Winners Beyond BAT with Alan Zhang

In this episode, I sit down with Alan Zhang (Principal & Portfolio Manager at Ox Capital Management) to map China’s tech landscape through an investor’s lens. We break down how Alibaba, Tencent, and ByteDance are approaching AI, and why the “AI OS” is the real endgame. Finally, we analyze what’s changing in China’s consumer internet, EV ecosystem, and embodied AI pipeline. We also unpack China’s delivery wars (Alibaba vs Meituan vs JD), why quick commerce is structurally different from traditional e-commerce, and how markets price geopolitical risk into China tech valuations.

Alan Zhang is a Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia; previously, he spent years as an investment analyst on the Asia team at Platinum Asset Management.

He studied Actuarial Science and Commerce at the University of New South Wales, and he’s even taught advanced econometrics. So if you like the intersection of fundamentals, market structure, and Asia platform businesses, well then, this one’s for you.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

01:34 Alan’s background: quant → Asia equities

03:11 US vs China AI: frontier vs “two-legged” approach

05:25 “Uninvestable” China and what changed

07:31 Beyond BAT: Xiaomi, Meituan, Mindray, MicroPort

09:24 BAT AI strategies and the AI OS thesis

13:45 Tencent: tools, data, distribution, and model strategy

16:33 AI-native phones: ByteDance × ZTE and what’s next

26:51 China EV landscape: BYD, Huawei, Xiaomi, Zeekr

31:28 Why phone OEMs can compete in EVs

34:16 Embodied AI: robotics parts, redundancy, and Unitree

39:38 Valuation + geopolitics: why Asia tech trades discounted

41:53 China delivery wars: subsidies, quick commerce, Meituan’s edge

50:27 12–18 month predictions + what investors miss (healthcare)

AI-Generated Transcript

Grace Shao (00:00)In today’s world, there’s no shortage of information. Knowledge is abundant. Perspectives are everywhere. But true insight doesn’t come from access alone. It comes from differentiated understanding — the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend — someone who can help us see things differently.

So today, joining me is Alan Zhang. And I’m Grace Shao. Alan, really excited to have you. I’m excited about today’s conversation because we’re going to get into the investor’s perspective on Asia tech and emerging markets — with a proper markets-and-math backbone.

Alan Zhang is Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia. Before that, he spent years as an investment analyst on the Asia team at Platinum Asset Management. He studied actuarial science and commerce at the University of New South Wales, and he’s even taught advanced econometrics.

So if you like the intersection of fundamentals, market structure, and Asia platform businesses, this episode is for you. Alan, welcome.

Alan Zhang (01:31)Thank you, Grace. Pleasure to be here.

Grace Shao (01:34)Alan, to start, why don’t you tell us about yourself — your background — and what it is that you cover now?

Alan Zhang (01:40)I grew up partly in Hong Kong, mainland China — Shenzhen particularly — and in Australia. I spent close to a decade in Australia doing my schooling and education, and worked for a firm called Platinum Asset Management, then co-founded Ox Capital with Joseph Lai.

I studied actuarial science, so I’ve had a lot of experience manipulating numbers, cleaning up data — and that helped me tremendously in public equities. Nowadays there’s no shortage of financial data, and the ability to understand them — and the intent behind them — is crucial to investing.

Grace Shao (02:34)Yeah, yeah.

Alan Zhang (02:46)At Ox Capital, we also built a tool called the Mode Model, which distills more than a million financial data points from various sources to help us understand our coverage region a lot more. In terms of my coverage, I build quant models, I look at equities, and I also help with portfolio positioning based on macroeconomics in Asia.

Grace Shao (03:11)That’s interesting because you started off in quant, but now you’re looking at equities — the fundamentals, right? You’re covering a lot of ADRs, and a lot of China’s big tech.

Let’s talk about that. What is the China big tech internet ecosystem looking like right now? How does it compare to the US?

Alan Zhang (03:20)In the US, they are focusing more on frontier models, while Chinese companies are taking more of a two-legged approach — tackling AI with different approaches. The US has invested a lot of resources into advancing frontier models. On one hand, we see successful cases like Gemini, Anthropic, and OpenAI, while we also see a lot of AI subscriptions cutting their prices by more than 90% in the last few years.

If you remember in 2023 and 2024, many subscriptions were priced at a few hundred — some over $1,000 a month — based on investment assumptions. Now they’re cutting prices to sub-$100 a month. Some may never make their money back based on those assumptions, but it’s not being discussed today because the benefit of AI far outweighs that blip, and large-cap companies are investing enough to offset the impact.

If we look at China, they haven’t gone through this episode — and I don’t think they will. Anyone who looks at Asia understands Asian users will never assume people will pay over $1,000 a month for subscriptions. China is working on frontier models, applications, and infrastructure at the same time.

In summary, China is still the runner-up, but they’re developing AI in a more balanced manner. And it’s also good to see the US pivoting — in the recent 12 months, we’re seeing more US companies investing in software and applications rather than just frontier models.

Grace Shao (05:25)China was deemed uninvestable, especially for Western investors. Your fund is based in Australia and Hong Kong, and your LPs are non-Chinese. For public investors who want exposure to China’s AI upside — what are they looking at? What are they thinking?

Alan Zhang (05:46)Usually the big tech. China went through the property adjustment and the antitrust campaign in the internet space. It was painful — people called it uninvestable because they couldn’t see new growth drivers. And if they could, they were too insignificant compared to the two most important industries at the time: internet tech and property, which were both recalibrating.

But things are different now because investors can see new growth drivers scaling up. In hindsight, these adjustments also helped innovation: talent that dreamed of landing a job at Meituan, Tencent, Alibaba went to smaller firms or startups; capital that made easy money in real estate went to new areas.

Economic transformation is still a work in progress, and investing in China becomes more attractive if we see AI, consumption, and advanced manufacturing play a bigger role. We’re still in that phase. But we’re glad to see some companies bottoming out and making progress under the current setup.

Grace Shao (07:19)In a pragmatic way, does that mean we’re looking at BAT? What companies should we be looking at for exposure to Chinese AI and economic transformation?

Alan Zhang (07:31)Besides Alibaba and Tencent, people should look at relatively smaller cap — but still large-cap — companies like Xiaomi and Meituan. And also industries outside the internet. For example, Mindray in healthcare, or MicroPort in surgical robotics — they can implement AI into their products and make their portfolio more attractive.

Grace Shao (07:41)When we chatted offline, you said a lot of companies are overlooked. Beyond BAT — what are some “1.5 tier” or “second-tier” companies that are huge by market cap but not well known in the West?

Alan Zhang (08:09)People will naturally see them more over time. Tencent and Alibaba were making active efforts overseas; now as the market matures, more companies are going global. If I’m on a roadshow, people ask about Keeta, which is a subsidiary of Meituan. Xiaomi is opening more stores in Europe — even Africa and South America. People will naturally see them more.

If you come to China and compare what’s here to where you live, you’ll see a clear difference.

Grace Shao (09:24)Let’s double click on BAT — Alibaba, Tencent, and ByteDance. At a high level, how do you compare their AI strategies? Are they playing the same game, or different playbooks?

Alan Zhang (09:52)Same, but different. They’re all investing heavily in frontier models and infrastructure. Ultimately, they all want to build the AI OS people will use. The DoorDash–OpenAI collaboration was a good example of what AI and a commerce company can do. Whether it’s an app within an app or an app within a phone — that’s still an open question.

Alibaba is e-commerce and cloud. They have to build a competitive model or their cloud becomes commoditized. Tencent is a platform — they build tools. In LLMs or AGI, late movers can have an advantage because users may be indifferent as long as security and usability are similar. ByteDance, as a private company with strong feed algorithms, has been AI-native for a long time — even back in 2018 they were investing heavily in AI and user intent.

So they’re all trying to build an AI OS for users, just from different starting points.

Grace Shao (12:29)I love that framing — I’ve been writing that 2026 is about the AI OS. Tencent has signaled they’ll double down on LLMs. It’ll be interesting to see whether late-mover advantage shows up — and whether they need to spend less on pure infra.

How should we think about Tencent’s positioning? They’re late on LLMs, but AI is already integrated across touch points — WeChat, gaming, fintech, mini programs. Should they continue using open-source models like DeepSeek, or focus on proprietary models like Alibaba integrating Qwen?

Alan Zhang (13:45)They’ll do both. With Yao Shunyu reporting to Martin Lau, they’ll try to build their own model like every tech giant. At the same time, Tencent’s bread and butter is building tools — AI tools to help merchants and users and improve the experience.

Whether it’s an app within an app or an app on a physical phone — like the Doubao phone we saw — Tencent has the ingredients: ecosystem, quality data, and distribution.

Grace Shao (14:37)When you say “building tools,” how is that different from Alibaba building tools for businesses? And how is that different from ByteDance’s “app factory” approach?

Alan Zhang (15:10)One example: in WeChat’s input bar, if you long press, you can translate. People type in their own language and WeChat translates to the recipient.

I also visited their AI showroom recently. They showed mapping genetic pools and building a genetic bank for seeds and animals — they have quality data. They can also build full simulators for flights and cockpits — one of only a few companies that can do that. They’re investing in spatial intelligence and data banks, and building tools inside WeChat.

I think it’s only a matter of time before they move more properly into e-commerce and release something like what DoorDash and OpenAI shipped.

Grace Shao (16:33)On hardware — can we talk about ByteDance and ZTE’s partnership? ByteDance worked with ZTE and launched an AI-native operating system on a ZTE phone. Instead of building their own phone, they partnered with OEMs. What do you make of that?

Alan Zhang (17:11)As a user, I looked forward to it. A product like this may take longer to be widely available because it disrupts a lot of vested interests. But the trend is inevitable — AI OS will be valuable in ways we can’t even measure.

This is what I envision for Xiaomi and Tencent too. Companies like these — and Apple — are planning for that day, but they’ll move when stakeholders are ready. OEMs have the protocols to make it happen. Tencent also has content and intent — ads revenue — plus distribution. Tencent and Xiaomi will try to tackle this new market.

Grace Shao (18:13)Is ByteDance moving faster because it’s private? Xiaomi and Tencent are public companies — does that slow them down?

Alan Zhang (18:29)Absolutely. ByteDance can try something new; if it fails, it doesn’t impact the core. If Tencent or Xiaomi do this, they can agitate business partners and users.

Grace Shao (19:10)For an American audience, is there an apples-to-apples comparison to US peers?

Alan Zhang (19:32)It’s difficult. These companies are mature and make decisions based on their own opportunity sets. In many spaces, Chinese companies are leading, while the US is still exploring new frontiers. Tencent has been relatively quiet until recently, and they work quietly with industries to understand how their AI stack helps.

In 2015, Tencent founded a learning program called Tencent X — “X” stands for another Tencent. They work with business schools, bring entrepreneurs and business leaders to site visits and exchanges, and use the process to understand how to develop their stack to empower Chinese industries. A successful example was Pinduoduo — through this program, they found Colin Huang and supported the company through traffic. Tencent can find more companies like this in their own way.

Grace Shao (20:46)[Connection drop]

Grace Shao (21:04)Could you restart that sentence?

Alan Zhang (21:08)[Repeats Tencent X explanation]

Grace Shao (22:07)Looking at 2026 — what consumer AI applications might look different? Any sprouts inside super apps that people aren’t noticing yet?

Alan Zhang (23:07)2026 will likely be an interpolation of 2025. I don’t expect a completely new form factor. Most Chinese companies are already super apps, boundaries are ambiguous, and they’re fighting for the same consumer pockets.

But ads revenue will shift. Previously, ecosystems charged a lot for ads because of captive customers. With AI, people are reconsidering how they use apps — budgets will relocate to new apps.

Grace Shao (24:20)On infrastructure: it feels like everyone is shipping models — not just BAT and the “four tigers,” but also Kuaishou, Meituan, Xiaomi, even EV players. Why?

Alan Zhang (24:58)They have enough users, and AI improves experience and broadens reach. For example, older users didn’t use search much, but with AI they can adopt faster. AI makes products more interactive and easier to use.

EV companies want more engaging products. Cars are becoming commoditized, so they invest in infotainment and ecosystems. That’s why every sizable Chinese company will try to build a model. And we’re still in the investment phase — nobody knows who wins, so everyone tries. It’s not as expensive as it sounds.

Grace Shao (26:25)Isn’t it costly for EV companies?

Alan Zhang (26:32)It’s costly, but a lot of money is spent on chips research and manufacturing. The LLM itself isn’t as expensive as people imagine.

Grace Shao (26:51)Let’s double click on EVs. Who are the biggest players in China beyond BYD and Zeekr?

Alan Zhang (26:55)BYD and Huawei. Emerging ones: Xiaomi and Zeekr.

Grace Shao (27:15)How do you position them?

Alan Zhang (27:21)Xiaomi’s selling point is ecosystem. You can call “Xiao Ai Tong Xue” — the voice assistant — to operate devices through the ecosystem, especially with HyperOS 3.

BYD’s advantage is manufacturing — they can build similar-quality cars cheaper through supply chain management.

Huawei has HarmonyOS and strong brand equity — customers pay up, so they can stack a more luxurious experience into the car.

Grace Shao (28:15)How does that compare to “luxury EVs” like Nio — are they still relevant?

Alan Zhang (28:24)They’re still relevant. Li Auto is more family-oriented than luxury. Nio targets younger consumers who want the driving experience. Huawei’s models skew more toward corporate executives and founders — generally 40 and above.

Grace Shao (29:08)So there’s a shift — five years ago it was BYD, Nio, Xpeng, Li Auto; now Xiaomi and Huawei are making strides because of AI operating systems. Is that right?

Alan Zhang (29:28)Yes. China’s auto market has many brands and licenses, no shortage of production capacity — and there’s overcapacity. The “anti-involution” campaign has targeted autos. The industry is commoditized, so companies need differentiated advantage. Xiaomi and Huawei have ecosystems; BYD differentiates through cost and can scale domestically and overseas.

Grace Shao (30:41)Why are Xiaomi and Huawei able to lead? Does that mean EV-first companies become less competitive?

Alan Zhang (31:28)EVs have fewer parts than ICE cars. Historically you needed over 10,000 parts; now EVs might have a few hundred to just over a thousand. You can break it into powertrain, battery, chassis, and battery management — and the rest is non-core. Many parts are commoditized except the battery and system.

Xiaomi and Huawei can repurpose capabilities from phones: chips, screens, packaging. Xiaomi can repackage Qualcomm chips and repurpose them to be auto-grade; Huawei can do similar. Cars also have bigger screens than phones — manufacturing capability transfers.

EV-first companies like Nio, Xpeng, and Li Auto spend on manufacturing and also on chips, because their bigger vision is robotics. They’ve said chips for EVs alone wouldn’t pay back — the bigger scheme is robotics.

Grace Shao (34:16)So in embodied AI: you have Unitree, “Galabots,” UBTECH; you have EVs; you have Xiaomi/Huawei tech stacks. Who wins? Is it just cost and price?

Alan Zhang (34:54)Cost, price, and redundancy for physical movement. Even traditional automation companies like Inovance are building robots. A robot shares parts with EVs — optics, gears, batteries — but also has new parts like PLC controllers where you need redundancy. On these fronts, many are on a level playing field.

Grace Shao (36:12)Do Chinese EV firms have an edge in spatial intelligence, or is it mainly cost?

Alan Zhang (36:21)China is still runner-up in spatial intelligence and will spend time to catch up. But China has a short feedback loop: optical components and supply chain are local; ideas can turn into products quickly and iterate fast. Not an advantage yet, but not far behind.

On who wins: too early to say. Unitree is the one that can make a more agile robot and do more stunts than other players.

Grace Shao (37:42)Where does AI show up in embodied systems — is it just visible “smart” functions, or more invisible?

Alan Zhang (38:19)Besides user experience, AI processes many parameters in the background. With enough computing, embodied AI can make simultaneous decisions — what to move and what not to move. Humans blink, walk, and raise hands at once; without AI it’s harder for robots to act like that. With AI, robots can handle more parameters and make simultaneous moves.

Grace Shao (39:38)How do you price geopolitical risk into valuation positioning? Export controls, trade wars, domestic regulation — how should investors look at China?

Alan Zhang (40:17)The market is already pricing a discount. Asia tech trades at a discount to US peers — Samsung and SK Hynix versus Micron; BAT versus the Magnificent Seven. Tools may be less available, which can slow advancement, but it’s also encouraging to see alternate solutions like DeepSeek. Over time companies can become more technologically independent.

For large caps, investors may feel safer sizing up. For small caps, we start small and see how it plays out. Entrepreneurs are agile and prepare for change.

Grace Shao (41:53)A reader question: China’s delivery wars. Alibaba vs Meituan — subsidies, vouchers — why is this happening now?

Alan Zhang (43:43)Meituan has led quick commerce — 30-minute delivery — and it surprised me Baba took so long to react, because quick commerce will take share from traditional e-commerce. A few years ago Meituan delivered iPhones at launches — a wake-up call for JD. The new delivery war kicked off with JD’s initiative around April; JD spent heavily to buy consumers, and Baba joined a month or two later.

Money could be better spent elsewhere, but I understand Baba — if they lose relevance in e-commerce, other businesses stop making sense. E-commerce is the core.

Despite growing daily volume from 30–40 million to 80 million — sometimes 90 — it’s discouraging Baba hasn’t improved delivery efficiency much. Meituan was already profitable at around 40 million drops a day by carrying multiple deliveries per trip and improving dispatching. It’s sad for investors that many platforms are still loss-making due to subsidies, but Meituan’s underlying efficiency advantage remains. As a consumer, the subsidies are great.

Grace Shao (46:29)Why does Meituan have such an advantage in dispatching and logistics compared to Alibaba, which has massive logistics and warehouse footprint?

Alan Zhang (47:39)It comes down to the core. Meituan built it through local business development — ditui — integrating merchants into inventory and payment systems. Inventory is kept locally, so Meituan focuses on dispatching and rider movement. Their algorithm can even predict demand and move riders toward hotspots ahead of time.

JD invested heavily in centralized logistics hubs and infrastructure — that makes them slower to pivot. Baba used an asset-light model early, working with ZTO, and is more centralized — mostly Hangzhou. Meituan is more decentralized and localized. In quick commerce, doing well in one city doesn’t guarantee another — but once dominant, you can use profit from one pocket to subsidize another. Traditional e-commerce is more centralized.

Grace Shao (50:03)That’s a fascinating lens — culture and management style mapping to business model outcomes.

Alan Zhang (50:04)And risk. In food delivery, you can’t hold inventory. Meituan works on the assumption you don’t hold inventory. Baba and JD have more of a culture of holding inventory and keeping products in storage longer.

Grace Shao (50:27)Closing: biggest prediction for China tech in the next 12–18 months?

Alan Zhang (50:51)One for EVs, one for internet. In EVs, OEMs with a pure domestic focus and without an ecosystem will lose relevance in 12–18 months — consumers are making up their minds. In internet, with Tencent hiring Chief AI Scientist Yao Shunyu, we’ll see more AI functionality built into Tencent’s ecosystem.

Grace Shao (51:54)What’s one company or subsector global investors are sleeping on?

Alan Zhang (51:56)Healthcare. It can be resilient regardless of overall spending. The market is focused on frontier-model spending and ROI, but healthcare companies aren’t budgeting for “latest and greatest” models — they’re looking at applications that improve products and ecosystems. Even if we stopped advancing frontier models for four months, there’s tremendous value to extract from current models.

I see a mindset shift among healthcare executives to build AI into products and sell superiority — historically, tech adoption was cost-driven; now it’s revenue-generative. Mindray in Shenzhen, or MicroPort in the Yangtze Delta — great companies. Surgical robots and medical devices are not far behind other systems.

Grace Shao (54:15)Final question: what’s one differentiated view you have that’s non-consensus?

Alan Zhang (54:37)Instead of focusing only on AGI timelines or capex or cloud consumption, we should think about daily businesses and smaller-scale businesses extracting real value from AI — even financial companies. I’m excited to see new form factors and more AI functions in consumer products.

Grace Shao (55:24)So focus on practicality and real use cases — not just headline spending.

Alan Zhang (55:33)Absolutely. Look beyond the top three, top five — and don’t go too far down the risk spectrum.

Grace Shao (55:39)All right. Thank you so much, Alan. Thanks for your time today.

Alan Zhang (55:43)Thank you, Grace. Pleasure to be here.

AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Get full access to AI Proem at aiproem.substack.com/subscribe