Are You Considering Chinese AI In Your Strategy?

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0:58Amit Joshi:I'm Adi Ignatius. I'm Alison Beard, and this is the HPR IdeaCast.

1:09Amit Joshi:All right, so Alison, today we have a topic that's really interesting to me, and that is the parallel development of AI ecosystems in the West and in China. So you think of how the two pursued divergent strategies with the Internet. It really is a separate Chinese Internet. The question is whether that's going to happen with AI. And the likelihood at this point is that there isn't, that there can exist multiple ecosystems and that companies can tap into them. This sounds very interesting. I know that you spent a great deal of time in China earlier in your career, but I imagine it's changed dramatically because of the advance in the Internet and now AI.

1:45So what did you learn?

1:46Amit Joshi:Well, when OpenAI debuted in 2022 with Generative AI, we all suddenly had that at our fingertips. You know, China was way behind. They're not now. So they've caught up and they have competitive advantage in some really clear areas. So my guest today is Amit Joshi, who is a professor at IMD. And he's suggesting that rather than this being a winner-take-all or you have to go with one or the other of these ecosystems, that global companies can really work with both, that there are advantages to Western AI, to Chinese AI, and if you run a complex business, you might want to be engaging with both. I imagine, though, that a lot of companies are maybe hesitant because of concerns about data privacy, security, that government policy might change and prevent them from continuing that strategy.

2:37Amit Joshi:Absolutely. Look, I mean, we have these concerns when we deal with any AI company. what are the biases, et cetera. I think with Western companies dealing with China, absolutely, there's a whole new level of security concern. But there are ways to protect your data. And again, Chinese AI companies are doing some things really well. So at the very least, it's worth educating yourself. So here's my conversation with IMD professor Amit Joshi, co-author of the HBR article, How Savvy Companies Are Using Chinese AI.

3:13Amit Joshi:One of the big questions for global business, obviously, is the technological race between China and the U.S., particularly in the development of AI. There are questions, of course, about who's winning, but also whether the two countries will end up pursuing separate parallel paths. So I want to set some context. So let's go back to 2022, when most of us first became aware of generative AI's remarkable capabilities. Open AI, an American company, suddenly makes chat GPT available to everyone. It's an unforgettable moment for many of us. What is happening in the field at that time in China? So in 2022, when the U.S.

3:49market suddenly pivoted to generative AI, China was still all in on traditional AI, machine learning, as we call it, right? I mean, at that time, the real war, it seemed, at least on the surface, was between American machine learning and Chinese machine learning. And then we had the huge Chinese giants, the Alibabas, the Huaweis of the world, JD.com, can send, obviously, investing massively in traditional machine learning, getting more data sets, putting in more infrastructure for that. And they were completely almost oblivious to this huge development in the Gen AI space. Okay.

4:25Amit Joshi:So fast forward a couple of years, and now it's China's turn to shake up the world with the emergence suddenly of DeepSeq, at least to those of us who didn't know. So people in the West are amazed, They're skeptical even about some of DeepSeek's claims. Talk about what happened in those intervening years and the significance of DeepSeek's emergence. So the first thing that happened as soon as OpenAI launched ChatGPT was shock and awe in China, right? I mean, they were completely taken aback. This was a race where they were supposed to be equal partners, if not winning it on some aspects. And now all of a sudden, they seem to be laggers.

5:04They're nowhere in the race. They have nothing that looks like it even on the horizons. However, credit to them, and this is something they're amazing at, they pivoted very, very quickly. So if you fast forward just a couple of years from there onwards, this is January of this year, I believe it was, is when DeepSeek actually launched. DeepSeek was launched, and in the interim, they managed to do this faster than what OpenAI had done. They managed to do it for cheaper than what OpenAI had done. And at least at the time it launched, it rocketed right up on the AI leaderboard. They evidently managed to do it better than what OpenAI had at that time.

5:42Amit Joshi:That's great context. So now we have two essentially parallel but distinct AI ecosystems. And I want to talk about how they differ in terms that are understandable to people like myself, both in terms of how they're configured and how they perform. So let's start with how Chinese AI companies are, how they've developed the product and how they're taking AI solutions to the market. So if you will, I'd just like to rewind to how the U.S. systems were built, right? I mean, when the U.S. systems were built, remember, OpenAI was this tiny little startup with a couple of hundred people in San Francisco.

6:20They essentially used the infrastructure, the chips, the storage, the data that was already available at the time. So they used the Azure platform. They used the NVIDIA chips, obviously, which were not, let's face it, they were not designed specifically for generative AI. They were designed for other purposes, originally for graphics, but then eventually for traditional machine learning. And they built it off of that. The first things the Chinese did is they said, hey, since we know what we're doing now, how about we actually create the infrastructure that's kind of designed to do these kinds of things?

6:54So let's take something that kind of vertically integrates this infrastructure. Let's put it all together and then make sure that the stack that we are building is meant for this. So the customization and infrastructure that they did, it was not about saying, let me try and build the best general purpose AI tool that I can get away with. What they said is, can I customize my storage, my chips, my data, my training for this one particular purpose? So this was one of the first things that the Chinese did. Very successfully, obviously.

7:35Amit Joshi:I think this probably points to a comparison maybe more generally between the kind of Western approach to business and a Chinese approach to business. Where one, as you say, is maybe leading the technology wave and at a certain altitude. and where the Chinese maybe are super focused on consumer segments and applications. So talk more about that and maybe even examples of what Chinese companies are doing with their AI products. So again, the contrast between what's happening in the West versus what's happening in China is pretty amazing in this sense, right? I mean, let's take the usual Western models, whether it's ChatGPT, whether it's Gemini or even open source models like Llama.

8:15what we're trying to do is we're trying to build the best model that we can do using the highest of the best quality chips, the best storage, the best data, or the most amount of data that we have. Contrast that with what the Ant Group did. The Ant Group, they said, we want to build an AI medical app, right? That's going to be available to people who have the Alipay app. So it's an AI doctor that's available on Alipay. I could use a general purpose Gen AI tool, but what if I create a healthcare-specific model that uses data specifically from hospitals, that uses infrastructure that allows this model to make quick inferences faster than a general purpose model, and then put that on the app?

9:05Nothing contrasts what we are trying to do in the West versus what they are trying to do better than this example, in my opinion. So the American president, Donald Trump, his AI plan, at least to the extent that he's laid it out, is set at trying to achieve U.S.

9:23Amit Joshi:dominance in the field. And I'm interested in your view, is this a winner-take-all battle, a sort of Sony versus Betamax, or can more than one of these ecosystems survive? I do not think this is a winner-takes-all battle. I think if we fight it as a winner-takes-all battle, we are completely missing the point. I have a feeling that this is a space where we will have multiple foundation models that will probably be focused on certain areas. That'll be better for one versus the other. But I do think this is something where multiple different models, multiple different technologies will coexist. So in that sense, this is less like social media where we have one dominant social media platform and one chat platform and one search, etc.

10:07But this is more akin to mass production, where we've got about a dozen or more car companies in the world, all of them more or less successful, all of them differentiated in some way.

10:19Amit Joshi:So let me just push on that a little bit, because we do sort of have separate and distinct internets. I think in the early days of the internet, we didn't even imagine that was possible. But there really is a distinct Chinese internet, and it is almost a parallel and unbridgeable kind of system. but you think AI is different. Explain why. First of all, specifically, if you stick to social media, a lot of the power for social media comes from network effects, the facts that the value that one person gets from using, for example, Facebook or WhatsApp is heavily dependent on how many total users there are on this.

10:53This is not easily transferable, this concept, to open AI. I mean, as long as open AI is providing me with quality answers, I don't really care if a billion and other people are using it. Now, there are some technical things, reinforcement learning with human feedback that's happening in the backend that might marginally make OpenAI's answers better than, for example, Lama, which is lesser users. But the quality difference is going to be relatively minor. So because this is not driven primarily by network effects, at least as of now, my sense is that this is going to be akin to an economies of scale kind of a business rather than a network effects business.

11:39Now, in network effects business, we know it's winner takes most, if not winner takes all. But in economies of scale business, we know for the last 80 years that multiple businesses can survive in parallel. They can coexist.

11:52Amit Joshi:Okay, so this starts to get to the crux of the HBR article that you co-wrote. And that is that global companies have to figure out how to navigate these two very important parallel AI universes. So let's start with a basic question. Can global companies engage in both of these ecosystems? I think they can. We've already started seeing a few examples of companies that have started exploring both ecosystems and figuring out which can work for both, right? So let us remember one thing. The leading Chinese model that's out there right now, which is DeepSeek, is an open source model, which basically means I can take it, I can modify the weights and I can put it on my servers with minimal cybersecurity risks or with minimal privacy risks, etc.

12:36It'll never be zero, but they are minimal. Knowing this, it then becomes imperative for Western companies to try and understand saying, OK, I have access, obviously, to the Western models, but I also potentially have this Chinese model. And by the way, like we were discussing previously, the Chinese models have been trained on the Chinese internet in addition to the internet, which the Western models are trained on. They can do different things. They do specialize in different aspects. So is it possible that moving forward, Nestle or Airbnb or Siemens or GE, they discover that, you know, if I really want to do high-end innovation, if I really want to do cutting-edge stuff, if I want to do new drug discovery, if I'm Pfizer, then it's much better for me to use some of the cutting-edge Western models.

13:32On the other hand, if I want to do customer service, if I want to do supply chain optimization, maybe the Chinese models are better. And then I set up my system in a way that depending on the problem, the AI can triage what's happening.

13:50Amit Joshi:Talk in more detail about one or two of these companies and how they're engaging with Chinese AI. One is BMW, right? I mean, what BMW is planning to do is, especially for cars that are for the Chinese market, but in general, cars in the Asian market, it's integrating DeepSeq, which is obviously the Chinese large language model, into its vehicles. So a lot of the AI in BMW vehicles is going to be done by DeepSeq. So staying still in the automotive field, Bosch, which is obviously a massive tier one supplier to automotive companies. they've just finalized a high-performance computer for AI-enabled vehicles, right?

14:27So they're going to make massive amounts of these based on the Chinese AI. Then, of course, in the other space, in the consumer package goods space, we've got folks like Procter & Gamble. And what P &G is doing is it's using Chinese AI along with Western AI to hyper-personalize the messages that is going out. So it's partnering with Duyin in China to do what's known as interest-based e-commerce. And what this essentially does is it combines short videos. The short video e-commerce is huge in China. It's not huge outside of China. But combine that with discovery and understanding which products to recommend in an e-commerce purchase.

15:07So really, really, we've got companies all the way from consumer packaged goods to durables like automotive to industrial goods like Bosch that are kind of already stepping into this dual paradigm.

15:22Amit Joshi:Are the Chinese better at, let's call it hyper-personalization with AI? Are they more advanced or more focused on that than Western counterparts? So what the Chinese are better at is industry-specific hyper-personalization. So if you just take general hyper-personalization, I don't know if they're better. But if you ask me, are the Chinese better at hyper-personalizing in healthcare? My answer to that would be probably yes. Are the Chinese better at hyper-personalizing in B2B marketing? My answer to that will probably be yes. So I had a conversation with Chris Tang, who's the CMO of Alibaba. And what he said is what they're using their AI for is to allow the vendors on Alibaba.

16:02So they've got a couple of hundred million vendors on Alibaba, which is basically the most dominant B2B vending site, you know, essentially in Asia. And what they essentially do is, I mean, the classic thing, right? I mean, if I'm a vendor and I'm selling 50 different types of mugs to buyers, to retailers all over the world, I will never, ever have the resources to do the marketing materials, to do all the kinds of pictures and descriptions, et cetera, for all the 50 types of mugs that I'm selling. I'll probably go to an agency and have it made for the five best-selling mugs that I have on my portfolio.

16:40What I can now do is I can use Alibaba's AI to create marketing material, images, descriptions, etc. for the remaining 45. And now all of a sudden, I have this scaled up, amazing personalization that's possible, which still yesterday was simply not doable.

17:01Amit Joshi:Then what about the opposite? To what extent are Chinese companies operating in the US AI ecosystem? I think that, to the best of my knowledge, is extremely limited so far, primarily driven by restrictions, but also because I think that they are currently more focused on their own internal market. Their own internal market is growing so rapidly, including the government sector, by the way. The government sector has now turned into a massive user of the Chinese ecosystem, of the Chinese AI ecosystem, for everything from governance to city management to taxes to you name it. So I think the growth in the pie that they're seeing in their own backyard is just incredible.

17:44They're not currently actively pursuing the U.S. market, to the best of my knowledge. But I bet that's going to change.

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19:06Amit Joshi:So now let's talk super practically, all right? So if people are listening to this and thinking, okay, that's interesting. I need to think about, if I'm a global company, I need to think about whether and how I want to be in the Chinese AI ecosystem in addition to the Western ecosystem. Where do you start? I mean, how do leaders, what exactly should they be thinking about or looking at if they're trying to answer that question? There's a few things I would recommend. I mean, if you're an executive, right? If you're an executive listening to this, go play with DeepSeek right now, right? Right, figure out a way, a safe way, a sandbox.

19:41Download DeepSeek, make sure it's cut off from any private secure data if that's a problem with you, but play with it. Understand how it is similar, but also how it is different than the Western models, for example. What works better on DeepSeek versus what works worse? How does it do on hallucinations? How does it do on answering factual questions, et cetera? Because it's not a level playing field, it's better at some at worst for some. Secondly, and more importantly, in my opinion, keep a broader eye on what's happening in China, not just in the LLM market, but also in the larger business models market.

20:16To me, the largest story that will probably come out of China eventually, I think two years from now, we won't be talking so much about how the Chinese models might be better or worse than the Western models. I think what we might be talking about two years from now is look at this cool business model that they have built based on AI. Because this is exactly what we saw with the mobile revolution, right? Were their mobile phones better than ours? I don't know, maybe, depends who you ask. Did they have significantly different business models that were based on them? 100 % yes. So I think this is something executives and businesses need to keep a very close eye on, which is what kind of different business models, What kind of different use cases are the Chinese building based on this technology that they now have?

21:04If you're an American company that's based in America and using American models so far, I think it's in your best interests to actually look at these models. It's quite likely that these models are not only going to be better, but they're going to be cheaper to do some of your tasks. Why would you want to cede that advantage to competition simply because competition did the due diligence and looked at the Chinese models?

21:31Amit Joshi:So China is a particular challenge for Western businesses. My wife is in the academic world with China. When she visits China, like everyone else in her world, she gets a burner phone. She gets a burner laptop. She doesn't risk her data. I don't know if that's excessive paranoia or if it's proper paranoia. So now we're suggesting that Western companies engage in China's AI ecosystem. Won't some of those same concerns about data privacy, data integrity, how do we think about that? Yeah. And as if that was not enough to think about already, let us also throw in regulations because the regulations are probably going to be significantly different across these geographies and how Western companies need to handle that.