12 Aug 2025 · 32m · Alison Beard, Adi Ignatius, Amit Joshi · 18 quoted facts
Amit Joshi argues that AI will not become a winner-take-all contest between the US and China. He says AI is closer to car manufacturing than to social media, so several differentiated ecosystems can coexist. Global companies should test Chinese AI alongside Western tools, because Chinese systems are cheaper, built for specific industries, and may bring new business models.
The tension is between opportunity and risk. Chinese firms went from being caught off guard by ChatGPT in 2022 to launching DeepSeek, and companies such as BMW, Bosch and Procter & Gamble are already using their tools. But Joshi and host Adi Ignatius stress privacy, security, censorship bias, regulation and US-China politics. Joshi's answer is to sandbox, hedge and route each task to the best model.
Joshi says social media is driven by network effects, where each extra user makes the product more valuable. AI answers are not, so he expects an economies-of-scale market where many differentiated providers survive.
Rather than one best general model, Chinese firms customized chips, storage, data and training for narrow jobs. Ant Group's healthcare model on Alipay is his example.
Because DeepSeek can be downloaded and run on a company's own servers, privacy and cybersecurity exposure is minimal in his view. He says it is never zero, and future non-open models may require trusting a full foreign stack.
Joshi says savvy firms already use a triage engine that sends each query to the cheapest, fastest or most accurate model. He suggests Western models for frontier work like drug discovery and Chinese models for tasks like customer service or supply chain.
Joshi compares this to the mobile revolution, where the phones mattered less than the new business models built on them. In two years, he expects the talk to be about Chinese AI-based business models rather than model quality.
Not according to Amit Joshi. He argues it is not winner-take-all, and that global companies can use both, picking whichever model is best for each job.
Joshi says China was far behind when ChatGPT appeared in 2022 but pivoted quickly. DeepSeek launched in January 2025, was built faster and cheaper than OpenAI's model, and briefly topped the AI leaderboard.
Joshi says risks around privacy, security, ethics and regulation are real. DeepSeek is open source, so a company can run it on its own servers with minimal but not zero risk, and he advises testing it in a sandbox cut off from private data.
Joshi names BMW, which is putting DeepSeek into cars for Asian markets, and Bosch, which built an AI vehicle computer on Chinese AI. He also names Procter & Gamble, which uses Chinese and Western AI for personalized marketing with Douyin.
Joshi says to set up a safe sandbox, download DeepSeek, keep private data out of it, and compare it with Western tools on tasks like factual questions and hallucinations. He also suggests following a China-focused newsletter to watch new business models.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Amit Joshi says that in 2022, when the US pivoted to generative AI, Chinese companies were still focused on traditional machine learning.
“in 2022, when the US market suddenly pivoted to generative AI, China was still all in on traditional AI, machine learning, as we call it”
Amit Joshi says OpenAI built its early generative AI on existing infrastructure such as Microsoft's Azure platform and NVIDIA chips, which were not designed specifically for generative AI.
“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.”
Amit Joshi says Chinese companies vertically integrated their AI infrastructure so that chips, storage and data were designed for generative AI.
“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.”
Amit Joshi says Ant Group built a healthcare-specific AI doctor, trained on hospital data, that is available to users of the Alipay app.
“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.”
Amit Joshi argues that AI is more like an economies-of-scale business than a network-effects business, so multiple providers can survive in parallel.
“this is going to be akin to an economies of scale kind of a business, rather than a network effects business”
Amit Joshi says DeepSeek is an open-source model whose weights can be modified and which can be run on a company's own servers.
“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”
Amit Joshi says Chinese AI models are trained on the Chinese internet in addition to the internet used to train Western models.
“the Chinese models have been trained on the Chinese Internet, in addition to the Internet, which the Western models are trained on.”
Amit Joshi says BMW plans to integrate DeepSeek into its vehicles for the Chinese and wider Asian markets.
“It's integrating DeepSeek, which is obviously the Chinese, uh, large language model, into its vehicles. So a lot of the AI in BMW vehicles is gonna be done by, uh, DeepSeek.”
Amit Joshi says Bosch, a major automotive supplier, finalized a high-performance computer for AI-enabled vehicles based on Chinese AI.
“Bosch, which is obviously a massive tier one supplier to automotive companies, they've just finalized a high performance computer for AI enabled vehicles”
Amit Joshi says Procter & Gamble is partnering with Douyin in China on interest-based e-commerce that combines short videos with product recommendations.
“it's partnering with Duoyin in China to do what's known as interest-based e-commerce.”
Amit Joshi says Alibaba's AI lets vendors on its B2B platform generate marketing images and descriptions for products they could not previously afford to promote.
“What I can now do is I can use Alibaba's AI to create marketing material, images, descriptions, et cetera, for the remaining forty-five.”
Amit Joshi says the Chinese government has become a massive user of Chinese AI for governance, city management and taxes.
“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”
Amit Joshi says a bank he works with in Asia has used a triage engine to route queries among different language models since the early days of ChatGPT.
“there's a bank that I'm working with in Asia that has had this system in place pretty much since the early days of ChatGPT.”
Amit Joshi says that when he asked DeepSeek about the 1989 Tiananmen Square events, it answered that nothing happened.
“it says, basically, I mean, I'm I'm paraphrasing here, but it said nothing happened. It was a nice, sunny day.”
Amit Joshi compares AI to the mobile revolution, saying China's key difference was the business models built on the technology rather than the devices themselves.
“Were there 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.”
Amit Joshi says Manus, an AI agent company that recently moved from China to Singapore, builds agents using a mix of Western and Chinese models.
“Look at these, look at tools like Manus, right, which is a Chinese company which recently moved to Singapore”
Adi Ignatius says that at the time of recording, Donald Trump was expected to meet Xi Jinping before the end of 2025.
“Donald Trump is probably going to meet with Xi Jinping before the end of of 2025, and that could improve relations.”
Adi Ignatius says a recent headline reported that President Donald Trump was going after Intel because its CEO allegedly had ties to Chinese communists, which Intel called ridiculous.
“the latest headline is that President Trump is going after Intel because its CEO, you know, has had ties, quote unquote, with Chinese communists.”
Joshi explains China's surprise at ChatGPT in 2022 and how it caught up quickly, setting up the rest of the discussion.
“they pivoted very, very quickly”
Chinese firms built infrastructure for narrow purposes, unlike the US approach of building the best general model on existing chips.
“customize my storage, my chips, my data, my training for this one particular purpose”
Joshi argues AI is driven by economies of scale, not network effects, so many providers can coexist as car makers do.
“this is more akin to mass production”
Open-source models make it feasible for Western firms to run Chinese AI, with tasks routed to whichever model suits them best.
“which basically means I can take it, I can modify the weights”
Real companies already combine Chinese and Western AI, from cars to personalized marketing for merchants.
“using Chinese AI along with Western AI to hyper personalize the messages”
Joshi recommends hands-on sandbox testing and watching Chinese business models, not just the models themselves.
“go play with DeepSeek right now”
The conversation covers political volatility, scenario planning, and how DeepSeek handles sensitive topics.
“go ask it what happened in 1989 in Tiananmen Square”
Joshi sums up what Chinese AI does well and points to agent-based AI as ripe for partnerships.
“all their applications are calibrated for the real world”
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I'm Adi Ignatius.
I'm Allison Beard, and this is the HBR IdeaCast.
All right, so Allison, 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. So, what'd you learn?
Well, when OpenAI debuted in 2022 with generative AI, and 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, the 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, you know, continuing that strategy.
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, that 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.
One of the big questions for global business obviously is the technological race between China and the US, 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 though. So let's let's go back to 2022, when most of us first became aware of generative AI's remarkable capabilities. OpenAI, an American company, suddenly makes ChatGPT 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 US market 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 dot com, Tencent, 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, so fast forward a couple of years, and now it's China's turn to shake up the world with the emergence suddenly of DeepSeek, at least, you know, to those of us who didn't know. So, so people in the West, you know, are amazed. They're skeptical even about some of DeepSeek's claims. Talk about what happened in those intervening years, and, you know, 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 laggards. They're nowhere in the race. They have nothing that looks like it, even on the horizons. However, you know, 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, which is, uh, January of, uh, of 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.
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, you know, in terms that are understandable to people like myself, both in terms of how they're configured and how they perform. So let let's start with with, you know, 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 US systems were built, right? I mean, when the US systems were built, remember, OpenAI was this tiny little startup with a couple of hundred people in San Francisco. They 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, well, 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? So 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.
I think this probably points to a comparison, maybe more generally, between, you know, kind of Western approach to business and a Chinese approach to business, where one, as you say, is maybe, you know, leading the technology wave and at a certain altitude, and where the Chinese maybe are super focused on consumer segments and and application. 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 what's happening in the West versus what's happening in China is is 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. What we are trying to do is we are 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?
Nothing contrasts what we are trying to do in the West versus what they are trying to do better than this example, in my opinion.
Hm. So the American president Donald Trump, his AI plan, you know, at least as to the extent that he's he's laid it out, you know, is is set at trying to achieve US dominance in the field. And I'm interested in your view. I is this a winner-take-all battle, 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, you know, one dominant social media platform and one chat platform and one search, et cetera.
But 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.
So let me just push on that a little bit, cause 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, you know, there really is a distinct Chinese internet, and it is almost a parallel and and, yeah, unbridgeable kind of system. But you think AI is different. Explain why.
First of all, specifically if you stick to social media, right? A lot of the power for social media comes from network effects, the fact 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. This is not easily transferable, this concept, to OpenAI. I mean, as long as OpenAI is providing me with quality answers, I don't really care if a billion other people are using it.
Now, there are some technical things, you know, reinforcement learning with human feedback that's happening in the backend that might marginally make OpenAI's answers better than, for example, Llama, which is, uh, lesser users, but the quality difference is gonna be relatively minor. So because this is not driven primarily by network effects, as 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.
Now, 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 eighty years that multiple businesses can survive in parallel. They can coexist.
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 can global companies engage in 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, et cetera.
It'll never be zero, but they are minimal. Knowing this, it then becomes imperative for Western companies to try and understand saying, okay, 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. On 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.
Talk in more detail about one or two of these companies and how they're engaging with 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 DeepSeek, which is obviously the Chinese, uh, large language model, into its vehicles. So a lot of the AI in BMW vehicles is gonna be done by, uh, DeepSeek. So staying in 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?
So they're gonna make massive amounts of these based on the Chinese AI. And then, of course, in the other space, in in the consumer package goods space, we've got folks like Procter Gamble. And what P and 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 Duoyin 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.
So, really, really, we've got companies all the way from consumer package goods to durables like automotive, to industrial goods like Bosch, that are kind of already stepping into this dual paradigm.
Are the Chinese better at, let's call it, hyperpersonalization with AI? Are they more advanced or or more focused on that than Western counterparts?
So what the Chinese are better at is industry-specific hyperpersonalization. So if you just take general hyperpersonalization, I don't know if they're better. But if you ask me, are the Chinese better at hyperpersonalizing in healthcare? My answer to that would be probably yes. Are the Chinese better at hyperpersonalizing in B two B 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.
So they've got a couple of 100 million vendors on Alibaba, which is basically the most dominant B two B vending site, uh, you know, essentially in Asia. And what they essentially do is, I mean, if the the classic thing, right? I mean, if I'm a vendor and I'm I'm I'm I'm selling fifty different types of 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 fifty types of mugs that I'm 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. What I can now do is I can use Alibaba's AI to create marketing material, images, descriptions, et cetera, for the remaining forty-five. And now, all of a sudden, I have this scaled-up, amazing personalization that's possible, which till yesterday was simply not, uh, not doable.
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. They're not currently actively pursuing the US market, to the best of my knowledge, but I bet that's gonna change.
So now let's talk super practically, all right? So if people are listening to this and thinking, okay, that's interesting, I I need to think about, you know, if I'm a global company, I I need to think about whether and how I want to be in the the Chinese AI ecosystem in addition to, you know, 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? Figure out a way, a safe way, a sandbox, download DeepSeek, make sure it's cut off from any private secure data if 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 and worse 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. To me, the larger 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 the school business model that they have built based on AI. Because this is exactly what we saw with the mobile revolution, right? Were there 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 at 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.
If 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 gonna be better, but they're gonna 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 look at the Chinese models?
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. You know, I 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 gonna be significantly different across these geographies and how Western companies need to handle that. I do not want to underplay the security, privacy, and even ethical issues that come with using these systems. They exist, okay? For now, DeepSeek is open source, so you can actually take it and you can put it on your own servers with reasonable amount of safety, a reasonable amount of confidence that nothing crazy is happening in the back end.
Not 100%, obviously, nothing is 100% on this planet, but reasonable. But yes, moving forward, there are going to be probably different models that are not open source that will need to be based off of their complete infrastructure stacks. And in that case, Western companies will need to make an informed decision on whether or not they want to play in that ecosystem. Absolutely yes, both from a privacy, security, a perspective, but also from a regulations and and then finally from an ethics perspective.
Yeah, and then you've got just the uncertainty about relations between Washington and Beijing. You know, as we record this, they're better in some ways than they have been. Donald Trump is probably going to meet with Xi Jinping before the end of of 2025, and that could improve relations. But you don't know. I mean, I mean, you know, the latest headline is that President Trump is going after Intel because its CEO, you know, has had ties, quote unquote, with Chinese communists.
Well, you know, anybody in authority in China is a communist. There can be problems in that area. This is not necessarily one, and Intel has already said that this is ridiculous, but it is a volatile relationship. And I wonder if politics could force companies to choose at some point, and is it responsible to do scenario planning as you're thinking about engagement in the ecosystem, I guess, to what degree do companies need to think about these, you know, kind of worst case scenarios?
They have to absolutely factor that in. Irrespective of what's happening currently politically in the United States, I think we can all agree that we've generally, across the world, entered an era of more uncertainty, of greater volatility, for a variety of reasons, right? Whether it's political, whether it's environmental, whether it's around regulations or whatever that is, right, there's just more volatility. Companies will need to hedge their bets. I mean, it's not gonna happen that just because a couple of presidents don't like each other or, you know, putting tariffs on on on different, uh, parts of the world, that you're gonna stop doing business in that part of the world.
And if you then want to continue doing business in that part of the world, you need to be integrated into that ecosystem, into that particular technological stack. So, if nothing else, from a pure scenario planning perspective, from a pure hedging perspective, organizations do need to look at this. And a and and and a related aside, you know, what's what tool is really great for doing scenario planning? It's actually DeepSeek. And you know why? You know why, funnily enough, is because you can actually drill down and see its chain of thought.
So there's this little button that you click, and then it actually shows you how it thought about the answer that it actually gave you on scenario planning, which can be tremendously useful for executives. So for folks listening, I I encourage you to try it out.
Let's say that some of these risks are manageable and kind of get back to where where we were and not worst case. So, again, there's this idea that companies may be running parallel gen AI models and applications. I just want to understand, in really practical terms, what does that look like? What does that mean? To what extent is that simple and everyone's already doing it, or to what extent is that going to force people to kind of change how they think about business?
So I think at some level, most savvy organizations already doing it, but they could be doing it between Gemini and ChatGPT. So most organizations that have thought this through and have a reasonable technological architecture, what they're doing is, you know, if a query goes into the company GPT, for example, they've got a little engine in there that says, hey, you know, for this particular query, going to Llama might actually be cheaper, faster, use less tokens, and give a more accurate answer than going to ChatGPT.
Yeah, so just as an example, there's a bank that I'm working with in Asia that has had this system in place pretty much since the early days of ChatGPT. And then they keep adding the different LLMs that come in, and they keep optimizing their triage engine that does this. So, at some level, savvy organizations already working on this. Having said that, adding Chinese large language models and Chinese it's not just about adding one more LLM to this.
They will have to think this through a little bit deeper because of the reasons that we spoke about previously, which is around cybersecurity, privacy, ethics, as well as regulations.
Well, and bias, you know, we certainly know the Chinese internet is scrubbed of, or the censors certainly try to successfully scrub the internet of things that the Chinese ruling Communist Party thinks are are damaging to their image or to China's image. I assume we would have to have the same concerns about Chinese trained large language models, that they may be scrubbed of certain pertinent, relevant material that would be a a bias that could be tricky for for Western companies.
Without a question, and and obviously, again, again, I I encourage, uh, listeners to try this out for themselves. I mean, if you're gonna use DeepSeek, go ask it what happened in 1989 in Tiananmen Square and see what answer you get.
What do what do you get? Have you done that?
I have, multiple. This is the first thing I did when I went to DeepSeek, and it says, basically, I mean, I'm I'm paraphrasing here, but it said nothing happened. It was a nice, sunny day. And you can go on and on about controversial topics, and it'll essentially just back out, right? It'll essentially say, Hey, look, I don't have the answer to this question, or I'm sorry, I cannot, uh, provide an answer to this question, which is fine.
I mean, this is this is something, but but you're absolutely right. I mean, there are some aspects, I mean, especially for organizations that are gonna be dealing with things like this, that are gonna be dealing with kind of ethical outcomes, these are things that they will need to consider. Which is not to say that the Western models are completely cl- free and clear and and and completely above board. They have their own sets of scrubbing that goes on, which also we have to account for.
Okay, so we've talked about some of the risks. So I want to throw it back to you. Your research has suggested that what Chinese AI companies are doing in some ways is so extraordinary that Western companies would be foolish not to at least experiment with them. So so make the case, what is it that the Chinese companies do so well that Western companies really need to see if there's a place for them in that ecosystem?
So what the Chinese have done really well, and which in our paper we call the three C's, but which in in simple words is they have created an ecosystem that from the ground up is customized for Gen AI. They have taken that in a low-cost model, and then finally, they have built it such that all their applications are calibrated for the real world. They're not calibrated to win competitions, that's an aside, but they're calibrated for healthcare.
They're calibrated for pharma. They're calibrated for supply chain. They're calibrated for CPG. This is something that Western companies should not ignore. I'm not saying that we cannot do it in the West. I'm not saying that the Western models are not capable of that, but this is something that the Chinese are trying differently. They've been very, very successful. There's a lot that we can learn, just as they learned from looking at our models. There's a lot that we can learn from looking at theirs, and it would be silly of us not to do that.
And then, of course, the big one, in my opinion, what business models these folks build based on these tools, that's gonna be the real kicker.
Yeah, this will be fascinating to watch how this unfolds. And there's an option for, uh, Western companies to form, you know, not just to plug into, you know, a DeepSeek or or another LLM, but to actually form deeper partnerships. What's your advice in terms of of how Western companies might think about that?
Most Western companies, most companies around the world, are now looking at agents and agentic AI, et cetera. Agentic AI is, in many ways, ripe for these kinds of partnerships. So please do look at how some of these tools can fit in to your overall AI portfolio, overall AI strategy. It's unlikely, almost impossible, that you're going to be sticking to just one tool, one type of AI, or one infrastructure. You will be using a variety of these anyway, inside most medium to large organizations.
Look at these, look at tools like Manus, right, which is a Chinese company which recently moved to Singapore, which is building agents on top of these tools, and it's using a combination of Western models and Chinese models already. This is what I would advise executives. Please don't miss out on this opportunity.
So this is fascinating, I mean, this is the market to watch, and I think you've, you know, you've put your finger on the fact that there really has been this sense of distinctive and parallel development, and now we'll kind of watch how it evolves. And it seems really smart to say, you know, companies need to look at all of this that's out there and and figure out the best solutions for themselves. So so why don't we end, you know, what's something listeners could do right now to kind of educate themselves better about these possible opportunities?
Figure out your favorite or or go online, go to Google, go to your favorite search engine, find out a few sources of information, a newsletter from China that you would want to subscribe to, to get more information about what's happening in their ecosystem. Then go ahead and play with a couple of these things. I'm a huge believer in the fact that these tools are not something that you can learn from watching a YouTube video. This is something that you really need to get your hands dirty with.
So create a sandbox for yourself, don't put anything private or confidential in there for obvious reasons, but then fool around with it. Ask it nasty questions, ask it politically incorrect questions, and ask it normal questions and see what kind of output it gets and compare.
All right, Amit, thank you for the article that you co-wrote for HBR and and thank you for being on IdeaCast today.
It was my pleasure. Thank you so much for having me.
That's IMD professor Amit Joshi, who co-authored the HBR article, How Savvy Companies Are Using Chinese AI. Next week, Allison will speak with futurist Nick Foster on how to reframe your future planning. If you found this episode helpful, share it with a colleague, and be sure to subscribe and rate IdeaCast in Apple Podcasts, Spotify, or wherever you listen. If you want to help leaders move the world forward, please consider subscribing to Harvard Business Review. You'll get access to the HBR Mobile app, the weekly exclusive Insider newsletter, and unlimited access to HBR Online.
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