AI Agents and the Future of Global Trade with Alibaba’s Kuo Zhang - Ep. 291

0:10NVIDIA AI Podcast Host:Hello, and welcome to the NVIDIA AI Podcast. I'm your host, Noah Kravitz. Since 1999, Alibaba.com has served business-to-business e-commerce buyers and suppliers from over 200 countries and regions around the world. Kuo Zhang, president of Alibaba.com, is here with us today to talk about how AI agents like Alibaba's recently launched Actio will be reshaping our massive global trade ecosystem over the next decade. And while we've talked a lot about agents in recent episodes of the podcast, there are few people in the world who have Kuo's perspective on how technology shapes global commerce, which is why I'm so excited to welcome him onto the podcast.

0:52NVIDIA AI Podcast Host:Kua, thanks so much for taking the time out to join NVIDIA's AI podcast. Thank you for having me. And I know you're joining us while you're traveling, so an extra special thanks. Appreciate you making it work. So let's get right into it. Maybe we can set the stage, or you can set the stage. Tell the audience a bit about what Alibaba does, and Alibaba.com in particular, and then you can talk a little about your role as president of Alibaba.com.

1:19Kuo Zhang:Okay. So Alibaba.com is the first business of Alibaba Group. It is founded in 1999 by Jack Ma and the 18 founders. So it started as a yellow page, and now it's kind of evolving to a leading B2B platform in the world. It is connecting around 50 million buyers, B2B buyers on a yearly basis, and more than 200 ,000 suppliers globally. And it's now enabling more than$60 billion US transaction in yearly basis. So this is about Alibaba.com today. And actually, I joined Alibaba since 2011. So my first role is in Tobol and Timor. It's kind of in China, domestic B2C business. And I joined Alibaba.com since 2017 and become the president about five years ago.

2:19Kuo Zhang:So yeah, this is quite a journey.

2:22NVIDIA AI Podcast Host:Yes. I can only imagine how, I mean, maybe, I don't know if you can speak to this in a short amount of time, but I can only imagine how technology and from your perspective has changed so much, you know, on the inside, on the buyer and supplier side. And of course, what Alibaba has done and is doing, building platforms. Because as a consumer, the way that, you know, I purchase things way down at the end of the chain has changed so much. And so I can only imagine from your perspective and the work that you've done and continue to do now, just really how things have transformed.

2:57Kuo Zhang:Right. So what do you say is correct? I want to echo that is one of our dream is to make global trade as easy as online shopping. Yeah. Think about how we kind of reshape about the e-commerce. So the people buying stuff is really easy. And now when the buyers want to source or want to buy something from a kind of supplier, overseas especially, it's still, they will meet a lot of challenges, including the language barrier, Yeah. Time difference, the time zones and the culture difference, the trust issues. So how to kind of pay, how to settle the payment, how to settle the logistics, how to settle the kind of after sales services, so on and so forth.

3:53Kuo Zhang:And yeah, so there's a lot of things to be done by technology. So before AI, actually, we already set up kind of the first, the demand and supply system, the search and the product listings and the online communications, kind of the live shows. And then we build up the transaction systems, including the payment networks and the logistics networks. And then set up the entire trust systems, kind of the B2B extra payment for the buyer and suppliers in the B2B scenarios. So that's, I think we already set up a kind of a standard e-commerce platform for the B2B. And now we know that AI era is coming.

4:42Kuo Zhang:So we see a lot of improvement space in here as well. That's why we introduced Axio.

4:49NVIDIA AI Podcast Host:So let's talk about Axio. from the materials I read and I looked at some videos online. It's described as an AI agent designed to help you do business, which sounds simple enough, but as you've alluded to, there's a lot that goes into doing business B2B, particularly on a global scale. So can you tell us, what does Axio do, and how does it fit in, why is it so important to Alibaba.com's overall vision?

5:15Kuo Zhang:Sure. So I can share with you our kind of vision but we do it and I can show you some data to prove it. Perfect. So Axio actually is an AI native application. So when we build this application, we build it on top of the kind of SOTA model. We see when the people are now using Axio, it's the kind of the behavior is different. Like they're using the traditional search engine or traditional platform before. The first four, they are using the kind of natural language or long sentences to describe about their request. So previously, they may use the language like I want to buy a kind of, how to say, the portable energy storage to buy something like that.

6:05NVIDIA AI Podcast Host:Like a generator or a battery?

6:07Kuo Zhang:Yeah, it's like a battery. battery. And now they can describe in a kind of full sentence, like what is the scenario this battery is using. So what a type of look like is like a kind of suitcase is portable with what kind of protection and the dimensions of the size, the kind of weight. You can put it together and then the actual engine can understand what you are requiring and kind of break down into different elements and match the products and match the suppliers. So it's a kind of a completely different user scenarios. And we see that the people are putting much longer sentences, more natural sentences into the Axio system.

6:52Kuo Zhang:I think that's one. The second is now we're introducing an agent model, actually an agent model to the Axio. So now it's not only doing the search functions, but also is acting as an agent. So meaning that you can give them a very complex task. You can execute upon the task and deliver the result to you. And what we see is that in Axio, the user, the audience in Axio and in Alibaba.com is only 30 % of coverage, meaning a lot of people actually is using Axio to do the first online sourcing for the global trade. and it's completely kind of lower the barriers for people to enter in this field.

7:36NVIDIA AI Podcast Host:So how does just, you described it, but kind of to dig in for a second, how does the experience differ? Or maybe you can unpack a little more of all the different things that have to go into actually buying an item business to business globally. And you kind of alluded to, you know, building the network of trust, the technical infrastructure, the logistics, payment, other trust factors. Can you describe maybe some of the things that traditionally have been done manually that can take up quite a bit of time, particularly, as you said, for someone new to doing this, that now Axio can sort of take care of and automate?

8:18Kuo Zhang:Sure.

8:18NVIDIA AI Podcast Host:I can give you two examples. Okay.

8:21Kuo Zhang:So this example is actually my team tell me that just one week or two weeks ago, how they see the people that are using the system. So one example is this kind of a supplier for Polyvian Games. It's a kind of mini size of Olympic Games helping in Latin America. Okay. About six countries is attending that games.

8:53NVIDIA AI Podcast Host:Right.

8:53Kuo Zhang:And once the suppliers actually sourcing the items for these games, you can imagine they are sourcing all different products from metals to gears to clothes, protection stuff. And it's neat to kind of apply to the local compliance regulations. previously they need a kind of team with expertise to sourcing from all kinds of suppliers maybe hundreds of suppliers to support such kind of game so now what do we see is that they upload a file like excel so telling about all the specifications they need the items are in hundreds or thousands of them and you can just upload this file to xxu and xxu can understand what your requirement.

9:41Kuo Zhang:Understand this is kind of, this product is going to be used in Latin America. Need to follow the local compliance and guidance. And then it will simultaneously execute these tasks. And previously, you may take weeks, even months to finish this sourcing list. Now using ASU, it can be finished in hours or in minutes. And then it can give you all the kind of suppliers who can make this product and give you a suggestion. Then you can use that to send inquiries and it even can take this step further to communicate with these suppliers. I think that is one of the examples I can tell you how the agents can collaborate together to help you.

10:29Kuo Zhang:The other, I can give you one more. So this is a kind of requirement from expertise. So who actually, how this sourcing experience just need this agent to help them to execute on the complicated tasks. The other examples I see is that the people just have ideas. For example, one of the ideas is that I want to design clothes for the ADHD children, child. So what a type of materials is and how to design this kind of child, which is it can help the ADHD children. So then the XU can help you to start from the marketing research to see what the existing product is and give you suggestions step by step and then give you the suppliers and the product recommendations and even can help you with the design prototype.

11:25Kuo Zhang:So all this kind of stuff from the marketing research to the product design or product redesign all the kind of find the suppliers who can make the product all this stuff can be executed by

11:38NVIDIA AI Podcast Host:this agent so it's really almost the full business life cycle exactly and so you sort of read my mind you almost immediately got to what i was thinking which was how far down the the line can it execute like you you kind of mentioned you know providing that axio could provide the sources and then And, you know, I would send inquiries, but actually the system could send the inquiries and agent to agent collaboration. Can you talk a little bit more about that, either what it can do now or sort of what you're working on or is possible, if you can speak to that?

12:15Kuo Zhang:Okay. So first I can tell you how we design the system. Yeah. And then I can tell you about the boundaries. Like I think you have a lot of questions that where we go. Right. Where we stop, right? So the system is working like this. So you first start from your questions. You send a request. Either it's a kind of in natural language or it's a multi-model.

12:40NVIDIA AI Podcast Host:Right.

12:41Kuo Zhang:You send up a drawing, a design, or kind of a file, a PDF, Excel list. It interprets your request and then orchestrates into a kind of set of tasks that you can execute upon.

12:55NVIDIA AI Podcast Host:That can be at the level of a business already running that has sophisticated sourcing needs, or like the example you mentioned with the games in Latin America. Or it could be something like an individual, as you said, for instance, who has an idea for clothing design, tactile clothing for ADHD wears. And so it could be somebody who doesn't know anything about sourcing, even as you said, it could help the system can help them design. So any level of expertise coming in.

13:22Kuo Zhang:Exactly. And for that part, actually, it can involve the human, actually the users to interpret. So like here are the tasks I orchestrated or designed for you. What do you want to check? If not, then it will execute upon these tasks using all the SOTA model to give you the best result. So that's the second step. And the third step is to re-evaluate. So in many of the cases in this kind of global sourcing or global trading scenarios, so the decisions is not always quantitative tasks. Many of the time it's qualitative tasks or decisions as well. They need to evaluate whether the decisions or the answers that we give is the right answer.

14:10Kuo Zhang:It will be proved by the platform like Alibaba.com to see if this is a good answer or if this is a good output. If not, then we will iterate the whole process and see how we can kind of evolve and help the people. And whenever actually there's a decision cannot be made by, let's say, the machines. Like when you make a deal, so what the process, what kind of conditions that you can offer, then it will involve the human to make the final decisions. or when the kind of the AI actually exceeds this boundary, it does not have this kind of knowledge. It will come back to the humans to make sure that they understand and can execute upon.

14:55Kuo Zhang:So this is how we kind of execute this whole system.

14:58NVIDIA AI Podcast Host:You mentioned in your example, sourcing materials for an event in a particular part of the world, some of the just situation-specific, cultural-specific, location-specific things that have to be dealt with for different projects, even if they might look the same sort of on the surface. How do you build for a global audience? Alibaba, alibaba.com has been serving a global audience for some time now. But when you're building AI systems, agentic systems, how do you ensure that the AI-driven solutions work across these diverse markets and use cases?

15:32Kuo Zhang:Right. So the first four is a kind of a combination of the world model and the domain-specific models. The word model meaning that, so when there's a kind of specification for a kind of country or region, this specification or the rules will be learned by the model. So when you kind of ship product to that area, so what kind of laws or regulations that we need to apply to. And the domain-specific kind of information or the knowledge is maintained by alibov.com. So you know that we have more than managed 200 different suppliers and they upload all kind of certificates and all different kind of product details that we can understand.

16:17Kuo Zhang:And together, we know that how we can apply different product into different scenarios, into different countries. And also it's kind of a human and the machine interaction system. So in some of the cases that we involve humans to make the decision or make the kind of judgment as well, if it already exceeds the boundary of the AI system or the knowledge base. And the third, but not least, is that we already, actually Alibov.com already managed a system which we comply with all the regulations, all the rules. And also we already managed to process more than$60 billion kind of transactions. So we know that how to build a systematic approach to protect.

17:08Kuo Zhang:So these three layers actually together that we can deliver a better result for the global trading.

17:16NVIDIA AI Podcast Host:When it gets into more kind of nuanced or maybe sort of qualitative as opposed to quantitative information, insights, cultural nuances, you know, just things that a person might sort of know and act on intuitively, but maybe have a hard time or just never think to express in words. Do you approach training the systems in the same way? Are those types of knowledges and capabilities kind of more dependent on learning by use? How do you infuse the systems with, you know, the sort of local nuanced information that can be really important to closing deals? Okay.

17:56Kuo Zhang:This is a very good question. And I think we are keeping working on that as well. So what we're doing now is we can say that it's three layers. The first, of course, is the data set. So knowing that we have more than 260 million products and suppliers and transactions and the buyers, that we, through this data, that we understand what the demand and supply look like. And we can kind of abstract the domain knowledge from that as well. So I think that's the first layer. The second part is about the industry know-how. So that part actually is based on not only the model, but also the industry expertise.

18:45Kuo Zhang:So I can give you an example. So when we say we design a product for a specific scenario, so how we evaluate that design is a good design. It's like the question is how we evaluate the answer is a good answer. So that needs to be kind of rely on the expert systems to evaluate all these answers and to kind of keep improving the system.

19:11NVIDIA AI Podcast Host:Right.

19:11Kuo Zhang:And the third is about the platform itself. So when we outcome kind of a result based on the requirement, and then we will put that result into our platform like alibor.com. And then we will see the conversion rate and we'll see how the kind of the buyers and the sellers are iterated with this output. So if this is not good, and then we will iterate the system, the models, the data set as well. So that is how we kind of solve these problems. So you can imagine that we are leveraging our data, our kind of industry know-hows, but we need to iterate the system in real time based on the system's feedback.

19:54NVIDIA AI Podcast Host:I'm speaking with Kuo Zhang. Kuo is president of Alibaba.com, and we've been talking about Accio. They're relatively new. When did Accio launch, Kuo?