Where does Europe fit in the so-called China-US AI race?

Joining me today is Alex Lu, who offers a unique perspective. Alex works at the intersection of three very different AI worlds: China, Europe, and enterprise transformation. Having spent more than a decade in France and now advising European companies on AI adoption (often Chinese models), he offers a perspective that is often missing from the broader AI conversation, which is typically framed as a competition between the United States and China.

In this conversation, we explore how European companies are actually approaching AI implementation. Rather than racing to deploy the latest models, many are focused on organizational design, employee adoption, process changes, and measurable returns on investment. Alex explains why European firms tend to be more cautious than their Chinese counterparts, how concerns around AI sovereignty shape technology decisions, and why companies increasingly find themselves balancing U.S. frontier models, Chinese cost-efficient models, and European alternatives such as Mistral AI.

We also discuss the economics of AI adoption, including the emerging concept of “tokenmaxxing” or rather if that is even the wise path forward, whether AI is truly replacing jobs, how companies should think about ROI when AI introduces variable costs, and why the future may involve token budgets becoming as commonplace as mobile data plans. Finally, we explore Europe’s position in robotics, industrial AI, and regulation, and whether Europe’s strength may ultimately lie not in building the largest and best-performing models, but in defining how AI is deployed responsibly at scale.

To find the previous episodes of Differentiated Understanding, see here.

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.

Season two will host a series of guests from early-stage investing, as well as builders, researchers, founders, early adopters, and product managers. For more information on the podcast series, see here.

AI-generated transcript (for reference only)

Grace Shao (00:01)

Hi Sheng Yun. Thank you so much for joining us today. Really excited to have you.

Alex Lu (00:05)

Yeah, thanks very thanks for inviting me. I’m also very excited to have this conversation with you.

Grace Shao (00:11)

Yeah, awesome. So tell us about your journey. I think you’re in a pretty unique position. You know, like I said in the intro, you know, a lot of the conversation about AI right now is often positioned between China versus US, But you actually work predominantly with European companies in adopting AI and their digital transformation. So tell us about your your background and how you got into this.

Alex Lu (00:31)

Yeah. so thanks a lot. So actually, I went to France. I spent more than 10 years in France. I went to France in 2004 and I studied in a school called Ecole Polytechnique. and then when I graduated from the school, I started my work in in Europe, mainly for automotive industry and afterwards for the consulting industry. And still when I was in the consulting industry, I worked mainly for for the auto sector. So

I have a very traditional background of automotive. That’s why some of the work I’m doing currently in the in the AI, we can come back on that, is in the automotive manufacturing sector and mainly for European companies. Because I started my career in Europe, so I know I don’t I know them pretty better, pretty good. And the the the other thing point I want to mention is the school I started actually the Ecole Polytechnique was

Let’s say it it was a famous school in France or in Europe, but it it’s not so famous in in the world. actually this is in France they have a different educational system. but still with the with the rising of of AI in Europe, especially the French large language model called Mistral AI, the school becomes famous because the founder of the of of of Mistral AI comes from the the same school. So basically it’s also a a little bit like

Tsinghua university in China is like the the Tsinghua in in France, having the best talents for for the AI. So nowadays, when I continue my work in the AI transformation for companies or AI implementation for the companies, I work a lot with European companies. Firstly, I know that my I as I said before, and secondly, is when we look into the global competition between China, US, and Europe.

In the AI landscape, it’s pretty clear. It’s like China and and US or US China being the tier one or first ranked models. And Europe is kind of lagged behind. So most of the European European companies, they have this kind of attitude of being a little bit complex, I would say. on one hand, they are kind of seeking for, of course, for the best technology in the in the world to enhance their company’s competitiveness.

There comes the question, how I can define my AI strategy for next year’s between Chinese and US tech stack in AI. And the second question they raised often is while we are European companies, we want to keep keep our AI sovereignty, which is a very important topic in AI. again, we can come back on that. So their question is: okay, between this US and China tech race.

Is there any place for European companies regarding the foundation model companies or application companies or even corporate clients? What could be the playground for European companies? So these are major two questions are often received from European companies and you will you can see the thinking angle is they European companies want to at the same time keep it keep the AI sovereignty and at the same time keeping their competitiveness. That makes the question a little bit complex. Yeah.

Grace Shao (03:49)

Actually why don’t we just double click on the unpack that a little bit? What’s your view on it? Like what what do you advise your clients to do then if if they are kind of cut caught in a pickle or unsure how to build out the next stage of their infrastructure kind of being caught in between China and the US?

Alex Lu (04:08)

Yeah. So the the first thing I I always shared is in in in this tag race actually China and US we are not I want to twist twist a little bit the angle saying this is a competition between China and US. Actually, if we look into details, actually China and US are taking different directions in terms of the AI development, if I can say, because let’s say if we look into the US,

AI ecosystem or the AI development. I think a lot of efforts are put on the foundation model or kind of foundational research regarding how AI can be become AGI can be bring beneficial benefits to the humanity, or how we can guide Rails AI so that okay, one day we will not go into the direction of science fiction movies. So this is a little bit the the push from the US AI companies. While in China, actually

the ecosystem or from the national perspective, China’s AI is more about applications and more about how we can have the s beneficial from the whole society from the AI and how I can combine AI with my traditional technologies or traditional business to to to to to grab more values. So if we think in this angle, actually it will give us two different pictures. One is we cannot say that it’s kind of from

front to front front competition, because these two nations are just take different angles. The second thing is if we look into details based on these assumptions, we will say one nation is pursuing having the most advanced AI technology and one nation is pursuing most kind of most beneficial AI for the society regarding cost effectiveness, et cetera, et cetera. So then it comes to the question that you raised for European companies is

We always brainstorm and conclude on the simple question is what kind of AI are we looking for for European companies? Are we looking for, let’s make it simple, I take some an analogy. Are we looking for kind of you need all the employees to be the PhD employees having the most intelligence in the world? Then that will be the US foundation models. Or if we want to say we have the most cost efficient and best performing employees, virtual employees in your company.

Then we might consider Chinese models, foundation models. Then this is the trade-off. I think the companies should figure out. And the answer will not be so simple like that, saying, tomorrow I will switch to all US tech stack or Chinese tech stack. I would say the two ecosystem, as in the past in the digital area, will still continue for European companies, meaning that they need to juggle with Chinese tech stack in certain markets.

maybe in Chinese market for sure, but for other markets, developing markets where the Chinese foundation model are taking influence and as well as with US models. So this is the thing. And I think the other angle answer to to to their question is I I usually take the statement from Jensen Jensen Huang saying the AI is kind of five layer cake.

So what we are talking about is only one layer, which is the foundation model. And if we go deeper, then we will have infrastructure like data centers, like powers, chips, and electricities. And if we go upper, we will have the applications. So I would tell European companies or I told European companies often is I think the use cases in Europe makes a lot of sense because the cost is there and the employee was pretty

much expensive than Chinese employees. So if we deploy the same model, let’s say, and it of cost the ROI return on investment, you make the business case very easily in Europe than in China because the labor cost is kind of lower. And the the advantage of Europe, one I would say one of the advantages is about power and electricity. I study in France and in France y you would see they have the most advanced nuclear nuclear power technology in the world, at least in the past. And

I think the French government is also think about how we can build more power plants in the in the country to support Mistro’s AI development. And I listened to the founder of Mistral AI, Arthur Mensch. he explained to European Commissions how we can keep the AI development in the Europe is just he make a very simple analogy, meaning that intelligence equals to token. So we all know that, and he said token equals to electricity.

So if we want to make our society more intelligent in Europe, then we need to build infrastructure and more efficiently and more sustainably. And and my last point is I looked into the report released by Stanford, the HAI index. And very interesting because US is far away beh in advance compared to other countries in terms of the number of data centers, it’s around

2000 or more than 2000. I I didn’t remember the exact numbers. And the second and third, it’s not China in terms of the number of data centers. Based on the data, it’s United Kingdom and Germany. So Europe, Europe has the capability to build power plants, but I I I tend to believe these power plants are not currently used to train US models, in my opinion. So again,

This is the European competitiveness if we want to talk about AI. So if we enlarge a little bit picture, we say that okay, it’s five or five layers cake, then again, China and maybe is better performing better in terms of electricity, maybe a little bit less performing regarding the chips. Same situation for Europe. So it’s not only about the most performing models, right?

Grace Shao (10:13)

That’s an a really interesting take. So help me understand like what do you actually advise on these companies for? So you gave me a big high level picture, right? Well give me some examples on the kind of work you’re working on. It’s because I think on this podcast, we often invite people who are builders, founders, investors, and they give us a lot of high level views, which is great, right? But but I want to hear from you, how are you actually helping companies go through this AI transition? And

What are the bottlenecks? Maybe further down we can talk about that. What are the challenges? What are the exciting areas? But just help us understand what are the day to day tasks that you’re working on.

Alex Lu (10:49)

Yeah, thanks. again, I I might share two different perspectives from my experience when it’s again with the European companies. It’s very interesting example because actually I build a product doing this kind of market intelligence, market research for European companies and the value proposition at the time it at that time is we can save time for your employees and they a and and we make your organization more efficient.

And basically we find when we when we s when we sell this kind of value proposition to different companies, I see very interesting different answers. One is on the European side, he would say, this is very interesting, but before implement implementing, we need to think about a kind of tomorrow’s process process, meaning that if we put your AI product into our organization, how our employees will work with the product together, what’s the process look like?

And how many new skills would my employees need to perform or to better use your products? So I think the European company’s mindset is they will need some time to conceptualize the AI products or AI use cases. And then they will need to conceptualize and project, especially once the product is in place, what my company will look like. So they will spend

A little bit more time than Chinese companies to figure out the the regarding the talents, regarding the organization, regarding the process. And in my opinion, it might be a right right approach, in my opinion, meaning that they put human before the techno technology. And this is what I observed when I implement AI for companies saying that, okay, I bring you the best technology, but often we might have improved efficiency by 10 times or five times.

In one single process, but actually there will be some bottlenecks in other organizations, in the in the rest of the organization, then you cannot you cannot increase the efficiency of the whole workflow, let’s say. So that’s why a lot of people in US they talk about AI native organization to kind of remove the bottlenecks in the in the organization. And I worked another example, I worked for a European company, and it’s very interesting. He said, I receive

high level management. He said, I received so many reports from my employees, I I don’t have enough time to review and to approve them. That’s the case because we increase efficiency of the working level of the people, then the bottleneck becomes suddenly the a the leadership. And then we need maybe an empowered leadership by AI in the future to make the whole organization more efficient.

Or we need to think about a new organization where we include AI agents and human beings together because the two natures are producing things on a different scale. So this is mo most of the time, this is the European companies. And for the Chinese companies, the mindset is totally different. if we implement the AI solution, the same product to a to a Chinese client.

The the the answer would be, that’s very interesting. You save 20 or 30% of my employee’s time, but you know I cannot let’s say lay off the employee and to make some savings. So just tell me for the time we saved where he can work to produce more. So it’s always in the mindset, okay, we have some some time saving, but you I cannot pay 80% of the salary to the same guy. So

In in order to so I I need to pay him hundred percent salary, so I y your business case doesn’t work for me. So where where we can grab more values. Yeah, so you see a di

Grace Shao (14:37)

That’s really interesting. That’s a really interesting approach.

Yeah, because it’s like the company is reflecting actually a broader, I think, social, even cultural and perspective on how they’re perceiving AI. And in the China and US, often the conversation is so fixated on improving efficiency and people who are utilizing AI are actually more burnt out because they are like 10Xing themselves or whatever these days.

Alex Lu (14:49)

Exactly.

Grace Shao (15:03)

But you know, in Europe, that conversation is so different. And you can say maybe much more humane. However, like you said, the bottleneck right now is then how do these companies become the next generation? Like still relevant in the future, once this becomes normalized. So it’s interesting. I wanna go back to that a little bit later as well. I I wanna touch on something before we get further into I guess the comparison of you know, the adoption and everything is

Alex Lu (15:17)

Yes.

Grace Shao (15:30)

You and I met each other essentially online because I found out about your work that you helped a lot of European companies adopt Chinese model. I found that was very, very fascinating, right? you advised them on how to basically integrate, say, the Minimax and C AI of the world. Now, a lot of these model companies, when I speak to them, they say their priority right now is to basically sell globally. And of course, the Western markets are some of the most lucrative markets. the US.

headwinds are mostly in geopolitics, compliance, Europe. How do you view that as a market for them or opportunity for them? Like, is it equally challenging for these companies to sell to enterprises in Europe or do you think there are more opportunities for them right now and they’re they’re kind of taking off a bit more?

Alex Lu (16:16)

Yeah. I I I I if I think the conclusion if I I can state at the very beginning is kind of in Europe definitely there are more opportunities than for Chinese large language model companies than in in Europe, than in US, sorry. I have two proofs for that. Firstly is I discussed with a a CTO who is also a schoolmate from my school Polytechnique.

And actually he was very interested also by my newsletters on LinkedIn and then when they he asked me the question so apart apart from Deep Seek, what are dip other models and Chinese models that we we that we can use to improve our efficiency? Because when we meet all of he said he told me when we meet most of the IT implementation companies, they came with the solution like Anthropic or ChatGPT or OpenAI or or Google Gemini.

So we don’t see so many options. He mentioned the word options of Chinese models. And we know that Chinese models are more cost efficient. And then we can talk about token mapping. I think it’s kind of related topic. So this is one thing. I think in Europe, actually for the companies from the business perspective, they are also looking for different variety of different models so that they can bring what I said before, a best cost performance ratio models in the in the organization.

So this is one thing. And then I I told him that most of the Chinese large link model companies, firstly they started their business in China and then they tried to inf have the global influence. Like the most advanced one is zero dot zero one dot AI, but the other ones they are trying to catch up, like that AI you mentioned also Minimax. so I I think the thing is what I see today is the ecosystem of Chinese models.

Are not currently penetrating into the European markets. But definitely there’s a a room for Chinese players. The second thing is I always take the comparison with the other industries like EV industries, like car industries. because you you will see Europe put a lot of tariffs on Chinese vehicles. because okay, you you see a lot of Chinese vehicles because of

Europe wants to protect their own industries, et cetera, et cetera. But at the end of the day, they are not putting hundred percent tariffs. They are putting somehow reasonable tariffs on the Chinese vehicles. So the bottom line I want to mention is I lived in Europe before and I know the mindset of Europe European people. The mainstream, of course, we have different views. I think the mainstream for European people, most open ones.

Are saying okay, we need fair competition. The EV cars is just because okay, the European commissions are claiming that okay, you produce in China, but we in Europe we produce in a more sustainable way, so our cost is higher, blah blah blah. So if we take this comparison, I think definitely there will be some places for Chinese companies in condition that we play fairly in the European market.

And then we might come back to the third point I mentioned before, of course, there’s a a point of AI sovereignty. the biggest, the biggest player of European AI ecosystem is still Mistrol, so it’s the biggest player in the foundation model. And of course, Mistrol should be one of the choices options when we suggest to European clients as the large language models.

So I would see that if tomorrow the Chinese model enter these European markets, they will face a fierce competition with Mistro because Mistro basically they have a government back, let’s say, from France, and they have a very good positioning in the ecosystem. you would see in in two or three weeks you’ll there will be a VivaTech in France and Mistrol for sure they will be on the stage and for sure they will be

French or German presidents, French president and German chan chancellors. And with their unique positioning, I think most of the European companies they were firstly considered Mistrol, but still if Chinese companies can bring something on the table, business wise, the European companies will not only limit to only one model, there will be some balance between different models. And today, the balance I see is Mistral versus other US models.

Grace Shao (21:11)

No, I just think it’s really interesting ‘cause I think it also totally makes sense when I to talk to people who are in the Korean market or, you know, covering the Middle Eastern markets. sovereign AI is just such a top of mind like conversation for companies, whether it’s for compliance reasons, or regulatory reasons, whatnot. So it makes a lot of sense that Mistral’s position very well in Europe. However, are there any other players that maybe we’re overlooking outside because we’re not that familiar with the European market? Any other

foundational model labs that we should know of coming out of Europe.

Alex Lu (21:45)

Yeah, there will be apart from Mistro there’s another large language model whose name is H, but it’s less famous. And then you’ll have it’s not if we can say it is kind of word model by Yen Laquen, the ex researcher in Metafair, and he just came back to France and lab raised raised a large amount of money for the for for his model. It’s called AMI, yeah, AMI.

Grace Shao (22:01)

Mm.

Interesting. Okay, so let’s talk about the token maxing thing you touched on just now. So offline we talked about this a little bit recently. There’s been getting some buzz. It’s quite funny, you know, whether I’m it’s like big tech in the US or big tech in China. When I talk to them, people are saying, Okay, our managers are pushing us to token max. If we don’t basically use AI in our job and figure out ways to essentially replace ourselves, we get replaced, which is the irony in all of this. It’s it’s all kind of sci fi. but

Grace Shao (22:41)

Then the joke’s kind of been played now on the companies because you know there was just headlines coming out saying, this one guy basically spent like more than half a million dollars on tokens in a month, and that’s obviously more than his salary. And then companies are realizing, wait, this token maxing strategy is not cost efficient at all. So from an operational standpoint, I know you are someone who work a lot with companies to implement AI and find

Alex Lu (22:54)

Yeah.

Grace Shao (23:10)

the most cost efficient way for their for their operations, right? And not just costs, like you mentioned, it’s like a balance of costs, you know, and and operational sustainability as well as obviously company morale and everything. So how do we view this trend? Where is this going? Is this sustainable? Like just just give us some high level views on this.

Alex Lu (23:32)

Yeah, there there’s a a lot to talk about this, because the token is becoming really a trendy topic for individuals and as well for companies. so to answer your first firstly to answer your questions, I don’t think that’s sustainable. My view is the token mapping is kind of marketing for infrastructure companies. and of course, as you say, there’s a lot of people burn a lot of tokens and more than their salaries.

Then the question would be if I pay your salary or if if I pay your tokens. We’ll come back to this point afterwards. I discussed with some some Chinese companies. Very cost cons cautious. I think the the the thing is today when we actually for for the tech companies in China, there’s also some ranking of token consumed. but it’s kind of indicator of how people are use AI. But

It’s not is it the right indicator? I don’t think so. basically I think in the in the in current status we didn’t we didn’t find a very good metric to measure the performance of a human being empowered by AI. that’s the thing. So we take a kind of proxy indicator, which is the token for and of course there’s a lot of waste of token in in in in in the usage and I’m I’m not sure that every single employees

would be the master of AI if we don’t provide the sufficient upscaling in terms of the AI. Because from individual perspective, sometimes we use by coding, but if we don’t master the basics of coding, then we might waste some time and as well as some money and tokens in the by coding. So this is my view. So the token maxing is kind of marketing stuff and and the the day when we find out

Again, for the AI organization or for the organization, how we can measure the performance of individuals with AI, then we might have a clear picture and no longer token max. And the other interesting thing you you mentioned already, but I read also is Microsoft they are kind of switched to their copilot because ever s if everyone used used the entropy cloud model then become too expensive for the whole organization. It’s just not just not cost efficient.

And brings me to my point is when discussed with some Chinese companies. So, you know, Chinese companies are very cost conscious. And they are thinking is I think that it was a joking, but this is right angle of thinking is show we in the salary of our employees to allocate a part of the tokens monthly for our employees. meaning that okay, if the

If in the in the in the past situations hundred percent of the salary tomorrow might be eighty-five percent of yesterday’s salary plus fifty percent by tokens. And the tokens you can you can use and if you don’t use tokens efficiently then it’s the savings for the company. So this is it’s

Grace Shao (26:43)

That’s really crazy. But I kind of see what you mean.

Like so essentially it helps you with your job. So that’s why it’s on you. But then what if you just don’t w but what if you don’t want to use AI? What if you just like I can do my job perfectly fine the way I did it before and I don’t want to token max and I want to keep my hundred percent?

Alex Lu (26:50)

Exactly. That’s the question that the Chinese company needs to answer, but you reflect on your point mentioned that the token consumption is sometimes much more expensive than the salary. So it causes Chinese company companies to think that okay, I spent salary, I spend tokens for the intelligence, I spent two times to hire employee. So why not combine them together and doing kind of tomorrow’s package is your basic salary plus tokens?

Grace Shao (27:32)

So actually on on that,how should companies think about it then? Because, you know, it’s really easy to say, okay, this is an AI native company. There’s 20 people in this company. Everyone’s token maxing because it does bring the 20 people’s efficiency to say like 400 people, whatever it is, right? However, what about the traditional companies, especially the ones that you work with? Like a lot of them are OEMs, manufacturers, you know.

It it doesn’t make that much sense for them to really jumping on this AI bandwagon as well then. Or how do you advise them then? Or how do you think how should they think about it?

Alex Lu (28:06)

Yeah. I I think for the for the traditional companies or European companies, it doesn’t make sense for everyone to give the token maxim because as I said, I’m pretty aligned with the European approach saying that okay, in order to release or unlash the value of AI, we need at least to upskill a little bit our employees. We cannot expect employees like with thirty ex years experience in the industry and tomorrow he switched to a kind of AI expert in the

in the in the in his company. So I I just want to combine our question with my previous comment saying that today if you look into the Chinese market today there are some big big traditional telecommunication companies like China Mobile they are proposing the token plan for individuals it’s like your smartphone monthly monthly plan yeah

Grace Shao (29:02)

Wow. Like data plan.

Alex Lu (29:05)

It’s a kind of data plan, exactly. So the token is becoming kind of infrastructure like electricity, like water, or like your smartphone, monthly subscription. So this might be the way the companies might pursue, saying that, okay, yesterday I might give you a kind of monthly plan for your telephone. So I can reach out to you and you can read the emails and you can use the telephone to walk with emails, work teams or with Zoom, etc. etc. And tomorrow it might be a com kind of monthly subscription.

For different employees, then you have a monthly token plan you can use for your personal, not for professional work in AI. I guess that might be the way that the China might be moving for individuals and for companies. And again, for European companies, they are not there yet, but when I discuss this vision and this kind of trend, and they are pretty interested, they might be moving in the same direction.

And for the companies, at least not at the national level, but at the company level, to provide kind of a monthly subscription to a limited number of people who master AI, and the first wave of people adopting AI is their coding team, their IT team, their digital team. So they will be the first employees to use this kind of concept of monthly subs subscription to tokens.

And of course, for manufacturing companies, there’s a lot of people working in the factories, in the plants, or or in the on the production lines, and they are not be impacted, they will not be impacted by this kind of AI wave. But still, I think the things are are moving slowly and it’s it’s changing so quickly. but this is currently my discussion with European companies.

Grace Shao (30:48)

That’s actually very interesting. I it makes a lot of sense actually to build it in in as like a infrastructure like 5G data. And then it’s really, it’s really like there’s a cap on how much the company will pay for, but then how you utilize it should be, and you’re more mindful of how you’re utilizing this, right? And not wasting the tokens and and buy the that thus you know, wasting your energy, compute everything. So

Grace Shao (31:13)

I want to bring it back to the Chinese pricing models really quickly. I know you work with a lot of European companies, they are the buyers essentially. You also help them connecting with the Chinese vendors, essentially, which are like the Chinese LLM labs, Minimax, Drupal, Moonshot, etc. Now, how should we understand the pricing model of these companies right now? Because it’s obvious that they are pricing themselves much cheaper to US peers.

Some might you know, obviously argue that their performance might not be as on par like on par or as at the frontier. however, even when they do play catch up, you know, the reflection of it is it just s seems like a complete different cost structure. Help us ex understand that, like they’re thinking, why they’re pricing it much lower and how that plays out in the long run.

Alex Lu (31:46)

Mm-hmm.

Yeah. Actually, there are two perspectives on that. maybe I will firstly talk from the client’s perspective and then I I might conclude with the recent price decrease by Deep Seek. maybe you you have already read about it. so f from the European companies as I said before, the thinking is w that the that that’s that’s the statement for the companies I met. we do not need entropic models for all the time.

That’s for sure. Because this is very expensive even for a company. So for sure they will need a kind of different options from different models, like the best U US models and the cost-performing, the best cost-performing models from Chinese models and the AI sovereignty models like Mistro. So basically there will be three combinations and then there will be engineering of technical issue that meaning that how to manage these models to

Perform the right tasks. So, meaning use cloud to perform the most complex tasks and use Chinese models to perform kind of less complex tasks. And I think European companies they understand this. And they, of course, they are looking for Chinese models for the cost effectiveness. And I would say this is also one of the bottleneck of US models because they are very

In a relative way, very expensive. Therefore, it it’s the bottleneck of the massive adoptions. Only the European big, big companies can afford like continuous use of US models. well there are a lot of SMEs in Europe. So this is this is the thing. And for the Chinese model suppliers, I think the the way I I see the the the price issue is if you ask me

Can Chinese companies increase their token prices? I would say surely, because if you look into the financial report of ZAI or Minimax, actually they are not they invest a lot in the research to develop these models. And the expectation from the industry AI industry is if you want to train or pre-train a next model, you will cut it will be more costly than the previous pre-trainings. so for sure.

Chinese companies can increase their token prices. And w that that’s what they are doing actually after the open cloak, if you read into the news. And the thing is, compared to the US model, still the Chinese model are very cheap. I think there’s one very strategic thinking thinking angle is if you think about the Chinese models, most of them they are open source models. And the the the the thinking angle is, I think, for the Chinese model players is

We want we open source these models because we want people to use these models. Because they can deploy it on their own infrastructure, they will have more freedom, or they can use our open source model to train their own models. and maybe they they will use our our tokens by or they will they will understand or know our models better by open sourcing. So if if we combine this thinking angle, I would say.

The Chinese model strategy might be to increase the influence in the world, maybe in the developing markets, where people are more cost conscious, and to help people to use this AI to adopt AI in a cheaper way. And then in the long term, in the future, that’s very Chinese, maybe again to increase the prices once we take the market positioning.

It’s like the price competition for the last decade regarding this digital sharing economy or digital era. Nothing has changed. So a very aggressive c pricing strategy to at least to to have the market share and then once we have the market share then we can establish our our our our position in the market and ca kinda do a lot of monetization stuff.

That’s the one thing regarding the increased influence globally and taking the lead in the AI industry for the developing countries, in my opinion. Of course, go going to Europe is is part of the their strategy. So this is from the Chinese model’s perspective perspective, and it’s a very special case, of course. It did this is Deep Seek. DeepSeek released just the before and right after the release, during one month, I think for the developers we enjoy the

75% of discount regarding the token price. So it’s very deep discount. And recently, I think one or two weeks ago, DeepSeague announced that they will keep this 75% discount for for for forever. So it’s kind of they they just discount their token prices by such huge amount of discount. I’m pretty surprised. and

Again it di it it launched a price war in the market and you see recently Xiang Mi decrease also their token prices and I don’t know if other players will will follow in Chinese market at least. But if we think about Deep Seek cases, it’s a very special case because Deep Seat this year it doesn’t create a lot of buzz in the AI community in the US. I think so. I I’m not living in US but I read some newses. news, sorry. I think the

nowadays DeepSeek, I’m not saying that we have the best performing model. and and and in terms of of the tok coding performance, DeepSeak is is not at the top top level compared to other models. But the interesting thing is DeepSeag this time is trained on the Huawei ASEAN chips. so again, I think the price decrease of DeepSeag combining with their recent news of raising money and hiring some harness engineering

Across the world, I would suspect that DeepSeek by decreasing their prices, they just want to break through the ecosystem established by NVIDIA. This is my thinking, and and that’s why after the President Trump visit to Beijing, there are 10 Chinese companies are not authorized to buy Nvidia chips, but up to now you see few others.

Grace Shao (38:09)

That’s interesting.

Alex Lu (38:23)

I think there’s a thinking from the national wise from from the nation thing that okay with Dipsy can we break through the Nvidia chips plus CUDA? And if because that’s so cheap, so most of people they might use Deepsi in the future and they might be used Huawei as ASN chips because Deepsi got trained on these chips and it’s best support DeepSeak’s performance. So this is another angle. Yeah, so you would see

Grace Shao (38:23)

Mm-hmm.

So the open source strategy. Sorry, go on. It’s basically a strategy

to get people in to get the developer into its ecosystem, its own community first, which is what Jensen’s been saying the whole time. Yeah. no, I I agree with you on that. I actually I I wanna and steer away from the chips today because I I am quite fascinated. So you work with companies, adopt AI, but how does that actually what are companies really using AI for? Like we hear about stories.

Alex Lu (38:54)

Exactly.

Grace Shao (39:16)

you know, companies are token maxing, whatnot. And obvious the obvious one, like you mentioned, is in coding capacity in IT, but no again, not every company is in tech, you know, not every company needs coping co coding capacity. sorry, let me just say that. Not every company needs coding capacity. So like what are we seeing actually on the ground, especially for maybe more brick and mortar stores or old school traditional industries? Why would people all want to adopt AI right now?

Alex Lu (39:47)

the the the adoption rate actually for European companies is pretty low, to be honest. most of companies, if we say at a large scale, they don’t adopt sufficiently AI and they just are afraid of missing out something. So this is a FOMO. they are just feared of missing out some opportunities, and if they don’t use AI today, they might be less competitive in the future. So the the f the most common use cases I see in

companies for coding and for it and sometimes it’s easier to measure the effective effective sorry effectiveness of ai that’s in the most most of the time in the sales marketing department so meaning that if you use ai you can produce produce more contents and with more contents you have more impressions with more impressions you might have more conversion rate you might have more conversions and you might have more sales revenues so

This chain is actually well formed. So by using AI, you can track the individual metrics on the chain, and then you can kind of monitor the results by using AI. And most of the time, I get a very simple question of European companies, and very difficult question actually to answer is: what’s the ROI of implementing AI? What’s my return? then it’s a very difficult question because in the

digital, 10 years ago in the digital era, I can tell the ROI, I can estimate why, because the incremental cost of using digital products is kind of almost zero. You just need your digital products and then it makes more efficient, it makes more automate. Well, in AI, that’s very difficult because if you think about it, if you use more AI, you will consume, as you say, more tokens. So, meaning that

An employees, you need to pay the salary. If he is a heavy AI user to produce more content, then you will need to pay his tokens bill. And then the ROI might not be so immediate. Or there might not be ROI actually for the individual use cases. Then we come back to the question: is okay, by using this AI, how we can make the whole organization more efficient and how we can generate more revenues for the whole organization.

While for the individual users, maybe there’s no business case. So I think again, the the the the the difference compared to 10 years ago is the people who use AI and who use heavily AI, then he will have a bill to to pay. That’s a variable cost. That’s very important. And secondly, is the variable cost will be really

The beneficial of the variable cost will really depend on the skills of each individual. You may pay $100 for employee A or employee B. If B master better AI, then you will have 10 times more results, financial results, compared to the first case. So again, I think you asked the right question. the ROI question is definitely a very good question. and most European companies they seek about ROI before investing. So they are very cautious.

While again, if we compare to the Chinese companies, we are more pragmatic. So let’s implement a POC. it costs a little bit, but let’s implement it. If it doesn’t work, never mind. We waste some money, but we we we continue, we iterate or we continue with another use cases.

Grace Shao (43:17)

So you think the Europeans are taking a more cautious approach, but actually more cautious on what the potential ROI is. Then I bring it to the question that is a bit more philosophical and like a societal, not so businessy, is then isn’t the headline or the mainstream discussion on AI is replacing our jobs completely overblown then? If companies are not even investing in the like, you know, buying tokens, I don’t think they’re replacing people and comp just replacing roles with. Like AI, are are they? How do I understand this?

Alex Lu (43:50)

For the tech companies, I think your statement or the statement is true for the tech companies because they’re traditionally there are a lot of coders, there are a lot of programmers, and and actually I see a lot of developers, individual developers in the market because they work for tech companies and now with the with AI. That that would be very challenging. And again, currently for European companies, if I would say

They’re still at very, very early stage compared to to China. the cost is one thing, and we can take at the other angle, causes equals to conservative. So they are a little bit conservative and they care a little bit more about their employees. So actually I I I will not see in European market AI replace a lot of human workers. It’s not happening today. Will will that happen tomorrow? I think so.

Grace Shao (44:46)

Mm-hmm.

Alex Lu (44:49)

but again we need to find another society structure or we need to find other job opportunities for the human beings when AI comes to the companies and replaces some of them. It we’re not like very aggressive like at the tech companies like Meta or other tech companies. it will happen slowly, but of course AI has impact on the on the employment on employment, even for European companies. and

Grace Shao (45:13)

Mm. The economy itself will evolve and and jobs will look different.

Alex Lu (45:21)

Exactly. it that that’s exactly what I I was in Europe ten years ago. It’s exactly the discussion around industry four point zero if people remember. We say that okay tomorrow we’ll have some automated machines in the plant. So it’s kept it’s not it’s happening currently in China. We call it a dark light factory. So it’s very automated. you can run the factory without turning the light on.

so basically at that time in Europe we had a very big debate on where the employees employ employers should go once industry four point zero is in place. And the answer was there were sorry, the answer was there will be some upscaling and new job opp opportunities created with industry four point zero, and we need more skilled people to master these machines. And that’s that’s the same thing for the AI.

Tomorrow we will need people who can orchestra, who can manage the agents, AI agents, instead of doing the same job as a simple agent.

Grace Shao (46:23)

Yeah, I see. So so on that, I wanna ask, you know, given Europe’s strength in industrial, like industrial strength manufacturing, where do we see opportunities for companies to really couple that with the development evolution of AI right now?

Alex Lu (46:41)

You mean the the use cases, right, for the companies?

Grace Shao (46:44)

Use cases, new opportunities, new potential businesses. where could we see p like, you know, new businesses come out or, you know, new business revenues for current industrial companies?

Alex Lu (46:55)

Yeah. for European companies currently the use cases we’re discussing is more around kind of efficiency use cases. So for example, they want AI to help them to do some root cost analysis because if you run a a plant and if the machine is kind of done, the production line is kind of stopped, and then you you you lose basically a lot of money because you missed up.

opportunity of producing X unit units of of your products of your cars. So basically people care a lot a lot about how I can analyze the root causes of of a machine being done. And this traditionally was a very heavy task. We need we need a lot of experts to be involved and because there’s a whole system of different machines in the same plant. And the machine is kind of the product production line is kind of

made in a industrial sequential. So every parameter on different machines might have an impact on the chain. So we need to involve a lot of experts and by using AI actually we can we can understand better. We can do some causality analysis and do some root cause analysis and find the root causes more easily and in the future to do some predictive predictive maintenance and to improve the efficiency of the companies. So this is currently happening.

People are asking for that. And some companies are also asking for these kind of knowledge management platforms. Like we we need knowledge management for new enrollment of employees, for HR policies, for reimbursement policies, for new employees onboarding, etc. etc. So a lot of around that. And if we look into the vision and into the future, I think European companies are start to think about it.

I’m talking a lot a lot about European companies, but that’s the same thing for for the companies in China, it’s just kind of more advanced. So sorry, I I’ll come back. So if we take into the vision of European companies, actually they are also thinking about the future, which is how I can use AI to increase my revenues and to make the pie a little bit bigger. And then it comes to the discussion of agentic economy.

Meaning that can I use my agent to kind of sourcing, to kind of sourcing for my company? Can I use my agent to do some business development, to write emails, to do some code calls, to reach out to potential clients? So these are the things that people will come to think in the next wave, saying that okay, if we have a very good engineering of our agents, guidelines of our agents, what an agent can say, what he cannot say.

what he should say in which context. So once this is done, again it’s very European, they need to use everything kind of under control. Then I think we are ready to to go for the athentic economy so meaning that agent can do business in in the place of the companies.

Grace Shao (50:04)

I see. And if I were to say I’m the founder of AI native company, how would you advise me other w because it would be very different from what you’ve been saying about advising more traditional industries?

Alex Lu (50:10)

Yeah, it it i if you are a AI native founder, I think I’m I’m doing the currently the same position. there are a lot of things to consider. For example, in terms of the technology, the foundation model is evolving very pretty quickly. So how I make sure that my AI agent idea or concept or business model will not be revolutionized or disrupted by this

Foundation models. This is something we need to think about. The second thing is I always tell myself and also people in the say same AI community is we we don’t start to build our products from scratch without discussing with the clients. So why not in in a more safer way, why not discuss with the clients, build products for certain clients, and then kind of

Conceptualize the products and build more standardized products that we can sell, we can say, we can sell to market and we can scale in the future. It means the build of the product comes always from a specific demand of the clients. And once if there’s a demand, then we can do something, we can build things. Why this? Because, in my opinion, all the AI native funders, I think we are pretty aligned is produce.

something or build a product in the future will be much easier in the past. And if we compete with AI in terms of the intelligence, there’s no way a human being can catch up with AI. And we should place our time where the AI cannot compete and where we still need a human being. I I I make very simple analogy to some friends of mine saying emotional intelligence, meaning that how we can establish relationships with the people, how we can build a trust.

So still I think if I’m a founder or if AI native founder, he should go out to meet clients, discuss with clients, build a trust and have some demands from the clients because building the process will be pretty easy and the cost of failing is pretty low. So build fast, fail fast, scale fast and it works even more in the in the future.

Grace Shao (52:36)

And then my question on that is how do we actually understand how to build guardrails and safety around this? Because you talked about how Chinese companies you work with are often a bit more like gung ho, let’s go, we’ll t we’ll fix it if after it’s broken, kind of mentality. Whereas the European companies maybe are seen as a bit of a slow adopter in many ways, you can say more cautious, more humane, and protecting their concurrent employees. But, right, like

End of day, if this is the future evolution of our economy, how do we go forward with this? And then how do we actually build more intentionally?

Alex Lu (53:13)

Yeah. technic technically, actually there are a lot of skills, there are a lot of technical stuff in the area to build the guardrails for the agents, like Anthropic, I they are doing doing a very great job, and also some Chinese foundation model companies and also agentic companies. So all of all of that they call that the harness engineering. So they put every concept into the harness saying that okay, we need to build a harness and to make the guardrails.

So this is the technical perspective. But still, this technical perspective is very from the developers or programmers. And if we bring the case into a real company case, then it really depends on each use cases on each company. I would say for any new human employees which is who is a new hire in the company, at least when I join European companies, there’s always a code of conduct.

You see, it’s it’s simply a a document that we need to learn. We need to we need to we need to be compliant in the future in in our work or professional work within the company. So I would say for the AI agents that the same thing. they are very important in the future, a kind of infrastructure to evaluate the performance of the AI agents, meaning that if the AI agents is delivering the performance as we wished before, so there’s a kind of benchmark evaluation.

And also the evaluation should include also is the AI agent performing correctly as we wished in terms of the code of conduct. And the code of conduct should in my opinion, be written by human human human beings. It’s like an extra bic team, they have they have written a a hundred-page of constitutional constitution for for for for cloud. And then each company should write their code of conduct for.

every agent in every department. And a lot of Chinese founders then they are entrepreneurs, they are also joking at okay, we develop an AI agent today for companies but the next question will come shortly is when should we retire our AI agent it if it doesn’t perform correctly or why when we should replace them. So you see the evaluation or benchmark of of the AI agents would

shortly become a a a pro a p a problem in the market when we adopt massively the agents.

Grace Shao (55:45)

So then each organization will have to institutionalize this, essentially you’re saying, and have their own standards of code of conduct, whatnot. That makes a lot of sense. Yeah. And right just like how companies right now regulate data usage, even company devices, whatnot, right? Like this will all just be part of the compliance that employees will have to learn. I want to ask you one last question, which is what’s one differentiative view you hold?

Alex Lu (55:54)

I think so. In terms of the AI?

Grace Shao (56:16)

In terms of everything, it’s a question I like to just kinda throw throw it at people when they come to the podcast. It’s a it’s a wild card.

Alex Lu (56:24)

Okay. I think one of the points I always mention, it comes back to my background, is today the AI race is between US and China. So we say that European is kind of lagged behind. but do not forget that actually technology is one thing and the usage of technology is another thing. And again, if we come back to our

my my statement saying that implementing AI is not about technology. It’s not it’s about process culture and organization and human being. So I think the placard of the Europe is they’re pretty good at regulations. And if you think about they issued GDPR before the Chinese PIPO, which is protection of personal data. And they have this kind of European AI Act. And then if I think about how anthropic

They penetrated these enterprise solutions versus ChatGPT and generate today more revenues than open AI in terms of AR, because of the simple concept of responsible AI. Then I would say tomorrow, if the AI comes to the enterprise level, enterprise implementation, and if everyone should be responsible in the company with their own agent or with their own developed AI, maybe Europe has a part to play in that.

in the in the AI in the in the world of AI, because their initial statement is kind of we want AI to be regulated, we want AI to be responsible. So this is my point of view.

Grace Shao (58:06)

Thank you so much. You know, today you’ve been really generous just explaining to me and and the audience just how AI is really being implemented into these big companies and the more European perspective. is there anything else you think we’re missing or any misconceptions we might have about the relationship between European companies and Chinese companies or how Europe is perceiving AI? Is there anything you think we’re missing or do you think we covered it all mostly?

Alex Lu (58:36)

Yeah, I I I think we covered most of them, but I just want to mention one thing is even though we say that okay, there’s two different nations in the world, US and China, competing AI, or in we we we take different directions of AI. And still I received a lot of recently questions from European companies, and they are really, really interested by Chinese tech companies. So you would see

they are pretty open and they come frequently nowadays to China and they have the mindset of learning what Chinese companies are doing, what Chinese foundation models are doing, and especially seeking their use cases. so one thing I would say is when I receive them, we show some very advanced Chinese use cases. They would say, you are in a different environment because we have different laws, we have different regulations compared to you Europe.

but they are quite interested about what’s happening in Hong Kong because the regulations in Hong Kong is pretty closer to European markets. So still, I I see we might have a lot of potential collaborations between China and Europe in terms of the AI, in terms of the physical OI. We didn’t mention the robotics, and definitely it’s an area where European can have more playground, not only

About the humanoid robots, they want also to have their places in the hardware value chain for the robot robots. Like a lot of

Grace Shao (1:00:10)

I’m sorry, it’s I know we’ve hit our time, but what what is your view on that? Because you know, European companies traditionally been the leaders in robotics, right? Industrial robotics, like machinery. where do they stand now in the world? You know, are are the Germans and the Japanese still leading the space or or how how are they gonna be kind of presenting themselves or positioning themselves on the supply chain right now?

Alex Lu (1:00:15)

No worries. Yeah. So for the very traditional industry robot robots that let’s say it’s like KUKA, you have a lot of robotic arms. So they are still kind of leading the world, so you have a lot of robotic solutions implemented in the in different car makers’ plans. but for the humanoid robots, actually Europe Europe is lagged behind again because it’s not all only about the value

About the not only about the supply chain of the robots itself, it’s also about again the software and the large language models behind the robots. So the mindset of European companies today is: okay, we understand China again has the most advanced humanoid robotic companies in the world. US has maybe advanced in software in large language models or word models. China is pretty good at the supply chain.

So again, the same question they ask themselves. But the the recent demands I receive from European companies are are two. The first one is as a traditional European companies, we know that they know that the value chain of making a car is quite similar. Let’s say it’s not hundred percent the same thing, but there’s sixty or fifty percent are common of making a car and making humanoid robots.

So their thinking is okay, can we participate in the wave of these kind of robots with the development of China? So like motors, like electric motors, like actuators. Yeah, German, German guys are pretty good at at this apply. So that’s the first thing. The second thing is demand is a lot of European companies saying that okay, we have the real use cases in Europe because we are lack of workforces in our plants.

It could be an aging population, it could be some strike of labor unions. So they in order to keep the plant working, as we said before, about the predictive maintenance, they are very welcome, the Chinese robotics in the European markets. Again, the robots need to be compliant with European regulations, conditions, and they are very welcome. So the most common demand I receive is hey, hey, I I want to do a kind of analysis about

how I can be part of the supply chain in China and how I can leverage Chinese supply chain to be more competitive. The second one is okay, I have a use cases, then we need to think about how I can implement the humanoid robots in the European markets. And then we we can discuss about the business model of the robotic companies like Unitree of AJ Boss, because it’s not only about putting their robots in the factory, it’s about calibrating the robots, it’s about capturing the data, it’s about think about a closed loop of robust training. It’s about

the again, the guardrails how make sure robots will not harm a human being if they cross each other in the plant. So yeah, this is quite common nowadays for physical AI for European companies also, yeah.

Grace Shao (1:03:35)

Mm-hmm.

But in fact, actually you mentioned CUKA and it was bought out by Matee, right, a couple of years ago. So you’re also seeing a lot of Chinese companies like in the embodied AI, physical AI space actually actively buying out traditional brands in in Europe. How is that received actually locally?

Alex Lu (1:03:47)

Yes. actually for the for the embedded robots humanoid robots, there are not so many MA of Chinese players acquiring European companies. So basically I think for the humanoid robots, let’s say the robots like AJ Bot or like Uni3, China is much more advanced. And there was one robotic company in France, but they are kind of in financial difficulty. And another robotic company

They were in they are invested by Renault in France, but still their technology if you look into that is not as advanced as Unitree or AJ Rob A Gi bots, for example.

Grace Shao (1:04:42)

I see. one last question is just do you think it’s fair that we’re overgeneralizing all the European companies into just one EU right now? Or do you think actually a lot of different com countries have different goals, ambitions, or even, you know, future tracks for them laid out?

Alex Lu (1:05:03)

very good question. So I can only when I see European companies, sorry, actually I’m thinking about French and German companies. So actually I cannot represent all the European countries and for different countries like Spa Spain, Italy. I’m I’m not familiar familiar with the country. I didn’t live there. I I didn’t receive enough clients from from these countries. So actually you are right.

when I talk European companies, I’m more thinking about French and German companies. And of course, they are pr pretty different.

Grace Shao (1:05:35)

Okay. Well, thank you so much. Yeah, thank you. I just think it’s such a unique perspective because, you know, it it’s it’s more it’s easy for me to find someone who tells me the pure European perspective. It’s easy for me to find someone in the China US, but it’s harder for someone to for me to find someone f you know, who straddle between Europe and the Chinese market. You know, it’s obviously not as mainstream. So I’m really appreciative of your time and your insights and your sharing. Thank you so much, Alex.

Alex Lu (1:06:04)

Thanks,

Grace. Yeah, thanks a lot again for i inviting me and accepting me for the podcast. And thanks a lot for your audience. And yeah, let’s keep in touch if any chance happens. we can have another talk if needed.

Grace Shao (1:06:17)

Definitely.

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