AI in the EU

Welcome to the keynote from Cornell. I’m your host, Lutz Finger. I’m a faculty member at the Johnson School, where I teach several courses on AI and product development, including actually one where I replace myself with a virtual copy of AI, where I replace myself with an AI, I sort of say. It’s a certification program.

It’s open to the public, and it’s called Designing and Building AI Solutions. Beyond academia, I am as well a startup founder and the CEO of a gen AI platform for e-commerce. It’s called R2Decide, where we are transforming how shoppers engage with search, filters, and products. Now, as a startup founder, I’m very much focused, obviously, in how can I use AI?

Where do I get funding? And that is different all over the world, and we see different regulatory rules, different regimes popping up in how the US is working, how Europe is doing it, how India, how China. So those are different areas. And therefore, it’s an enormous honor for me to actually welcome today to the show Lucila from the AI office, from the EU.

And we are going to talk today about the AI office, obviously. We talk about the EU. We talk about how Europe might become, or hopefully becomes, the next AI continent. So it’s my pleasure to have you on the show.

Hello. Hi, Lutz. Thanks for inviting me. What I normally do in those situations, I would like you to give a quick brief overview about yourself before we actually dive into it.

Can you tell the audience a little bit about who you are? Sure. I’m Lucy Scioli. I work for the European Commission.

I’ve been working here for more than 20 years. And I am now currently the director of the European AI office, which is an office that is responsible for three main activities in the area of AI. First, to support innovation. So we put in place policies to support research, innovation in the European Union.

And that will include the AI continent action plan that was published a few weeks ago. Secondly, we’re responsible for the implementation of the AI act. You know that the European Union has a piece of legislation called AI act, which provides rules about the use of and the development and the use of AI, in particular, when AI carries risk for violating fundamental rights and safety. And thirdly, we promote the European approach to the AI internationally.

And so we represent the European Commission in the multilateral forum and in the discussions with non-European countries. Very cool. So when was the office established? The AI office?

Very recently, actually. In June, we started our works. We are more than 100 people who carry out these various activities of work. And we are recruiting and growing in number.

And to be successful, my KPI is that we have to be 140 by the end of the year. Oh, wow. Okay, that’s a good growth. Now, what led, which thought, which thought process in the EU led to establishing our own AI office?

Well, the first idea came across when we were negotiating the AI act with the European Parliament. The first ideas were about creating an AI office that would be an independent authority in charge of implementing the AI act. We have this kind of independent authorities in the European Union. We have it, for example, for data protection, or we have it for telecoms.

But we decided that for checks and balances at the beginning, in particular, since this is a very new area, it would have been better to keep this inside the European Commission instead of creating a fully independent authority, which probably makes more sense in areas where the domain is more mature, it is better known. So we decided to keep it inside the European Commission. And then secondly, we decided that we didn’t want to just become sort of harsh regulators that are there to, you know, basically imposing a regulation on innovative technologies. Right.

Right. We didn’t want to be innovative companies because the AI act is an innovation friendly kind of legislation. For us, innovation has always been at the core of our thoughts in our policies. And so we thought it would have been a much better idea to have the AI office work on both the implementation of the legislation, but also the research and innovation policies in support of AI.

We don’t think there is a contradiction between these two elements. We don’t think that the legislation risk to stifle innovation because if the legislation is designed in innovation friendly ways, then it can be a very important element for innovation. In fact, we think that if you want to have innovation in AI, you must have people and companies and organizations using that technology. Demand also creates incentives.

To innovate and demand in the AI is very much led by trust. Got it. So this is actually cool because this becomes core to our discussion here. Right.

You would you would say the AI office, it’s not a watchdog. It’s more supportive for innovation. It is indeed. And also the way we plan to implement our legislation.

It’s not about going after those who are not going after the AI. Right. It’s not about going after those who don’t comply. It’s more about helping companies to go through this journey and make sure that the developments in AI that they make are trustworthy.

Got it. And it becomes like so the central discussion here is really on how how is this AI continent going to happen? Because there was a lot of criticism in the past on potential stifling innovation. But before before we dug in, dig into this, let’s talk a little bit about Europe as a whole.

Why? If we look from the US, we know where Europe is, obviously. But like the question is, what is the European advantage in terms of AI? Is there a European advantage in terms of AI?

How is Europe positioned in that race? It’s a global race. We see it China, US, India and Europe. How is Europe positioned?

Right. I think Europe is not badly positioned because Europe has some strengths that characterize it. For example, it has a lot of talent. There are more engineers per capita in the European Union than there are in the United States or in China.

Europe has a very big and large and rich single market. It’s not as integrated as it is in the United States, for example, because the European Union is not a federal union. It’s a group of countries of 27 countries that want to cooperate closely. And they have established a single market where we have freedoms of movement, of trade and of capital.

But it is a single market and it is large. It’s bigger if you want than the United States. There are more people in the European Union than in the United States. And it’s particularly rich on average.

Europe is also very strong from the point of view of research. Number of publications is very, very high. We have excellent universities. And so on average, European population is very well educated and therefore likely to uptake.

Technology. Nice. So it’s essentially good engineering talent, strong research and a big market. There is an interesting fact to it.

Like in through large language models, we essentially a large language model or an embedding, how we call it, like takes information and embeds it in a computer language kind of thing. Let’s keep it at that high level. But that means that all those large language models, are multilingual by definition, right? For my startup, if I’m kind of like with sales advice for one of the companies, it doesn’t make a difference whether they talk in Italian, German or English to my model.

My model automatically knows those languages, meaning Europe actually has a way like is a big market. So far, there was always a problem of multilingual. And that is actually breaking down with the current technology. But certainly, the technology is one element that will help in making the country in making the continent more homogeneous.

Of course, there are also some differences between countries. I’m thinking, for example, employment legislation or capitals that are not yet flowing between countries exactly as we would expect them to do yet. But we are working on these aspects. And so we are trying to make the continent much smoother in terms of the single market than it is now.

But then there is another couple of elements, I think, that are unique for AI to the European Union. One is the fact that there are many developments that are brought forward by startups and scale ups. And they tend to be very strong at B2B. So you will not see in the future, you will not see in the European Union many models for consumers.

You know, as you see, for example, the United States, also in the United States, you already have social media, all these platforms that work very much for consumers models. In the European Union, it tends to be more for businesses. And secondly, we have some kind of infrastructure, which is probably unique for AI because we have a network of public supercomputers. And these are very advanced supercomputers.

We are even upgrading them now even more with more GPUs and so to AI capabilities. And this is a public network, meaning that universities, startups, companies can use these networks. And that should facilitate over time more developments, because the computing resources are made available basically for free, at least to the startups and to the universities. Companies, you know, bigger companies will have to pay for it, but everybody else can access it for free.

Which is a super fascinating discussion because we saw early on VC companies actually acquiring infrastructure like Nvidia A100 chips and then offering this to companies. And so I think that’s a really interesting discussion. There is also also looking at these new technologies in general, invest in you, but all like we do invest in you, but like what we really do is we give you access to an infrastructure that would have been otherwise scarce. And that model seemed to have worked.

Now, I saw that the EU under the idea of the AI continent actually focused a lot on this infrastructure play, which is, it’s an interesting discussion to have because the question is how much infrastructure is needed for a company to scale up, right? It’s definitely an interesting discussion for research. If you think about large like models, transformer models, developing new drugs or building out new foundation models, but, you know, some models are not as important as the infrastructure. So, I think it’s an interesting discussion to have companies.

We saw like OpenAI was now late with their number five, right? So, the progression of investment actually went down. I personally kind of like made a lot of jokes about Stargate that got announced and kind of like saying, yes, there’s a lot of investment. This is cool, but do we really need that?

How do you, how does the EU think about this investment area? Like how much can we really How do you think about this investment area? Like how much can we really get out of it? How do you think about this investment area?

Like how much can we really gain from having a network of supercomputers for research? No question, but for developing an ecosystem of a thriving economy, essentially. Well, you know, for the startups and the scale-ups, having access to computing resources for free is very, very important. Yes.

I mean, even American companies were very strong in AI, like the one you mentioned. And was supported in terms of computing resources initially by another company. And that is all fine. So, here, what we are trying to do is to give these startups and the scale-ups the possibility to have computing resources for free.

Now, it’s true that the bigger and the more advanced these models are and the more reasoning they will have, they will need more GPUs. And now it’s good also that NVIDIA is producing the blackwells. There are also these machine On the one hand, we say, look, we are now upgrading our supercomputers to more AI capabilities through the AI factories. But we are also planning to set up a limited number of what we call gigafactories, which are very, very strong supercomputers integrated with data centers.

And if one needs to train a very advanced frontier model right now, we need to have this kind of computing infrastructure. So I do not know if Stargate is too much or the plans exactly and what will happen, we will see. But obviously, if you want to train a model with, you know, more than trillions of parameters, you do need to have a very, very powerful supercomputer. And that’s what we call the gigafactory in the European Union as well.

Yes. Now, before I go on, because I have another, like, I want to look back to something you said earlier. But like, let’s do a short, quick public announcement here, like to the audience. If you listening in, you have the ability to ask live questions and we will bring this into the discussion.

So. If you don’t want your name to be mentioned, don’t say it, just ask a question. If you do put your name on it, I normally mention it. But this is the ability to ask Lucila Leif to have a view on where the EU is going.

So ask us. We will make sure that we somehow get into the conversation. So end of public announcement here. But like, you know me, this is how this keynote normally works.

There is also a general and to customers who gained most of the benefits. So there is a good argument to be made that Europe is actually extremely well positioned with their setup, with their companies in order to reap the benefits. Is there anything from your side as a policymaker, something you kind of want to stress or want to put together to enable the existing company landscape to be most effective? Well, on the one hand, we need to make sure that companies have access to the best infrastructure.

In the US, all these developments are driven by platforms who do have computing capacity. Think of the cloud capacity that you have in the United States. They have the data. So they have what is needed to bring forward the data revolution.

In the European Union, because these developments are driven by startups and scale-ups, it’s important, on the one hand, that the policies make available this infrastructure as much as possible to these companies. So computing capacity, we spoke about it, but also data. You know, you need high-quality data and a lot of data to train these models. So what we are trying to do in the European Union, for example, is to make sure that we have what we call European data spaces with high-quality data sets.

And we have mechanisms also for companies to pull data in competitive ways if there is a need for training models that need data coming from different sources. So we are linking then these data spaces to the supercomputers, and we are creating, what I called earlier, AI factories, which will also provide services in terms of training, for example. Is it fair to say, just to jump in there, is it fair to say, so essentially, Europe has not an established ecosystem as the US? Like in the US, you have big players, they’re creating an ecosystem, they are offering resources, they are offering access.

And, the way you think about it as a policymaker is that you’re saying, in order for us to be competitive, we’re trying to provide now the infrastructure as well as the space for data sharing to happen. Exactly. Yeah, yeah, this is exactly. So we are trying to provide the necessary infrastructure, computing capacity and data for companies to facilitate their, to be able to do that.

And, we are also trying to provide the necessary infrastructure for the development of AI. And, there is another element of infrastructure. I call it an element of infrastructure, which is skills and talent. So, we also support, you know, more master courses, more PhDs in AI in particular, to make sure that we have enough talent in European Union for these kinds of developments.

But then, once the models are developed, we also want companies and, our key industrial sectors to use them. Because, this is if you want the economic, the comparative advantage in the European Union has always been traditional sectors like manufacturing, automotive, aerospace, and so on. So, we want these sectors to make use of AI, to make sure that also these sectors remain competitive and drive innovation. Because innovation is not only the development of AI, innovation is also the use of AI, by other industrial sectors or service sectors.

And so, what we try to do with our policies is to facilitate what we call the application of AI. We call it the ApplyAI approach or the ApplyAI strategy, whereby we try to facilitate the adoption of AI by all these key sectors of our economy. Totally. And we need to facilitate that, probably because our ecosystem is, a bit less integrated than it is in the United States.

So we have to make sure that companies in these key sectors are actually making use of AI and if possible, AI made in the European Union. Got it. AI made in the European Union. This is actually like this is a nice new slogan here.

Let’s come back to the before we go. So there is a I think there is a three-pronged approach if I follow you correctly, right? Like enable infrastructure, enable data access and train to enable companies actually to use AI more effectively. If we and all together should create a vibrant ecosystem, which in the past like Europe, as you said, like has many engineers in Europe.

Europe has done a lot of cool research, but in the past, I think it’s fair to say that the past technology revolution has scaled up in the US and not in Europe. So we’re trying to you’re trying to create a system to actually enable this is done. If you go to the data spaces, AI is built out of data. The data is then ingrained in There are these they would be not as happy except they do an open source model.

If we create data spaces, how is that creating a competitive advantage? Because essentially, if you have a space where everybody shares their data, then there is no competitive advantage for the companies to gain. How does the EU think about that in a regulatory framework? Well, data spaces are not only about publicly available data sets.

They’re also about ways of sharing data. We have legislation on the way companies can share data with each other. It’s called the Data Act. You may have heard of it.

And it’s about facilitating the sharing. It’s about sharing of data between companies. And so what the data spaces do when it comes to business data, for example, is to give ways or to offer ways and examples of facilitating the share of data maybe along the supply chain of, for example, manufacturing rather than something else. So they’re not necessarily about having data or data sets that people can actually use.

So, of course, there’s a lot of data. But I think it’s important to have a data set that is accessible to everybody. Of course, the public data sets are open to everyone. I think we have some important ones.

I think, for example, the Copernicus data sets are also used by many American companies in terms of Earth observation kind of data. Yes. But other data spaces are then subject also to certain regulations. We are setting up the health data space with its own regulation.

And so it is very much about facilitating an exchange of data also between European organizations and actors and being subject to certain kind of data regulation. I like this a lot because when we compare, like if we look at the US industry, it’s dominated by players who have access to data. And we see this actually in the current AI. And I think that’s a very important part of the AI discussion.

The ones who have access to data are way better positioned in this part of the revolution. So by you guys creating an easy, like, I don’t know how easy it is, but a way to share data, you might actually leapfrog the approach between, like, you don’t need to have a huge conglomerate with all the data. Because smaller companies together can create the same value creation, like somebody who would own all the data in one go. Yeah.

And then I think what is also going to be important in the future is that we also will support the development and the production of more synthetic data. I think this is going to be extremely important for the world of AI because, you know, everybody’s scraping the internet. At one point, the internet is scraped and you just need to find more data. You have to, you will have to rely a lot on synthetic data.

So I think that both in the United States and the European Union, there will be a lot of attention to the development of synthetic data. Yes, yes, totally. And we see this already happening, right? But like a lot of companies, it’s a tricky, tricky balance, by the way, because synthetic data should contain the tiny clues the model wants to learn.

So we need to know what tiny clues we want to train. Very often we see this in, in privacy constrained areas like healthcare. But I expect way more to actually happening in the whole area of image generation. We see a lot of synthetic data companies coming up at the moment.

Let’s talk about, so let’s talk about trustworthy AI. So far we talked about the ecosystem, right? You described there are several approaches. You as a, you as a EU regulator or like, like I called you earlier on the spokesperson for the scales up.

You as a spokesperson for the scales about putting together access to infra, better way of sharing data as well as helping with education and making use of the talent that is there in Europe. Now in the last announcement on the AI continent, you talked a lot about trustworthy AI. And from my point of view, it’s a, it’s a double edged sword, right? If you say trustworthy AI, who decides what is trustworthy and how big is the hurdle to you need to jump over to be actually trustworthy.

Talk me through this. What does it mean to be trustworthy AI? And maybe before you do this, just an acknowledgement to the audience, guys, there is no 100% in humans and there is no 100% in AI, meaning AI like humans has always a percentage where it’s wrong, right? It’s, it’s the same approach, whether it’s human brain or an AI brain.

So, but like, talk me about trustworthiness. Trustworthiness is about making sure that the AI we use is a system or a technology that one can trust. And the reason why there are situations where one cannot trust it, it’s because AI is, of course, trained on data. And there are contexts where we don’t just want to have an AI which exacerbates the biases we may have developed historically.

I’ll give you an example. If I’m a company, I’m looking for an AI, which is more advanced, more advanced than the AI we use, the engineer and I do that, for example, on LinkedIn or through any other application for hiring human resources, then if the AI that is looking at the CVs and that is offering me the profiles that are found on the market is only trained on past data, is likely to just give me, in the European Union, white male engineers. Because this is what we have in the majority of engineering positions at the moment. But of course, nowadays with the new generation, there are more women, of course, on the market, only that the AI will probably think that in the past I’ve been hiring a lot of male engineers and therefore it will give me a CV of a male engineer.

So I think it’s important that for certain kind of applications where discrimination, for example, is important, certain characteristics are kept in check. This is one example. It’s a very good example. And by the way, in my course, the public EECOR Now course where I talk about models, I usually There are also these examples in general.

use this very example as a biased one, right? Because it’s easy to see. Now the question, so meaning if an AI selects candidates, and as you said, it was trained on biased data, then it will select biases, right? So the AI just reflects the data it was trained on.

And if the data is biased, the AI is biased. It’s always the data. So now, but the question for me is what is trustworthy? So let’s keep that example of the hiring.

At the moment, and those numbers are totally made up. So don’t take my, I just made the numbers up. So let’s say in the EU, 70% of the engineers are men and 30% are women, right? What is a trustworthy AI?

Because it should be 50-50, right? Is a trustworthy AI an AI which does 50-50? Like gets you 50 women and 50 men? Or is a trustworthy AI which is 70-30?

Or is a trustworthy AI which selects the best talent for the job, meaning the highest success ratio? What is trustworthy in this case? And why is the EU deciding this and not the company who’s building the model? Well, I think that a trustworthy AI would be an AI that does not consider gender as a factor of, as a relevant factor for the choice, but is based on the capabilities reflected in people’s CDs.

Now, we think that it’s important that for certain applications where, you know, this kind of discrimination can really have a negative impact on our society, these things are kept in check. And what we are asking with our regulation is that companies that put on the market this kind of AI first check that the bias. They also, we also ask them to document how they have developed the system. And we also ask them to provide information to the user on these kind of elements and whether the user has to be attentive to the system.

There are also一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定!一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定 sounds一定 sounds all<|tl|><|tl|> unemployment a serious data scientist would be documenting what she or he is doing. And it’s in this sense that I don’t think that the AI Act is particularly burdensome because it is asking simply to document before the AI is put on the market. And this requirement is only limited to certain applications. We spoke about the recruitment case.

Other examples are, for example, for universities, when they use AI to select the students that may have access to university. In the European Union, we had incidents where the universities would base their selection on the schools where the students come, the high schools, basically, where the students come from. And the high schools based in richer areas tend to have a higher number of students normally going to university. And therefore, this kind of AI would be really discriminatory towards those maybe less well-off students that have studied in areas or in schools where maybe less students normally go to university.

So we find that this is another example of a discriminatory AI. We had incidents like this in the European Union. And so we have decided also, for example, the AI that is used in universities in this sense to be considered high risk. And we have done the same for when the AI is used in healthcare, for example.

And then for the AI systems that may have an impact on safety. So, yeah. This is, yeah. Yeah.

I think this makes all sense. And we have technically, and I don’t go into technical details here, but like come to the course if you want to know more. There are many things how we can actually manage bias. But the fact is all data is biased, right?

And no model is unbiased. Like essentially, we want a model to figure our tiny clues. If we like those tiny clues, we say tiny clues and we say the model is good. If we don’t like the tiny clues because they’re, they actually point to something which we didn’t intend to see, then we say it’s biased, right?

But it’s the same mathematical impact here. Now, the question comes down for me to who decides that threshold and who decides what to focus on, right? When we, let’s take self-driving cars. Self-driving cars are going to be awesome.

They’re going to be in the market. I’m living in San Francisco. We have them on the street. There seem to be more self-driving cars by now.

The normal cars. And they will come. They will come as volunteers. But self-driving cars make mistakes.

They are not unfallible, right? So we will have a situation where self-driving cars will kill people. That means a robot kills a human. At what level is accepted versus not?

This is an error rate. Bias is an error, right? Bias is an unwanted error. Which you might reduce.

But what is acceptable and what is not acceptable? And why is the EU deciding this? Because it’s a safety issue. But how do you come to the conclusion?

Well, the error rate or the accuracy will be decided in standards, which are not decided by the EU or by us. It’s decided by industry normally, working on the standards. So. As I said, the Act only addresses a very limited number of applications.

And so we will be requiring that for those applications, there are accuracy rates that are decided by industry that work and participate in the standardization organizations. So just to say that you’re right, there will never be an AI that is infallible. And so our system of ex-ante checking, if you want, is about minimizing the risk of accidents once the AI is in the market. Accidents can still happen.

And then in that case, there will be authorities that will intervene, that will look at the accident. But they will simply conclude that if they can see that the developer or the deployer of that AI system has done its homework in terms of making sure that the system is safe, that the system is basic, minimizes bias, the documentation is there, the information given to the user is correct, then there will be no consequences. Got it. So it’s a mechanism to minimize the risk that an accident happens.

You cannot avoid it completely. Got it. Now, if you, there are two questions now in my mind. Minimizing risk.

First of all, is the risk level. Depending on where you as a regulator put your focus, as well as once a risk happened, what happens to the company? Let’s do them in two different steps. So if you want to minimize risk and you compare now another country outside of the EU versus the EU, the other country is more relaxed with the risk.

So it doesn’t apply the same minimization needs, meaning like, okay, yeah, you launch it. Maybe something goes wrong. We will improve later versus the EU, which says, okay, I have a certain risk standard. And like, essentially, how much do you focus on innovation versus how much do you focus on risk avoidance?

These are, in my mind, at least, two areas which are a trade off. How do you, how does the EU deal with it? And what does it mean for the ecosystem, which you described earlier on? Yeah.

You’re trying to support. Well, as I said, the EU is looking at risk and probably setting a higher standard than maybe other parts of the world. Although, let’s admit it, there are regulations on AI also in other countries, not only in the European Union nowadays. And it looks at risk.

And it may set a higher bar than other parts of the world, but only for some applications of AI, not for the whole market. I would say it’s probably targets, I don’t know, 10% of the market. And that’s very important. And the other important element is that it does that across the European Union.

So it’s one set of rules across the European Union. Recently, I was reading the AI Stanford. Yeah. There are many, many different pieces of legislation on AI in different states.

So a developer who wants to sell in the United States probably has to be careful in each state to meet the requirements of the relevant legislation. While in the European Union, the idea is if you’re developing an AI that may be considered to be high risk, and you need to put in place a machine that can select the machine that in place a certain checks before you put this on the market you do this once in and you put this you do this in any country of the european union you want and after that this ai can circulate freely in the european market so this is this is very important also to keep in mind so while there is a trade-off and i totally get it there is a trade-off however the value you actually see here is um that it’s at least a standardized trade-off across all different countries can you like you are in the field you’re talking to scale up in startups um what’s their reaction to the ai act what what do you hear from european companies back to you i have a mixed reaction in the sense that there are some companies who think some startups who think that the rules may be burdensome but i can tell you that the majority of the startups i talked to they totally get it and they see it as an advantage so they they see that the ai they sell they can refer to it as being trustworthy because it’s compliant with the ai act and in this way they find it much more easy to sell you know we live in a world where the media talks very negatively about ai and the ai is gonna kill everyone and is gonna take over the world and the fact that people can actually and organizations and business users that can actually trust that technology they consider this an advantage so the trustworthiness of the technology can be used as a branding a commercial branding and therefore can be turned into an advantage awesome thanks let’s this is actually an interesting one we’re living in a world where i is seen as um um something um dangerous that depends on where you look like if you look in silicon valley then obviously people are way more positive about all the value that can be created with ai i personally have said many times i don’t believe in agi kind of ruling the world um that’s technically in my view still a lot of bs moreover it is still us humans who have agencies and so when we let’s move into the last part where we can’t think a little bit about how do you change perception for ai how do you enable people to be trained to build to actually create a world where people can actually be able to do things that are not well our main message has always been that artificial intelligence is good for the world is good for the european union is good for everybody so our main objective is to stimulate the development and the use of ai across the european union and that’s why we put in place all the actions that you can find in the AI continent strategy. We want developments in the European Union in terms of AI. We want to have more technological sovereignty.

So we want to be able to make these developments in the European Union and not just depend on other parts of the world. And we want to use it. But to use it again, we need to create trust because there is sometimes a lack of trust towards this technology exactly because it’s a black box and you don’t know how it’s going to behave. And we need to have people understand that AI is actually a positive technology and use it with more, let’s say, friendliness than is suggested.

And that’s why I think at the end of the day that even a very targeted piece of legislation can be useful to support investment. But alone it cannot do much. And that’s why we also have to support our infrastructure, our talent, and the adoption of AI in general in the European Union. This is going to be key in the next few years to come.

Got it. No, this makes sense. Now, what would you tell to people like me? So, like I told you my story before, right?

I tried to come back to Europe and I’m not… There are many Lutz’s like this around. I tried to come back to Europe a few times. I’m an AI guy.

I realized that I couldn’t really build like a lot of products. There was a lag. I have now an own startup. My advisory board tells me, look into the US.

Don’t look into Europe. Despite being a lot of Europeans on the advisory board. What do you tell innovators and people who build stuff like me? Well, I would tell you to come to Europe now.

Now it’s the time because we are really trying to build this AI continent. So not only you will find the infrastructure that you need, and this infrastructure will be made available to you for free, but also you will find the talent you need. And we are setting up a lot of people. We are setting up certain elements to facilitate the life of startups.

For example, we are strengthening what we call the capital union. So the possibility to have much more venture capital and more equity than it has ever been possible in the past. And we are also simply facilitating the life of the startups and the scale ups. We will be publishing a strategy pretty soon to put in place what we call the 20-day scale up.

And we are also thinking of a 20th regime. So a regime only for the startups and the scale ups to facilitate their growth in the single market. Basically smoothening the differences between countries in some of the administrative activities that companies have to put in place when they are active in different countries. So I think the moment really is now.

And I really hope that the brilliant minds of you all will be able to come together and the minds of Europeans all over the world really come back to Europe because we need them. And we would love to work with them, of course. That’s a very nice ending. The time is now.

I can tell you just as a fun fact in preparation to this podcast, I reached out to my network and talked about questions to ask and so on. I always do this for every session I do. And one friend of mine, an INSEAD graduate like me from my INSEAD network, he kind of said, yes, I see very much what the EU is doing. And I start, he’s in a VC, I start to invest in companies because I know that my next funding round will be way easier if I go to Europe in those areas.

He’s in healthcare. So very cool. Time is now. If you are out there and listening, if you have an innovative idea, you want to go to Europe, think just about what you heard.

And let us know. Thank you. Bye. Thank you.

Thank you.