20VC: Why No Models Today Will Be Used in a Year, Why Open Will Always Beat Closed in AI, Why Proprietary Data is Less Important Than Ever And Why EU AI Regulation is a Disaster with Alex Lebrun, Founder & CEO @ Nabla

Alex Lebrun is the Co-Founder and CEO of Nabla, an AI assistant for doctors. Prior to Nabla, he led engineering at Facebook AI Research. Alex founded Wit.ai, an AI platform that makes it easy to build apps that understand natural human language. Wit.ai was acquired by Facebook in 2015. Prior to Wit, Alex was the Founder and CEO of VirtuOz, the world pioneer in customer service chatbots, acquired by Nuance Communications in 2013. In Today's Episode with Alex Lebrun We Discuss: 1. Third Time Lucky and Lessons from Zuckerberg: How did Alex make his way into the world of startups with the founding of his first company? What worked with Alex's prior companies that he has taken with him to Nabla? What did not work that he has left behind? What were the single biggest takeaways for Alex from working with Mark Zuckerberg? How does Mark prepare for meetings? How does Mark negotiate so well? 2. Open vs Closed: Why does Alex believe the winning AI models will always be open? Why are open models not as transparent as people think they are? What are the biggest downsides to both open and closed models? Does Alex agree with Emad @ Stability that we will have "national data sets"? 3. Incumbent vs Startup: Who wins in the AI race; startups or incumbents? How important is access to proprietary data in winning in AI today? How does Alex respond to many VCs who suggest so many AI startups are merely "a thin layer on top of a foundational model"? Is that a fair critique? Which startups are best placed to challenge incumbents? Which incumbents have been most impressive in adopting AI into existing product suites? 4. Models 101: Size, Quality, Switching Costs: Why will the best companies switch the models that they use often? Will any models in action today be used in a year? How important is the size of the model? How will this change with time? In what way is new EU regulation around models going to harm European AI companies? 5. Location Matters: Who Wins: When looking at China, US and Europe, who is best placed to win the AI war? What are the biggest challenges Europe and China face? Why is the US best placed to win the AI race? What does it have to overcome first? If Alex were a politician, what would he do to ensure his country were best positioned?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  So the new regulation is a disaster. It means 100% of the LMS that were trained these last three years would be illegal in Europe. It's not connected to the reality. The intent is good, but the limits it puts on how to train a model and how to operate a model makes it in practice compared to what we do today makes everything illegal.

• 0:18Harry Stebbings?

  Welcome back to 20 VC with me, Harry Stebbings. And today we continue the deep dive into the world of AI. If you haven't listened to the shows with Imad at Stability and Jan LeCoon, then they are a must. But I'm so thrilled to follow those with this incredible discussion with Alex Lebrun, co-founder and CEO at Nabla, an AI assistant for doctors. Prior to Nabla, he led engineering at Facebook AI Research. Alex came to Facebook through his founding of Wit.ai, an AI platform that makes it easy to build apps that understand natural human language. Wit.ai was acquired by Facebook in 2015. And prior to Wit, Alex was the founder and CEO of Virtuos, the world pioneering customer service chatbots, acquired by Nuance Communications in 2013.

• 0:59Harry Stebbings?

  I'd also want to say a huge thank you to Julien Corneille for the intro today, without which this episode simply would not have been possible. But before we dive into the show today, this episode is brought to you by Tegus, the go-to research destination for bold investing. Tegus curates expert insights, analysis and financial data to give you powerful perspective for your investment decisions. With lightning fast access to over 60,000 transcripts across 20,000 companies, you'll discover a wealth of unique insights to fuel your fundamental research, gain perspectives, synthesize information, model outcomes, and ultimately make better decisions, all on Tegus. As a 20-minute VC listener, you can try it for free at tegus.com/gross.

• 1:40Harry Stebbings?

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• 2:25Harry Stebbings?

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• 3:05Speaker 02

  Hello. You have now arrived at your destination.

• 3:11Harry Stebbings?

  Alex, I am so excited for this. I've been looking forward to this one for a long time. I've been thinking with a couple of the AI shows, I can't wait to do this in person with Alex. So thank you so much for joining me today.

• 3:20Speaker 00

  Thanks, Harry.

• 3:20Harry Stebbings?

  Now, I would love to start with a little bit of context because we're three startups in at this point. So how did you first make your way into the world of startups?

• 3:28Speaker 00

  So twenty-two years ago, I fell in love with a chatbot. Her name was CBL. And really, really, I was, you know, summer nights in Paris, in my basement, alone with my computer and I.

• 3:38Harry Stebbings?

  This could go in many directions.

• 3:41Speaker 00

  Up to you. I was mesmerized by this chatbot, you know, the fact that the machine is trying to understand you, language and can generate some words. And I was really, really struck by this thing. And I decided, okay, I will spend my life building chatbots. And so I founded a company doing customer service bots twenty-two years ago, very early. And this is how I started this series of companies in the domain.

• 4:03Harry Stebbings?

  Can I ask you, given it was twenty-two years ago, and I know this is off schedule straight away, but just how do you think about market timing today?

• 4:09Speaker 00

  When we started, I thought chatbots would become very, very intelligent after three, four years and get to AGI and replace humans in call centers. And the more I worked on the problem, the more. I realized it was really, really difficult to do that. Actually, the first time we released the bot in Europe was in twenty oh four to the French railway company. And we were very, very happy to have this customer and the deputy CEO tried it. And she asked, my name is wrong on the reservation. Our bot answered, hello, wrong on the reservation. And then we realized, okay, there is still a lot of work. It's not smart at all. And so the more I worked on these things, the more I realized it's not ready yet. So suddenly, for the last year, two years, we reached a point where market timing might be finally, finally good.

• 4:54Speaker 00

  You know, where the product is ready and people have evolved to maybe ten years after Siri, people have changed. So maybe the market timing is now.

• 5:01Harry Stebbings?

  So if we project forward and thinking of market timing, how did Nabla come to me and what was that founding moment?

• 5:07Speaker 00

  I was sitting at a terrace at Facebook headquarters in Menlo Park. We joined Facebook through the acquisition of my second startup. And sitting at this terrace with like cocktails, looking at the sunset, suddenly it reminded me of a scene in Silicon Valley. You know, the show.

• 5:21Harry Stebbings?

  Yeah, I love.

• 5:22Speaker 00

  Where they go on the roof at Holi and there are these people doing barbecue and say, yeah, we are vest and rest.

• 5:29Speaker 00

  And, and I saw myself in this vest and rest situation. And after four years at Facebook, I suddenly realized, okay, it's time to go out and back to the arena. And we've learned so many things at Facebook AI research, it's time to try to push these things to the real world.

• 5:43Harry Stebbings?

  Can I ask, what would you say are the biggest takeaways from your time at Facebook and the AI research at Nabla?

• 5:49Speaker 00

  So first thing that amazed me when I arrived at Facebook is I thought all big companies were slow. And actually, when we arrived at Facebook in early twenty fifteen, there was maybe six thousand employees and it was going so fast. It was a machine, very well oiled machine, engineering was so efficient. I learned it's possible to be that big and very, very efficient. And when you are in this situation, there is nothing you cannot do. Just after I joined, I started to work on a project and I wanted to hire two hundred people to human concierge to train my bots in real time. It was not in the budget, of course, but a week after I had a meeting with Mark and in about ten minutes, Zuck challenged a little bit my idea. And he said, okay, let's do that. And I could hire. Two hundred people in Menlo Park.

• 6:31Speaker 00

  And when you have this speed and agility and unlimited resources of a company like Facebook at the time, I was really shocked by this at Facebook.

• 6:39Harry Stebbings?

  What did you learn from working with Mark? You mentioned that meeting there and the speed of decision making from him. Is there anything that you took away from working with him?

• 6:46Speaker 00

  Many things. One thing I like, he spent not a lot of time outside the company. You know, he had like thirty minutes meetings back to back from morning to evening. And he prepared really well every meeting. So you have to send in advance and like a short note about what decision you are expecting from him. If you fail to send this document twenty four hours, you know, by the minute before your meeting get cancelled. And so in many cases, you arrive in front of him, he's read your document, he's gathered all the information, the data he needs. So the meeting is very fast, you know, he will challenge what you ask for maybe and make a decision. So this way to work, I think there is much to learn. I'll give you an example of that. When I wanted to hire my two hundred human concierge, my reasoning was we will train this AI.

• 7:28Speaker 00

  It will work really well and we will steal business from Google in terms of social, like exactly what chat G P T may do in the time to come. I prepared answers to every possible objection. He could object about everything I was ready to say why my idea would work. And I arrive at the meeting and first minute he tells me, Alex, I agree with you, it will work. So instead of hiring two hundred concierge, let's hire one hundred thousand and we will kick wheel tomorrow. And then I completely reversed my course and say, no, no, no, Mark, I'm actually, I'm not sure it will work. It would be crazy to hire one hundred thousand people. And I gave all my reasoning on the opposite sense, like we should be careful, this thing might not work and so on. So very smart of him.

• 8:09Speaker 00

  I don't know if it was on purpose, but he learned more about the truth of my project, attacking it from this direction, this angle. And it was very, very interesting.

• 8:17Harry Stebbings?

  Can I ask, and we mentioned kind of NABLA being your third company. What worked with the prior ones that you've taken with you? What didn't work that you've left behind? And does it get easier?

• 8:27Speaker 00

  It's not getting easier. You learn some lessons, you don't do the same mistakes again, hopefully, but get new dangers, you know, new issues. The one I suffered from when I started NABLA is what I call the Kim Jong-un entourage. Trap. You you see this video of Kim Jong Un and he's visiting something and all the generals are around him with a notebook and everything he say will be written down. If you fail to do that, I guess you disappear the next day. And nobody, of course, will ever, ever say, I disagree with you or challenge any of these decisions. And starting my third company after two exits, I felt like my investors were always agreed, my team always agreed,

• 9:05Speaker 00

  people around us. And so I was not challenged enough. And then we made some early mistakes that probably were not challenged enough.

• 9:13Harry Stebbings?

  What were some early mistakes?

• 9:14Speaker 00

  So when we decided to start in healthcare, we were a engineering team, you know, very strong in AI and engineering, but not so much in healthcare. And we made actually a good decision to start as a B two C healthcare company to learn. But then we lost our way a little bit and we went too far before realizing that it was not the right path and that we should go back to a B two B business model. We probably took too much time to make these decisions, just an example.

• 9:42Harry Stebbings?

  Can I ask you, what do you do then to stop the Kim Jong paradox or problem? Like, how do you surround yourself then with people that do challenge you? What did you change?

• 9:50Speaker 00

  The solution first is, I think, is with external people, you know, even my investors were too close to me. But if you go to people further away, they have less, who are very successful and they have no incentive to please you. And you get more information, more data from them. And internally, it's a matter of just telling your team, okay, you should change our decisions. And we did that a lot at Nebula. And sometimes too much, you know, they challenge everything after that. Also, after you make one or two mistakes, the team will more naturally come forward. And this is before you reach the right level of challenger.

• 10:22Harry Stebbings?

  It's funny, one of my friends is Gustav Soderstrom, who's the C P O at Spotify. And he says, talk is cheap, and so we should do more of it. I want to start on kind of where we are in terms of the landscape itself. And we look at the developments today. And I think Chat G P T has brought around kind of consumer excitement to a level that we haven't seen, obviously, in A I for years and years. Have there been fundamental developments in the last eighteen months technologically that have led to where we are today with our excitement level? Or is it the continuation of years of behind the scenes? Development.

• 10:50Speaker 00

  For the general public, it looks like a very big step function with huge advancement every ten years. I think from the inside, it's much more continuous. So for instance, ChatGPT, you know, is based on GPT four, which is based on GPT three, was released three years ago. GPT three is a large language model. It was invented before that. Transformers, where the paper was released in twenty sixteen. The progress is continuous, but the public perception of it is very, very discontinuous.

• 11:18Harry Stebbings?

  Can I ask you, what do you make of the VC hype cycle? What do you and what do the teams around you and the true AIOG's think when they say, see all the VCs just chasing everything AI?

• 11:29Speaker 00

  From our standpoint, it's really ridiculous. I've been building AI companies for twenty two years, so I've seen the cycle several times. So after a while, you're not surprised anymore. The only thing you need to know is when to add or remove AI from your deck, and it would change about every three or four years.

• 11:46Harry Stebbings?

  Can I ask you, when I was with you at Ambassadors, and obviously as an ambassador today, I'd probably say this, but you know, the common criticism, especially of generative AI, is bluntly, it's a thin, relatively valueless layer on top of kind of foundational models. Is that fair or not when we look at generative AI applications today?

• 12:04Speaker 00

  I don't think it's fair. When the C language came out around nineteen seventy two, some people said, hey, now it's so easy to build software. Every software is just a thin layer on top of C. And of course, it's not true, and we know it. And the same when databases came, you know, in early eighties. So it's not fair to think that any AI application on top of LLM is just a thin layer. LLM is a new kind of resource. It's like a new kind of infrastructure that everybody can access, sure. But then it's completely novel. It's very hard to control. You have hallucinations, it's deterministic, it's very hard to configure. You have several knobs you can change, you know, between fine tuning or how you prompt it and many other. It's very hard to control. And also, it changes every week.

• 12:49Speaker 00

  You have new LLMs coming out that claims to be better than the ones from the week before. How do you decide when to change your LLM? How do you do it without breaking your existing users and product? There is a new infrastructure, there is a new game, new rules. The companies who are the faster to understand these rules and play along these rules will win and build the best products.

• 13:11Harry Stebbings?

  So if we have two, sorry, I'm trying to understand. So if me and you start a new startup today in the travel and expense management category, and we're using whatever model we choose to use, do you have an advantage over me if we're both using the same model?

• 13:25Speaker 00

  I think I have because I know how it works internally. So I know the limitations. When something is wrong, I know where I should poke to find a solution. I also know when there are many, many and more and more LLMs available, which one will probably be the better suited to my problem. And I know what kind of machine learning I have to build around by LLM to balance for the weakness of it.

• 13:46Harry Stebbings?

  So we see companies switch between LLMs very frequently.

• 13:50Speaker 00

  It's how it will work, of course. You have every two weeks incredible things coming out of research. And if you just assume it's static, in a year from now, your product will be very dumb compared to your competitors' products. Or more expensive to run, or very slow.

• 14:05Harry Stebbings?

  We had Imad on the show from Stability, and he said, you mentioned hallucinations. He said hallucinations are a feature, not a bug.

• 14:13Speaker 00

  I mean, by construction, the LLM has to output something. And so if there is nothing to say, you make something up that looks natural. So this is probably what Imad meant. By design, he cannot not output something. And so this will be a hallucination.

• 14:29Harry Stebbings?

  And you also mentioned the changing nature of LLMs and models. He said that no models today would be used in a year. Do you think that's right?

• 14:37Speaker 00

  Absolutely agree with that. Will you drive a car you drive today in ten years? I don't think so. There is so much progress so fast that I don't see why we would use the same in one year.

• 14:47Harry Stebbings?

  We mentioned your proprietary knowledge. If me and you were to start a startup in the same space at the same time, how much of an advantage is it to have existing proprietary data? Say, being a four to five-year-old company with four to five years of customer data versus being a new startup with no existing proprietary data?

• 15:03Speaker 00

  I think this is always one train late. So having a lot of proprietary data was very, very important for the last cycle. Five years ago, maybe it's less and less true. You need some to bootstrap your model. So for instance, at Nabla, we built a data set of 30,000 medical With patient consent, we have to pay doctors. It's very hard to build this because we need this to bootstrap our product. But then with the new, you know, pre-trained models, fine-tuning is very efficient with a little bit of data. There is a, I don't know if you've heard about Lima, a model that came out three weeks ago.

• 15:38Harry Stebbings?

  Can I actually just interrupt you and just ask, for those that don't know, what's pre-trained and what's fine-tuning? Just so people understand, not monklashia.

• 15:47Speaker 00

  So a large language model is trained in two phases. First phase is unsupervised pre-training, so it's just fed with huge amount of text and it's trained to predict the next word. And so this is what we call unsupervised pre-training. Unsupervised because you don't need any human intervention. The text by itself is enough because it will try to predict the next word and on and on and on. And then the second phase is fine-tuning when you use different machine learning techniques. So for instance, reinforcement learning for chat G P T, where you train your model to follow instructions. Like for chat G P T is when I ask a question, you should tell me something that looks like a right answer or looks like a reasonable answer to what looks like this question.

• 16:31Speaker 00

  And so the fine-tuning is the second phase where you give more precise instruction on how it should behave. To do that, yeah, there are several techniques. So everybody is still learning, you know, it's like huge bag boxes, these things, and everybody is still discovering new things. But three weeks ago, there was a paper about Lima. This Lima paper shows that with only one thousand question and answer examples, so very, very small data sets, they get something for use for fine-tuning, so the second stage. They get something that performs better than G P T three and almost at the level of G P T four with only one thousand Q A, you know, for fine-tuning. So the thing with L L M is it looks like a huge pre-training with text. It's learning mostly through pre-training with huge amount of text.

• 17:16Speaker 00

  And then with a small amount of data, with high-quality data, you can get a lot, you can get what you need and train your model to do what you expect with very small data sets.

• 17:26Harry Stebbings?

  And we mentioned models there and we mentioned kind of size of models there and actually how you only need actually. Very small amounts today. Will there be new foundational model companies created, do you think? Or do you think we have the existing incumbents already?

• 17:39Speaker 00

  We probably have most of the existing companies. Many a few will be started. There is a lot of hype and it's public, so I can spill the bean about Mistral, a new one created by three engineers, two of them from Facebook, and I know them and you can still build this financial model.

• 17:57Harry Stebbings?

  I know them too, and they're fantastic. But how can you do it without being so far behind?

• 18:01Speaker 00

  To make a successful financial model company, you need incredible scientists and a huge amount of money. And if you check these two boxes, I think you can still do it. And you need focus. Google and Meta could have done what OpenAI did, you know, easily, and they didn't because it was not the priority and there is only legal people around. But I think a new startup with enough talent and enough money, and when I say enough money, I'm talking about billions, just to be clear, then I'm sure you can do at least as well as OpenAI.

• 18:31Harry Stebbings?

  We mentioned incumbents, we mentioned startups there, kind of encroaching on incumbent space. I think the big thing that I think about is like, who wins in this next wave? Is it startups creating amazing new products, leveraging foundational models? Or is it actually Adobe? Is it actually Apple? Is it Google with Bard? How do we think about where value accrues to startup or incumbent?

• 18:51Speaker 00

  So incumbents have a huge advantage through distribution. That's the one thing that is hard for us, startups. But incumbents have many disadvantages. I think first, they are very slow. You mentioned Adobe, I was sure you would because this is the one example that everybody has, but then who else is was as fast as Adobe? So I think most of the incumbents won't move fast. And in many cases.

• 19:16Harry Stebbings?

  Do you not think they're moving faster? I mean, I don't know if you'd call Notion an incumbent, but like Notion have moved very fast. Adobe moved very fast. Nirvan, the travel expense management company now, is like solely on OpenAI. I think actually they have moved very fast.

• 19:30Speaker 00

  That's fair. You know, some of them can move fast and it's not fair to say they are slow and that's it. I think incumbents suffer from problems because in many cases, they will do AI enhanced.

• 19:41Speaker 00

  Features and this is what all the one you mentioned did the notion you still have a document you edit you have a cursor you write hello by the way you can call chat G P T and to to to summarize a paragraph is what I call spreading a little bit of A I dust on the magic dust on your existing products Who knows how differently you can think about building knowledge for your company probably something will come and destroy notion and Google Docs and all of them with a totally new paradigm that is made possible by A I Certainly this incumbents won't do it so I think the best incumbents can benefit from A I to be competitive in their existing markets

• 20:20Speaker 00

  But I don't think they will invent totally disruptive things that will kill them and you know Kodak Kodak invented the digital camera and of course they never released it Because it would kill their main revenue which was the films.

• 20:32Harry Stebbings?

  Do you think that's why Google didn't innovate in the way that they could have done because actually the cost per query like chat G P T does is so much more expensive than the way that they do it today And actually it would have cannibalized their whole business if they were like hey let's embrace this new cost structure which is so much more expensive.

• 20:49Speaker 00

  I think the reason is simpler than that the reason is nobody could predict that L L M would be so useful and powerful before you train one at this scale And who in the Google all chart had the incentive to invest five hundred million dollars and just to see this without any business benefit for the company If you add to that the legal department who is not that happy that you are like releasing random child bots who can say anything then nothing happens And this is what happened to Google and probably also at Meta where nobody has the incentive to do that and so OpenAI as a private company they need to Show something to build products and they have this curiosity and the financial power and they did it.

• 21:31Harry Stebbings?

  When we look at the different approaches there's the open approach there's the closed approach we've mentioned some of the biggest names there You mentioned the Yan Le Kuhn episode he obviously is a big proponent for open how do you think about which approach dominates over the next few years open versus closed?

• 21:48Speaker 00

  So obviously I think the foundational model that will win will Be open. But that being said, you know, we should be careful because even an open model trained with open data, to me, is not that open. Because when you have like 300 billion parameters and it's a huge black box, you don't understand why the output is what it is. Is it really open? And so I'm just putting a little bit of salt on what I hear and like open models are, you understand everything. It's explainable, it's predictable. It's not true. An open model can be as hard to predict than that a closed model.

• 22:21Harry Stebbings?

  We mentioned kind of data sets before and I did just want to touch on it because I think it's important. And I am actually going to matter, Iman's episode, but he said about the importance of national data sets. Actually, for certain things, I think it really is actually very clear. Do you think we'll have national data sets moving forward, given how different nations have such different data?

• 22:40Speaker 00

  I'm not sure about national data sets. If you want to sell something to a nation, it's good to say that, but more per industry, per vertical that I said, of course, will improve the quality of the model. So if you train an L L M only on medical data, you get a better L L M for medical applications. But even if you feed the L L M with curated data, it's not guaranteed that the output will be good, will be perfect, that you can trust the output. So feeding a L L M with trusted data doesn't make the output trustable because of how L L M works. So there are lots of misconceptions about this.

• 23:11Harry Stebbings?

  Help me understand that then, because I think a lot of people will misunderstand that. So you have great, high-quality trusted data. What leads to then high-quality output and good decision and outcome?

• 23:21Speaker 00

  So an L L M is a huge probabilistic machine. It's like an autocomplete. I think this is the best image. And it will complete the beginning of your sentence at any cost. It will always complete it. And you may have like 10 sentences, let's say, in the input data set that are true. But maybe the L L M will start with the beginning of the first sentence and switch to the end of the second, another sentence, just because it looks good. But logically, factually, this output will be totally wrong. And I simplified a lot, but this is exactly how it works. And the form is perfect, always looks like, sounds like a very good answer, but the reasoning, the facts may be totally off, invented or wrong. And even if the input that I said is wrong.

• 24:01Speaker 00

  So when people say ChatGPT gave me a wrong output because it was trained on Reddit and Reddit is a lot of noise. Yes, but only partially. Even if you remove Reddit from the training data set, it doesn't mean you won't have like random answers.

• 24:15Harry Stebbings?

  We're kind of jumping around from Imad to Yan and picking different points, but Yan said in particular that it's a big jump to assume there's a correlation between intelligence and the desire to dominate. When we think about the most common question being the fear of AI really overcoming human power and dominating us. How do you think about Yan's statement about that correlation? Do you agree or disagree? And how do you think about that?

• 24:38Speaker 00

  I fully agree with Yan and I think if you really understand how machine learning works and you're not looking for free publicity. I don't see why you would say something like that, that getting more intelligent will make them want to kill humanity. I don't even understand the path leading to this thinking.

• 24:54Harry Stebbings?

  Why does someone like Jeff Hinton then feel that?

• 24:56Speaker 00

  I'm not in the head of Jeff. I don't know. I don't know. And I know Yan is also surprised and.

• 25:02Harry Stebbings?

  Do you think Elon Musk's decision to be very proactive in terms of the petition to pause AI development? How do you think about that?

• 25:09Speaker 00

  It's weird. At the same time, he said that I know he was trying to build a team to compete with OpenAI. He hired somebody from DeepMind, like the same day where he announced we should pause for six months. And it looks to me that the people who are proponents of this pause or to more regulation are the one who feel that they are in advance. And so it's like a way to say, guys, let us, we are in front. I don't want more people to start the race.

• 25:33Harry Stebbings?

  Can I ask you, you mentioned creating chatbots twenty two years ago and kind of the length to where we are today. I always think it takes longer to adopt than we think. Bluntly, when we look at where we are today, are we really at the precipice now? Or actually, is it another ten years before we see that adoption cycle?

• 25:50Speaker 00

  Finally, it's here. When I see people using ChatGPT and learning to do their job differently with the help of ChatGPT. I really feel we are on the verge of finally having a huge impact with chatbots. For the first ten years, I tried to convince people that chatbots were ready and to buy my chatbots. And they say, no, it's not. And then for the ten following years, people told me, oh, chatbots. Incredible, we can replace human with chatbot. And I said, no, no, no, no, don't do that. It's not ready. And finally, now I feel we we are at a point where many things will be impacted. But which doesn't mean you're replacing people with them. It's a great tool, but it doesn't mean it should. Because of the chat form, it's tempting to think, OK, let's just replace this guy, this worker with a bot. In many cases, I don't think it's ready for that.

• 26:35Speaker 00

  But it still have a huge impact of how work is done.

• 26:38Harry Stebbings?

  You said it's not ready to necessarily replace. When we think about NABR and healthcare, I seem to be quoting him a lot. But Imad, with AI, you can change the nature of a doctor. Will AI replace doctors?

• 26:48Speaker 00

  AI will not replace doctors, but doctors who use AI will replace doctors who don't. Definitely. AI can have a huge impact on doctors, the way they work. And those who will embrace this change will strive.

• 27:00Harry Stebbings?

  Can I ask you, in the next one to two years, how will those doctors who embrace it? How will they use it?

• 27:05Speaker 00

  The first thing I learned when I started to operate a healthcare company is that for the first 50 years of computerization, computers have been a bad news for doctors. Before computers, they see patients, they care. There is a little bit of admin work, but it's done by somebody else and not so much paperwork. And they're very, very, very happy. And suddenly, people like us come to them and say, OK, now there is an electronic patient record. You need to document, you need to fill this form, you need to do this and that. And we have trouble to get the money from the insurance company. So we need to document more to make sure the claims go through and so on, so on, so on. So, you know, the other day I visited a small clinic in Paris and all the doctors looked very happy. And I said, what happened? You know, it's Tottenham.

• 27:49Speaker 00

  The computer system is down today. We are just using papers and pen. And they were so happy, all of them. And so you have to realize that the state today is a very bad state where computers were bad, bad news for them. Because they are drowning under this administrative work, documentation, for many reasons. Doctors spend an average 49% of their time doing this kind of administrative task as opposed to caring for patients. It's bad for everyone, it's bad for them. Three out of four doctors suffer from symptoms of burnout. It's bad for patients.

• 28:23Harry Stebbings?

  So you're three out of four suffer from burnout?

• 28:25Speaker 00

  Yeah, three out of four doctors have suffered from burnout symptoms for the last year. And the main reason of this is they all mentioned the pressure of all this admin work, basically.

• 28:36Harry Stebbings?

  What is that admin work? I didn't live this life.

• 28:39Speaker 00

  The biggest work they have to do is clinical documentation. So they have to document everything they hear, their decisions. And in big systems that we call EHR, you know, electronic health records, which are in dinosaur systems. And the reason they need to do this documentation more and more, first is the financial reason to get reimbursed by the insurance companies, especially in the US. You need to have like a very solid file with all that. Otherwise, the insurance will take the first opportunity, the first pretext not to pay your claim. The second reason is for legal protection, because medical malpractice trials, you know, it's so expensive, your best protection is to document this in the right way. And so this is why mainly, I mean, work started to grow bigger and bigger.

• 29:22Speaker 00

  And these EHRs are very, very old and clunky systems. I checked the other day, there was one specific clinical action that took 227 clicks, mouse clicks, to be performed in the system. And I posted this on LinkedIn and many doctors answered and said, no, actually it's more than 300 clicks. And so this is the stage today.

• 29:42Harry Stebbings?

  So when we look at that, though, and the thing that strikes me there is like, okay, we could actually have recordings of meetings, transcript, speech to text, and then auto suggestions, meeting summary notes, whatever we want to do. Trouble is, it's healthcare data. It's not me and you chatting about shopping, where we could have preferences. No one really gives a shit actually so much about, you know, what you want to buy from a supermarket. With healthcare data, does regulation and consumer protections get in the way of efficiency?

• 30:09Speaker 00

  Yeah, so first, the solution, I think how AI will impact doctors, it will bring an AI assistant to every doctor, to every clinician. This AI assistant will be aware of what's happening, what the patient is saying, what data we know about this patient, what documentation, what work needs to be done.

• 30:25Harry Stebbings?

  Sorry, just to break it down, how does it know what the patient is saying? We record meetings?

• 30:29Speaker 00

  We don't record the meeting, but we capture the audio. We don't store it, we just capture it and then drop it. And so, yeah, it gets a lot of context from the audio environment, what we call an ambient system.

• 30:39Harry Stebbings?

  And the data itself from the consumer, how do we do that? From wearables? How do we ingest the data?

• 30:45Speaker 00

  So this AI assistant is connected to the EHR, so it will get The data from the existing source of truth, which is these patients' records.

• 30:53Harry Stebbings?

  Are the EHRs interoperable? Do they have the ability to move data between different applications and silos?

• 31:00Speaker 00

  So in theory, there is a new law in the US.

• 31:03Harry Stebbings?

  In theory?

• 31:04Speaker 00

  That you know, Epic spent billions to millions to try to block, but it finally was after, I think, 20 years of battle has passed. And so, yeah, they should be interoperable. Like the new model EHRs are like CRM systems today, as you have an API, it's easy to integrate.

• 31:21Harry Stebbings?

  Can I ask, I worry with healthcare about the incentive mechanism. And what I mean by that is like specifically, if we make people much more productive and we maybe remove the need for certain nurses, actually, more people will be unemployed. And when we think about politicians who are allocating money for the NHS, which is obviously our healthcare system, they need nurses' votes. And so saying, hey, we're going to remove a load of low-level nurses through intelligent automation workflows, whatever that is, it goes against the political incentives by being more efficient. Does that become a problem?

• 31:54Speaker 00

  It's not a problem. The World Health Organization says that we are missing 18 million clinicians by 2030. So the state is not, it will put people out of the jobs. The reality is we are desperately looking for clinicians in every country in the world. So if you can make their lives easier, it's a big win for everyone.

• 32:13Harry Stebbings?

  What are the biggest barriers then to AI having the impact on healthcare that I think we both believe it can?

• 32:19Speaker 00

  There's many barriers. I think the mistake that many AI startups are doing in healthcare and what we did initially is we focused directly on the patients. It's too much, too fast. You know, we cannot jump. Healthcare system is so complicated. You cannot, regulation, you know, for many, many reasons, you cannot do that. Technically, we're not ready to do it. We don't have the data. We should first focus on clinicians, the way things are done today, change your life with AI. And then together we'll go to the next step. So I think to answer your question, that one of the main problems is that we are trying to go too fast to a patient-facing perfect system. I'm spending a lot of time in hospitals, I'm spending nights in the emergency services call centre in Paris. Listening to everyone.

• 33:00Harry Stebbings?

  How is that? And what do you see?

• 33:02Speaker 00

  So it's interesting, you know, I met a doctor in a cafe in Paris. I made a demo of NABLA on my phone and he told me, Alex, you're going to come to the emergency services call centre next week all night with me. It turns out this guy, who is like the chief emergency physician in France, Patrick Pelou, invited me to spend nights over there. What I learnt spending nights over there is it's a little bit like a call centre. You have a first team is taking calls. Overall, in France, it's about forty million calls a year. And the first responder, they don't make medical decisions. They are not doctors, they're not even nurses. Their goal is to just to understand what is the address and what is the situation? What is the emergency? It's very hard to get that because people are panicked and you have lots of people calling for very small things,

• 33:45Speaker 00

  really close things, and you have real emergencies hidden. So their job is to sort through these very, very complicated situations when people are calling. And they actually write a note, structured note with this information. They click submit and then you have like regulation doctors who make the decision. Should we send the real emergency services? Should we send firefighters or should we just have a doctor talk to this person over the line? And so it's a two-step decision. And a lot is done around documenting the situation. And these things, you know, it would be so easy to do with what we do at NABLA and you know, what we know how to do with AI today. The goal would be to keep the person who is answering the phone

• 34:26Speaker 00

  because they are know-how in terms of empathy, calming people down, understanding the context. Very complicated situation again. Typically, you have several people that are screaming, nobody knows how many victims, what's happening. Very, very complicated situations. And these people, I spent hours and hours listening with headphones next to them. They are experts cutting through this darkness and understanding what's happening. So we want to keep these people, but they are typing with two fingers on the keyboard and missing some information, it takes a lot of time to get and so on. So having an AI assistant work alongside them, listening to the call and documenting. I tried it, you know, it works already perfectly with what we have, would be a perfect team. So this is an example.

• 35:06Harry Stebbings?

  How many of the calls lead to ambulance? Is sent out. Do you have that number? Is it like one in ten, one in twenty?

• 35:13Speaker 00

  What I saw the few nights, you know, maybe one in five results in services being sent. And in these cases, three in four are firefighters who have medical, you know, doctors and so on. But they don't handle the most critical medical agencies are handled by what's called SAMU in France. And it's one in four.

• 35:31Harry Stebbings?

  How receptive are doctors to you when you talk about AI and improving their work with AI? Are they like, yes, bring it on? Or like, huh, seen it before?

• 35:40Speaker 00

  So we've seen a change in that in the recent years. And up to three years ago, you know, almost every doctor would tell me, okay, you don't understand our problems, go away. I'm already fighting against my EHR every day. I don't need more stuff. And it really changed recently. The core reason has nothing to do with AI is that the health systems are collapsing everywhere, you know? I know the situation with NHS. Here it's worse in France, in the US, all the major hospital groups are losing money.

• 36:06Harry Stebbings?

  Why is it worse in France?

• 36:08Speaker 00

  So in France, we have a huge shortage of doctors. And there are like zones in France where you have to drive two hundred kilometers and wait for three weeks just to see a GP. So this is obviously a big problem. Globally, the problem is the same everywhere. It's access to healthcare. In France, you can get good doctor quickly if you know somebody in the system. In the US, you can get it if you have a lot of money. It looks different, but eventually it's a problem of not enough doctors.

• 36:32Harry Stebbings?

  Are there less doctors than ever, or are we getting more unhealthy than ever? Is the demand increasing or is the supply decreasing?

• 36:38Speaker 00

  Both, since it takes ten to fifteen years to train doctors. Decisions you made twenty years ago have a big impact today. So first, there are less and less doctors. Many of them are aging and will go be retired soon. And so the number is going to drop. And in terms of demand, everywhere in the world, you have a huge rise of chronic disease like diabetes. That takes a lot of medical resources, you know, chronic disease. We always see as engineers, young, healthy people, we see the easy things with medicine where, oh, I'm hurt. I go, there is a quick diagnostic decision. Maybe I spend two days in the hospital and then it's over. But what takes resources in the healthcare systems is not that, is our aging people and chronic disease and often combined.

• 37:21Harry Stebbings?

  I actually had a fascinating stat and it was like ninety percent of the cost of healthcare is spent on the last six months of life. Can I ask you, my first ever investment, fun fact, was like a WhatsApp for doctors and nurses in the UK. It went to a hundred thousand doctors and nurses in the NHS and then it came to getting paid. And they just don't pay and they deliver great value, a hundred thousand doctors and nurses. How do you think about actually building a business, be able to delivering value in a sector that just doesn't pay and doesn't have a willingness to pay?

• 37:50Speaker 00

  So this is the biggest difficulty when you are trying to disrupt healthcare, actually, especially for startup. You have the payer, you know, in insurance or public payer, you have the providers who are providing care and you have the patients and it's a very complex relationship with three parties. And typically, the one who benefits from a product is not the one who is paying for it. So typically, the patient will benefit, for instance, from your better healthcare app, but it has to be paid by the payer, by the NHS. And if the provider disagrees, they can block everything. So you have to solve these three mass problem from day one, which is very, very, very complex. And so you have to find a balance between, as a startup, if you optimize a very small process, it's easy to deploy because it follows the lines of the existing system.

• 38:33Speaker 00

  So for instance, if you do a better way to take appointments, online appointments instead of calling, you don't change a healthcare system with that. Even that is hard to push, but it's easier because you don't change the fact that there is an appointment, there is a consultation, there is a synchronous visit, so you fit into the system. If you do something too ambitious, it will never fit in this system. It's, I think it's impossible to go to market with something too ambitious. So I think as a startup, we have to find the right level of ambition where it fits enough in the system to be adopted, but it's not too much disruptive from day one, otherwise it's impossible to go to market.

• 39:07Harry Stebbings?

  And who pays you?

• 39:08Speaker 00

  So learned very hard lessons. If you are thinking about creating a healthcare startup, the very, very first question I want to hear is, who is paying for this product? Who is paying? What is the business model? Then you can, in a startup, you know, in Y C, in all startup schools in the world, they tell you, don't start with a solution, start with a problem. But in healthcare, even starting with a problem is not enough. Start with who is paying. And then how you do frame the problem for this person to pay? And then what is the solution for this problem eventually?

• 39:37Harry Stebbings?

  If we think about this responder to the call though, and we take intelligent notes which allow them to be better and more empathetic, who's going to pay for that? The hospitals don't pay.

• 39:46Speaker 00

  So in emergency services, the only potential payer is the government. And so not a good, good go to market, you know?

• 39:53Harry Stebbings?

  Yeah.

• 39:53Speaker 00

  It's just first go to market. And that's why I didn't start with that. Now, you know, we've deployed our product in the US. Thousands of doctors are using it every day. We are well funded. Now, if the emergency services in France really want my product and the sponsor is a trick blue is like the god of emergency in France and the minister of health looks at me in the eyes and say, we really want it. It may be the right timing, but it would be a very, very bad idea to start with that as a startup. And maybe if you're lucky, you do a pilot and then you wait, you wait and you is what I call death by pilot.

• 40:26Harry Stebbings?

  And so you go to the US and you sell to private clinics?

• 40:29Speaker 00

  We started with the US because the problem we are solving now of clinical documentation is huge in the US, bigger than in Europe. And so the willingness to pay for the existing providers, and again, you need to an entry point in the system is very high. We found this go to market. We also made it possible for physicians to use our product without any approval from anyone bottom up. They can go to nabla.com, you know, it's a web application. It's a Chrome extension. They use it tomorrow and then they love it. And then we'll talk to their boss and say, we'd like to use this in the full clinic.

• 41:02Harry Stebbings?

  Can I say, imagine going to the US there because of the bigger market? I think there's a common suggestion to a lot of AI founders today that if you want to build in AI, you've got to be in the valley. It's all about being in San Francisco. It's where the talent is. Paris and France has become also an AI hotspot. How do you advise founders first on whether they need to be in the valley or not to be building the best in AI?

• 41:24Speaker 00

  I think it used to be true, but it's not true anymore. When I built my second company with AI, I happened to stumble upon Adam Schaefer, the creator of theory, parking lot in the valley after an event. And he helped me a lot to think about with AI. He gave me the confidence to follow what I had in mind. Owning to this confidence, I was able to refuse offers from Google and others who wanted to buy us too early. And so it had a huge impact on my company. If I were in Paris, no Adam Scher, nobody has done this before, maybe I would have sold it to the first 10 million. Okay, let's sell it. It's good. And so it used to be true. I think that you needed to be in the valet with having this kind of people around you. Now I think it's less true because it's everything is distributed. The talent is very, very distributed. It will

• 42:08Speaker 00

  get more distributed. And I think you should get close to your customers.

• 42:11Harry Stebbings?

  Do you think French startups still sell too soon?

• 42:14Speaker 00

  Yes, French startups. So in France, you have very good AI engineers, machine learning engineers, because the education system is free, is very focused on mathematics. So we produce lots of good engineers, but we suck at growing companies. Really, we are not bad at that. I don't know why. Maybe we lack discipline, maybe we-

• 42:31Harry Stebbings?

  Is it you suck at growing companies, or you just sell too soon? The author comes in and asks you, 50 million, it's a lot of money. You only raise one round, you'll take in 10, 15 each as co-founders. C'est bon?

• 42:43Speaker 00

  Exactly. C'est bon. It's enough. And so if you are normal, it's hard to refuse a $1 billion offer if you haven't done, you know, 10 million before. But this is what Zuck did, what Google founders, you know, Sergey and Larry did. So maybe we're not focused enough or disciplined enough, and we don't have examples around us to grow this company. Hopefully, it's changing. We have very good scale-ups in France now, but it takes some times to-

• 43:07Harry Stebbings?

  What do you think Europe needs to do to keep pace with the US in terms of AI and being an ecosystem that attracts the best AI talent?

• 43:15Speaker 00

  So Europe is probably 10 years late compared to US and China. And with a new regulation, we are probably going to take 50 years more.

• 43:22Harry Stebbings?

  Talk to me about that. Why do you think that is? Because the regulation is so prohibitive? Unpack that for me.

• 43:28Speaker 00

  So the new regulation is a disaster. If you look at it, the people who wrote this, they went too fast, you know. Good regulation is about good timing and involving the right people who are actually doing stuff in this domain. I think Europe being late felt like, okay, let's be at least first in regulation.

• 43:43Harry Stebbings?

  What was it about the regulation which made us so bad?

• 43:46Speaker 00

  Some aspects say, for instance, that you should be accountable. I'm simplifying, Be accountable of the all the data you use to train your model. For instance, you should make sure that you have a proper license, like explicit consent from everywhere it comes from. And I see why it's a beautiful idea. Who would disagree with that? But in practice, it means 100% of the L M's that were trained these last three years would be illegal in Europe. It's not connected to the reality. The intent is good, but the limited put on how to train a model and how to operate a model makes it in practice compared to what we do today makes everything illegal.

• 44:23Harry Stebbings?

  What do European startups do then?

• 44:25Speaker 00

  We moved to the UK, maybe for one reason Brexit.

• 44:27Harry Stebbings?

  Move to the UK?

• 44:28Speaker 00

  Yeah, finally Brexit, maybe it was good. You know, I'm half joking. If the regulation, if it's really like this and nobody can challenge it, startups like us may have to move. Or maybe you can keep the team in France, but we have to physically train the models elsewhere. I don't know. It's a big, huge danger.

• 44:45Harry Stebbings?

  What would you do if you were in charge? If I put you in the Hague or in the EU and you were given the regulatory powers over the next five years of AI in Europe, what would you do?

• 44:54Speaker 00

  First, I'd wait a little bit because it's too early. We are still in the demo phase of AI, you know, L L M's. It's not really used a lot, it's just beginning to be used. And as I said, you know, regulation timing is key. It shouldn't be too late, but if you're too early and nobody knows what actually are the risks, not the risks you think because you read some science fiction books, but the real risk, nobody knows yet. And so I think first I would wait maybe a year or two to learn from the field what should be regulated and why. And then I would involve people who build these models, of course, but also people who are using it, you know, the users, the general public, which was not done so far. So they have like experts who are smart people, but there is no reality of L L M's yet.

• 45:35Harry Stebbings?

  What do you think about China? We talked about Europe there. China, how do you think they've embraced AI and the next wave of AI?

• 45:41Speaker 00

  They have a key advantage that there is no G D P R or very few regulation internally. And so the data qualities they have and the amounts, both the quality and quantity of data is incredible in China. And so for instance, for healthcare data, they probably have everything about every individual in China. And if you are supported by government, I'm sure you have access for for research to all this. They are lucky for that.

• 46:04Harry Stebbings?

  If you think about geographies, who's the winner and who's the loser in the next 10 years? China, US, Europe?

• 46:11Speaker 00

  They all have issues, their issues. So we talked about the advantages of China, but they are very close, they are getting more and more close, they have maybe the wrong incentives at the research level. It's hard to predict when we come out of China. We talked about Europe, where regulation is probably the biggest problem that will keep Europe behind. And we are already behind, that's my concern. The US, you know, is always in a very good position. Immigration laws may be something that will be a problem eventually in the US because talent is so distributed. We can learn, deep learning, very easily from everywhere in the world on a tablet today. It will be more and more like this. And if it's so hard to go to the US, to work for US company, eventually it will have an impact, I think.

• 46:53Harry Stebbings?

  I want to move to a quick fire round. I say a short statement, you give me your immediate thoughts. Does that sound okay?

• 46:58Speaker 00

  Okay.

• 46:58Harry Stebbings?

  Okay. Do you agree that some of the biggest businesses to be built in AI will be built in services businesses, helping enterprises implement AI?

• 47:06Speaker 00

  I disagree. AI will enable a new generation of players in every industry that we kill the incumbents eventually.

• 47:12Harry Stebbings?

  What do you think is that timescale?

• 47:14Speaker 00

  Five years. Existing services companies, like consulting companies, are embracing AI. They will eventually remove the big data from their website and put AI, and then they will put LLM to replace the words. And they will take this business, I think. There was a company called Element AI a few years ago in Canada, a very ambitious company who tried to do services like that. And they eventually failed. They were acquired by ServiceNow for sort of months erased.

• 47:38Harry Stebbings?

  What was the biggest element you would like to change about the AI community?

• 47:41Speaker 00

  Make it more diverse. I'm really tired of talking to people like, just like me. It's a cliche to say that, but it would be a lot of value if we get more diverse. I have another quick anecdote. When I was at Facebook, somebody discovered that the model that was supposed to tag images, that was 100% successful to finding tennis balls in pictures. Actually, if you remove the tennis ball from the picture, it was still finding the tennis ball. So it didn't learn to find the tennis ball, it learned the context of a tennis ball, like a racket. In the second phase, it changed the color of the people on the picture. And if you had more black pixels in the hair, suddenly the model thinks, oh, it's a ping pong ball. It was very early days when We discovered that some models have bias.

• 48:23Speaker 00

  And this bias in this case is it didn't learn to look at the ball. It learned to look at the race of the people around. And it's actually making this decision tennis versus ping pong based on the race of the people around.

• 48:33Harry Stebbings?

  Iman said all models have bias. Is that fair?

• 48:37Speaker 00

  Yeah, more or less, but all of them and many models have huge bias. The guy, you know, very strong researcher who found this thing at Facebook very, very early before everybody talked about bias. He's Mustafa Sissi, he's from Senegal. I don't think it's just luck that somebody with a different angle than we have, different culture, found that huge hole in the models that we hadn't found before.

• 48:59Harry Stebbings?

  Does AI kill traditional media?

• 49:01Speaker 00

  I don't think AI will kill investigative journalism, finding the right sources, the right information. So this part of media, I don't think will be replaced by AI. Now the generation part at the output probably will be disrupted a lot by AI.

• 49:16Harry Stebbings?

  What's the most painful lesson you've learned that you're also pleased to have learned?

• 49:20Speaker 00

  That fundamentally, people don't really change.

• 49:23Harry Stebbings?

  What makes you say that?

• 49:24Speaker 00

  I mean, you can help people to grow. It's true for yourself. So you can correct some weaknesses. You can be stronger at what you are strong at already. But I think you have to accept that some of the fundamental characteristics of yourself and the people around you won't change. And if you expect them to change, it leads to big failure and mistakes.

• 49:45Harry Stebbings?

  What do you think is the biggest misconception that people have around AI? You hear many discussions. What do you think of that? Oh, can't believe they're saying this again.

• 49:54Speaker 00

  It's that they think it's conscious. They are influenced by the form, like, oh, perfect answer. The perfect form and it's a chatbot. It answered my question and influenced by the form. They make conclusion on the deep inside, you know, of the system and say, oh, it's conscious. And for me, the biggest proof of that is that in 1966, when the mother of all chatbots was released at the MIT, you know, Eliza, the very first bot. For a few months, people around the world thought that AI was solved, that there was a human, you know, sentient AI in Eliza, which is a very, very simple pattern matching system with regular expressions. But because of this, the way it was presented, people fell in the trap. And I think we keep falling in this trap again and again and again for sixty years.

• 50:39Harry Stebbings?

  What's the strongest belief you had, which turned out to be wrong?

• 50:42Speaker 00

  It's very hard, yeah, because when we change belief, we tend to forget that we have the contrary belief before and.

• 50:48Harry Stebbings?

  For me, like with Panda, it was funny. I thought like if you showed enough value, you would get people to pay. If you could prove value, you would get payment.

• 50:56Speaker 00

  Yeah, it's a very good one. And we had exactly the same situation at the beginning of Blabla. When I thought if patients love your healthcare product enough and you prove the health benefits of your product, then payers will pay for it. And it's now that I'm saying it is so wrong, it was obvious, but probably a big beliefs we had that was painful and expensive to learn.

• 51:18Harry Stebbings?

  Who's been the most helpful angel?

• 51:20Speaker 00

  Probably Yan Lekha, especially when we doubt about something or he's very confident that we are on the right path and it means a lot.

• 51:29Harry Stebbings?

  What makes the best VC?

• 51:31Speaker 00

  So I worked with Andreessen Horowitz with my previous startup. It was incredible because they never call, they never ask for anything. We didn't even have board meetings. And every time I need something, in twenty minutes, I get huge help. Maybe it's to find a real estate problem because I don't have an office. Maybe it's organizing a huge event and I don't want to hire a marketing team. I want to use them for one week. Or I have like a strategic decision to make and they give me access to the best people in less than a day. And so the fact that you are in control and you ask for help and they deliver it. We could go with that AI to a very nice company with only thirteen employees. Because we are relying on Andreessen Horowitz for all these things. It was incredible. But for my first startup, I did some strategic mistakes, go-to-market mistakes.

• 52:13Speaker 00

  And I wish my VCs back then would have been more involved with me and coached me. And it really depends on the founder stage. A neutral VC who is not here is much better than a bad VC. Who can have a very negative impact on founders. I'm helping founders every day.

• 52:28Harry Stebbings?

  Do you think VC in Europe is good? Or do you think it's still massively behind VC in the US?

• 52:33Speaker 00

  Again, it changed a lot. So ten years ago, VCs in Europe were all from the financial industry. There is nothing about financial industry. Finance in being a VC, a little bit of course, but it's mostly about entrepreneurship. So 10 years ago, all the VCs I was working with in the US were former entrepreneurs. And all the VCs I knew in Europe were former bankers. In most situations, you don't need your VC to be a banker, but you need them to be an ex-entrepreneur.

• 52:57Harry Stebbings?

  10 years' time, where's healthcare?

• 52:59Speaker 00

  Hopefully 10 years' time, you know, every physician has their AI assistant doing a lot of stuff for them and helping them to be 10 times more efficient, you know, seeing more patients, spending more time with every patient, taking better decisions. I also think that at the higher level, decisions that require a big picture of the data, you know, large, large view will be taken by AI, not by people. So for instance, in an emergency services regulation center, when you have thousands of people needing help and you have some resources, what is the state of your resources, what is the demand? How you make these decisions at the city scale? Today is done by humans, but it's a miracle that it doesn't collapse. And sometimes it collapses when it's a big event.

• 53:40Harry Stebbings?

  2033, where are you then? Where's Alex then?

• 53:43Speaker 00

  So my dream is to build a healthcare system from scratch, without any of all these limitations and constraints we mentioned. So controlling full stack, patient experience, provider, hospital, data-driven prevention. So my dream is to be able to build that. And to do that, I still need to learn a lot in healthcare and I need a few billion dollars.

• 54:04Harry Stebbings?

  Alex, listen, I love doing this. Thank you so much for joining me and thank you for putting up with my prying questions.

• 54:09Speaker 00

  Thanks, Harry.

• 54:12Harry Stebbings?

  I so love that discussion. It's also so special to do it in person. If you like the show and want to see more from us behind the scenes, of course you can on YouTube by searching for 20 VC. But before we leave you today, this episode is brought to you by Tegus, the go-to research destination for bold investing. Tegus curates expert insights, analysis and financial data to give you powerful perspective for your investment decisions. With lightning-fast access to over 60,000 transcripts across 20,000 companies, you'll discover a wealth of unique insights to fuel your fundamental research, gain perspectives, synthesize information, model outcomes and ultimately make better decisions. All on Tegus.

• 54:51Harry Stebbings?

  You can try it for free at tigus.com/gross. And speaking of tools we cannot live without there, SecureFrame is the leading all-in-one platform for automated security and privacy compliance. SecureFrame simplifies and streamlines the process of getting and staying compliant to the most rigorous global privacy and security standards like SOC 2 and many more. SecureFrame's industry-leading compliance automation platform, paired with their in-house compliance experts and former auditors, helps you get audit-ready in weeks, not months, so you can close more deals faster, delivering a suite of modern governance, risk, and compliance solutions. SecureFrame makes it fast and easy to achieve and maintain compliance, so you can really focus on serving your customers, scaling your business, and growing your revenues.

• 55:35Harry Stebbings?

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• 56:17Harry Stebbings?

  As always, I so appreciate your support, and I can't wait to bring you an incredible 20G Growth on Wednesday.