Dust’s Gabriel Hubert and Stanislas Polu: Getting the Most From AI With Multiple Custom Agents

27:03So rather than encapsulate the technological breaks that are useful and abstract them away from users, exposing them at the right level gives people a ton more autonomy and ready for the ability to design things that we had never thought of. Some of the scenarios that would come up, we literally cannot imagine that sounds. That idea makes sense like the fragmentation and providing people with a Lego blocks to see what sort of use cases emerge. just to make it a little bit real though, can you share a couple of use cases that you've seen in your customer base that have been unique or surprising or particularly valuable, just something to make it a little more tangible?

27:39There's obviously a ton that people are thinking about. The category of obvious use cases that have been interestingly and quickly deployed are enablement of sales teams, support teams, marketing teams. And that is essentially context retrieval and content generation. So I need to answer a ticket. You know, I need to understand what the answer to the ticket is and generate a draft to the ticket. I need to talk to a customer. I need to understand which vertical they're in and how our product solves their problems and draft and you may not follow up on their objections. I need to prepare a blog post to show how we're differentiated from the market.

28:16Again, like I'm going to go and plow into what makes us special and generate with our tone of voice. Those were pretty obvious and quite expected. What I've been excited about is to see two types of things, one very individual assistance, personal coaches. People generally actually quite young people in their first years of career asking for advice on a weekly, on a daily basis. How did I do today versus my goals where do you think I should focus my attention in the coming days? Can you actually break down my interactions on Slack and in notion over the past couple of days and say where I could have been more precise?

28:50I'm getting the feedback that I'm sometimes talking to theoretically. Can you point out the ways in which I can improve on that in these two notes that I'm about to send? And so that's exciting because our bet was, you know, we want to make everybody a builder. We want to make everybody able to see that it's not that hard to get started. And by reducing the activation energy there to see the small gains immediately, rather than wait for the next model or the next version that's going to really solve everything for them. And personally, this case has been great for that. The second family of use cases that I'm excited by are essentially cross -functional.

29:24So where the data silos exist because the functions don't speak the same, they speak the same language, but they don't speak the same language. And so understanding what's happened in the code base when you don't know how to code is powerful. Having an assistant translate into plain English, what the last pull request that's been merged does is powerful. It's powerful to people that were blocked in their work, didn't know who they should bug to actually get an update, so marketing to engineering, sales to engineering. The other scenarios already, extracting technical information from a long sales call is powerful because it means that the engineer doesn't need the abstraction of a PMM or a PM to get nuggets from the last call with a key account.

30:09They can just actually focus the attention of an assistant on that type of content, on their own project and get those updates. So I'd say that's the family of assistants that we're excited by because they really represent, I think the future of how we'd love fast -moving, well -performing companies to work, where the data that is useful to you and the decisions you should make is always accessible. You don't need to worry about which function to decide it on it or create it, you can access it. And that fluidity of information flowing through the company helps you make better and faster decisions day and day out.

30:44Yeah, any other examples that I'm missing, Stan, that you think you're excited about? No, I think what I wanted to add is the fact that, as you said, the usage is extremely fragmented. We see over and over the same scenario, and so we have data to back that kind of a proposition, is as we built dust as a sandbox, which is makes it extremely powerful and extremely flexible, but also has the complexity of making activation of our users are not trivial because when you have an horizontal sandbox like products, you're like, yes, but forward. And so generally the pilot phase that goes with our users starts by clearly identify two cases.

31:25So they really kind of try to answer the question, what are the use cases that should care about for my company and try to identify a couple of them. And we always see the same pattern. We see first use cases get deployed, usage starts. We try to move literally to another use case. Second use case gets deployed, usage picks up a little bit more. And then we generally go through a phase where the usage is kind of flat, increasing slowly. And eventually it reaches kind of a critical mass of usage. And all of the sudden, Skyrockets do something like 70 % of the company. And that's kind of the pattern of kind of activation of our users.

32:01And the Skyrockets do 70 % the usage picks up the time. The original least case that were identified by the stakeholders become just anecdotical compared to the rest of the usage. And that's where we we feel like does provides out value. And it's very hard to know for us what are all those use case because for we have examples of company with a few hundreds of people and a few hundred assistants. And so it's just it's just hard to answer the question, what are the best use cases like? Those are great examples. And that calls to mind an analogy that I would like to try out on you guys, and you may tuk on this analogy, but this is what just showed up in my brain, which was a lot of those use cases you described, you could imagine some sort of vertical application being built around those use cases.

32:47And the analogy that comes to mind is, there are gazillion vertical applications, and yet where does a lot of work happen? Spreadsheets. Why does it happen in spreadsheets? Everybody knows how to use a spreadsheet. They're there. They're flexible. You can customize them to your heart's content. And so the knowledge that I'm wondering about is this almost like the spreadsheet of the future? You know, some of these applications may get peeled off at a vertical specific applications, but even then people are still gonna come back to the personal agent because it's just, it's there, it's available, it has access to your data, it's familiar, you know how to use it.

33:23You can build what you want quickly and simply and effectively. Like is that a reasonable analogy for what this kind of It's an amazing analogy for another thing that I'm thinking about, which is it took me the longest time to get Stan to spreadsheets when we started working together. And this is way back when this is, this is like, I don't know if it was 20 years ago, 15 years ago. And then at one point Stan uses it for something and is like, oh wow, this is kind of a cool, replant interface where you can just get the results of your functions in real time. And I was like, yeah, that's now the worst thing you say.

33:55Here's like, it's a cool, replant interface for non -engineers. I get it now. And yeah, I think it's also interesting for that. the experimentation cost is very, very low. You think about the way in which some of our customers try and describe the gains that they're experiencing or that they're seeing and their excitement for the future is some functions we've had 80 % productivity gains. Some functions we're seeing, 5 % productivity gains and we're not even sure that we're measuring them right. But we're seeing gains when the specialization of the assistant is close enough to the actual workflow that is able to augment.

34:30The distribution problem of that with a verticalized, a verticalized set of assistance is almost impossible to solve. How are you going to get that deep into that function at a time where budgets are tight decision -making on which technology is going to be a fit is sometimes complicated when sometimes that's where the performance gains are the most obvious. One of our users has seen like 8 ,000 hours a year shaved off two workflows for an expansion into a country where they decided not to have a full -time team. And so basically, sparingly some of the boring details, but like the ability to review websites, compare them to incorporation documents in a foreign language, have a policy checker that was making a certain number of checkpoints very clear to the agents that were reviewing the accounts, all in a language and in a geography that none of these people were yet familiar with because they were really exploring the country.

35:24And immediate gains, like very, very easy iteration on the first version of the assistant, two weeks to launch it into production, roll it out to three human agents that were then assisted by these assistants, and their CTO sharing, like, you know, we're seeing a north of 600 hours a month. I'm thinking our pricing's terrible, but what I'm excited by is that that case could not have been explored or discovered with a verticalized sales motion. Because I just don't know how you get to that fairly junior person in a specific team and actually are able to pitch them and deploy that quickly. Whereas if you have that common infrastructure that people understand the breaks of, not everybody knows how to do some products, not everybody knows how to do a pivot table, but everybody understands that they can just play around with the basic things and probably get help from somebody close to them.

36:17That's the other thing we've seen. the map of builders within companies, this heat map of people. What some means about it is that it's people who are just excited about iterating, exploring, and testing new stuff, which I think correlates well to high performance or high potential in the future. It's like, dust is heat seeking for potential and talents across your Palestinians, because the people using it the most, are people who are the most comfortable saying, I don't feel threatened by something that's gonna take the boring and repetitive side of my job away from me. I'm excited to have that go away and focus on the high value test.

36:51I think for this first six months, I was one of the loudest voices saying, what is that main use case? Did you guys heard many, many times? And then eventually I realized, like this is a primitive, we're talking about spreadsheets. You could talk about, frankly, a word document. You could talk about office suite. When I interface with dust, I think about it like slack. Except I'm not slacking my colleagues, I'm slacking assistants. And they actually do this kind of work for me. And I can show them the kind of work. So it feels, Pat, to your point, something like a spreadsheet meets the ergonomics of a slack as it It's brought to me as opposed to I have to go to it And that that is it took me a while to get there and now I see how the fragmentation is the power of what you're going after Well, thank you, bro I have a quick question on sort of the psychographic of your user because you're You're common that it's like heat seeking for the people who are sort of ambitious and innovative and stuff like that I don't know if you have a name for them, but let's call them the makers, the people who are not afraid to try new things and try to build stuff.

37:58Have you come up with a systematic way to find those people or do they tend to find you through word of mouth or some other thing? Because that's not LinkedIn profiles. Don't say, you know, Gabriel, maker, right? Like, I think it's a super interesting question at a couple of levels, but our mission is dual, right? So the things that predict a great outcome with dust, I'm coming out of a core and trying to think about what was most powerful about this call I had yesterday with the Chief People and Systems Officer of the company that could not stop interrupting the five minutes into my pictures.

38:30Yes, I did the talk on this. Yes, I've already read about this. I've got a blog post on this. Okay, when can I demo? Where do I put my credit card? That's calling you next week. And it's the top -down motion is enthusiasm and optimism about this technology changing most things for most people who spend most of their days in front of a computer. You need that. That's a necessary condition, because I think it unlocks three things. One, it unlocks the belief in a horizontal platform for exploration, the ability for security to be in the support of business rather than a blocker. And genuinely, sometimes, example setting.

39:05Like, we have founders and leadership teams. They're just like, how have you augmented your own workflows last week? And leadership meetings are being asked, they're doing off -sites, It's about like how are you going to get better at answering to some of your team's queries faster with us? So once you have that, then you have the right sandbox. I'd say that the right petri dish. I don't think we fully cracked the builder identification. So right now it's more like bait. It's like the products is incredibly easy to use. Anybody can create an assistant, even if they have not been labeled a builder by their organization.

39:38And it's just the sharing capabilities of their assistant that are somewhat throttled. But we can see from the way in which people explore the product, create assistance for themselves, share them with their teammates in a limited way, a great predictor of that type of personality. And if you ask me to look at LinkedIn and predict who are going to be in that family, I'd say the number one discriminator is somewhat to a degree. It's a bit ages, but like people who are maybe earlier on in their careers, who have a mix of tasks that they obviously know they can get an assistant to help with. So they have use case one just laid out for them, people who have repetitive tasks, and people who have scripted their way out of a lot of repetitive things before.

40:21Just to be explicit, we had the conversation, I think it's okay to say, it is people under 25. We were saying yesterday the power users, the people that are using this all the time at the companies are the people under 25 because they aren't set in their ways, just to be explicit. And that doesn't mean everyone. You can be 70 and constantly innovating in a new way. But in general, they don't have the pattern that they've been set to. And by the way, that's true of a lot of the next generation of productivity. Notion, which Pat works really closely with. That is a under 25 power law type business.

40:54And you know, the teammates here under 25 keep pushing me to transfer over to Notion. And it's just a different type of thinking. It feels like a very similar motion at dust. Yeah, I think that the one thing we had, the we have, which is useful, is that the immense speed of see success of ChatGPT as a now obviously world famous product has made it really easy to set up pilots by just telling teams, do you know what, send a survey out? Ask people how often they've used ChatGPT for personal use in last seven days, like rank by descending order and that's your pilot team. That's the people you want to have poke holes at, kick ties, because we've asked the entire world to move from calculator technology, punch the same keys, you'll get the same result.

41:45To stochastic technology, ask the same question, you'll get a slightly different result. This has not happened. This is the biggest shift in the use of the tools that we have since the advent of the computer. We're asking an entire cohort of the workforce to move to a stochastic mindset. And the only way you get that is by having a risk reward ratio that you're comfortable enough. You know what? I'm not asking it to be right 100 % of the time. I'm asking it to give me a draft that saves me time. Many, many, many times over. And that distribution of ROI is something that I'm comfortable exploring with and it's rating on.

42:17And I think that that is really one of the predictors that we see in people who've tried chat GPT or in people who are just curious with new technology is they expect that some of it's going to be a bit broken. but the upside scenario to them is so clear and so 10x that they're willing to make that trade -off or that local risk to get things started. So you guys have a lot of very strongly helped beliefs, internally and externally, and the good news is you've consistently been right about the strongly helped beliefs. You've named a few of them. I mean, you've talked about this shift from deterministic to stochastic way before it was mainstream.

42:52You talked about rasterization and vectorization. I think about that that can be unpacked if you'd like. It certainly would need big unpacked on the show. If we go down that rabbit hole, you talked about no GPUs versus PMF, right? Can you just walk through some of the beliefs that dust lives by? It can either be philosophical as a couple of these are or tactical, like the no GPUs before PMF. Yeah, the first one is really the continued belief that focusing on products is the right thing to do because it really feels to me like we are only scratching the surface of what we can do with those models.

43:34Right now we are starting from the conversational interface so that's why you use the Slack analogy and I really truly believe that that analogy, the Slack analogy, will not sustain in time because the way we interact with that technology will change is started with the conversation interface, but it will hand in a very different place in the machine. Basically, those models are kind of the CPUs of the computer, the APIs, and the tokens are really the bash interface. What we're doing right now is merely inventing bash scripts, and we have yet to invent the GUI, we have yet to invent multi -processing, and we have yet to invent some new things.

44:14we are really at the very beginning of what we can do from a product standpoint with step technology, whether it evolves or whether it stays like it is. One word that I think is going to be important and I feel recent news has actually helped confirm or is an interesting new drop in the bucket for is one of our product models is augmenting humans not replacing them. And it's not just a naive version of saying like we're not here to get people fired. It's really that we think there is a tremendous upside in giving people who will still have a job in five to ten years time, the best possible exoskeleton, and that it's a very different kind of company and kind of product conversation to be like, all right, how many dollars are we going to take away from your op -ex line next year versus this is the number of latent opportunities that you are not able to explore as a business because your people are dragged down in pushing like, stale slideware around or not even knowing what dependencies they have on the rest of the company.

45:18This is how much friction you've imposed on the smart people you've spent so much money hiring because half of their day or part of their week is spent doing things that we should literally not be talking about in 2024. So that's one. And the thing that comes back to the... It's the trooper. You've been saying that from the beginning, Gabriel. And in the beginning, you didn't use the word productivity. Like, you didn't want to use the word productivity. I want to know if that shifted and if so, the nuance around why you chose not to. I think productivity, there's two terms that I was hesitant on.

45:49Productivity to me sometimes feels like an optimization when there's two ways to be productive. There's doing the same things faster and there's doing just better things. And I think the mixed effect of productivity is enshrined in effort versus impact. At the end of the day, your boss is never going to be mad if you spent no time doing the things you were assigned to do but brought in the biggest deal for the company. Nobody's actually going to make any comments on that being the bad decision because I think the more you grow in your career and the more you're close to the leadership of the company and the more you realize it's not about the effort, it's really about the impact.

46:23And the impact comes in sometimes unplanned, hyperplanaried completely like left field ways where it's like, of course we need to focus on this and it's current hindsight but you need to free up time -space energy and mental cognitive space for that. The other one was Enterprise Search. I just feel like Enterprise Search is one that we didn't want to put on the website because retrieval of information is obviously a use case that people are very excited about very quickly. But we're just very convinced that looking for the document is a step that people are not particularly passionate about. Nobody wakes up in the morning and is like, I'm so happy that I'm going to get just get the right document the first time around when I do the search.

47:00People just want to get that job done, and it just so happens that using context from three different documents across seven data silos helped them get it done faster or better. And so I think the search bit is just, it's never the job to be done. Nobody really wants to search. They want to complete. They want to prove. They want to test. But the search bit is a step that we think will get abstracted and go back to Stan's point. I think that the interfaces and the experiences we have with this technology will sort have really tried to forget about what the original data source was quite fast potentially, once we've gone over the trust hurdles that exist today.

47:36The thing that this all comes back to is collaboration, collaboration between human and non -human agents. And I think projects by Anthropic are an amazing example here. We thought about co -edition last summer, we have an amazing intern from MIT with us last summer, and who spent their time working on a co -edition interface. How do you chat to an assistant to make something that you're thinking about better, whether it's an app or a project or a document or a script? And this is something that obviously the recent release by Anthropic has made very palpable to many more people. That is to me the interface and the interaction that we need to get right.

48:19And that will be in the future. So we say augmentation and we'll stick to it because I think it really helps us focus on the interfaces that help humans and non -humans make progress faster. It's gonna be about proposals. How do I get to have a human in the loop with a proposal that's written just in the right way to decide if we swipe left or swipe right on it? It's gonna be co -edition. How do I have the language of the human in front of the assistant be as easy to interpret and as you know, full proof as possible for the final project to move into its final form as quickly as possible. And so you need that interface, that interaction between the agent and the human.

49:02And you forget that when you replace too quickly. When you're focused on just replacing and removing, you've built something that is fire and forget essentially. And you'll see the gains, you'll see the dollar gains. But if you've automated 100 % of your customer support tickets, you still need the insights from what people are pissed off about. You still need to understand and have your finger on the pulse of why people are stuck. Otherwise, you're slowing down your product development efforts. And product development efforts today live and die by some of the comments that are coming in from support tickets.

49:35And so how you've made that problem go away and become like, actually, maybe cheaper, sure, but also virtual and harder to connect to, is not, I think, a super long -term view of how your product and business is going to serve your customers best. Because you still need to think about the ultimate interfaces that are going to enable the decision -making to make it better and strategic and the best option for your customers in the future. So keeping the human and the loop always. I mean, it is, human lives one way to stay at, but it is driven, this human driven. like the whole point of all of this technology that we are building is to serve humans better.

50:13And as soon as you remove that, you've made a terrible mistake because someone else was not going to do that and they're going to actually have a better experience with customers and employees and stakeholders. And then they're going to win. Obviously, the scenarios in which you're going to catch me and you're going to be like, this one, we know that humans get it wrong way more and so we should obviously replace it. And this is a complex and nuanced problem. So I'm sure there's certain areas of it where pure replacement has fully understood non -external, like with no -negative external value.

50:48But I'd venture that we're pretty poor at modeling where value is created and how it's funneled through the parts of our company today. And economists have been graded showing that when you don't price negative externalities well, we end up in a messy situation. And so this is the question that I post to leaders who are asking, what should I automate first? I'm like, well, I don't know, which parts of the company do you worry about the most? And often I just find that CEOs are panicked about what their customers say on support tickets. And so making that problem go away, making that problem less visible, might be great for some obnox conversations and your stop price could have unforeseen consequences if you haven't funneled it through in the right places.

51:27But also, I think there's so much more to do than to shave 3 % off your balance sheet. The spectrum of opportunity that you're giving your team, if there's technologies in their hands and if they're able to come up with ideas, is broader than just firing people out of their job. I'm not saying you shouldn't do that. I think I don't want dust to be perceived as naive in this ecosystem where the disruptive of nature of this technology is going to take some people's jobs away because those jobs were currently being done by humans for lack of a better alternative. I think in certain situations you could see those jobs as having been created because we were waiting for the robots, having been framed in a way that was because we were waiting for the robots.

52:13But I don't know that that's what leaders of companies are excited by. I think that the upside, the future, the way in which we need to be resilient, anti -fragile for what's to come and what our competition is going to come up in. Those are the ways in which energy and support I feel should be fueled to support teams. You guys, second time founders, you started your first company over 10 years ago. You were an early acquisition of Stripe. You guys were there super early on. What have you learned and done differently this time as second -band founders? I think really understanding that a few explosive bets are more likely to get you anywhere meaningful than over optimizing too early on on something that is still meaningless in the market.

53:02That's one thing that I think we think about differently. So like exploring versus exploiting and all those frameworks. That's one. I think the transparency that you get the trust and empowerment that you give to your team is, I don't think we were against it. It's more that we were clueless about how much more empowering you could be. So the idea, one of the best words from my stripy is was paper trail. And it was, you had two people in a card or have a conversation and then one of them would take the time to just write a paper trail in Slack or in a document. So you know what, we just had this exchange and we've moved the needle in this direction.

53:43And it saved end other humans, the time and effort to go in a meeting room or figure out that this decision has been made. And it feeds a graph network of trust and respect for your co -workers, that is, I think, second to none in how you can then just achieve more as a team. So, culturally, you need to sort of push that to begin with, because especially people who are earlier in their career will not always feel comfortable with how information should be shared. So I think that's one where we're examples and point. Big markets that you really believe in for a long time. We love technology when we started our first company like 12, 30 years ago that's like, this is great.

54:24This is amazing. These are QR codes. Everybody's going to use them. And it's like, no, we have to wait for a pandemic to sell QR codes. Okay, we'll do that next time. And so like, rolling falling in love with the technology and not really fundamentally understanding how big the business could be if it's successful and asking that question early in Unabashedly is one thing that I feel is different. So what do we kept? Our experience together. I think it's an advantage to having built a company with a person because you've explored everything. You've explored the beauty, the terrible, the joy, the pain, and you know pretty much the entire API in and out.

55:01And so that makes the animals a much more efficient co -funding, I mean co -founder interaction and co -operation. I think it's a really big and fair advantage. I think the biggest one that I think is completely different from me and that Kebra mentioned is about empowering people. It's really as a founder, it's not you. I mean, it's not to you early and it's to you to build them to build the initial spark. But then for the sake of the company, you are not the one that has to build. the other one that has to make, create an environment for people to be in public, to build those things, and explore, and create new stuff.

55:45And the best value you can give is, I don't like to use that word, that what neither shick was coming to mind. It's not necessarily the evil shitties, really guidance, and trying to create an environment where every is as the chance to do what they want. But yes, in the guided environments where it's everything works as a whole, but that wouldn't be the biggest difference in something that we learned about the electronic striped at these festivals. So guys, let's move to a lightning round. We've got a couple questions for you. All right, lightning round. Question number one, Stan, you share these predictions for where the world of AI is going on Twitter from time to time.

56:22At this moment, what is your top contrarian prediction for where the world of AI is going? And don't give me this by modal a little bit of this little bit of that. Let's hear a point of view. What's your top contrarian prediction for where the world of AI is going? I see it.

56:44It's the lightnings, I have to answer something. It's going to be tough. I think we're on a very, very, very, very tough period. How so? The excitement will go down and maybe it'll take times to get to the next stage of the technology. There's tremendous value to create, but people will not sit yet and it'll take a long time for it to diffuse through society. So there is massive amount of value to create, but it's going to be, we might have tough times in promise. Alright, short term pessimist, long term optimist. I'll tell you that. Alright, Lightning Round question number two. And this is for both of you.

57:20Who do you admire most in the world of AI? Ilya is just incredible. I've had the chance to walk with him. He's my favorite people in AI. He's extremely smart, but he's not a genius builder. He's a genius leader. He's just a visionary. And I think that's incredible. Kaparti, I know him. I actually don't know him, but I admire him a lot. And in terms of pure genius in AI, I think it's Shimon and Jakub at Hoven AI. They have crazy last names all that people look at. But Shimon and Iaku are. I'm impressed by those who've been around for a while and are good. They're acting as good resistance and condensator elements in the system.

58:05They're just providing the friction to remain optimistic but cautiously so. And to me, one of the first, I can't remember if it was a tweet or a podcast or an article, but the hearing younger Karen should be like, you know, we can make pretty good decisions with a glass of water and a sandwich. And these things require power station -sized data sources and are not making great decisions on some things. So we we feel something is missing. And it's like elegant that he putting that back into into into perspective has been interesting to me. I guess it's hard to not cave to the hype, I think. And so in some ways, pushing for a simple ideal like being open, which I think Yann account is doing quite aggressively, despite that not always probably being the easiest decision.

59:02And also saying, you know, we probably haven't solved everything all the time. is nice. And from my personal experience, the researchers that have worked for or with him have learned and taken from that quite a bit. And so that that, and it's not French, but you know, some touch of modesty, touch of temperance. I've appreciated it in my discovery of the generative side of artificial intelligence. Like after 10 years of just doing your prediction and classification from fraud and risk and onboarding its stripe and healthcare claims management and things like that. It's nice to feel like there's some people who've seen a lot, done a lot and are just questioning rather than affirming.

59:54All right, so that brings me to the third and final lightning round question. You chose a for your most admired gay real. And dust is proudly made in France. Paris has been in an epicenter, certainly an epicenter for all things AI. Your take on the Parisian ecosystem, and what do you want to say for the French founders listening to this podcast? Other than I'm starting with English, it's their fault not ours. Yeah, I think the French system is awesome because we compared to where it was 12 years or 15 years ago, it was our first company. No, we have tenants because there's been a generation of scallops that went through the market and train all that talents and most recently that kind of explosion of AI talent as well, which is super exciting.

1:00:46So I say it creates a pool of tenants and with the right conditions to create the incredible companies. Obviously, it's not, I mean, tackling the US market from France is a challenge. And so that's the thing to be beginning to work on, of course. Yeah, I think there's, if you have ambition, there's a lot more to do. And then as long as you're not naive, where there are still some realities. You can fight some aspects of narratives. You can't fight gravity. Or at least you shouldn't. You should probably work with gravity way more than you should fight it. But there's a ton more we can do. And I think we have to behave a little more like tech countries like Israel, I think, in mixing Ruthis Ambition, a recognition for where talent is and how it's already connected and has high trust's connective tissue, which I think is a great catalyst and accelerant in making great companies happen.

1:01:47But a recognition for where the markets are, where people are buying, where people are paying and how quickly people are making decisions on shifting new technologies, especially in that space. I think the biggest advice is as a French funder, if you've always been friends, you have that feeling that something magical must be happening in US. Something special that was being something special about those people. Well, I'll tell you, I've been at Strife, I've been at OpenAI, I'm working with Sikko Yaw. These are all our normal humans. You don't have any magical capabilities. it is just like us.

1:02:21And it's very important to be ambitious and believe strongly that you can make it, you can do it wherever it is from France versus the US. Wonderful. That's a good place to end it. Thank you, gentlemen. Thank you, guys.