Uncapped #52 | Mike Volpi from Hanabi Capital

24:25And the big dollars are still in the front end of the curve. So I think there will be. The asterisk there is that as if you're creating an open source model that's very close to the front end, you're by definition using a lot of compute and you need to have capital. You need to have money to get that compute. And you're seeing even the most staunch open source firms not be so open source anymore. The latest Quinn model is closed. MuseSpark is closed. That's not a coincidence. They're spending a lot of money training those models. They're not just going to like open them up. Do you think the frontier labs will stop allowing third party to use their frontier quite as much because of the compute crunched stuff?

25:07It is likely that that will be the case. Let's say that that happens. Then what are the impacts of that? If, you know, the if the big labs start to say, hey, you can sort of, you know, you can access our old models, but the new stuff is only first party. Well, I mean, what it'll do is it'll cause the open source stuff to be even further behind. And by the way, temporally, you can have blips. So like, you know, Jensen can get the idea that he really wants an open source model. so you can go to Reflection or whatever and give them$10 billion. One generation, they can catch up and then they'll fall behind.

25:34But it's a one-time thing. It's a one-time thing. Yeah, I don't think it's continuous. I do think that you'll have, I mean, open source will be relevant. It's relevant in one more context, which is at least in today's world, and I don't know if this will sustain, but in today's world, if you post-strain a lesser model enough, it will perform a small number of tasks just as well as the big models can, right? Essentially, open source can exist as the underlying layer of companies that are post-training them for specific personalized or business or corporate use. That exists. And there may be a business model somewhere out there.

26:12If these post-training kinds of companies are quite successful, that they can provide financing for open source to be closer in to the big models. But open source out of the kindness of your heart, which was the Quinn, the Deep Sea, Kimi, Lama. Doesn't make sense. No. I mean, who's going to go spend$50 billion to give it away? I mean, it just doesn't make sense. It made sense for a minute. Or what do you think was the argument for it at the time? I don't think people forecasted how much money it would take to train these bigger models. Yeah. I don't think people expected it. I think, you know, decisions like why was Lama free?

26:45My guess is the folks at Meta looked at all these people and said, we can't exactly monetize this, but we'll try to commoditize their market by putting out something that's free. And I think that that's passed. That's passed, clearly. Yeah. I mean, it seems like once we saw that it was tapping into labor budgets for real instead of like software spend at that point, it's not really about price. It's about value. Yeah, exactly. That's the open source side. And then what about the Neolab side? Because that's like obviously the other potential vector here. And I would argue that's probably like one of, if not the hottest venture segments right now that is getting backed at, you know, huge dollar amounts, huge prices.

27:22Like what's your read on why that's happening? Yeah, well, we largely have not invested in Neolabs because of my thesis that the big labs are going to win. It is difficult to imagine that there will be an algorithmic improvement that will allow a new lab to be competitive with the capital that the big labs are throwing at it. Not to mention the fact that the big labs, they're not sitting there clueless, like thinking like, oh, yeah, there's no other algorithms here. They're doing their own research of what the next generation will be. I don't see a scenario where there's a more clever architecture to a model that outperforms dramatically the big guys.

28:00Now, there are a couple of areas that I find somewhat interesting, and those usually sit in pockets where there is a set of proprietary data that the model can be trained on that is not available to the big labs. So if you think about the core of the training that happens for the large models, it's the Internet. The internet and the internet is largely available to everybody. If there's pockets of the internet that are not available, you can pay for it and open it up and get that stuff. Any model that sort of bases its training on internet available data is likely to not be as competitive with the big labs.

28:36However, there are pockets where you need or have proprietary data. We're investors in periodic labs. Why is that interesting to us? They create their own data through their own labs and they do training and reinforcement based off of that. We are interested in robotics. Why? Well, robotics data is not broadly available on the Internet. However, you can either tele-op it or have remote workers create data, but you have to basically generate the data that your model trains on. So long as that data universe is reasonably private and not available to everybody else, then the people who have the right data strategy can actually stand out.

29:17So there are pockets where I think different kinds of models can be used for different kinds of orthogonal directions than the classic core models. But the Neolab that comes along and says, you know what, I'm really good at RL. Right. And, you know, because we do RL better, we're going to beat the other guys. And I'm like, you know, I got the memo on RL. They didn't miss that memo. Spend a lot of money on our own. On the robotics example, let's say, so, you know, just to run with that, like data is like the thing that could differentiate it. How do you then think about investing in, let's say, scale for robotics data?

29:51That's going to be a data company selling to the big labs versus I'm going to invest in a new lab that is going to get that data. Or do you think both are workable? As a longtime investor in scale, what I would say is the business of providing labeled data to large labs is a tough business to be in. At scale, we were there early and we served the labs well. But every single time a new contract will come up, it's a dogfight. It's scale, it's surge, it's Mercor, it's Handshake, it's Turing, whoever it is. Because ultimately, the job of annotating that data isn't that hard. It's a low barrier to entry job.

30:32And what the labs want is that data provided to you at low cost. I think essentially the same will be true for robotics data that is provided to the labs. It's going to be very competitive, low barrier to entry. And it's going to be largely dependent on who bids lowest. So then what would you need to believe to back a new robotics model? One is the architecture of the post-pre-training is important to understand in robotics, because generally, like all other things, if you do heavy doses of post-training and robotics, the robot will do one task really well, but that doesn't generalize. In order to do have robots that generalize, you need a lot of pre-training data.

31:11And that pre-training data is very important on the specific embodiment of the robot. So like what physical shape that robot takes is very important. So companies that generate a lot of their own pre-training data, often created in-house, is actually a material differentiator, in my view. Purchase data is nice, but not a material differentiator. So if your robotics company says, I get all my data from scale, I get all my, not that you should buy some of that data. Please do, because I'm still on the board at scale. But I don't think that's the differentiator. Differentiator is how and what kind of data do you collect in-house.

31:45And you don't share with anybody else. And you created largely, I think, around embodiments which are explicit to your robot, your architecture, particularly since the hardest tasks in the robotics right now are largely manipulation tasks or dexterity tasks. Like, what kind of gripper do you have? Is it three fingers, four fingers? Is it a little claw? Like, those things matter a lot in the pre-training. Do you think a company that makes robots would be a good source of that data for themselves? Like, is that a good way to back in? And like if somebody's building a robot, they have them out in the wild.

32:18They're presumably collecting a lot of potentially proprietary data. Like, is that one of the vectors? Absolutely. Yeah. I mean, one of the advantages I'm pretty convinced that Elon will have with Optimus is that he has data collection mechanisms all over. I mean, look at self-driving. It's just like once you're in the wild, you start getting the flywheel going. Absolutely. And, you know, we're investors in a company called Mind Robotics, which is the robotics spin out of Rivian. Same thing. Their own factory will be their data collection instrument, and they will have a proprietary funnel, which will be materially differentiated than other people.

32:49What's your read on the overall compute situation? So like one framing of this is that basically like the demand for AI is just going absolutely vertical much faster than compute can get online. What do you think is like the likely ways this plays out? You know, if like the labs, you know, Anthropic adds 10 billion of ARR, you know, a few more months. It's just like, how do you think this ends up shaping, you know, maybe down through the stack? You know, we were talking about the labs, but like, what will the ripple effects be? And like, how do you think about this dynamic if demand really is surging at this rate?

33:18It used to be that the demand for GPU compute was largely on training. Now with this explosion in inference, you have the twin effect of bigger models being trained and tons and tons of inference running on the same systems. So that means that the load or the demand for compute is kind of skyrocketing and it's likely that it will continue for some time. The supply side of that is basically TSMC wafer starts. Yeah. There's only so many of those. So anybody that bought compute historically probably got it cheaper than anybody that will buy it in the future. And I think one of the very smart things that Open Day I did is they bought a lot, a lot, a lot of compute ahead of time.

34:04As a result, I think Google aside, because we talk about TPUs in a second, but Google aside, they probably have the lowest cost of compute of any player in the market right now. Yeah, there's the price of it. And there's also just the availability. The availability. It's just like, you know, money doesn't get you more wafers kind of thing. You just got to wait. Yeah, exactly. On the other hand, the value, particularly on the inference side, that the value that people are seeing from the inferencing of these models is very high. Like, you know, you sit there and you do a spreadsheet and you ask Claude to do the spreadsheet for you and you go, wait a minute, I'll pay triple the amount of money that I'm paying for.

34:39So in some sense, we're getting into the fact that you'll be able to sell that the output of that compute at a much higher price point in the future, given how productive and useful it is. Today, we live in a world where NVIDIA is the gatekeeper to all compute. Again, TPUs aside, because that's a whole interesting side conversation of what Google decides to do with TPUs. I don't see that sustaining. And I do see more specialization in the kind of compute. You don't think NVIDIA's dominance will sustain? No. What do you think will happen? I think you're going to have compute that is orient towards specialized tasks that are not necessarily dependent on NVIDIA.

35:15So for example, I'm a big fan of Cerebris. Totally. Why do I like that one? It's not a particularly good training chip, although Andrew will argue with me that that's not true. But, you know, let's leave that as where it is. In inference, it's a godsend. It's super fast. And it's made certain compromises that make it very, very good for that. I think that's one. I think we're going to see more of that style of architecture that's very good for inference, for example. I think you're going to see more of these. There's even further evolutions of, you know, back in the old days, we used to call them ASICs.

35:47But if you look at etched, or Talus. These are companies that are fundamentally baking in some parts of the neural network weights into the chip itself, which will make it even faster than Cerebris, even less flexible than Cerebris. So I think that you're seeing this evolution towards mission-specific silicon rather than general-purpose silicon. And that will happen and will take the load off of everything being NVIDIA. But I do think that we're going to do a lot of work in that bottom layer of the infrastructure to liberate ourselves as an industry from a stranglehold of chips. Yeah. I feel like the U.S.

36:23probably can't invest enough fast enough in this. Already a bunch of the GPUs are being built in Arizona. I think some of the Cerebris chips are made in Arizona. I know Intel is trying to come online with their fabs to do independent silicon. I don't know that that's still worked yet, but I do think we're going to see a huge amount of capacity. And then obviously there's a geopolitical issue of Taiwan. And, you know, I can't imagine that that same situation will be true in five, six years. That would be great. Yeah. What about now flipping to the other side? You know, we've been at the bottom of the stack, like the application layer.

36:57Obviously, over the last few years, there's been a bunch of amazing application companies built. Do you feel like there's durability there? And if so, what do you think the source of it is? Or, you know, what are the places that you think are risky? I'm not talking about pre-SS. I'm talking about like an AI native, you know, seeming application. I guess let's start there. In a given business, there are two things that are fundamentally proprietary. One is we talked about data. Every business has a bunch of data and it's their most relevant data. And the second one is business workflows. So how you do and conduct business.

37:28I think companies that can capture the data and the workflows that exist inside of a business or in a given industry vertical that the big labs are unconcerned about or don't have access to those things can survive as application companies. If your basic concept though is, I take a document, I send it over to OpenAI API or Anthropic API, it comes back and I show it to you this way. It's not enough. That's not enough. What will also happen is over time, the OpenAI's, Anthropics, and Googles of the world will look at the most interesting TAMs and saying, wait a minute, I'm going to do that. So there's Anthropic for legal or Anthropic for finance or whatever.

38:17This is not their core business. So they're going to have varying degrees of success. But it'll do 80 % of the job. But it'll do the damage. Business verticals, like if you're in construction, there's a bunch of workflows in construction that it's not likely that OpenAI or Anthropic or Google will care about. Also, I personally, I don't know, maybe this is something I need to let go of, but it would be surprising to me if people no longer wanted UIs. But we'll see. If everything just became chatting. No, I'm in that camp. You know, it was interesting. When you were talking to Brett, he was talking about like systems of record and how systems of record are important.

38:51I actually think one of the reasons why Salesforce sustains is because every salesperson knows how to use Salesforce. So actually the human interaction with the system is the single highest moat that anybody has from breaking in. That might change over when agents do the work versus humans. But as AI pilled as I am, I think that takes a while. And so that retains some, the UI does retain some level of sustainability for some period of time. What do you think about AI, like services as software, fully do the work type of things where, you know, instead of selling you, you know, accounting software to an accounting firm, we're just going to sell you completed accounting work kind of thing.

39:31Like, do you think that's where stuff ends up? Are you more excited about that? Are you more excited about the software looking companies? Well, I answer the question two ways. One, I think it's a high bar today for AI systems today to actually offer accounting as a service. You may not need the entry-level, lower-level, white-collar work, but you still, in today's world, need a lot of contextual and other forms of knowledge that accountants have about their clients, about the systems, about how all things work. So I think we're still in an era where purely delivering a sort of service from AI with no humans involved.

40:11I think we're a little early for that. Particularly because in some of these, you need the verification of a human stamping. It is actually part of the value. I think customers obviously will want some human to have supervision over it. And then humans, we have some very interesting, sporadic contextual knowledge about things that's actually surprisingly helpful. To the point earlier about VCs and what makes us different than introducing you to customers and employees. There's just like random little bits of knowledge here and there that we don't know what it's going to be. I think that exists and persists for a while.

40:42I will say, though, that SaaS business models, like classic product models, are going to go through a very important transformation, which is when you spent a lot of money developing a piece of software and then sold it to many, many people, you architect your business to be a product business. And VCs for years have looked at things and said like, oh, I want a product business, not a service business. But now if the cost of software goes way down, I think you go in one of two directions. Either you're a low-cost provider, you're like the Amazon of software, low margin, available to everybody, good delivery and so forth.

41:15Or what you offer is a more customized edition of the software that either has agents associated with it or is really plugged in and integrated with your system in a particular way. I think what that has done is sort of recast, you know, 10 years ago when I was doing VC and we said like, oh, they have a professional services business model. you're like, oh, no, no, no. Pass, pass. Not that one. Now they're like called FDEs and they're really cool. Palantirite are cool. Super cool. And the interesting thing of, you know, what do I think of an FDE is it is a job that is a go-between between a business problem and a technical problem.

41:53That's the fundamental value of an FDE. Now your business problem tends to be pretty specific to your business. Your technical problem is something that's built on a stack and structured and so forth. And somebody needs to say, Jack, I understand your business problem. This is the way how we solve it. And that's the software company of the future. In some cases, you will solve it by populating a framework or an infrastructure with agents. In some cases, you still plug in a human here and there. But that's what a software company of the future, I think, looks like. And one of the interesting things, just from a business model perspective there, is these deal sizes are so gargantuan that an FDE just doesn't matter for the for the COGS relative to the compute costs and other things.

42:33If you're doing a 10 million dollar enterprise deployment, who cares? A hundred percent. But, you know, keep in mind, like I started my career peddling routers and switches. Right. And that's when you used to go to the network engineer on the other side. It goes like, well, my switch has 24 ports while your his only has 16 ports. And therefore and like for that difference, you could charge$100 ,000 a year. But when you're talking about the CEO of T-Mobile saying like, I need to reduce my churn by 2 % and you do that for them, that's hundreds of billions. That's right. Right. And no surprise, the contract values that these people are willing to offer are huge.

43:10Why? Because you're actually solving a business problem for them. You're not providing them with the technology. And that's why the FDE stuff makes so much sense. So that's a bit about like the sort of an native stuff. Just quickly, I'm curious your sort of pulse on the pre-AI SaaS. The sort of public software companies have obviously gotten, you know, hit in the last year or so. And every time the labs release some new thing, you know, it's like the stocks get traded down further. Do you think it's like oversold? Do you think it's undersold? How do you feel about it? You know, I'm not a very good public investor, so it's hard for me to say if things are over or undersold.

43:43But I do think all of those companies have like one of two paths forward, right? Which is one is hang on to what you've got, grit your teeth, fire more people, increase your EPS, and just, you know, do what you're doing. Profit available for the duration. Fire half your staff and increase the EPS. Private equity, essentially, to yourself. But there's going to be a set of companies that will be able to embrace the AI thing and do kind of a transition of their business into more of an AI-centric business model. I don't think the market has really differentiated that because if you look at the multiples, everybody's sort of sitting at five times, four times.

44:23And also everyone's saying that. Yeah. Yeah. And I mean, look, I think there's a big difference between, I don't know, Figma and Workday. Yeah. Right. I think the chances that Dylan's going to figure something out are much higher, a lot higher. And I think his, well, I'm an interested stakeholder, obviously, but I do think that some of those stocks have been oversold. Now, I don't I can't tell you if they should be trading at a 12 times multiple or a six, but it just feels a bit oversold right now. given the capability of the founders. The beautiful thing about founders and leaders is that however the business looks today, it doesn't have to look like that in five years, right?

45:03If Tesla traded at car company multiples, it would look different. It would look very different. The reason it doesn't do that is because people look at Elon, even though - SpaceX will be the same, of course. Even though there are no cars that are, well, I mean, there's a handful of cars that are self-driving and there's no robots in the market. People go like, He will successfully transition the company at that stage. Therefore, the company is worth more. I think SaaS is like the car business right now. Everybody thinks the car business sucks. Everybody thinks SaaS sucks. Some of those leaders will be able to transform their companies by hook or by crook into something like what Elon did.

45:37And I think you have to look into the management of that company and say, do they get it? And then I think you'll see differentiation of the good ones and the bad ones in the SaaS universe. I completely agree. And then how about outside of AI? Is there anything that you're investing in that's not AI? Right now, it's like 90 % plus AI, but I would say defense tech is an interesting area. In defense tech too, there's a lot of AI mixed into it. Sure. But I do think that we're seeing geopolitical shifts that are reasonably permanent for some time. Take, for example, the dynamic between the U.S. and Europe and defense spending.

46:22I don't see European defense spending being reduced. It's probably going to be dramatically increased. And so there's opportunities there. It's the right tailwinds. I do think that with companies like Anduril and to a degree Palantir being successful at breaking into the market of selling into the U.S. defense infrastructure will be an inspirations for both sides. I was going to say it does both things. It both it unlocks capital and talent, but then it also sort of warms up the DoD to believing that, you know, these companies can work with them and take their understanding seriously and all that.

46:55Absolutely. And I don't think that's going back. You know, this sort of like the, you know, Pandora's box has been open on that front. So I do think that that's an interesting sector. You know, it's interesting because there's always these philosophies like, oh, do you invest in weapons and not invest in weapons? And when I was at Index, we didn't do defense investing at the time. And I remember looking at Anduril and thinking, this is going to be a winner. And I had a conversation with Trey Stevens. And I was like, hey, I don't think we can do this because we don't do weapons. And he was like, I don't think you get the mix here.

47:27weapons are a system that deters violence because the more weapons one side had as the other side have it, the less likely as they are to go to war. Now, this president has disproven that. But leaving that aside, I think the logic was pretty strong. And so now I'm more of the belief that I think the right kind of defense investing is the right thing to do. Yeah, I think so too. Before you joined Index, you were like a founder, you're an operator. Do you feel like that mattered looking back? Like, was it valuable to you to have done that? Do you think, would you have been an even greater investor had you just been an investor the whole time?

48:03Or do you think that if you sort of like think back and reflect on those chapters that it, like the net effect was that it made you a better investor than just more experience would have? First of all, I really enjoyed being an operator. It was super fun to be at a fast growth company, to see things explode and grow. You know, I joined Cisco. We were a few hundred people. I left, We were 55 ,000. Oh my God. You know, like. In how long? I was there for 13 years. Wow, that's crazy. It grew to that size in about 10. Wow. And by the way, you feel somewhat like you participated in something that's reasonably momentous.

48:37Like, you know, I got there when nobody had the internet and because of the products that we made, everybody got the internet. Wow. I mean, that's only, you know, you count the companies on one hand that have had that head count growth. Yeah. Yeah. Yeah. Not just the head count growth, but the societal impact. of what was created. Yeah. Just because of the pure enjoyment of having been there, I'm happy I did it. How does that, how is that relevant in the context I do, of the rest of my life? I do think that the old Steve job things applies, which is you got to connect the dots looking back. Now that I understand VC better, and by the way, I still get, I still learn every day, but I understand it a little bit better.

49:14I didn't actually realize how important having that card of having been an operator was. Because there's a lot of VCs, everyone's smart. Everyone's got a degree from a good school. Everyone's got a big fund. You got to differentiate yourself, right? Like when an entrepreneur is selecting a VC, I am the product. What makes this product different than any other product in the market? And in my case, having had that experience of growth, recruiting, customers, relationships, all that was enormously useful for me. I'm not of the camp that you have to have been an operator because, you know, you look at Peter Fenton, the biggest thing he's operated was a lemonade stand.

49:58Well, shout out to Peter. Yeah, this is for you. But he is one of the greatest VCs of all time. He is. And so we all, as a venture capitalist. And there's many other examples like that too. Absolutely. I mean, Michael Moritz was a journalist. John Doerr was a sales guy. So we all come at it from different angles. I think the important thing is to recognize that you have to have an angle and that that angle has to be relevant to the entrepreneur. And so for me, having been an operator was mega. But it's I don't I don't view it as like it's a must. It's an approach that happened to help me. And it was most importantly, it's fun while you do it.

50:35Yeah, absolutely. When you think about what types of founders are thriving today, has it changed versus before this AI thing? Are there different mindsets, different types of educational backgrounds, different work experience, different age? Have there been updates that are material to you that change what kind of founders you're interested in? I think the biggest difference is the level of maturity that some of the younger founders have relative to even 15 years ago. There is so much content available, so much peer community available that you take the average 20-year-old, 21-year-old, and they're showing up with so much knowledge compared to a generation ago.

51:15I know. I was 21 15 years ago. And when I meet a 21-year-old today who's like a founder, I'm just like, oh, my God. Like, I wasn't there when I was 27. Dude, I was 21 in the Stone Ages. Yeah, there you go. Two tootons ago. But I'm just like, it's crazy what people know now. And I think it's going, I think it's accelerating because I think, you know, now a 12 year old today is going to be using AI tools. By the time they're 20, they're going to be eight years into this. And it's not just like book knowledge or AI knowledge. There's also a lot of just commercial knowledge. Yeah. What do you think that's about?

51:44Is that from the online content? Where is that from? Yeah. I mean, it's all of the above. You know, we're absorbing content from all sorts of sources. It's peer groups. It's online. It's stories that we tell, podcasts that we listen to. Here we are. Especially yours. That's right. But I think people accumulate knowledge from all these sources at a very young age. It's incredible. It's extraordinary. I mean, it's absolutely extraordinary. Can I ask you an offensive question? Yeah. So all these young founders, you're not old, but you're not a young VC exactly. And you're doing it very successfully.

52:14What is the mindset? Like, what do you have to do to be not a 25-year-old VC? First of all, thank you for calling me old. You're not old. If you were, let's pretend you. Look, I think the challenge that we old dudes run into, especially when we've had some amount of success, is that we think we know better. You've got to approach these things with a beginner's mind because every 21, 22-year-old knows a whole lot of stuff that I don't know about. If you approach the relationship with the like, hey, I'm the all-knowing grandfather. Let me tell you, kid, how this is. Yeah. It's not going to work.

52:54It's not going to work. You just have a conversation with people and you say, that's interesting. Let me give you my first principles answer to what you're saying. I think this is the right approach. But if you don't agree, tell me why. And let's have a conversation as equals. So I would say, forget your age. Treat the person like an equal. And sometimes you might have an experience that you can pull out and say, well, you know, five years ago, I saw this. It might be wrong today, but I'm just going to throw it out there for you to consider. And if they like it, great. And if they don't, let's move on to the next topic.

53:22Right. But it's treating people like equals is the biggest thing. And this is so hard for people to do, because I mean, one of the things I think makes that particularly hard. By the time somebody is, you know, been working for 30 plus years, they've had successes. You don't want to relearn everything. You know, I think it takes a certain mindset to do it. But when you look at some of like the best tech executives, you look at a bunch of the best investors. I mean, I look at like, you know, take Jeff Bezos, take Vinod. You take a lot of people. I do think that if people are able to reset in beginner's mind all the time, it does seem like the most effective people are both extremely experienced and, you know, are able to think from the beginning again.

53:59Totally. I mean, not to get philosophical about it, but we all start as younger people. Maybe you didn't because you're a very confident man. But I started with a lot of lack of confidence, like, you know, can I do this? Will I be able to do this? And then over time, you sort of like, oh, I figured this out. I figured that out. And then you get to be 59 like I am. And you're like, hey, I want to feel confident on this foundation. I thought you were 49, by the way. I didn't realize. Very nice of you to say. I wish I was 49. But, you know, you get on this foundation of self-confidence, which is built on the fact that you think you know.

54:30At that point, being able to tear it all down and say, actually, I don't know, is a very hard thing to do. It breaks the entire structure of what gives you comfort to be the person that you are today. Do you feel like you've got that successfully broken down? Not always, but I try hard. I would imagine it feels good. Like I would imagine it feels kind of freeing. It doesn't always feel good to people. It doesn't feel good to feel dumb. New. New. Yeah. It's very naked, right? It's a very uncomfortable feeling. But it's permission to like explore and have it all be light again. I agree with you if you can get there.

55:05Yeah. But I think for a lot of people, that's a lot of breaking down of layers and layers and layers of stuff that's been built up. I do think it's the ultimate expression of self-confidence is when you can accept the fact that you don't know something. That's what I think, too. I think being able to raise your hand in a conversation with a group of people, say, what was that word you just said? Yeah. Super confident thing to be able to do that. People don't want to. No, I agree. But that's a tough thing for people to go through. Does it change for you the types of people that you want to build your firm with at Hanabi?

55:32When you think about, you know, the sort of dynamics of the market, does it make you want like a wide range of teammates, experiences and ages and backgrounds and all of that? Or do you think it's more about this mindset at the core? I think this mindset is absolutely the core forever. But I do think that a modern day fund is structured with a lot of people that are cohort relevant with the current entrepreneurs. I am not, and I try my hardest to be cohort relevant through the things that we talked about. It's much easier to teach somebody of the current cohort a little bit about VC rather than teach an old dog how a 25-year-old thinks.

56:10Well, Mike, this was a total pleasure. I really appreciate you doing this with me. I learned a ton. Thank you. Super fun. Thank you. Have me back anytime. Great.