Cheryl & Maxine: Okay, three, two, one. Hey, I'm Cheryl. I'm Maxine. This is First Check, part of Day One, the network dedicated to founders, operators, and investors. If you want to be a better early stage investor, this is the show for you. So TLDR, if you don't want to suck at investing, listen up.

Cheryl Mack: Man, like, I'm just so excited to talk to Ash. To the point where when you said he was coming to town and I was like, Ah! Maybe he'll have lunch with me. And for the record, that lunch was amazing. It was very tasty. Very, very tasty. Oh, I meant the company. Oh,

Maxine Minter: um, I was good. I actually, I'm so excited for this podcast with Ash.

Maxine Minter: I think he is one of the brightest people I've ever met and definitely the most inspiring in my early investing career. I feel like I have learned so much from him, everything from the way he thinks about company evaluation, Through to like the discipline around investing through the discipline in life that is required to support investing.

Maxine Minter: Really like just the wide ranging, very thoughtfully considered, well developed opinions he holds on investing. I have found really inspiring. So I'm so excited to have this conversation with him.

Cheryl Mack: I guess he's got like, he's got such a wide range of experience, right? Like early days at AngelList. Then actually investment banker first, something about oils.

Cheryl Mack: I read on his LinkedIn or, uh, your batteries or something. And then angel list and then starting his own fund and, and even like living in Italy and just, you know, supporting companies from, from Europe as well. I think. So just talking to him about some of those things, I'm really excited to ask him like, Hey, what has that been like at the early days when you first started investing?

Cheryl Mack: And also we wrote a book about AI, AI first, the AI first company. Yeah. He must have been investing in like looking into AI from the earliest days before you and I even like knew the concept of AI. So, I think we'll

Cheryl Mack: hopefully get to ask him a little bit about that.

Maxine Minter: Right. Yeah, one of the items on his incredible CV is that he co built Zeta, which was At least I understand the first AI specific fund in the world.

Maxine Minter: And so he was specifically investing in AI and certain applications of AI, way before, um, actually his partner, uh, at the fund is now the head of MIT. And so they were very early in space. Um, so yeah, there's just so many topics. I can't wait to cover up with him. And I wonder what he thinks

Cheryl Mack: about investing in AI now.

Cheryl Mack: Oh, yeah. We'll have to wait and see. We will wait and see. All right, let's get on to it. Let's dive in.

Cheryl Mack: This is gonna be fantastic. I'm excited. I hope you're excited.

Ash Fontana: Yeah. Well, this is the first time I've suggested myself for a podcast rather than being asked.

Cheryl Mack: It was so smooth.

Ash Fontana: I was actually also

Cheryl Mack: surprised when Maxine was like, Hey, Ash has to be on the podcast. I was like, really? We're still in the phase of like us asking other people to be on it, not

Cheryl Mack: having someone like Ash come to us and be on it.

Cheryl Mack: Now I feel like we're basically celebrities. Well,

Ash Fontana: exactly. I liked what you guys are doing and I thought that I might have something To offer people that are in this phase of their career, um, where they're either just starting to write first checks or thinking about it or thinking, really experienced and then going back to writing first checks.

Ash Fontana: So I've done all of those three things and, uh, I really liked how you're approaching the topic.

Maxine Minter: Thank you. I would plus one that. I think you are probably one of the best placed people to share all of the different versions for folks that are thinking about starting either at the very beginning of their investing career or managing other people's money, right?

Maxine Minter: Like that they have just started thinking about funds. You have an incredible vantage point across multiple peaks in the ecosystem

Cheryl Mack: at

Maxine Minter: different moments in history that I think are really valuable. Yeah.

Ash Fontana: I've done a lot of things poorly. So

Maxine Minter: you

Cheryl Mack: learn the most when you do them wrong though, right?

Ash Fontana: Something like that. That's what we like to believe.

Cheryl Mack: So the first question we always ask our guests when they come on is, what is the first thing that you ever invested in? Anything from like a book, you know, or less podcast guest invested in books, uh, to your education, to a stock.

Ash Fontana: I think the most significant investment I made was in mockups for my first website business.

Ash Fontana: And so we had a designer friend and we paid him a bit of money, probably a couple of hundred bucks at the time, which when you're in high school was a lot. And he did these really beautiful mockups for this website we were creating. It was a website where you could buy services related to school formals and running events.

Ash Fontana: And we took those mock ups and like laminated them and then we, we went to sales meetings with them and we sold our first subscriptions, like signed our first contracts, got the first money in the door so that we could then afford to build the website and run the business and everything else. Because, You know, we were both okay at programming and design, but we weren't, our designs weren't that good.

Ash Fontana: So we decided to pull a little bit of money together, do the mock ups, see if people bought it and then go away and build it. And that was the right move. And, uh, but it was, it was a big investment for us at the time and it worked out.

Cheryl Mack: And is that the investment or the company that ended up funding your whole college?

Ash Fontana: That did fund, uh, college for me. That's a pretty good

Cheryl Mack: return

Cheryl Mack: on your first investment there.

Ash Fontana: Yeah, I mean, college in Australia is not that expensive. Um, but, uh, and I got some other help from the government and everything else. But yeah, it did effectively fund all the college, uh, and it was fun. And it's just the experience of going through it, right?

Ash Fontana: It's like, Before content management systems were really around, or when like that content management system space was changing and it's when web based marketplaces were really becoming a thing. Um, and so at the time I didn't really know it because I was just building a little web based marketplace, like a tiny one that really had no exposure beyond Australia.

Ash Fontana: Um, to any significant market opportunity never really became that big, but I realized later by going through the process of building that I understood a lot of other web place marketplaces, uh, a little bit better.

Maxine Minter: How painful was it to build websites in that moment in history? I tried to build one at the same time.

Maxine Minter: Oh, my God. And then when Squarespace became a thing. Yeah. It was just like a true blessing. Yeah. Like, the idea of modular blocks.

Ash Fontana: Yeah.

Maxine Minter: Really. I Not

Ash Fontana: having any graphical interface at all. Yeah. Yeah.

Maxine Minter: Any drag and drop. Any drag and drop. Or any, you know, like ability to work with your own website without being technical at the time.

Maxine Minter: Yeah.

Ash Fontana: Yeah. We were working with the very first version of something called ColdFusion, um, which was remarkable at the time because it was really fast. I think. I think building websites at that point in time, and we're talking like basically the year 2000 or slightly after, was easy until you wanted to do anything interesting.

Cheryl Mack: And then it became really hard. Just past Hello World. Yeah,

Ash Fontana: exactly. Like, you know, if you wanted to add payments, if you wanted to add, Like more complicated listings and different types of media, it became a lot harder. Um, but that's how we evolve is like an ecosystem or that's how like the web evolved, right?

Ash Fontana: It's like you, you wanted to do more. And so then another product appeared and then another one, another one. And all these products became really big, eventually became big companies.

Maxine Minter: Your kind of early investing career in

Ash Fontana: startups.

Maxine Minter: What was your entry point there? And how did you get enamored with it?

Ash Fontana: Yeah, I'd say investing in startups, um, for me began with starting my own startups, you know, whether it was a website at high school or a company I started with two friends of mine about a year after I left university in New York. Um, that was probably what I invested in first, um, in terms of my own time.

Ash Fontana: And then I really started seeing the startup investing world in more detail when I joined AngelList. And I joined when there was about five engineers and the founders and, um, and two and one or two designers. And. Through that, I really started to understand venture capital a lot better. Now, I should say before that, I was really into investing in stocks, like when I was in high school.

Ash Fontana: I was really into value investing and understanding companies from their fundamentals up, um, rather than, say, macro down. I was really into that. And I had explored the idea of going into venture capital because I was in, initially I was meant to do investment banking, but I very quickly got placed because of my interests and sort of background knowledge into more private equity in technology.

Ash Fontana: So I was working in battery technology and making private equity investments in small battery companies in 2010. And then I thought about going into venture capital. Then I started a startup. Then I went to AngelList and then I left AngelList and started a fund. And so that's how it started. And that's roughly how it evolved.

Cheryl Mack: And do you remember the first, cause you do a lot of AI investing now, do you remember the first time that you got excited about AI?

Ash Fontana: I do. And it was, it was probably in primary school. Um, and it was actually to do with studying the brain. And so I was really obsessed with anatomy as a kid. And then The anatomy that is, um, fun to study because you can just memorize stuff and, you know, pretend to know it is bones and muscles.

Ash Fontana: But the anatomy that's really, really hard because you can't really see it is the brain. And so I started, like, challenging myself to understand more about the brain. And, you know, obviously didn't get very far, um, because I never became a neurosurgeon, but I was really interested in that. And then I think that somehow led me to a Ray Kurzweil book about singularity and all that sort of stuff.

Ash Fontana: And then he obviously invented Kurzweil keyboards and I was really into music at the time. I was playing a lot of music and then I started thinking about synthesizers and then electronic music. And so I sort of went from being interested in the human body to reading one, it's nonfiction, but science fiction book.

Ash Fontana: And then going back to my hobby, which was music and thinking about how you could get machines to create music. And then, you know, by then I realized there was a thing and it was called artificial intelligence and it was made up of all these statistical methods and whatnot. So that's how I got interested in it was sort of.

Ash Fontana: That late primary school into high school era.

Cheryl Mack: It's so interesting to me because the AI that we know now is this like hot startup tech, like AI that will, you know, change your life and design your house for you and what all these things, but at the, at the earliest moments of AI, it was, it was much more about like studying the brain and all of these.

Cheryl Mack: It's really base fundamental things that doesn't factor into my day to day thinking about AI anymore.

Ash Fontana: Yeah, I think it's a really good point to make and it is helpful to recontextualize it in that way. And in fact, this was something I did with my book about AI that came out a couple years ago. Is I made sure at the beginning that, To say like, hey, this whole thing started with people experimenting with the nervous system of frogs and then trying to recreate very simple neuronal circuits on computers and so on and so forth and I think that's really important because We need to remember that, you know, these are now very big complicated systems But they started by trying to simplify a complex system and then built out from there.

Ash Fontana: And I think it helps you understand AI a little bit better. If you go back to, you know, what were these original simple neuronal circuits that we're trying to model? What was a perceptron? And what was that whole movement about? Because then you can sort of build an understanding from the bottom up.

Ash Fontana: Rather than just sort of jumping into a, uh, a really easy to use library or framework. And then sort of hacking your way back to, um, building some sort of intelligent system. Uh, another good angle to approach AI, I think, is from probability. And just starting with statistics and a probability textbook, which is something I've also done.

Ash Fontana: And I did sort of midway through my AI investing. I was like, hang on a sec, I need to go back to this and really get at it from that angle as well. Um, because then you, you build an understanding up from, you know, basic bedding equations really, um, or basic statistics. So, uh, I think it is important to remember that AI had a basis in that.

Ash Fontana: Um, and also if you think about it just from a, uh, tooling or like a utility perspective, The first AIs were programmers trying to automate their work, because they were lazy.

Maxine Minter: Like all best tools. Yeah,

Ash Fontana: exactly. Like all best tools. They were trying to get leverage on their time. And that's what AI needs to do.

Ash Fontana: It needs to be a lever for something. Um, and, you know, if we think about where AI is going to be really impactful in saving us time and money and whatever else. Um, and that's what it's always been about,

Maxine Minter: like so many other tools. I really like the vantage point of understanding this space from a probabilistic perspective.

Maxine Minter: I have, I'm familiar with the neuronal one. That's net new information to me for me. So maybe just purely selfishly would love you to continue that exploration. Can you paint that picture for me a little bit more? Like you're thinking about the way that you would think about. An early stage company, building in the AI space.

Maxine Minter: How do you use

Ash Fontana: that,

Maxine Minter: your knowledge of the history of, yeah. I could

Ash Fontana: selfishly go on about this for hours and hours, but I would say this, uh, which is. If you think about why the brain is really powerful, it's because it's a network of neurons that can somehow manage to make connections between different neurons in different parts of the brain really quickly.

Ash Fontana: And somehow intuitively. And so for example, you know, we hear a song and it reminds us of a time and a place and a smell or a certain person or something like that. And it's completely bizarre how quickly and effectively we can bring back that memory. Um, but we can because of the way that the brain is a very, very highly efficient computer and a very, very well networked set of neurons that sort of sit layers upon layers upon layers of each other.

Ash Fontana: Um, and so what I'm getting at is, I think keeping that in mind helps you try to figure out what AI applications are going to be really powerful and the ones that are going to be really powerful, at least I argue in my book and elsewhere, Are the ones that have a data learning effect and the core of a data learning effect is critical mass of data process to turn that data into information, but a data network effect and just remembering that, you know, all the data that something's collecting that a piece of software is collecting has to be relevant to the data.

Ash Fontana: It's previously collected for it to add additional insight. And so I think thinking about the brain is a very, very effective. Network or very, very dense network of neurons helps you remember that, you know, any highly effective AI, not natural intelligence, artificial intelligence. Has to have that similar density of data, um, for it to be something that can deliver a result really quickly.

Ash Fontana: That's my super abstract way of connecting those two things. Wow.

Maxine Minter: Yeah. Maybe to kind of apply it. Also on the other side is there's a kind of upper max on how much you can learn in a particular field.

Cheryl Mack: Yeah.

Maxine Minter: Right. And so like, once you get to all this. Cause what, you don't have enough data in that particular.

Maxine Minter: Well, like let's take. Okay. EQ, for example.

Cheryl Mack: Yeah.

Maxine Minter: Right? Like, there are, if you have a very low EQ, there's a huge amount of return for you to learn the like next increment up the ladder of EQ. And so in Asher's example, your ability to kind of like collect new information, integrate it, turn it into information and integrate it, then allows you to like materially change your prospects by like stepping up that thing.

Maxine Minter: Once you become like top 10 percent of the world on EQ, like if you become top 9 percent of the world, it's not, you're not getting that much better. Yeah. Top eight. Top seven. Is that a fair application of the

Ash Fontana: Yeah, I think often you see these, uh, diminishing returns to scale on data. Um, you, you very much see these in a lot of, uh, AI based applications, and you see.

Ash Fontana: the data learning effect, um, sort of peter off, but not always. And I think you see this in humans too, but I won't go down that tangent. As in, I sort of agree with what you're saying in that you can reach, um, a natural limit in terms of a skill or knowledge or whatnot. I think it's probably true of knowledge and not, not skills.

Ash Fontana: Um, I'm not sure. And I don't know that we should go down that tangent right now, but I will say in AI first applications, you see both. You see a lot of applications that have a diminishing return to scale on data, but some that have an increasing return to scale on data. And figuring out which one it is, is incredibly important.

Ash Fontana: Because the former won't have a very durable, won't be very durable, won't have a very sustainable competitive advantage. The latter has an incredibly powerful form of competitive advantage, and no one can catch up. It's a rich get richer sort of thing. Um, dynamic and Matthew effect in, in, uh, at play, uh, and figuring that out is not easy in my experience.

Ash Fontana: And like, that's what I spent 10 years, the last 10 years of my life doing. It's figuring out how much of an advantage a startup is going to have and in the venture capitals, um, sort of context, figuring out if in the 10 year period in which I have to return the money that I've promised I'm going to invest well, is that startup going to end, be at the end of that 10 year period and still be so competitive?

Ash Fontana: That it can have pricing power and earn a really good margin and therefore be very capital efficient and therefore return a lot of the capital that was invested in it.

Cheryl Mack: Wow. I like, I mean, for us, I think trying to think about that as, as a way, like a heuristic of making investment decisions, I think is really important because we aren't like Australia is not creating the next, or at least I don't think we believe that Australia is going to be creating the next like neural network machine learning generative, um, model, but.

Cheryl Mack: Like, there are a lot of AI companies here who are doing some really exciting things and are absolutely capturing market share. But using that heuristic around, well, actually, we need to understand how competitive is this company going 10 years is a really important question. I, yeah.

Ash Fontana: I think this is a really good question for the whole ecosystem, Australia and the machine learning ecosystem in general.

Ash Fontana: Which

Cheryl Mack: is.

Ash Fontana: Yeah. You know, there's a couple of sub questions here. Um, is it worth putting a whole bunch of money into building foundation models? And maybe vertical specific foundation models like a foundation model for? Um, the natural environment and whether a foundation model for industrial robotics, a foundation model for, uh, certain types of languages, um, or certain types of language models, is that worth, you know, for example, the government or even the private investment industry here investing in, or should we invest in things that sit above that or should we invest in things that sit below that?

Ash Fontana: Um, like chip companies, uh, or where should we be playing? And I certainly think that at the moment, there is broad brush, far too much money going into companies that don't have a competitive advantage. And, and this seems like a contradictory statement, far too much money going into foundation model companies.

Ash Fontana: And actually there's far too little money going into companies that take a very basic version of like a foundation model model. And just spend a huge amount of time tuning it with their own and collecting their own data and blah, blah, blah. So I think the sweet spot is, is in the middle of those two things.

Ash Fontana: And that's what I've been doing for 10 years. And I think that's where most of the returns are going to come from, but it is a question for Australia and you know, you, you say, well, Australia's not really, um, someone the other day said to me, well, Australia's not really, you know, at the forefront of this machine learning wave, are they?

Ash Fontana: Like, they're not really known for that. They're known for quantum, maybe, and this and that. And I said, maybe, but Canva. Like, arguably, it has more users of an AI first product than a lot of other companies in the world. But what have they done? Have they built a foundation model to, um, generate images? No, they, Stable Diffusion did that and all these other companies did that.

Ash Fontana: Um, Midjourney and whatnot. Have they just taken Midjourney and thrown it into Canva? No, they did something more sophisticated than that. So, what is an AI company and what should we, what part of the ecosystem should we, Be investing in, I think, is an incredibly important question.

Maxine Minter: As you said, I think a lot of people are grappling with it, not just in, well, I wouldn't necessarily think of like the number of investors investing into AI companies at the moment.

Maxine Minter: I wouldn't just ring the fence around like machine learning, the machine learning ecosystem, right? Like more and more it becomes an import. Into an app layout development. And so there's lots of people using these tools that don't really understand them in the way that there's lots of people building SAS businesses that don't understand the fundamentals of computer science.

Cheryl Mack: Yeah.

Maxine Minter: And so maybe you can kind of continue to explore that and find, like, should we be investing at the app layout? Are these actually the same substrate to be building on? Yeah. Or. Are they not?

Ash Fontana: It depends on what sort of return you're going for. Um, and it depends if you have another source of competitive advantage, you know, uh, just to pick a couple of examples that are, that are quite simple, but, um, I think add some color or help us sort of understand what we're talking about here.

Ash Fontana: A lifestyle SAS business, like something that is a really good product that automates something for a pretty niche industry like lawn mowing companies. Um, if you're going for a certain sort of return, uh, as in, you know, not some thousand X return, if you want to build a cash flowing business and blah, blah, blah, you know, that's a really good investment that doesn't necessarily need AI, maybe throw some AI in there to help sort of, you Pass invoices or something like that, or do some customer service stuff.

Ash Fontana: I think like the AI question there is not like fundamental to you because of the return profile you're going for. I think there are a lot of other businesses where you might be going for a more of a venture site scale return and AI might be helpful in building that product and building that business.

Ash Fontana: But it's not the core competitive advantage. The competitive advantage is coming from something else. Like you have a super valuable integration or you are already the system of record in that industry. And you've built apps on top of that system of record. Um, sort of like Salesforce, right? Like is Salesforce core competitive advantage AI?

Ash Fontana: They're incredibly good at AI. They've invested a lot in AI. But I would argue it's not, it's, it's ecosystem actually, like, and Benioff superpower is building the, building ecosystems. And so, you know, it's AI is more of a sustaining innovation for, for Salesforce and not core to its competitive advantage.

Ash Fontana: And so I just, I think it depends on what you're going for and what you have, and that will determine whether, you know, you have to really make the bet that AI is going to give you a competitive advantage or help you create a product that is of a certain scale or not.

Cheryl Mack: I would love to go deeper

Cheryl Mack: into all of these, I know, but you did come on this podcast with a, another, uh, slightly.

Cheryl Mack: Adjacent topic in mind, which is around, um, some thoughts you have around first checks and I why that is a really good strategy for you at this moment. So I'd love to just learn a little bit more about what your thoughts are there. Yeah,

Ash Fontana: yeah. That is the name of the podcast and we to talk about that. I was thinking

Cheryl Mack: that.

Cheryl Mack: But you spell it a little bit differently in your country now, where you live. Although in Italy, I don't know how they spell Czech. Can you say it like Italian? Can you say it in Italian?

Ash Fontana: Uh, we don't really use Czechs. Yeah. We're a little bit more sophisticated with our ranking these days than America. Um, so a founder once said to me, and I was the first investor in this guy's company, and he said to me.

Ash Fontana: You know what? The only value an investor ever adds, because we met value add investors, is when they write you your first check. And it, you know, hearing that as his first investor, you would think I was offended, um, because it would suggest that like everything I've done subsequently was completely useless to him, which is not true in that case.

Ash Fontana: Uh, but I actually really liked that because it's the case that the existence proof of adding value Is that you allowed the entity through which all subsequent value is created to exist. Um, and so I think first check investing is the only way to really ensure that as an investor, you're adding any value to the world.

Ash Fontana: Um, and if you're not the first investor in something. I think figuring out if you've done anything useful is really hard. Um, so that's one thing I would say about first check investing. The other thing I would say is it's been my experience and people could argue this all day long, but,

Cheryl Mack: and we might,

Ash Fontana: and we might,

Cheryl Mack: we've

Ash Fontana: got some time.

Ash Fontana: We, it's been my experience that the culture of a company is set really early. Like what's the bar of excellence for engineering, design, product and whatnot. That, that, that bar is set really early and also like how people treat each other and everything that's all set really early and determines who you can hire subsequently.

Ash Fontana: And the first product you usually build at a company is the product you're still selling in 10 years. You might've built other products and add ons and whatnot, but that first product is really the product that exists forever. And the positioning of the company initially, Really determines whether it, you know, succeeds or fails is in you either have good competitive positioning at the start or you don't.

Ash Fontana: Um, as in you're either entering a super crowded market or you're entering a market that's sort of on the come and you're, you're entering way before everyone else and you give yourself enough of a head start to, to truly, uh, have a chance at succeeding. And all of those things are determined really early.

Ash Fontana: And so I think like the fundamental decisions you ma you make with a founder early on about where to. where to go, what to build, who to hire are really, really important. They're the decisions that have an outsized effect down the line. Um, so that's why I've always liked being first and why I continue like being first if I can.

Ash Fontana: And there's a nuance here. There's like first first first check or first institutional investor that like really sits on your board and makes stuff happen. There's investing enough money to let someone leave their job and go full time on something or investing enough money so that someone can really.

Ash Fontana: You know, incorporate the company, hire their first person, and launch their first product. I think they're both important. You could say that both are first checks because they put someone into business. But either way, and I've done both. Either way, I like, I like doing both of those things and not later.

Ash Fontana: Like, I don't like being the angel check that's coming in at the same time as a VC fund. Because by then, you're just marginal money. Um, and I don't like being a VC fund. That's coming in, you know, at the series BLC because again, by then you're just, you're marginal money.

Maxine Minter: And that shifted for you a little over your career, right?

Maxine Minter: Yeah. As you mentioned there at the top, you've done a whole bunch of different roles around the investing landscape. And you know, when you were building Zeta, you were doing first institutional checks, but that didn't mean you were always the first dollar into the bank account for them.

Cheryl Mack: Yeah. And

Maxine Minter: then now you're doing personal checks.

Maxine Minter: Yeah. And so, are you also thinking, are you seeking to be that kind of first dollar in the bank account or?

Ash Fontana: Trying, yeah. Trying. Yeah, trying to be the first or one of the first. I think the nuance there is. It's very easy to lose all of your money if you only ever invest by yourself and you're writing small ish checks, call it like 20 to 200k, because it just doesn't last very long with a lot of applications that people want or need to build these days, um, or seeking funding for.

Ash Fontana: So you sort of want to maybe go in with at least some other people so that you have enough cash to go from zero to one. So maybe like in the group of checks that allow a company to go from zero to one is how I would generally say I'd like, I'd like to invest.

Cheryl Mack: On that point though, like I do find that I, I find myself in that situation a lot of time where I know that my check when I write with the syndicate is about 150, 200 K.

Cheryl Mack: But often the startup will, I estimate need about a million bucks or like at least 500, right? So I can have conviction and get excited about something, but then if I don't know what else, like how else like round is coming together, then it can be difficult to make that first decision. And I know a lot of investors, particularly in Australia, tend to like feel that pain point of like, well, you know, Like, let me know how your round's going and then I'll come in.

Cheryl Mack: Like, how do you manage that balance of wanting to invest, but also being conscious that like, if you're only writing 200k, then the chances, and you're the only money, the chances of them getting to that

Ash Fontana: zero to one are lower. I tend to solve this in a way that, um. is not very common, which is through coaching or advising, as in, I will put in a lot of time as a coach or an advisor.

Ash Fontana: Coaching is paid if they can afford it. And if they can't, I'll advise and then say, look, we'll figure out an equity deal later or something like that. Or you'll just let me invest sometimes if it comes together really quickly. And I try to Give them as much help as I possibly can so that they get to a point where they're more compelling to a few other, at least a few other investors or to a critical mass of investors.

Ash Fontana: And then we can all invest at the same time. So more simply just put in a bunch of time to try and get them there. Um, that's one way. Um, another way is to just do it anyway. Um, and what I mean by that is you just write that check, but you just break down the milestones and Try to figure out a higher resolution picture of risk mitigation points along the way, because it's very easy to say, all right, let me know when you get to this much traction or you launch product like that's a very low resolution picture of what it would take to raise more money or get a critical mass of investors, but they're actually higher resolution pitches you can build.

Ash Fontana: So like. I know an investor that has invested in something like your company before and really appreciates that this is actually the critical point. And I think with 50 or a hundred K we can get to that point. Get them involved. No one else in the world will be interested at that point. But they will be interested enough, because they've seen this play out before, and they think that's a crucial point and whatnot.

Ash Fontana: Then they'll give us enough money to get to the point where everyone will see that this is the real deal, and this company is fundable. So sometimes I do that, where like I'll invest just enough. If I have specific knowledge of a risk mitigation point, that's like just a little bit further. And I have specific knowledge of an investor that will invest at that point.

Cheryl Mack: You must need to know the ecosystem pretty well then. Like how often do you know that like next point of that person?

Ash Fontana: Um, pretty often, but like, that's not because. Because I have some extremely good knowledge of the global technology ecosystem. It's because I only really do one thing.

Maxine Minter: Right.

Ash Fontana: Which is machine learning and AI.

Ash Fontana: Okay. Um, so. So if

Cheryl Mack: you're really focused then it's easy

Ash Fontana: to know. Yeah. It's

Maxine Minter: one of the benefits I think of being a specialist.

Ash Fontana: Yeah. Yeah.

Maxine Minter: A couple of episodes ago, we did one on diversification, kind of thinking about consolidation versus diversification and the trade offs folks are making. And I think this is a really important thread to pull out, right?

Maxine Minter: Like if you specialize, right, sure, you consolidate and expose yourself to a bunch of risks, but you can also de risk in different ways than if you are diversified.

Ash Fontana: Yeah. Um, I think there's nuance there, which is, yeah. For me, the diversification comes through the end market that you're approaching. And so I invest, I just do machine learning and AI, but they're exposed to all sorts of industries from insurance to healthcare, to pharmaceuticals, to infrastructure and roads to whatever, agriculture, all sorts of stuff.

Ash Fontana: So they're exposed to a whole bunch of markets that have a whole bunch of different macro considerations. That have all sorts of different cycles. The one thing I'm trying to understand is the technology cycle. Like I invest in technology cycles, not market cycles and the market cycle diversification is sort of natural because the technology is applicable to multiple industries and that's why I don't really get.

Ash Fontana: Industry specific funds, unless they're attached to a corporate that is in that industry. That totally makes sense. Yeah, makes sense.

Cheryl Mack: Uh,

Ash Fontana: the industry specific funds don't make sense to me. They seem to be a really bad way to construct a venture capital strategy. Private equity or later stage stuff maybe.

Ash Fontana: Um, but I think technology specific funds, uh, are really smart in venture capital. Or it's, that's a good way, a basis on which to have a venture capital fund strategy. Because You can understand the de risking points of building certain types of technology, like a consumer hardware specific fund, or, you know, again, AIML, or you see this in bio a lot, like a lot of biotech funds focus on like very specific Um, types of technologies or disease states or therapeutic areas because the technology risk or the science risk is around like a certain biological, set of biological processes.

Ash Fontana: So I think that's really smart.

Cheryl Mack: Can you make the argument that technology changes so quickly that like on the life of a 10 year fund, is the same technology still going to be relevant?

Ash Fontana: Uh, I think, uh, you can, but I think that's even more of a reason to specialize. Okay. As in, you can't keep up with the changes unless you're highly specialized.

Ash Fontana: Like, there's no way, even 10 years ago, today, there's definitely no way. But even 10 years ago, there's no way you could keep up with all of the research in machine, deep learning computer vision, which is, was my original specialty, not just machine learning, but deep learning computer vision, unless you were specialized.

Ash Fontana: Because then they were like, you know, About 10 really good papers coming out every couple of weeks, really good papers, maybe every month and reading all of those papers, understanding all of those papers, understanding the implications, understanding how they connected to other innovations, understanding who wrote those papers and where they're going to start companies.

Ash Fontana: And that's, that's a month's work right there. And then the next batch comes out. And that's, I could do that because it's all I did. And that became quicker and quicker and quicker as in more and more papers started coming out, the innovation became a lot, cycle became a lot faster. But by then I had such a good basis in the research and I had such a good network that I could ask, you know, is this relevant?

Ash Fontana: Who's doing this? What did I built up in the beginning that as it got faster, I could keep up with it. And I don't think you could have done that if you jumped in four years later. It's like when the first transformer paper came out, I was on onto that because again, it's all I did. I'm not particularly prescient or smart or anything.

Ash Fontana: If that's all you do, and you missed that, then you just had your eyes closed. Um, and if you got into Transformers in 2017, then you were able to, you knew when like Cohere was starting, and when Anthropic was starting. You knew that, because you'd been tracking these people since the day they published a paper, or even before.

Ash Fontana: And you knew that research was going on at Google before. If you jumped into Transformers when everyone started hearing about Transformers, As in, when GPT came out, or when GPT 2 or 3 came out, it was already too late. Like, what are you going to invest in at that point? I don't know, what are we

Cheryl Mack: investing in then?

Cheryl Mack: Well, everything, if

Ash Fontana: you want to invest in the fundamental technology, like everything was already worth billions of dollars. Yeah, okay. So I think you have to specialize to keep up in, in very fast moving, uh, tech environments. Yeah, it's

Maxine Minter: an

Ash Fontana: interesting perspective.

Maxine Minter: How do you think about that? Because I think one of the, um, interesting perspectives or spicy opinions you've shared recently, Is fund or solo.

Cheryl Mack: Oh yeah.

Maxine Minter: Right. As an angel or you know, at scale solar capitalist, but without anyone else's capital behind you or, and you've done both. You've done both. You know, especially from our last conversation on this, you know, you're trending individual, you're trending. It is actually, you can big qualification, but if you can invest yourself, you should, as opposed to run a fund.

Ash Fontana: Yeah. My view on this is. It's very particular and there's a lot to disagree with.

Cheryl Mack: Great. Let's get into it. I'll be the disagreeer.

Ash Fontana: Who's red teaming? Who's not? Who's not? Um, there's a lot to disagree with here, but, um, picking up from where we left off, I think AI is super competitive now. A lot of funds, a lot of people investing, as I said, at the foundation model layer at the sort of what I call like light AI, it's like AI plugged in to SAS layer.

Ash Fontana: And there's some stuff in between, but it's just very, very crowded. And when something's crowded, I don't want to be there. My view is as soon as you're competing, you're losing. You should just aim to be non competitive situations. Otherwise you have no hope of even thinking about winning most of the time.

Ash Fontana: So that's one reason I've sort of pulled back from deploying a lot of other people's money. Because I think it's very, very competitive at the moment. Second reason is I think we're in a very long equity bear cycle and a very long debt bull cycle. And then there's all sorts of other risks around stagflation, which will cause a lot of volatility in equity markets, which will make a lot of things difficult.

Ash Fontana: It'll make it hard to raise funds. It'll make follow on rounds hard to get done because everyone's struggling to raise funds. Except a select few, it will cause a huge amount of disruption in the industry. A lot of the middle of the venture capital ecosystem is going to fall out and blah, blah, blah. And so I think that makes.

Ash Fontana: Venture capital, which is essentially private equity, very hard for the next three to five years at least. Um, and so that's another reason to pull back from raising funds and managing other people's money. Another reason I think to sort of move away from fund investing to first check investing is just sort of a collection of lessons I've learned, um, around, you know, uh, what causes funds to fail, um, what causes funds to succeed.

Ash Fontana: What are good incentives to set for funds? You know, management fee versus carry and where the industry is at versus where I think it needs to go. Um, there are a lot of views around that, that you're just not really allowed to have if you're running a fund. Um, because you can't, you can't run a fund business if you, if you have those views.

Ash Fontana: And whereas if you're an individual, you can have whatever views you want and people can take it or leave it. And you'll either succeed and be able to eat. Or you won't and you have to get a job.

Cheryl Mack: And which bucket are you in currently?

Ash Fontana: Uh, I'm okay. I don't really invest for me. Like, um, I don't really keep anything.

Ash Fontana: I invest sort of for like a charitable trust. Yeah.

Maxine Minter: I'd be really interested as you're thinking about like that portion, there's a whole bunch of macro reasons that you mentioned at the top there, but it sounds like there's some micro reasons, like specifically for funds, where incentives lie. I wonder if you could

Cheryl Mack: flesh

Maxine Minter: out any of those ideas because I think there isn't a whole lot of innovation in the business model.

Maxine Minter: No. Like

Cheryl Mack: the

Maxine Minter: 20 and

Cheryl Mack: two model.

Maxine Minter: Yeah.

Cheryl Mack: I've heard that the U. S. has a lot of different, whereas like Australia is, you know, it's I think it's a relative

Maxine Minter: statement. Is it? But like, as a objective statement, there's very little innovation in the business. In my opinion, there's very little innovation in the business.

Maxine Minter: Being thoughtful about incentives, being thoughtful about construction. Yeah. Thoughtful about value creation, who you're creating value, all of these kind of things. It sounds like you're hinting at some of those areas. Yeah. And for your, just maybe not casting a broad brush, but just specifically for your decision to invest.

Maxine Minter: Just your dollars and not raise capital and not invest other people's money. How do you think about those incentives? Yeah,

Ash Fontana: I think you're right. Like the model hasn't changed since my great, great, great, great, great grandparents were sending people off from venison shipping expeditions. It really hasn't changed.

Ash Fontana: Like that's what, that's what Carrie comes from. The concept of Carrie comes from, you send people off on an expedition. You say, if you come back. You get to keep 20 percent of what you bring back and I'll keep the other 80 percent of the spice all that you got and it Hasn't changed. Um, that's not to say things have to change for the sake of changing, but I do really Have a problem and it's I wouldn't call an ethical problem because everyone knows what's going on But I'd say it's a bit of a moral problem with like charging management fees as opposed to just sharing costs I think it's fine to share costs of like fund admin set up, et cetera, but charging management fees on something that is purely an intellectual capital business doesn't really make sense to me because you don't have any cash outlays as a, as someone who is practicing the craft of venture capital in its most pure sense.

Ash Fontana: All you're offering companies is your advice and your time at most. Sometimes you don't even have to give much advice or time because they just have it all figured out themselves. And you're not spending money on anything really, um, except sourcing, which sourcing is just, you know, as a lot of people say, it's just shoe leather, like you've just got to get out there and be there.

Ash Fontana: Um, so I just really don't think that a lot of things that firms spend money on, uh, useful or relevant in the case of a lot of portfolio services. Or, or amount to that much in the case of just getting out there and going to meetings and having coffees with people, like just buy your own coffee, um, or have it at home.

Ash Fontana: Like, so I don't really get the notion of management fees. Also, I think as much as possible, you want to incentivize people to have big wins. And so, for example, I think sharing costs and then charging higher carry, like 30 percent or something like that is a much better model for a fund. And if I were to start a fund today, I'd start there and work backwards based on feedback from the people that I'm working with, uh, on, on getting the fund started.

Ash Fontana: Um, and I think another thing is just, it's really important to recognize, which is, The industry's performance is terrible. It's absolutely terrible. We don't beat the benchmark as an industry, the benchmark being the S& P. And we're certainly not beating it this year. Um, and I would argue in, if you take like the last one or two years in the next two or three years, we're not going to beat it as an industry.

Ash Fontana: And so what have we got to do? We've got to reduce the fee drag on returns, on net returns. And we've got to change the incentives so we are incentivized to actually make different sorts of investments, price different sorts of risks than people can otherwise get access to or are well priced by the public markets.

Ash Fontana: So it's, I think it's imperative on the industry to just really think about what we need to do to solve this returns problem or it won't exist.

Cheryl Mack: I think our last guest was saying that, uh, 45 percent of venture funds in the U. S. are invested Like aren't deploying at the moment.

Maxine Minter: Mm hmm. Mm hmm. That was during 23.

Cheryl Mack: Oh, yeah, 23 Yeah, or what weren't deploying last year which like that that contributes to the like fee drag right if you're not

Ash Fontana: still charging management face

Cheryl Mack: Yeah

Ash Fontana: and not putting any money to work.

Cheryl Mack: Yeah.

Ash Fontana: And 90 plus percent don't beat the benchmark.

Cheryl Mack: 90 plus funds in the US. 90

Ash Fontana: plus percent of funds don't beat the benchmark.

Ash Fontana: Yeah. Crazy.

Cheryl Mack: You invest in funds though, don't you?

Ash Fontana: Uh, not really. No. You have. I mean, sure. I've invested in super big funds that are amazing, like Sequoia and super tiny funds. that are amazing, like background capital, which no one's ever heard of, by sort of definition. Um, people like, like solo GPs that I really trust.

Ash Fontana: I've invested in some funds, but like, I really only invest in a fund, same as with a startup, if they really want me there and I'm like fundamental to the formation of the fund. in terms of helping them with their core strategy, getting their team together, um, and raising their fund. And then I'm not charging fees on that because I've helped them so much get it off the ground.

Ash Fontana: I just don't see the point in paying for something that I can do myself. Like I'm a venture capital investor. Why would I pay someone else to be a venture capital investor?

Cheryl Mack: Yeah. I

Ash Fontana: pay people to invest in property for me because I'm so bad at that. I'm very good at intellectual property. I'm very bad at real physical

Cheryl Mack: property.

Ash Fontana: Very, very, very bad at it. And so I'm very happy At least you know you're

Cheryl Mack: strength.

Ash Fontana: Yeah, I'm very happy to stay within my circle of competence, it's very small, I just stay there. Um, yeah, I'm happy to pay fees on something I don't know how to do. It's like, I'm happy to pay a dentist to work on my teeth, because they're the expert.

Ash Fontana: Um, but if it's, you know, something that I know how to do myself, like cook my own meat, my own dinner, like I'm like, I don't need to pay someone necessarily to do that, unless they're much better than me. Um, so no, I don't, I don't really invest in venture funds. I just. Like to help certain people start certain types of funds that I think should exist

Cheryl Mack: and are they using different models in the fees?

Ash Fontana: Yes, some of them are Using different fee models and some of them have different incentives. So two funds that I've been helping a bit in the last year I won't name them, because I don't need to, um, and because they're, some of them are public. One, for example, the GPs themselves are putting a gigantic portion of their net worth into the fund.

Ash Fontana: Like they're putting most of their personal net worth into the fund. And they're going for a very heavy carry, very low fee model. Um, and they're starting a fund that's going to invest mostly in Europe. And I think we need more good Series A investors in Europe. So, I've helped them. And another fund is using a heap of software, a lot of really good process, um, and community and good sort of networking tools to build a fund that invests in hundreds of startups per year.

Ash Fontana: And so I'm very happy to help them because I think they're really innovating on the venture capital model. These are people that came from AngelList. And they're seeding just a huge amount of companies again, mostly in Europe, not just, but mostly. And I think that needs to happen, both the innovation and seeding more companies.

Ash Fontana: So that

Cheryl Mack: you can write more

Ash Fontana: personal checks. Uh, that, that is helpful. Yeah, I mean, I do co invest. You're like, oh

Cheryl Mack: no, I haven't thought, I hadn't thought about that. I

Ash Fontana: do co invest with them, but, but I don't really, I don't invest solely in Europe. Like, I, in fact, the super majority of the companies I invest in are in the U.

Ash Fontana: S.

Cheryl Mack: Any Australian?

Ash Fontana: Uh, not

Cheryl Mack: that I can

Ash Fontana: think of. As in, yes, like, a couple. But, percentage of portfolio, it'd be 10 or so 10 percent

Cheryl Mack: and the rest of the world Asia anything there

Ash Fontana: not a lot. I stay away from markets I don't know very well. Yeah, fair

Maxine Minter: fair. So as you have been I mean already you have been part of so many incredible moments Ecosystem development as you think about from here on

Cheryl Mack: out

Maxine Minter: Think an open question for me is Right.

Maxine Minter: Is there a season to be an investor in our lives or is this actually a forever thing? Can you be an excellent VC investor regardless of fund or individual or maybe it differs for a very long time or do you have a career?

Ash Fontana: Big topic. Um, so to start this off with an opinion of someone That I cannot name, but I can say has recruited probably the most successful venture capital fund partners in history.

Ash Fontana: She's a myth. She said the ideal, like the ideal time or the most productive period for a venture capital investor is 35 to 45. So that's something just to think about. Not my opinion.

Cheryl Mack: That's a very, 10 years? Like that's it?

Ash Fontana: That's the ideal. Yeah, well, it's three investing periods, right? Yeah,

Cheryl Mack: okay.

Ash Fontana: Three by three, uh, roughly.

Ash Fontana: That's something to think about. Here's my view on this, and I break it down a few different ways. I think, uh, I find this sort of skill luck spectrum or dichotomy, um, that, uh, is a bit more of a spectrum, uh, really useful, which is, are you in a skill based profession or a luck based profession? You know, a skill based profession would be like Great.

Ash Fontana: Okay. Yeah, a skill based professional would be like making shoes. A luck based professional would be like a movie star. I think VC is far more luck based than skill based.

Cheryl Mack: Yeah.

Ash Fontana: And I map that spectrum to, are you in an industry that requires high competence? Or good networks. So you need to be very very competent at executing a certain skill certain way.

Ash Fontana: Or do you need to have a huge network so that you can get very lucky. And again I think VC is very much network based. And so to get back to the question what's the ideal period? I think you start as early as possible. So you start building your network as early as you possibly can. As soon as you have any semblance of credibility such that you can go around the world and start building a network in a certain area.

Ash Fontana: And I think you finish when you no longer have the energy for networking. And for some people that's pretty young. And based on certain lifestyle factors. But some people, like my partner Mark at Zeta, he's older, but has all the energy in the world for networking all day long. Um, and so it, it really, the age, I don't think is not the thing.

Ash Fontana: It's when you don't feel like you have the energy for networking in a very extreme way. Now, I think, what does this mean? Do you get better or worse as an investor over time? I think it means you get, you definitely get better because of the network effect, like quite literally Metcalfe's law. Every additional node you add to your network, uh, it provides more value for every existing member of your network.

Ash Fontana: Um, and so I think the bigger your net, the network you can build, the better, the better you will be, the more useful you will be, and therefore, the more lucky you will get and your career should be only determined by your ability. To, um, to get started networking and keep up the energy for it.

Cheryl Mack: That makes me feel super good about my trajectory.

Cheryl Mack: I'm like, cool, I'm on the luck network path. And I absolutely love just like I'm an extroverted heart. So I'll never run out of energy because that's how I get energy. So I'm good to go. Okay, you're on the right path.

Maxine Minter: That has to be right, because it like resonates with exactly what I want to be true.

Cheryl Mack: I'm here for that.

Ash Fontana: I mean, there's some reasonably good like empirical evidence for this in, um, analogous professions. And Michael Mauboussin wrote a really good book about this, um, and covered a lot of different professions, but has, has he himself, the author has applied it to venture capital, um, very convincingly. And so it's not my argument.

Ash Fontana: There's some good numbers behind it. But it also matches my experience. Excellent.

Cheryl Mack: Two final questions. One final. So like you're in a position now where you've experienced like working at funds, starting funds, uh, also writing your own first checks. Uh, if you were starting again as a new investor, would you start from this strategy?

Cheryl Mack: Like only investing personally or, or what? Like?

Ash Fontana: Absolutely not.

Cheryl Mack: Okay.

Ash Fontana: I would work at a big fund that has investors to learn from that has a really good process. Don't necessarily take that process to your own fund or to your own first check investing, um, sort of activities, but at least know what a good process looks like that has existing networks to pull from experts to consult for due diligence or people to bring in to help companies, CFOs to bring in or whatever else.

Ash Fontana: That has other investors to follow to see how they build their network to see how they maintain their network to see how they Provide value to their network and that has scale so that you can just be in the game I mean rule number one of a job that Compound where your where your ability to do the job compounds over time as it gets better and better over time is Rule number one is stay in the game.

Ash Fontana: Don't, don't get, don't get yourself out of the game by losing enough, losing all your money.

Cheryl Mack: Don't run out of money.

Ash Fontana: And I think that's really easy to do if you're writing your own checks to start and you don't know what you're doing. Um, so no, if I did this all over again, I mean this, this is easy to say.

Ash Fontana: In reality, I would have never done this and I didn't do this because I find it really hard to work for other people and in big companies and whatever else, but I would have worked for, you know, a big growth stage or, uh, growth stage fund like Insight, Summit, Bain, you know, one of them. Um, or now I guess you could say like Sequoia Growth or Andreessen Growth.

Ash Fontana: Um, I would have worked for one of them and learned as much as I possibly can, uh, early on before going out and starting my own thing.

Cheryl Mack: Yeah. Okay. All right. Well, I don't know how many of our early investors can go work for big funds, but I'm sure there's lots of lessons to be learned there. I think

Maxine Minter: with enough time, effort on target and tenacity, they can get there.

Maxine Minter: They might just have to make some sacrifices along the way.

Cheryl Mack: Or they could just listen to our podcast where we talk to big funds and learn the learnings from that.

Maxine Minter: Totally the same. Yeah. One for one.

Ash Fontana: Well, yeah, we are like, I think we have to recognize we are lucky these days in that a lot of the knowledge is shared.

Ash Fontana: Like it used to be such a cottage industry, so to speak, uh, You couldn't learn anything from the outside. No, you can, you can learn something, but I'm, I'm sort of with you on that, but I'm also with Max and that like, you got to be there every day and you got to be in the office like next to these people all day long to really learn how it's done.

Maxine Minter: Yeah, I'm trying to learn from both the like explicit lesson and implicit lessons of what you operate.

Ash Fontana: And going through portfolio reviews of like hundred portfolio companies that last for days at a time, Sounds incredibly boring, but that's where the good part is. learning comes from.

Cheryl Mack: It's

Ash Fontana: like, why is this company doing really well?

Ash Fontana: Why is this company doing really poorly? What can we do about it? How does this fit into how this whole funds looking? Do we have to make some decisions in this fund to get dollars out earlier? Should we put more money in this company or not? Like, you know, all these very, very complicated considerations.

Ash Fontana: You don't even get to ask those questions when you have a small portfolio, a young portfolio of just like companies that started in the last few years. A portfolio of companies that are in like only certain industries are exposed to certain technology cycles. You just don't get to learn that much from small sample sizes.

Maxine Minter: It's a masterclass

Cheryl Mack: in doing drills on the thing you want to be excellent at. Or sitting in a hundred IC meetings. Yeah.

Maxine Minter: So, very sadly, we are nearing the end of our time together, which means that we get to ask you the question we ask at the end of our podcasts. What is your biggest big cojones moment? A moment where you felt super brave.

Ash Fontana: Um, so I've always been one to not really care what other people think. So I don't know what's brave or not because I don't know what's stepping out of the norm or not because I sort of am a bit oblivious to norms. And in a sense, this is like a, something that gets in the way of functioning in society.

Ash Fontana: Um, but in a sense, it's a superpower. I'll give you a couple. Um, when I was 11, I felt like my appendix was bursting. And so I called and adults were out of contact for various reasons. Mobile phones were tough in those days and whatnot. I caught, not only called my an ambulance, but, I ordered the surgeon to do emergency surgery even though she didn't agree with me that my appendix was bursting.

Ash Fontana: And I basically convinced her with my 11-year-old knowledge of the human body, which was not nothing but not as good as hers convinced.

Cheryl Mack: You haven't studied the neurons yet, right? No,

Ash Fontana: not yet. Uh, well, partially I convinced her to do emergency surgery and lo and behold, like it was within an hour of bursting.

Ash Fontana: Um, and so it, uh, that was, that was pretty brave for an 11-year-old to convince someone to open you up. It was

Cheryl Mack: very specific, like you had a very specific idea with what was going on in your own body.

Ash Fontana: Yeah. It was very, it was very odd that I sort of not only had any idea what was going on, but was able to sort of have a conversation with a doctor about it, um, and make it happen.

Ash Fontana: So that, that was pretty brave, I think, because no one likes having surgery, especially not 11 year olds. Um, but I, I don't know, I do all sorts of random stuff, like certain mountaineering objectives. Um, but I think relevant to this podcast. Uh, leaving the fund that I helped start and launch, uh, in a period where I, you know, for context, this was around the end of 2021, where we were an AI fund and AI was going hot.

Ash Fontana: Yeah. Going, going pretty strong. It's pretty hot. And the fun was doing really well and we had a great team and blah, blah, blah. Not to mention the obvious, like there are management fees on the table that you can just keep getting by staying. Um, but I left for a whole bunch of reasons, which is a whole nother podcast, but, uh, that no one will listen to and nor should they be interested in, uh, cause their personal reasons.

Ash Fontana: But yeah, I think leaving a fund is something that takes a lot,

Cheryl Mack: especially a fund that's doing really well and

Ash Fontana: at a

Cheryl Mack: time

Ash Fontana: when no problems. Yeah.

Maxine Minter: It's also a huge step just in life. It is your entire project, your craft, as you said, and to step out. from underneath that. In some ways, you're still the identity that you are.

Ash Fontana: Yeah. It is a, it's a big identity thing for a lot of people. They identify with the title and having something around them, having an office, having a place to go, having a team that cares about them, having a group of founders they work with. Yeah. And yes, like you get to keep a lot of that. Um, but for me, I think like having a diverse identity is really important for this reason so that you can be more rational about a lot of decisions in life that tend to.

Ash Fontana: Um, have you pulled by what you identify with and, you know, just as there were a lot of really good reasons to stay and keep investing in AI very aggressively then. There are also good reasons, which I've given earlier in this, in this podcast to go, which is that AI was arguably getting too hot. The macro cycles were getting very challenging.

Ash Fontana: Um, and so, you know, I, I ended up on one side of that debate and, uh, you know, I'm very happy with the decision, but at the time it was not obvious and a lot of people didn't think it was. You know, very smart.

Cheryl Mack: Big kahunas. I love it. Thank you so much for coming on our podcast today. This has been our first actually recorded in person episode.

Cheryl Mack: So I hope you know that you are our in person guest of honor.

Ash Fontana: Wonderful. Thanks for doing it.

Cheryl Mack: Thank you.