The Value of Being the First Investor: Insights from Ash Fontana

First Cheque

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Episode Summary:

In this episode of First Cheque hosts Cheryl Mack and Maxine Minter sit down with the distinguished investor Ash Fontana to explore the intricacies of early-stage investing and the evolving landscape of artificial intelligence (AI). The conversation delves into Fontana’s diverse experiences, ranging from his formative years building web-based marketplaces to his critical role in establishing the AI investment frontier.

Fontana shares a captivating narrative of his investment philosophy, advocating for the significance of first-check investments in shaping the trajectories of startups. With an emphasis on determining the long-term competitive advantages of AI ventures, the discussion uncovers the importance of specialization and the strategic deployment of venture funds amidst a rapidly transforming technology sector. Key themes revolve around adapting venture capital models, the intersection of network building, and the fortune of investment careers, providing listeners with a wealth of actionable insights.

Key Takeaways:

  • First Cheque investments are critical in establishing a startup’s potential and culture.
  • AI investments should focus on companies that exhibit a sustainable competitive advantage, potentially characterized by data network effects.
  • Venture Capital (VC) requires specialisation, especially in fast-evolving fields like AI, to keep pace with rapid advancements and make informed investment decisions.
  • The traditional VC model faces criticism for its performance and lack of innovation; exploring models with lower fees and higher carry could better align incentives.
  • Building and maintaining a robust network is essential for success and longevity in the venture capital industry.

Notable Quotes:

  • “The existence proof of adding value is that you allowed the entity through which all subsequent value is created to exist.”
  • “As soon as you’re competing, you’re losing. You should just aim to be in non-competitive situations.”
  • “I invest in technology cycles, not market cycles.”
  • “I find it really hard to work for other people and in big companies, but I would have worked for a big growth stage or growth stage fund, like Insight, Summit, Bain, one of them.”
  • “The bigger the network you can build, the better, the more useful you will be, and therefore, the more lucky you will get.”



This transcript has been A.I. generated.

0:00:00 – (Cheryl Mack): Okay.

0:00:00 – (Cheryl Mack): Three, two, one. Hey, I’m Cheryl.

0:00:04 – (Maxine Minter): I’m Maxine.

0:00:05 – (Cheryl Mack): This is first trek part of day one, the network dedicated to founders, operators and investors.

0:00:09 – (Maxine Minter): If you want to be a better early stage investor, this is the show for you.


0:00:12 – (Cheryl Mack): So, tl:dr, if you don’t want to suck at investing, listen up, 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, maybe he’ll have lunch with me. And for the record, that lunch was amazing.

0:00:36 – (Maxine Minter): It was very tasty. Very, very tasty.

0:00:37 – (Cheryl Mack): Oh, I meant the company.

0:00:39 – (Maxine Minter): Oh, it was good. I actually am so excited for this podcast with Ash. 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, 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.

0:01:15 – (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 I read on his LinkedIn or batteries or something, and then Angellist and then starting his own fund and even like living in Italy and just supporting companies 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 in the early days when you first started investing? And also we wrote a book about AI first. The AI first company. Yeah.

0:01:49 – (Cheryl Mack): He must have been investing in looking into AI from the earliest days before you and I even knew the concept of AI. So I think we’ll hopefully get to ask him a little bit about that.

0:01:59 – (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. And so he was specifically investing in AI and certain applications of AI way before. Actually, his partner at the fund is now the head of MIT and so they were very early in the space. Yeah. There’s just so many topics I can’t wait to cover up with him.

0:02:26 – (Cheryl Mack): And I wonder what he thinks about investing in AI now.

0:02:30 – (Maxine Minter): Oh, yeah, we’ll have to wait and see. We will wait and see.

0:02:33 – (Cheryl Mack): All right, let’s get onto it.

0:02:34 – (Maxine Minter): Let’s dive in.

0:02:40 – (Cheryl Mack): This is going to be fantastic. I’m excited. I hope you’re excited.

0:02:42 – (Ash Fontana): Yeah. Well, this is the first time I’ve suggested myself for a podcast rather than being asked.

0:02:48 – (Maxine Minter): It was so smooth.

0:02:50 – (Cheryl Mack): I was actually also surprised when Maxine was like, hey, Ash, asked 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 having someone like Ash come to us and be on it now I feel like we’re basically celebrities.

0:03:02 – (Ash Fontana): Well, 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 where they’re either just starting to write first checks or thinking about it, or thinking really experience and then going back to writing first checks. So I’ve done all of those three things, and I really liked how you’re approaching the topic.

0:03:22 – (Maxine Minter): Thank you. I would just want that. I think you are probably one of the best place 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, like that, they have just start thinking about funds. You’ve had an incredible vantage point across multiple peaks in the ecosystem at different moments in history that I think are really valuable.

0:03:43 – (Ash Fontana): Yeah, I’ve done a lot of things poorly.

0:03:46 – (Cheryl Mack): You learn the most when you do them wrong, though, right?

0:03:48 – (Maxine Minter): Something like that.

0:03:50 – (Ash Fontana): That’s what we like to believe.

0:03:51 – (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, our last podcast guest invested in books to your education, to a stock.

0:04:04 – (Ash Fontana): I think the most significant investment I made was in mock ups for my first website business. And so we had a designer friend, and we paid him a bit of money. It was probably a couple hundred bucks at the time, which, when you’re in high school, was a lot. And he did these really beautiful mock ups for this website we were creating. It was a website where you could buy services related to school formals and running events.

0:04:27 – (Ash Fontana): And we took those mock ups and laminated them, and then 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 we were both okay at programming and design, but our designs weren’t that good. 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.

0:04:56 – (Ash Fontana): And that was the right move, but it was a big investment for us at the time and it worked out.

0:05:00 – (Cheryl Mack): And is that the investment or the company that ended up funding your whole.

0:05:04 – (Ash Fontana): College that did fund college for me in.

0:05:07 – (Cheryl Mack): That’s a pretty good return on your first investment there.

0:05:10 – (Ash Fontana): Yeah, I mean, college in Australia is not that expensive and I got some other help from the government and everything else, but yeah, it did effectively fund all the college and it was fun and it’s just the experience of going through it. Right. 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.

0:05:37 – (Ash Fontana): 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 to any significant market opportunity. It 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 a little bit better.

0:05:58 – (Maxine Minter): How painful was it to build websites in that moment in history? I tried to build one at the same time. Oh my God. And then when Squarespace became a thing. Yeah, it was just like a true blessing. Like the idea of modular blocks.

0:06:11 – (Ash Fontana): Yeah. Really not having any graphical interface at.

0:06:15 – (Cheryl Mack): All, any drag and drop, any drag.

0:06:18 – (Maxine Minter): And drop, or any ability to work with your own website without being technical at the time.

0:06:23 – (Ash Fontana): Yeah, yeah. We were working with the very first version of something called cold fusion, 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. And then it became really hard.

0:06:43 – (Cheryl Mack): Just past hello world.

0:06:45 – (Ash Fontana): Yeah, 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. But that’s how we evolve as like an ecosystem. Well, that’s how the web evolved, right? 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.

0:07:11 – (Maxine Minter): Your kind of early investing career in startups, what was your entry point there and how did you get enamored with it?

0:07:18 – (Ash Fontana): Yeah, I’d say investing in startups for me began with starting my own startups, 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. That was probably what I invested in first in terms of my own time, 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 one or two designers.

0:07:55 – (Ash Fontana): 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, I was really into value investing and understanding companies from their fundamentals up 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.

0:08:33 – (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 Angelus and started a fund. And so that’s how it started and that’s roughly how it evolved.

0:08:49 – (Cheryl Mack): And do you remember the first, because you do a lot of AI investing now. Do you remember the first time that you got excited about AI?

0:08:55 – (Ash Fontana): I do, and it was probably in primary school, 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 fun to study because you can just memorize stuff, you know, pretend to know it is bones and muscles, 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 obviously didn’t get very far 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.

0:09:34 – (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 non fiction, but science fiction book, and then going back to my hobby, which was music, and thinking about how you could get machines to create music.

0:10:01 – (Ash Fontana): 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 that late primary school into high school, era.

0:10:14 – (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 all these things. But at the earliest moments of AI, it was much more about like studying the brain and all of these really base, fundamental things that doesn’t factor into my day to day thinking about AI anymore.

0:10:38 – (Ash Fontana): Yeah, I think that’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 of years ago, is I made sure at the beginning 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 these are now very big, complicated systems, but they started by trying to simplify a complex system and then built out from there. 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 were trying to model what was a perceptron and what was that whole movement about?

0:11:32 – (Ash Fontana): Because then you can sort of build an understanding from the bottom up rather than just sort of jumping into a really easy to use library or framework and then sort of hacking your way back to building some sort of intelligent system. Another good angle approach AI, I think, is from probability and just starting with statistics and a probability textbook, which is something I’ve also done 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, because then you build an understanding up from basic betting equations, really, or basic statistics. So I think it is important to remember that AI had a basis in that.

0:12:16 – (Ash Fontana): And also, if you think about it just from a tooling or like a utility perspective, the first AI’s were programmers trying to automate their work because they were lazy.

0:12:29 – (Maxine Minter): Like all best tools.

0:12:30 – (Ash Fontana): Yeah, exactly. Like all best tools, they were trying to get leverage on their time. And that’s what AI needs to do. It needs to be a lever for something. And, you know, if we think about where AI is going to be really impactful, it’s going to be impactful in saving us time and money and whatever else. And that’s what it’s always been about.

0:12:50 – (Maxine Minter): Like so many other tools and really like the vantage point of understanding this space from a probabilistic perspective I have am familiar with the neuronal one. That’s net new information 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. How do you use your knowledge of.

0:13:19 – (Ash Fontana): The human, that’s framework? Yeah, I could selfishly go on about this for hours and hours, but I would say this, which is if you think about why the brain’s 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 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.

0:13:58 – (Ash Fontana): And it’s completely bizarre how quickly and effectively we can bring back that memory, 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. 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.

0:14:27 – (Ash Fontana): 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 data network effect. And just remembering that all the data that something’s collecting, that a piece of software is collecting has to be relevant to the data it’s previously collected for it to add additional insight.

0:14:56 – (Ash Fontana): 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 any highly effective AI, not natural intelligence, artificial intelligence, has to have that similar density of data for it to be something that can deliver a result really quickly. That’s my super abstract way of connecting.

0:15:23 – (Maxine Minter): Those two things, 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.

0:15:32 – (Ash Fontana): Yeah, right.

0:15:33 – (Maxine Minter): And so, like, once you get to.

0:15:35 – (Cheryl Mack): All this, because what, you don’t have enough data in that particular.

0:15:38 – (Maxine Minter): Well, like let’s take EQ, for example. Yeah, 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 Ash’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. But once you become like top 10% in the world on EQ, like, if you become top 9% in the world, it’s not, you’re not getting that much better.

0:16:10 – (Maxine Minter): Top eight, top seven. Is that a fair application of the.

0:16:14 – (Ash Fontana): Yeah, I think often you see these diminishing returns to scale on data. You very much see these in a lot of AI based applications. And you see the data learning effect 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 a natural limit in terms of a skill or knowledge or whatnot. It’s probably true of knowledge and not skills.

0:16:45 – (Ash Fontana): 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 because the former won’t be very durable, it won’t have a very sustainable competitive advantage.

0:17:10 – (Ash Fontana): The latter has an incredibly powerful form of competitive advantage and no one can catch up. It’s a rich get richer sort of dynamic and Matthew effect at play. Figuring that out is not easy in my experience. And like, that’s what I spent ten years, the last ten years of my life doing, is figuring out how much of an advantage a startup is going to have. And in the venture capital sort of context, figuring out if in the ten 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 ten year period and still be so competitive 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?

0:18:02 – (Cheryl Mack): Wow. I mean, for us, I think trying to think about that 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 neural network machine learning generative model. But, 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 to be in the next ten years? Is a really important question.

0:18:37 – (Ash Fontana): Yeah, I think this is a really good question for the whole ecosystem, Australia, and the machine learning ecosystem in general, which is, you know, you know, there’s a couple of sub questions here. Is it worth putting a whole bunch of money into building foundation models and maybe vertical specific foundation models, like a foundation model for the natural environment and weather, a foundation model for industrial robotics, a foundation model for certain types of languages or certain types of language models.

0:19:08 – (Ash Fontana): Is that worth, 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, like chip companies, 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.

0:19:38 – (Ash Fontana): Far too much money going into foundation model companies. And actually, there’s far too little money going into companies that take a very basic version of, like, a foundation 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 in the middle of those two things, and that’s what I’ve been doing for ten years, and I think that’s where most of the returns are going to come from.

0:20:04 – (Ash Fontana): But it is a question for Australia. And you say, well, Australia’s not really. Someone the other day said to me, well, Australia’s not really at the forefront of this machine learning wave, are they? They’re not really known for that. They’re known for quantum, maybe, and this and that. And I said, maybe. But canva, arguably, it has more users of an AI first product than a lot of other companies in the world.

0:20:30 – (Ash Fontana): But what have they done? Have they built a foundation model to generate images? No. Stable diffusion did that, and all these other companies did that mid journey and whatnot. Have they just taken mid journey and, like, 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.

0:21:00 – (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. I wouldn’t just ring fence around like machine learning, the machine learning ecosystem, right. Like more and more it becomes an input 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 SaaS businesses that don’t understand the fundamentals of computer science. Yeah, and maybe you can kind of continue to explore that. And opine, should we be investing at the app layout? Are these actually the same substrate to be building on or are they not?

0:21:38 – (Ash Fontana): It depends on what sort of return you’re going for and it depends if you have another source of competitive advantage. You know, just to pick a couple of examples that are quite simple, but I think add some color or help us sort of understand what we’re talking about here. A lifestyle SaaS business, like something that is a really good product that automates something for a pretty niche industry like lawn mowing companies.

0:22:06 – (Ash Fontana): If you’re going for a certain sort of return, as in not some thousand x return, if you want to build a cash flowing business and blah, blah, blah, that’s a really good investment that doesn’t necessarily need AI. Maybe you throw some AI in there to help sort of pass invoices or something like that, or do some customer service stuff. I think like the AI question there is not like fundamental to you because of the return profile you’re going for.

0:22:32 – (Ash Fontana): I think there are a lot of other businesses where you might be going for more of a venture scale return, and AI might be helpful in building that product and building that business, 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.

0:23:00 – (Ash Fontana): Sort of like Salesforce, right? Like is Salesforce core competitive advantage AI? They’re incredibly good at AI. They’ve invested a lot in AI, but I would argue it’s not, it’s ecosystem, actually. And bending off superpower is building ecosystems. And so AI is more of a sustaining innovation for Salesforce and not core to its competitive advantage. And so I just, I think it depends on what you’re going for and what you have, and that will determine whether you have to really make the bet that AI is going to give you your competitive advantage or help you create a product that is of a certain scale or not.

0:23:40 – (Cheryl Mack): I would love to go deeper into all of these. I know, but you did come on this podcast with an slightly adjacent topic in mind, which is around some thoughts you have around first checks and 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.

0:24:03 – (Ash Fontana): Yeah, yeah, that is the name of the podcast, and we should probably talk about that.

0:24:06 – (Cheryl Mack): I was thinking that, 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?

0:24:15 – (Ash Fontana): We don’t really use checks.

0:24:16 – (Cheryl Mack): Oh, yeah.

0:24:17 – (Ash Fontana): We’re a little bit more sophisticated with our fair enough these days than America. So a founder once said to me, and I was the first investor in this guy’s company, and he said to me, you know what? The only value an investor ever adds, because we have value add investors is when they write you your first check. And, you know, hearing that as his first investor, you would think I was offended because it would suggest that, like, everything I’ve done subsequently was completely useless to him, which is not true in that case.

0:24:48 – (Ash Fontana): But I actually really like 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. 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. And if you’re not the first investor in something, I think figuring out if you’ve done anything useful is really hard.

0:25:17 – (Ash Fontana): So that’s one thing I would say about first chick investing. The other thing I would say is, it’s been my experience, and people could argue this all day long, but, and we might. And we might. We’ve got some time. 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 bar is set really early, and also how people treat each other and everything that’s all set really early and determines who you can hire subsequently.

0:25:53 – (Ash Fontana): And the first product you usually build at a company is a product you’re still selling in ten years. You might have 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 succeeds or fails, as in you either have good competitive positioning at the start or you don’t, as in you’re either entering a supercrowded market, or you’re entering a market that’s sort of on the come and you’re entering way before everyone else, and you give yourself enough of a head start to truly have a chance of succeeding.

0:26:31 – (Ash Fontana): And all of those things are determined really early. And so I think the fundamental decisions 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 decisions that have an outsized effect down the line. So that’s why I’ve always liked being first and why I continue like being first if I can. And there’s a nuance here. There’s like 1st. 1st 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, you know, incorporate the company, hire their first person and launch their first product.

0:27:13 – (Ash Fontana): 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. 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. And I don’t like being a VC fund that’s coming in at the series b or C because again, by then you’re just your marginal money.

0:27:41 – (Maxine Minter): And that shifted for you a little over your career, right? As you mentioned there at the top, you’ve done a whole bunch of different roles around the investing landscape. And 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. And then now you’re doing personal checks. And so are you also thinking, are you seeking to be that kind of first dollar in the bank account or trying.

0:28:04 – (Ash Fontana): Yeah, 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 smallish 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 or seeking funding for. 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.

0:28:36 – (Ash Fontana): 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 to invest.

0:28:45 – (Cheryl Mack): On that point, though. Like, I do find that I find myself in that situation a lot of time where I know that my check when I write with the syndicate is about 100 5200k, 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 that 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, like let me know how your round’s going and then I’ll come in. 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 zero to one are lower.

0:29:30 – (Ash Fontana): I tend to solve this in a way that 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. 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, and then we can all invest at the same time.

0:30:10 – (Ash Fontana): So more simply, just put in a bunch of time to try and get them there. That’s one way. Another way is to just do it anyway. 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. 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 100k we can get to that point, get them involved. No one else in the world will be interested at that point.

0:31:07 – (Ash Fontana): But they will be interested enough because they’ve seen this play out before and they think that’s a crucial point and whatnot. 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.

0:31:32 – (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?

0:31:38 – (Ash Fontana): Pretty often. But that’s not because I have some extremely good knowledge of the global technology ecosystem. It’s cause I only really do one thing right, which is machine learning and AI.

0:31:51 – (Cheryl Mack): So if you’re really focused, then it’s easy to know.

0:31:54 – (Maxine Minter): It’s one of the benefits, I think, of being a specialist. A couple of episodes ago, we did one on diversification, kind of thinking about consolidation versus diversification. Trade offs are making, and I think this is a really important thread to pull out. Right? Like if you specialize, 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.

0:32:16 – (Ash Fontana): Yeah, I think there’s nuance there, which is, 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. 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.

0:32:48 – (Ash Fontana): 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 industry specific funds unless they’re attached to a corporate that is in that industry, if that totally makes sense, like CMC, 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.

0:33:21 – (Ash Fontana): But I think technology specific funds are really smart in venture capital. Or 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 again, AIML or you see this in bio a lot. Like, a lot of biotech funds focus on, like, very specific 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.

0:34:02 – (Ash Fontana): So I think that’s really smart.

0:34:04 – (Cheryl Mack): Can you make the argument that technology changes so quickly that, like, on the life of a ten year fund, is the same technology still going to be relevant?

0:34:12 – (Ash Fontana): I think you can, but I think that’s even more of a reason to specialize, as in, you can’t keep up with the changes unless you’re highly specialized. Like, there’s no way. Even ten years ago today, there’s definitely no way. But even ten years ago, there’s no way you could keep up with all of the research in machine deep learning, computer vision, which was my original specialty, not just machine learning, but deep learning, computer vision, unless you were specialized, because then there were like, you know, about ten really good papers coming out every couple of weeks.

0:34:44 – (Ash Fontana): Really good papers maybe every month, and reading all of those papers, understanding all those papers, understanding the implications, understanding how they connected to other innovations, understanding who wrote those papers, and whether they’re going to start companies. 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.

0:35:11 – (Ash Fontana): But by then, I had such a good basis in the research and I had such a good network that I could ask, is this relevant? Who’s doing what? That 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 onto that, because, again, it’s all I did. I’m not particularly prescient or smart or anything.

0:35:35 – (Ash Fontana): If that’s all you do and you miss that, then you just had your eyes closed. 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’ve been tracking these people since the day they published a paper, or even before, 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 three came out, it was already too late. Like, what are you going to invest in at that point?

0:36:11 – (Ash Fontana): I don’t know.

0:36:12 – (Cheryl Mack): What are we investing in then?

0:36:13 – (Ash Fontana): Well, everything. If you wanted to invest in the fundamental technology, everything was already worth billions of dollars.

0:36:18 – (Maxine Minter): Yeah. Okay.

0:36:19 – (Ash Fontana): So I think you have to specialize to keep up in very fast moving. Yeah.

0:36:24 – (Maxine Minter): It’s just environment. How do you think about that? Because I think one of the interesting perspectives or spicy opinions you’ve shared recently is fund or solo.

0:36:33 – (Ash Fontana): Oh, yeah, right.

0:36:34 – (Maxine Minter): As an angel or, you know, scale solo capitalist, but without anyone else’s capital behind you.

0:36:40 – (Cheryl Mack): Or, and you’ve done both.

0:36:41 – (Maxine Minter): 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, if you can, big qualification, but if you can invest yourself, you should, as opposed to run a flound.

0:36:56 – (Ash Fontana): Yeah. My view on this is very particular, and there’s a lot to disagree with.

0:37:04 – (Cheryl Mack): Great. Let’s get into it. I’ll be the disagreer.

0:37:07 – (Ash Fontana): Who’s red teaming? Who’s not. There’s a lot to disagree with here, but 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 into SaaS layer, 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.

0:37:33 – (Ash Fontana): 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. 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.

0:38:03 – (Ash Fontana): 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. 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 venture capital, which is essentially private equity, very hard for the next three to five years at least.

0:38:36 – (Ash Fontana): 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 around, you know, what causes funds to fail, what causes funds to succeed? What are good incentives to set for funds, you know, management fee versus carry and where the industry’s at versus where I think it needs to go.

0:39:05 – (Ash Fontana): And there are a lot of views around that that you’re just not really allowed to have if you’re running a fund, because you can’t run a fund business if you have those views. 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.

0:39:26 – (Cheryl Mack): And which bucket are you in currently?

0:39:28 – (Ash Fontana): I’m okay. I don’t really invest for me. I don’t really keep anything. I invest sort of for like a charitable trust.

0:39:38 – (Maxine Minter): Yeah, 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 specifically for funds where incentives lie. I wonder if you could flesh out any of those ideas, because I think there isn’t a whole lot of innovation in the business model.

0:39:58 – (Ash Fontana): No.

0:39:58 – (Cheryl Mack): Like the 22 model.

0:39:59 – (Ash Fontana): Yeah, the 21.

0:40:00 – (Cheryl Mack): Yeah. I’ve heard that the US has a lot of different. Whereas, like, Australia’s standard, I think it’s a relative statement.

0:40:07 – (Maxine Minter): 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. Being thoughtful about incentives, being thoughtful about construction, 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.

0:40:25 – (Ash Fontana): Yeah.

0:40:25 – (Maxine Minter): And for your just maybe not casting a broad brush, but just specifically for your decision to invest just your dollars and not raise capital and not invest other people’s money, how do you think about those incentives?

0:40:38 – (Ash Fontana): Yeah, I think you’re right. Like, the model hasn’t changed since my great great, great great grandparents were sending people off from Venice on shipping expeditions. It really hasn’t changed. Like, that’s where Carrie comes from. The concept of Carrie comes from you send people off on expedition, you say if you come back, you get to keep 20% of what you bring back and I’ll keep the other 80% of the spice, all that you got and it hasn’t changed.

0:41:04 – (Ash Fontana): 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 it 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, setup, 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.

0:41:38 – (Ash Fontana): As someone who is practicing the craft of venture capital in its most pure sense, 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, except sourcing. But sourcing is just as a lot of people say, it’s just shoe leather. You’ve just got to get out there and be there.

0:42:05 – (Ash Fontana): So I just really don’t think that a lot of things that firms spend money on are useful or relevant in the case of a lot of portfolio services, or amount to that much. In the case of just getting out there and go to meetings and having coffees with people, like just buy your own coffee or have it at home. 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.

0:42:31 – (Ash Fontana): And so, for example, I think sharing costs and then charging higher carry like 30% 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 on getting the funds started. And I think another thing is just really important to recognize, which is the industry’s performance is terrible.

0:42:56 – (Ash Fontana): It’s absolutely terrible. We don’t beat the benchmark as an industry, the benchmark being the S and P, and we’re certainly not beating it this year. And I would argue if you take the last one or two years, in the next two or three years, we’re not going to beat it as an industry. 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.

0:43:33 – (Ash Fontana): So 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.

0:43:41 – (Cheryl Mack): I think our last guest was saying that 45% of venture funds in the US aren’t deploying at the moment.

0:43:49 – (Maxine Minter): That was during 23.

0:43:51 – (Cheryl Mack): Oh, during 23 still weren’t deploying last year. Which like, that contributes to the, like, fee drag.

0:43:56 – (Ash Fontana): Right.

0:43:56 – (Cheryl Mack): If you’re not.

0:43:56 – (Ash Fontana): Yeah. Because they’re still charging management fees.

0:43:58 – (Cheryl Mack): Yeah.

0:44:00 – (Ash Fontana): And not putting any money to work.

0:44:02 – (Cheryl Mack): Yeah, yeah.

0:44:03 – (Ash Fontana): And 90 plus percent don’t beat the benchmark.

0:44:06 – (Cheryl Mack): 90 plus funds in the US.

0:44:07 – (Ash Fontana): 90 plus percent of funds don’t beat the benchmark.

0:44:10 – (Cheryl Mack): Crazy. You invest in funds though, don’t you?

0:44:13 – (Ash Fontana): Not really. 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, people. I like, solo gps that I really trust. 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 and raising their fund.

0:44:48 – (Ash Fontana): And then I’m not charging fees on that because I’ve helped them so much get it off the ground. 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 capital investor? Yeah, I 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 property. Very, very, very bad at it.

0:45:09 – (Ash Fontana): So I’m very happy to at least.

0:45:10 – (Cheryl Mack): You know your strengths.

0:45:11 – (Ash Fontana): Yeah, I’m very happy. Stay within my circle of competence. It’s very small and I just stay there. 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. 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.

0:45:34 – (Ash Fontana): 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.

0:45:43 – (Cheryl Mack): And are they using different models on the fees?

0:45:46 – (Ash Fontana): Yeah, 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, year I won’t name them because I don’t need to and because they’re. Some of them are public and some of them are not one, for example, the GPs themselves are putting a gigantic portion of their net worth into the fund. 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 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.

0:46:25 – (Ash Fontana): So I’ve helped them. And another fund is using a heap of software, a lot of really good process and community and good sort of networking tools to build a fund that invests in hundreds of startups per year. 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.

0:46:52 – (Ash Fontana): Not just, but mostly. And I think that needs to happen, both the innovation and seeding more so.

0:46:57 – (Cheryl Mack): That you can write more personal checks.

0:47:00 – (Ash Fontana): That is helpful. Yeah.

0:47:02 – (Cheryl Mack): Oh no, I hadn’t thought about that.

0:47:04 – (Ash Fontana): I do co invest with them but I don’t invest solely in Europe. Like in fact the super majority of the companies I invest in here in the US.

0:47:12 – (Cheryl Mack): Any Australian?

0:47:15 – (Ash Fontana): Not that I can think of as in. Yes, like a couple but percentage of portfolio it’d be ten or so, 10%.

0:47:24 – (Cheryl Mack): And the rest of the world, Asia, anything there?

0:47:27 – (Ash Fontana): Not a lot, no, not really. I stay away from markets I don’t know very well.

0:47:33 – (Cheryl Mack): Fair. Fair.

0:47:35 – (Maxine Minter): So as you have been, I mean already you have been part of so many incredible moments in ecosystem development as you think about from here on out. Thinking open questions for me, 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?

0:48:04 – (Ash Fontana): Big topic. 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. 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.

0:48:32 – (Cheryl Mack): That’s a very. Ten years, like, that’s it.

0:48:35 – (Ash Fontana): That’s the ideal, yeah. Well, it’s three investing periods, right? Yeah, three by three, roughly. That’s something to think about. Here’s my view on this, and I break it down a few different ways. I think I find this sort of skill luck spectrum or dichotomy that is a bit more of a spectrum, really useful, which is are you in a skill based profession or a luck based profession? Skill based professional profession would be like.

0:49:01 – (Ash Fontana): Yeah, a skill based professional. 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.

0:49:09 – (Cheryl Mack): Yeah.

0:49:10 – (Ash Fontana): And I map that spectrum to, are you in an industry that requires high competence or good networks? So do you need to be very, very competent at executing a certain skill, a certain way, 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.

0:49:47 – (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. And so it really, the age, I don’t think, is not the thing. It’s when you don’t feel like you have the energy for networking in a very extreme way.

0:50:15 – (Ash Fontana): Now, I think, what does this mean? Do you get better or worse as an investor over time? I think it means you definitely get better because of the network effect, like quite literally Metcalfe’s law. Every additional node you add to your network, it provides more value for every existing member of your network. And so I think the bigger 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.

0:50:41 – (Ash Fontana): And your career should be only determined by your ability to get started networking and keep up the energy for it.

0:50:49 – (Cheryl Mack): That makes me feel super good about my trajectory. I’m like, cool, I’m on the luck network path. 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.

0:51:02 – (Ash Fontana): You’re on the right path.

0:51:03 – (Maxine Minter): You’re like, that has to be right, because it resonates with exactly what.

0:51:09 – (Cheryl Mack): I’m here for, that.

0:51:11 – (Ash Fontana): I mean, there’s some reasonably good, like, empirical evidence for this in analogous professions. And Michael Moberson wrote a really good book about this and covered a lot of different professions. But he himself, the author, has applied it to venture capital very convincingly. And so it’s not my argument. There’s some good numbers behind it, but it also matches my experience.

0:51:36 – (Cheryl Mack): Excellent. Two final questions. One final. You’re in a position now where you’ve experienced working at funds, starting funds, also writing your own first checks. If you were starting again as a new investor, would you start from this strategy, like, only investing personally or what?

0:51:58 – (Ash Fontana): Absolutely not.

0:51:58 – (Cheryl Mack): Okay.

0:51:59 – (Maxine Minter): No way.

0:52:00 – (Ash Fontana): I would work at a big fund that has amazing 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 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, CFO’s to bring in, or whatever else 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.

0:52:38 – (Ash Fontana): 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 ability to do the job compounds over time, gets better and better over time is rule number one is stay in the game. Don’t get yourself out of the game by losing enough, losing all your money.

0:52:58 – (Cheryl Mack): Don’T run out of money.

0:52:59 – (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. So, no, if I did this all over again, I mean, this is easy to say. 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 a big growth stage or growth stage fund, like Insight Summit, Bain one of them.

0:53:27 – (Ash Fontana): Or now I guess you could say like sequoia growth or Andreessen growth, I would have worked for one of them and learnt as much as I possibly can early on before going out and starting my own thing.

0:53:38 – (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.

0:53:45 – (Maxine Minter): I think with enough time, effort on target and tenacity, they can get there. They might just have to make some sacrifices along the way.

0:53:52 – (Cheryl Mack): Or they could just listen to our podcast where we talk to big funds and learn the learnings from that.

0:53:56 – (Maxine Minter): Totally the same. Yeah. One for one.

0:53:59 – (Ash Fontana): Yeah. We are. I think we have to recognize we are lucky these days in that a lot of the knowledge is shared. It used to be such a cottage industry, so to speak, that you couldn’t learn anything from the outside. No, you can learn something, but I’m sort of with you on that. But I’m also with Max. And that, like, you gotta be there every day and you gotta be in the office, like, next to these people all day long to really learn how that’s done.

0:54:22 – (Cheryl Mack): Yeah.

0:54:23 – (Maxine Minter): And trying to learn from both the, like, explicit lesson and implicit lessons of what you operate.

0:54:28 – (Ash Fontana): And going through portfolio reviews of like, hundred portfolio companies that last for days at a time, like, sounds incredibly boring, but that’s where the good learning comes from. It’s like, why is this company doing really well? Why is this company doing really poorly? What can we do about it? How does this fit into how this whole fund’s looking? Do we have to make some decisions in this fund to get dollars out earlier?

0:54:52 – (Ash Fontana): Should we put more money in this company or not? Not like, you know, all these very, very complicated considerations. 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 or exposed to certain technology cycles. You just don’t get to learn that much from small sample sizes.

0:55:18 – (Maxine Minter): It’s a masterclass. And doing drills on the thing.

0:55:20 – (Ash Fontana): You want to be excellent at sitting.

0:55:22 – (Cheryl Mack): In 100 ic meetings.

0:55:23 – (Ash Fontana): Yeah.

0:55:24 – (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?

0:55:40 – (Ash Fontana): 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 something that gets in the way of functioning in society. But in a sense it’s a superpower. I’ll give you a couple. When I was eleven, 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.

0:56:17 – (Ash Fontana): I not only called my own ambulance, but I ordered the surgeon to do emergency surgery, even though she didn’t agree with me that my appendix was bursting. And I basically convinced her with my eleven year old knowledge of the human body, which was not nothing, but not as good as hers.

0:56:34 – (Cheryl Mack): You hadn’t studied the neurons yet, right?

0:56:36 – (Ash Fontana): No, not yet. Well, partially. I convinced her to do emergency surgery and lo and behold, it was within an hour of bursting. And so that was pretty brave for an eleven year old to convince someone to open you up.

0:56:50 – (Cheryl Mack): It was like very specific. Yeah, you had a very specific idea with what was going on in your own body.

0:56:56 – (Ash Fontana): Yeah, 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 and make it happen. So that was pretty brave, I think, because no one likes having surgery, especially not eleven year olds. But I don’t know, I do all sorts of random stuff like certain mountaineering objectives, but I think relevant to this podcast leaving the fund that I helped start and launch in a period where, for context, this was around the end of 2021, where we were an AI fund, and AI was going hot.

0:57:35 – (Ash Fontana): Yeah, going pretty strong. It’s pretty hot. And the fun was doing really well, and we had a great team and blah, blah, blah, and not to mention the obvious, like there are management fees on the table that you can just keep getting by, staying. But I left for a whole bunch of reasons, which is a whole nother podcast, but that no one will listen to, nor should they be interested in because they’re personal reasons. But yeah, I think leaving a fund is something that takes a lot, especially.

0:58:01 – (Cheryl Mack): A fund that’s doing really well and at a time there are no problems.

0:58:05 – (Ash Fontana): Yeah.

0:58:05 – (Cheryl Mack): Wow.

0:58:06 – (Maxine Minter): It’s also a huge step just in life, right? 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.

0:58:16 – (Ash Fontana): Yeah, it is 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. And yes, you get to keep a lot of that. But for me, I think 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 have you pulled by what you identify with.

0:58:46 – (Ash Fontana): And 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 podcast to go which is that AI was arguably getting too hot. The macro cycles were getting very challenging and so, you know, I ended up on one side of that debate and 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.

0:59:14 – (Cheryl Mack): Big cajones. I love it. Thank you so much for coming on our podcast today. This has been our first actually recorded in person episode so I hope you know that you are in person. Guest of honor, wonderful.

0:59:27 – (Ash Fontana): Thanks for doing it. Thank you.


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