Leveraging AI to Predict Venture Success: John Sharp's Journey with Hatcher+

Okay, three, two, one. Hi, I'm Cheryl. I'm Maxine. This is First Cheque, 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, in short, if you don't want to suck at investing, listen up.

So, John is an interesting one. He's actually Australian but lives in Singapore. I first met John at a tech conference in Singapore. And one of the first things he tells me is, "We've analysed like 600,000 startup deals." And I'm like, "What do you mean by analysed?" And he's like, "We're doing this fund model where we invest in the top companies that get analysed through other programs."

And I was like, "You need to tell me more."

I can't wait for this one. I feel this topic is particularly interesting to dive into—the idea of algorithmic investing in VC, both direct or via funds. Just the sheer volume of effort to collect data on 600,000 companies really interests me.

For him, in particular, right? He was an operator, has been an investor for a while, and now he's building again, but in this different approach. I'm super excited to dive in and learn what he's learned from looking at all those companies.

Absolutely. His background is super interesting. I think he started in music and engineering so he can build, and then shifted into the investment space. He's been doing this long enough to have seen a few platform shifts. So I'm sure he can tell us a lot about that. Can't wait.

Let's dive in.

Let's do it. John, we are so excited to have you here today. Thanks for joining us.

Thanks for having me, Cheryl. I'm super excited as well.

Amazing. So I know you've been living in Singapore., so you may not have heard the First Cheque podcast before, but the first question that we always ask all of our guests when they come on the show is, what was the very first thing that you invested in? We've heard things about houses, education, and even household appliances. So, go for it. Tell us.

Wow. That's a really interesting question. I'd say the first thing I ever invested in was probably education, and I say that in a different way than most people would because when I was accepted into Melbourne Uni, my parents actually couldn't afford to pay for it. So, I had to defer my then interest in music for years until I could save up to go there. Then I decided instead to buy the textbooks and see what I could make out of that. So I spent $250 on three books: "Orchestration" by a guy called Walter Piston, "The Professional Arranger Composer" by Russell Garcia, and a book on orchestration, I forget what it was called, something about orchestrating voices by Nelson Riddle, the guy who used to do arrangements for Frank Sinatra. Oh, and the fourth book, actually, Dick Grove's book on jazz orchestration. So I bought these books and was travelling around Australia, grape picking, I was a firefighter, I was a lumberjack, doing all these sort of weird jobs. At the same time, I was going home and reading about how to orchestrate for the harp with Walter Piston and whatnot. Then I landed in Adelaide and bluffed my way into a studio orchestration job. And it ended up being ridiculously successful. So at age 19, I ended up getting smothered in work, and I went from earning like $150 a week in some crappy job to earning several thousand dollars a week, orchestrating and conducting the Adelaide Symphony Orchestra, which we're not allowed to call it in the recording sessions because they're not supposed to have a side job, but we used to do a lot of film music, commercials, TV stuff, all this sort of fun stuff. So for a $250 investment, that turned into a 10-year career, which is probably one of the funnest things I think anyone ever gets to do in their lives, which is, you know, conduct a room full of people, room full of really talented people, I should say, and produce, hopefully, beautiful music. So that was a really good investment. I have to say that paid off, but here's the interesting thing. That was the first industry I have to say that was disintermediated by software. And so that I think was a real turning point for me because at some point I had to make a decision. Do I stay true to the roots of Beethoven or whoever, right, Nelson Riddle? Or do I go off and embrace the emerging world of synthesis, digital waveform technologies, and software? And so at the tender age of, you know, early 20s or mid-20s, I guess by that stage, a difficult decision to make as to whether you stay pure to the traditional roots or whether you go off into this unknown new world. Wow, that's an interesting one.

Yeah, I've never had anything even close to that. When you started talking about orchestration, I was like, oh, like tech, software orchestration, and you're like, no, no, true music.

I thought like, maybe he's saying origination wrong, like deal origination is what he meant.

Yes, exactly, that's what I do now.

You have an investor hammer so everything looks like a nail.

But yeah, it was so, the super interesting part about that I think is, is the part where when I was renting out the Opera House in Sydney and charging $30,000 for a commercial, there was some group of guys down the street that had just bought a Fairlight synthesizer and charging $300 to go 'da da da da,' you know, on their synthesizer. And so there was this giant change that happened economically in the business where the typical film score went from being like a $100,000 gig, for example, to being a $5,000 or $10,000 gig. So everyone that is sort of based their lives like I did on that decision to do a lot of learning and a lot of training to get to that point suddenly was left in a situation where automation and software and dare we say AI had come in and completely transformed the industry.

Wow, that's so interesting. Do you remember the first time that it dawned on you that software was going to come and disrupt your industry, that it was going to come and eat your lunch? And was it a consensus at the time?

I lost a gig to one of these guys and the resulting commercial sounded atrocious, at least to my ears, because it was very early days for synthesizers. It was very early days. And I called up the guy who was the concertmaster at the Sydney Symphony because he wondered why we'd lost the gig. I said, "Well, we lost it to these guys out in Manly that have this like, you know, Fairlight synthesizer and whatnot." And he said, "What's a Fairlight synthesizer?" And I said, "It's this thing that replicates, you know, digitally the sound of an orchestra." And he said, "Oh, we got to fight these guys." And at that moment, I thought, here's this, like a 50-year-old dude, telling me that we've got to fight these guys. " And I thought, "I've seen Terminator. I know how this is going to go down and I've got to go with the software guys." So then I did the same thing I did like years ago. And I went out and bought books on digital waveform analysis and just started learning a lot more about that. And that became really fascinating to me, actually. It really broke me. And, and that was sort of the, the lean into software and networking and moving stuff around networks. And that laid down an entirely different road. And that road included scalability because suddenly if you're doing those kinds of things on a network, you can do work for Saudi Arabia or Singapore that you're not just locked to Sydney anymore or Los Angeles as we ended up doing stuff for, so you end up doing a lot more stuff when you broaden yourself out and you use software and you use networks. And so that was a real eye-opener for me. And I've never gone back, obviously. If you put me in front of an orchestra right now, it would genuinely sound like crap. I always thought that those people with the sticks didn't really do much anyway.

So I'm sure you'd be fine.

The guys with the sticks don't really do much, but it is an interesting management exercise, right? Because you're standing in front of 60 people with big egos, massive amounts of talent, and the guy with the stick has to basically... Let's call it the guy with a baton. That's a little more, a little more sort of artistic. The guy with the baton has to basically stand out in front of them and get them all to play at the same volume and the same tempo and with the same kind of dynamics and emotional context. And so it really is probably the most amazing management exercise I think that any manager can go through because we all have to deal with incredibly talented people and getting them to play together is one of the hardest things in management just leads to so many political fights and so much drama and so it's a really good training ground for all that because yes orchestras have a lot of drama and a lot of prima donnas and a lot of other things going on inside of there that,, that you can only imagine. I came away from conducting orchestras with the belief that a lot of what goes on in orchestras is much worse than what goes on in rock bands. Those guys are like complete wimps compared to what's going on inside your average orchestra. So a lot of respect also for those guys because they played hard. They partied hard. And, and just enormously talented people. But yeah, the guy out front has to respect those people, but they have to kind of respect the output that's coming out there. So yeah, a very interesting management exercise.

Oh yeah. I mean, we, from the executive coaching side, a big part of helping new leaders, especially if they haven't kind of experienced it firsthand before. New leaders try to access semantically or experiential circumstances when groups of people are doing hard things together. And a lot of folks, you know, have never played sport, they haven't spent a lot of time around sport. And so,, kind of orchestras or bands playing together is another entry point that people have seen, like the reality of building together, building trust, you know, that smooth handoff that happens when one player kind of hands off to the next. It's a very beautiful thing. I've actually never spent any time conducting, but it can be a wonderful entry point for a lot of new time managers to learn how to do that. So that's cool that that was your, that was your actual entry point, that you were actually conducting these large groups of people. I think that's a really interesting experience to have had relative to today's moment in AI prediction, just the kind of general disruption and widespread disruption that's happening at the moment. I'd love, maybe if you have some thoughts, maybe some pearls of wisdom for our audience, what did you learn going through that moment of disruption that is useful for today and to help people navigate this particular moment of disruption?

That's a really great question because I did live it., and this is years and years ago, decades ago,, when this happened, and it had a profound effect on their industry. I remember calling one of the very, very best people in our industry who'd moved to LA and finding him on top of his roof,, on his cellular phone, doing the roofing himself. And I said, "Dude, you know, there's people you can hire for that." He said, "Not on the budgets I'm getting now." And he'd gone from earning like literally hundreds of thousands of dollars a quarter to earning not very much money,, as a result of that. 'Cause he never really did make that move. But if you speak to your average musician today, they're completely across all the digital tools and everything. I went and spent some time with,, with the drummer from my rock band that I joined in my mid-twenties in Cincinnati recently. And he had just the most amazing digital ensemble of tools that created the most incredible abilities in the context of his living room that it would have been impossible to create 20 years before. So I think there's two paths. There's, there's the pathway by which you don't embrace it. You just ignore it. And you hope that by ignoring it, you'll get to the end of your useful career without too much disruption. But what happened to us back in that time happened so incredibly quickly that you had two paths. You either embraced or you went. You know, you went away. So I think the most important thing here is to embrace change and understand it. And if you don't understand AI, figure it out as quickly as you can. Because if you embrace the AI options that are available, you'll become expert at them. I mean, the nature of the way these tools are being presented to us. I just tried Notepad LM a few days ago for the first time, which is just absolutely insane. You insert a document, it outputs a podcast. I mean, it's just madness, you know, but it's so incredibly easy to use that there's no excuse not to use it or to become familiar with it. Now, is it going to be a podcast as good as Maxine, Cheryl and John? Absolutely. No way. Does it sound a bit like us talking? Yes, it does. Yeah. So you can see where that's going to evolve too. And if you feed it enough podcasts, then it's going to be a podcast. It's going to be interesting, right?

Absolutely wouldn't sound like us. It would be a creepy, creepy version.

Yeah, even your industry now is getting transformed, right?

Yeah, these notepad LMs is your, is your moment here that you'll have to adjust to and if it's going to flood the internet with You know, 220,000 fake podcasts a day that are all fairly listenable and, you know, with feature chirpy voices and supposedly intelligent banter., that this is a problem. And so you have to figure out how, how you leverage that to make that work in your favor.

But yeah, it's going to affect all of us and it's going to affect all of us in the areas that we totally didn't expect it to affect.

I think that's the, that's the bit, like it's going to affect you in the areas you totally didn't expect. For example, if Notepad manages to replicate Cheryl's shit talking, like it can have it.

No one can replicate Cheryl's shit talking. Not a

chance, not a chance.

But I do, I feel like the areas that you're not expecting the innovation to come in, I think those are the really interesting ones. I'm in San Francisco at the moment and it's demo day season., everyone. You know, like YZ just had theirs, EF just had theirs, 500 is having theirs, PAIRS was today, like it's

all

on.

And there's lots of the kind of traditional ones that you would expect, lots of innovation happening in kind of like influencer spaces, marketing spaces, et cetera. And this is just completely out of left field, like innovations in places I have never thought about as industries or the picks and shovels seeing a lot of that kind of LLM or like gen AI optimization tools. That is just like, wow, I just hadn't thought about that need, but it's so clear now that you talk about it., or, and then the whole bucket that's like, I've never even thought about, I have no idea how that would work. I'm going to go ahead and pass on that because I don't even have an access point, but I do. I think that the pace of change that's happening at the moment is unheard of, but it will be, you know, in 30 years, we'll have another version of this conversation and it will be considered a slow pace.

So we just made an announcement, a soft announcement, I'd say, in Sydney, actually, the last time I saw Cheryl was a few weeks ago, and we,, just slipped into the audience some,, some findings that we'd had recently with using our AI for Venture, and so we've been building a Venture Selection model using AI for close to eight years now. Dan hooked up my partner and I. Dan's amazing. Dan has been working in AI for 30 years. So much so, he's had numerous AI startups. I think the last one before us, the last startup he worked at before us, he exited for a couple billion dollars. He's done really well in that space. And the algorithms and models that he's built for Hatcher are starting to really bear fruit. So we're even seeing within venture, no one would have predicted that it was possible to score a company and then two years later, have that score be predictive of the valuation uplift that we're seeing there. But we're now seeing that happening with the Hatcher score. We can actually correlate a high Hatcher score now with a higher valuation markup or a low Hatcher score with a lower valuation, with a low valuation score two years later. And we've been, you know, we're pretty excited about this because this is a first in, in venture capital. We're still trying to get our heads around how we actually release this information., because it's a pretty bold statement to come out and say, well, we're able now to predict venture returns., but it's the old story. If you have an algorithm in a hedge fund and it works 51 percent of the time, you're going to make money. And so we have an algorithm that appears to work more than 51 percent of the time for venture capital. And so,, we previously had focused on the, as I think you guys are aware, we'd focused on how you construct a good looking portfolio in venture. Okay. And what the sort of modality and what the sort of shape of that looks like., and now what we're able to do is say, here's how you might want to think about selecting for that portfolio. So these are now, what we're getting now is to the point where for an average GP working in venture capital, we can do a lot to assist them in the selection process and make sure that their portfolios are, is. As good as it can possibly be. And that's based on a pretty rigorous approach that we've taken over the last few years to get to the point where we're able to know that that model works. So from A. I. Here's an example to your point of A. I. Affecting something no one ever predicted that it would. And everyone told us it was completely stupid to even focus on doing predictive analysis and venture because it wasn't possible. Well guess what, it is possible just like all the things that people say AI can't do, this ends up being another, yet another thing where AI can, can very successfully, you know, throw its hat in the ring and say here's some,, Yeah, we've got a surprise for you. We can predict what kind of,,, startups are going to be successful.

Yeah. So you and I, I mean, we hadn't caught up in a number of years,, but we, we met up again,, for the first time, probably in what, six or seven years at,, at a conference that you were at here in Australia. And you showed me some of those models and the scores. And, and I was really impressed with., how you, you know, were able to track the valuation in that range and, and where it was going. It might be helpful for the audience to,, just start at the beginning a little bit and, and explain exactly what Hatcher is and,, and what you've learned,, doing some of this analysis.

Yeah. So, so,, the beginning of this is,, is,, I,, I had an exit in,, 2000,, and 11 2012, moved back to Singapore., and, and started investing in startups. And that led to being engaged with a group of about 40 other angel investors. And we admit we made like 20 investments and we put about 20 million,, in, in the pot. And,, and invested that I put in like about 10 percent of it. And my buddies put in the rest. And, then we watched as over the next four years, this did absolutely nothing positive., it stayed at 20 million. And so between 2012, 2016. I'm sitting there looking at this kind of line and it hasn't moved. And I'm like, this is super annoying. So basically what we started doing is looking at why this number had stayed the same. Why was our portfolio still worth 20 million in 2016 versus 20? And I didn't understand J curves. I didn't understand very much at all about venture capital back then., so I started doing some analysis of what kind of portfolios might be predictable., and I did that with the monarchy. There were other co-founders at the time, and we seemed to get some good results when we loaded up a portfolio with a lot of startups like 500 or 1000. It turns out that Dave McClure is absolutely spot on with 500 startups, by the way, because that based on our later analysis seems to be really the cutting point where you can actually get a predictable return and his. Returns would seem to have indicated or validated his model We did a lot of analysis around that and I remember calling up and I had a buddy Dan Hookter in florida who was the smartest guy by a long shot that I knew In data and so I called up Dan and I said,, hey dan, you know I've been doing this analysis of a venture fund, portfolio and, it appears to me that if you You know, get a lot of investments and you put them in a portfolio that this could really create a much more predictable return. And he said, basically, you're dreaming, you know, forget about it, it's not going to work. You know, many people have tried,, no real value in doing it. And I said, well, look, would you mind just having a look at the numbers? And he said, sure, I'll have a look at the numbers, but yeah, don't, don't expect anything great. So the thing I love about Dan is that Dan is a full scientific method. You know, if he makes a statement and then the data says that he should revise that statement, he will. Yeah. Which is a wonderful thing to have in a partner, by the way, just amazing. So he called me back about 10, 10 days later. He said, look, I've looked at these numbers. He said, could you get me some more data? And I said, Oh, it sounds like you're interested. He said, yeah, he said, I think there's something here. And so that led eventually to about 600,000 rounds of data being analyzed. And now we are absolutely able to. Show that the right kind of portfolio is not 20 companies because that's just an absolute crap shot. That is all probability based. The right kind of portfolio is actually, you know, 100, 200, preferably 500 or 1000 companies in a portfolio that will return essentially an index for one of a better word of venture capital returns. Anything less than that, like anything in the sort of 10, 20 company area, like our first fund, you're playing with fire. You, you are making a statement that somehow you're a magician and you can take this incredibly small data set And make it work against the odds of that, that are literally one in 10, 000. The actual odds are only one out of every 100 business plans get selected. And only one out of every 100 of those goes on to be a unicorn or an absolute portfolio maker. So it's a one in 10, 000 bet, right? So if you're creating a 20 company portfolio, you've got one in five chances of having a unicorn in your thing and having an amazing portfolio. So that's why our first fund was basically where it was at. And the second fund we created around this model. Now the interesting thing about the first fund is we're now three X 20 percent IRR. Everything's great. Right? So, but that is pure probability. We ended up having two or three really decent bets in that portfolio that have gone on to create good value for those shareholders., but that's just, you know, that's just probability. We were one of the very lucky funds that has 20 companies as a portfolio that does three X because 75 percent of those funds do not do three X. That's the odds. I say, so we all hear about the ones that do the three X or four X or 10 X, right? We never hear about the funds that don't return the money or do less than that. Cause they just quietly go away or become zombies. So I'm very happy that our first fund is returning a nice multiple, but I would never go to investors and say, that's because of our genius. I would say that as purely probability based, because that's the way venture capital works. And if you want it to work for you, you need to recognize that just like the gamblers did in the 17th and 18th centuries. And go to the casino knowing the odds and knowing what kind of strategies you should deploy with respect to those odds. And if you work the system that way, you'll do really well. If you pretend that this is all based on genius, you're making a fundamental error of forgetting the fact that everyone in this industry is pretty smart, right? And so if you're setting yourself up to be a little bit smarter than that, well, there's not a multiple involved. Right. So I don't know. Some people are pure geniuses and they could probably make that claim, but not the entire industry. So if you want to succeed in venture, you've got to truly play the odds in a very intelligent way.

Right. And I mean, I think the reality is, if you spent any time around ventures and you spent any time, you know, with funds and recognition that the fund performance is distributed along the power law in the same way the underlying asset is distributed along the power law. And I will say, it's very common when people first start investing in this class. Because the power law doesn't pop up in a lot of other asset classes. So, you know, it pops up in maybe the creator economy and content, but it all, but it pops up in venture. And so it is something that,

but not bonds are no, not for, you know, treasury, treasury bonds or houses.

No, probably there's someone out there that's shouting at the screen right now saying, yes, it does to this very small extent. But I think the best analogy is like sports, right? You know, Moneyball was such a great movie and book. in respect because it just shone a light on the fact that people have been selecting for prettiness when they should have been selecting for talent., Billy, what's his name? I've seen your moment here. But the guy who's the manager with open eyes, you know, when, when he said about looking purely for talent, yeah, the guy played by Brad Pitt in the movie, when he set out for pure talent and, and based his whole thing on statistics and runs battered in and, and yeah, all the useful stuff. He did what no one had really done before. They just had a bunch of scouts going down this guy. It looks like he can hit. You know, and they hadn't spent enough time looking at the raw statistics of what wins games. Now, the difficulty occurs, as it did in Moneyball, is when people start replicating the strategy, everyone's on the playing field now with the same strategy. And so that's what's also going to happen with Venture. Like, we're one of the top 20, Global data driven VCs, according to this report that came out last year. That's great. Cause there's only really 20 people working hard in this space at the moment, but when they're close to 200, right, or 2000, it's going to be a bit harder. Yeah. When it's 2000 and it's the entire industry and it's every fund., and every fund is, is doing what we're doing, which is using large language models and algorithms to, to, to generate returns., then it's a completely different playing field, but remember, this is what happened in the 90s with public equities, right? We went from a bunch of like manual processes that Solomon Smith Barney to, you know, multi billion dollar dark pools. At Goldman Sachs, all trading algorithmically amongst each other, right?

Right. It's all, all trades are done by bots now, so there's, we don't, humans aren't even involved.

Right. Yeah. So we would be mad not to expect the same thing to happen within Venture. My prediction is within 10 years, You'll have some pretty aggressively algorithm driven, hopefully Hatch will be one of them,, very aggressive algorithm driven,, funds that are using their fundamentals and analytics that they're, that they're able to derive from these companies to build portfolios. And, the whole thing will completely change. It'll be a lot more like public equities at the end of the nineties and a lot less like the manual sort of archaic processes that we see now.

Mm. Yeah. I mean, I think that the, the key thing though is I would imagine there's an enormous amount of infrastructure that has to be built before you are ready to purely algorithmically trade and. If we think about the earlier stages, right, if we think about pre-seed, where we play even seed, increasingly today, Series A, like there isn't actually that many data points that you can access without a human being an intermediary.

So we hear that a lot.

Yeah. What kind of data points do you grab?

Yeah, exactly. We hear that a lot. There's not many data points, but. But then if you go to your average, like,, VC conference, right? And then Sheryl, this is, I've, I've heard you speak on this as well. Yeah, what do everyone talk about? Everyone talks about the team, right? So there's not a whole lot that changes with Maxine or Sheryl or John when we go become a CEO of a pre seed company, and now we're a CEO of a series B company. All that's changed on our CV really is three years or four years of working at this startup, right? But our CV is effectively unchanged. Where we went to school, maybe we did some course in the middle, but I doubt it because we're working 18 hours a day on our startup. So, there's a lot known about the team at the beginning and as that kind of comes in, there's a lot known about the market that you're going after, the problem you're trying to solve. And so, for a company like us, it does a really brilliant job of collecting all the data that's available on a particular thing, either by iterating with the founder over a series of like, we do this thing. Like we do pitch deck analytics, we do company description analytics. And so if we score a company description, 17 out of 50, the message we're giving the founder is, "Hey guys, there's not enough data here for investors to make a decision." And so we're going to keep prompting them and pushing them to fill up as many of those sorts of silos as we can till we get to a point where they have enough data to give us for us to start making an assessment. Now, that's just on the stuff that they know, but there's a lot of stuff out there that the world knows. about the problem that they're solving, whether it's breast cancer or climate change or, or rocketry or whatever that problem is that they're solving. You can very quickly find out who all the competitors are, which we have a database of 600, 000 companies. We know who your top 10 companies competitors are. When you pitch us, we just go bang and we devolve your application into math and we go out there and we find the top 10 guys, we've actually found the top 250. competitors. So we know what the competitive matrix is. We know what the team build up is. We know the problem. We know the market size intuitively within 15 seconds of you submitting your business plan. So the data points are actually out there. It's how you gather them. It's how you make sense of them and how you then construct a scoring methodology around that. We spent eight years constructing a scoring methodology. Takes a heck of a lot of work to get that right. But we're now at a point where that's a fairly reliable methodology. And so I would say that we are at the beginning of figuring out the selection piece of this. And then the next phase for this is how you go through the back office piece of understanding what's going on at that company and how well they're executing. Are they on track? What milestones should be in place for that company that every other company has? And we've started doing that with our valuation milestone., analytics where we actually target what the valuation journey should look like for that company, which is pretty cool. And so these are all things that we're gradually building as capabilities into the fast platform so that people can start moving into that journey of a data driven or data assisted, venture selection and venture building process.

So it sounds like at the earlier stages, the data points that are really valuable for that your system kind of orients on is team and team CV, market dynamics, competitive dynamics. And then as you're progressing them along that journey, kind of traction dynamics and execution dynamics. Thanks.

Yeah, also, we look at comparative valuation expectations, which are indicative of, you know, whether or not we're gonna be able to make money out of this as sort of venture investors.

Um, we're looking for market size.

Do you do that based on location?

Yeah, we looked at, we waited for the location, for the sector, for the stage. The stage is a really tricky one because a Series A in Singapore might be a seed in San Francisco. And so you've got to have these kinds of adjustments that you make. So we actually use round numbers.

Yeah. So we'll refer to round three rather than calling it seed or series A.

The naming is so messed up anyway. It may as well just be like fruits at this stage. Cause it's been changing, right? Like I'm doing my mango route.

I'm going bananas. What's the traction I need at banana? That's really funny.

I think, I mean, it's constantly moving, right? But. As you know, that has been the case for a really long time, right? Seed never used to be a thing. It used to be like VC, and they were all coming in at series A. In fact, Bessemer opens their investment notes on certain deals, and their one on Shopify actually makes me want to cry.

So they,,, it's like they are agonizing over the decision to invest in Shopify and they're asking themselves the question like, can you even build a good software business in Ontario, Canada? Shopify at the time had five million dollars in revenue, sorry, five million dollars in profit and it was, the valuation was five million dollars and they were like, do we do the

deal?

Don't know. I was just like, oh my goodness. Wow, what an endowment in Toronto.

But yeah, we all hear about these crazy deals like that, right? By the way, Shopify was the first time I ever saw an enterprise UI that I just thought was wonderful. Really? Like wonderful. Yeah. Yeah, I just thought it was just so incredibly well designed and well thought out. It made me weep for all the other UIs and UXs out there because I just thought it was just stellar. And their integration with PayPal, it was just brilliant.

It's just been beautiful. Anyway, I mean, I think like, obviously the game has changed so much since then, right? To build a business when Shopify first started, to be fair to Bessemer, so different from building a business as it is today, right?

And I think that's what. It's really interesting given the nature of what Hatch Plus is trying to do is to pull these data points and especially historical data points and make predictions about future valuation dynamics and stuff. And it just seems like such a dynamic system to be using historicals for prediction.

Now that's what humans are doing to be fair.

It's in that category of business where you've got to go big or go home. So what we're really building at Hatcher is an operating system for venture or for family offices or for an emerging class of alternative investments, let's just call it that. And we have to get AI across the entire infrastructure here for it to make sense. As I mentioned before, it's not just about the initial selection. It's about following through on that, which means this permeates your fund admin, permeates your managers, it permeates your reporting and what you put in the reports. And so it's a massive, massive exercise. As it turns out, we've spent. Literally tens of millions at all. So if I get to this point,, because we want to win, we want to be the guys in this space that have the integrated approach, but kind of building an iPhone approach by the way. So we're not betting only on our own technology. Like if we license our technology, for example, to Microsoft, which we just did,, for a venture. And they had some technology that was really cool. We'd license it back, right? We'd bring that back in and we'd say, okay, we're going to allow that to sit next to our stuff and make the best technology sort of win because we think that approach is a lot better. If I can offer people AI powered cap table management solutions or five instead of one and allow you to choose the one that works best for you. are the one that you've been using. Then you don't have the switching cost. You can come right on the platform. So our whole approach here is to create a platform that where I started here and goes right throughout the platform and the process automation sits on chains and supports all of those decisions that are made up here that are assisted by I. So what we're building here is a really massive bet that people are going to move like they did in the nineties. From completely manual or very, very manual systems to completely automated systems. And the reason we know that's going to happen is that the time frames, there's so much capital sitting out there that if I can present you with a system where you can deploy that capital with a really good feeling that you're going to make returns from that and allow you to operate fairly seamlessly, consistently with the same best practices in play at one of the top valley firms, then I know that I can make that bet on that technology offering. Because there's so many people that would like to be able to do that, but they don't have the analysts. They don't have the, you know, the ability to create those giant, you know, people based structures, but they do have the ability to invest a few tens of thousands of dollars in. You know, annual licenses of a patch of software. And so that's the business that we're in, trying to transform ahead of time. What we see is this massive opportunity in venture, but make it digital and make it accessible to everybody that wants to play in that alternative space. Because let's face it, I mean, with, you know, if I don't know what the Dow is today, but it's, it's pretty tough, bonds, not that interesting. Real estate, extremely well known and well understood throughout the world. It's getting much harder to make a buck there. And so alternatives and playing in that space intelligently is an area that everyone is rapidly moving towards: private credit, private markets, crypto, these are all areas of extreme interest to people, to investors now, and we're in the front driving seat of that. leveraging AI in, in those areas to help people make those investments that make money from those investments.

Super interesting. So of those, I think you said 600, 000 data points that you've analyzed, and I think you're kind of active.

600, 000 companies, actually. 600,

000 companies that you have, have analyzed.

What have you learned? What correlates to a successful business and what doesn't?

What have we learned? That's a really, really great question.. We've learned that the best qualified founders can sometimes be pretty flaky and run away at the first sign of trouble.. That's nothing new to anyone who's listening to that podcast,, and has invested, before. You are looking for resilience at the end of the day. That's, that's really the, the goal of, of all of us as,, as investors. I think we've learned that when you go into a crowded space in a developed market, that can be really difficult. If you go into an underdeveloped market with a cloned offering, yeah, you can do really well out of that. So there's a few basic rules that emerge. But I think these are already rules that are known to all the investors in this space as well. I think what we've learned from the AI is nothing that would be new to the ears of people listening to this podcast. There's no aha moment. What we've really learned goes back to our initial portfolio level analysis. What we've learned is that if you're making 20 investments or two investments, we've all met the guy that's made three investments and they're all unicorns. We've all been to dinner with that person. And those are

the

worst. Yeah, he's the worst. You know, I don't want to ever have dinner with that person.. They're statistically out there, right? You know, one in 10, 000 of those guys exist as well., and it's super annoying because it's just not what happens. Most people make three investments and they absolutely plummet in value and they have a hard time figuring out what went wrong. What went wrong is they just didn't make enough investments. Statistically, one in 100 of those is going to pay off big time, like a billion dollar exit, and maybe 10 out of a hundred are going to pay off really, really well, but not billion dollar exits. And maybe 30 out of a hundred, according to Fred Wilson, are going to do okay. Okay. And the rest you can just write off. And so if you're riding off 66 percent or 70 percent of your investments,, you know, you've, you've kind of, you're making more than three because statistically you just need to, right? So the

power law, the power law is everything. That's the

one thing, like you said, Maxine, it's, it's not something that doesn't, that comes up in other type of investment, but you enter the venture world and like, I can understand why it's confusing because you enter the venture world and you're like, why wouldn't I apply the same investing logic that I've,, that has worked for me in other assets?

You're so right. And like I said, I spent four years in venture, not understanding the power law or the J curve or some really fundamental things about venture. And, and so, yeah, I think there's some, some real basic good learning that you can do, but I'd say the basic thing too, to Maxine's question. The basic thing I think I've learned is the importance of doing a lot of investing in venture taking an index approach as opposed to trying to pick the winners because you know, the wrong founders will seduce you., the right founders will stammer and not say the right things at the wrong or they'll say the wrong things at the right time., and, and it's just, it can be confounding to try and pick winners in this space. One of the companies that I thought had the least chance for success is now one of our top performers. And our portfolio and one of the companies I thought had the greatest shot of success is right at the bottom and is out of business. It's

always like that, isn't it?

Yeah. It's super annoying, but that's the experience of every investor. Yeah. Like my, the best company that was ever put in front of me, I passed on because I just thought the guy had a really limited understanding of his business and, and, and it just a tiny, tiny, you know, set of assets relative to his valuation at the time that company is now an 8 billion company. And I was offered at a $ 10 million valuation. So that hurts. But on the available information at the time, there was absolutely nothing that indicated this was going to be an 8 billion company.

I think that's actually something you have to, you just have to truly internalize. To do this well, you have to worship at the altar of the power law. Like it dictates everything that you do. You really have to recognize how important that is. It is unavoidable and dictates the outcomes. And I think diversity. Yeah.

Just show up every day, pray to the altar, power law. Oh, please, please power law grant me

successes. Grant me successes. It's so true. And I think like, you know, when you are, especially picking at the earliest stages, and we spend a lot of time thinking about this as well, there are companies that I know I have passed on. Because there wasn't sufficient data for me to be able to make any reasonable prediction that it was possible that it would be successful. And they might be hugely successful, right? I think that they're, especially in the Australian ecosystem, we throw a lot of shade on the people who passed on Canva when it was, you know, a yearbook automation business. But like, if we take you back to that decision and the data that you had, Like what is the probability that you could have reasonably predicted that other than if you were doing founder investing, right? Market analysis would just not have gotten you to that outcome. Maybe the founder's analysis, but the market.

Well, also market analysis, how do you predict a pivot? Cannot. You can't. Every one of these companies, there's been like Canva, you just mentioned that had a consequential pivot., Airbnb had a massive consequential pivot, you know, I think it was the drummer of mine. From Cereal. When they went to like, you know, whole houses and. Instead of just mattresses on people's living room floors. Yeah, exactly. Mattresses on, that every one of these companies at some point has a pivot. And, and, and you're betting pre pivot in so many of these cases. And you're hoping that the founder has enough clue and support from his board and all the rest of it to be able to continuously pivot till they get it right. And the other thing is you're hoping that they have a board that understands that they're in the longterm, you know, pharmaceutical business. They're not in the short term, you know, sell something for profit business. And this is another huge problem that happens in these startups. if you have the wrong people backing the right kind of model. And, everyone knows that for, for, yeah, biotech or whatever, it's all about getting those FDA approvals and everything else. And you're not gonna make a dime until that happens. But when it happens, your valuation could, like, jump 40X, you know, like, the next day when that stuff comes out. So if you have people on your board that don't understand those dynamics, you're screwed. You may as well give up now if that's the majority of your board., and so that's another thing. I think it's so important. You have to have the right kind of backing for the right kind of model. There's so many things that have to go right for a startup to succeed in this world. And that's San Francisco, Singapore, Australia, Sydney, doesn't matter. You've got to have all these things aligned, which is why the CEO needs to keep all this stuff in mind. I think when they're, they're building their businesses, but look, it's a fascinating world., that we inhabit, which is why we do what we do, right?

100%. Absolutely. I feel like we can keep having this conversation forever, and sadly we're coming close to the end of our time. So, the last question we like to ask everyone who joins us is, what is your biggest big cojones moment? A moment where you felt really brave?

My big cojones moment was,, was when I was 19 and I bluffed my way into conducting the Adelaide Symphony Orchestra for a commercial for Australia's biggest department store on the basis of almost zero experience and,, and a modicum of talent and managed to pull it off and standing in front of those 60 people never having conducted before. Never having actually written live for strings before or anything like that, and pulling it off was certainly my big cojones moment. And it took me from a tiny, tiny salary and no prospects and being dirt poor, to being in a really great place in the world. So sometimes you gotta, you gotta have those big cojones moments, but that was very early in my life. And there's been a couple since, but that was,, That was a big one for me and, and proof that if you have confidence in yourself and you know your stuff, sometimes you just got to put yourself out there and, and yeah, follow your gut that you know how to do this.

I love that. So good. Thanks so much for joining us.

Thanks, John.

It's super fun guys. Let's do this again. I loved it.

I love it.