Of all investors featured in our Spotlight Series, Don Nguyen may have the most diverse background; a trader turned lawyer turned founder and angel investor. Our discussion covers everything from 'efficient market theory' (and why it's wrong), to the mathematics of follow on investing, to becoming a lawyer to present his family in a legal dispute. Now, Don focusses on Aerofiler, a company he founded in the legal automation space.
Read on to learn more Don's approach to venture investing
The Key Points
- Don studied law to resolve a personal dispute when professional lawyers couldn't solve their problem, and won.
- Tutored in uni by Scott Farquhar in the early 2000s.
- Market sizing is an underrated area of focus for Don. To generate competitive returns, 100x outcomes are necessary.
- A track record of execution is a better predictor of success than any degree or work history.
- Emerging markets provide opportunity given more reasonably priced rounds, which underpin a successful portfolio,
Find the full interview and an edited transcript below.
Talk us through sort of like some of the formative parts of your early career that kind of led you to where you are today as a founder.
So I went to uni in the early 2000s. My claim to fame was I was a year before behind Scott Farquhar, Mike Cannon-Brookes. So Scott Farquhar was my tutor at uni. So started off a pure tech capacity. After graduating, I went into stock market surveillance. We wrote algorithms to detect insider trading software which we sold sold to ASIC all the major investment banks.
And from there, I gravitated toward the algorithmic trading side, because I figured there was more money to be made on the trading than the compliance side. Fast forward a while that went reasonably well, I was happy how that kind of portion of the career went, and then just through, personal dispute that my family was having that, that led me to law, because the lawyers weren't solving things.
And so I ended up I think looking back kind of foolish decision that worked out well, going to do a law degree essentially to resolve that issue myself. With one year of law school contracts and civil procedure, we went to court and got the decision in our favour, which is good.
I ended up falling in love with the law, which I didn't expect. I thought it'd just be a short term thing. So I went to practice law practice the study of law I found was a lot more interesting than the practice of law. The practice of law essentially led to Aerofiler, a lot of manual work from building that sort of thing.
And so at the time, I think that the thing that really crystallised what I wanted to do was when a Partner said it's 10 years to get the Partner: two years of interesting work and eight years of manual labour. So I thought, why don't we just try and look at just compressing, solving, removing that eight years of manual work.
And then I got together with a few of the people I went to uni with and co-founded Aerofiler, which is a legal tech startup.
Tell us about Aerofiler. What are you trying to solve there?
So it's the contracting problem for our in house legal team. At the moment a lot of the tech stack for lawyers, they're progressing a bit more, but it's still shared folders spreadsheets, Outlook a lot of manual work.
So it's a classic SaaS play, where we're trying to digitise that remove friction points and streamline each part of the contracting process from drafting through to filing managing obligations.
How did you find time or the bandwidth to get into angel investing?
The first few investments I made, were from people I knew, my network. So I did all the due diligence myself. And then the, in terms of bandwidth, the capacity I had to manage deal pipeline very low. So I was just doing quite sporadic investments.
And then When I went through the 10 X program. That's how we connected and I learned about the TEN13 model where TEN13 will do the due diligence. It's a syndicated platform and I thought that was a better fit sort of for where I was trading off yeah, in terms of having some other people manage the deal flow, doing the due diligence.
So now, essentially, I've got capacity to look at a much, much larger potential universal deals.
And so do you remember your very first angel investment?
Yeah, a company called DITNO. Basically it stands for data is the new oil.
Clever, clever little wordplay there. So it's a cyber security company, I invested the early 2010s, I think that was just through a person I knew and I did some consulting for the company as well. So I was familiar with what they did, but that's how I kick off the journey.
And so was it an investment that you made at the time because really resonated with the founders and their vision or did it fit into some overarching sort of strategy that you had at the time or how did you get to a yes?
Yeah, partly, up until then my investing was about preservation of wealth.
So just investing in property index funds, things like that. And so I knew at the time I wanted to diversify into some assets with a high risk profile, the potential for larger returns. So I guess a large part of what I was doing was just driven by a portfolio strategy to, to diversify. And like I said at the time, I didn't really have access when you're just starting out, if you don't have.
Yes. So the network, you don't really have access to any deal flow. So I guess the investment fit into more, it was more sort of a portfolio play rather than, looking at a specific company. And that's, yeah, that's how I got the journey.
What was the process like when you made that investment from a DD point of view?
I guess from a tech perspective, I was pretty familiar with it.
So I knew that what they were trying to do was achievable. And then, from a DD perspective, they were trying to solve a particular problem in the cybersecurity space. And so I was yeah, I just spoke to some people that I knew who were familiar with the space to see if there was a gap there, what the need was and essentially that was the basis of the DD.
So if I thought the tech would work, if I thought qualitatively there was a need that, that was enough for me to pull the trigger for the initial investment.
And is the company still around or how has it gone since then?
The company's still around. So they're getting positive updates, slow and steady growth.
It's hasn't become the next Uber yet, but in terms of, getting a return on investment, just a solid founder, who has yeah, basically been very resilient, kept the company going through. Yeah, you can imagine over the last 10 years, a lot of challenging times.
Now you've got a few more companies in the portfolio, what sectors or industries are you currently invested in?
So at the moment double digit portfolio, a lot of, them have gone through TEN13 in terms of sectors I am. So I've got I guess part of my other background as well as just when I was doing all the stock market surveillance stuff I was doing a PhD in finance as well at Sydney uni.
So I didn't finish that degree, but the finance thinking informs a lot of what I do at the moment as well. So in terms of sectors, I, again I look at it from a portfolio construction approach. So even if, you've always the hot sectors of the day, like AI at the moment, however, I don't look to concentrate too heavily in, in one sector, because if your financial history, you can go all the way back to the Nifty Fifties back in the day where those 50 stocks now, they're not anywhere. Now you've got the magnificent seven of, Google and Facebook, but I try not to get caught up too much in the end and look at things from a portfolio perspective to not be too concentrated in any one area.
Do you have like certain caps in how much you can invest in a particular company or into a sector or industry?
Yeah, it's mainly, look at things on a deal by deal basis. So it's more like a lot of it's around I guess what I call bankroll management. If you with the first investment I made, I probably put quite a bit more in than I would now.
And you, once you realise it's a marathon, not a sprint, you construct, if you have a look at it. If you're looking at traditional fund, for example, you might have let's say, or 20, if I'm going all the way back to 2014. And so if you have a look at it and think, okay, I'm investing in 10 year cycles here, you want to have a little bit in, in every year, what does it look like from a, on a year by year basis going 10 years forward?
I think that, that. In a large sense provides a lot of protections, a little bit like dollar cost averaging. If you've got a strategy where you can consistently put in money between now over the next decade, it's going to be pretty hard to be too heavily invested in any one area if you maintain your discipline.
So do you have a dollar hard cap for each year?
Yeah, I've got a baseline strategy to say if let's say this is the model, if you will, if this is my strategy to invest over the next X years, this is that much I can put in with this frequency.
And then occasionally, if something comes in that looks really good and you want to do a follow on round that will yeah, there's some flexibility. To do that, keeping in mind that yeah, that, that will potentially diminish the ability to invest in future rounds, but at all, if you have rules for what, what the following size could be, you can still work, you have some flexibility, but enough discipline so that you're not just, putting all your money in one vintage and then potentially, yeah not getting it, not.
I just want to double click on the “follow on” conversation. A lot of the rhetoric in venture investing is follow your winners and continue to support them. Do you have a framework or a way of thinking about that?
I think it makes sense from the perspective of if if you knew which ones were going to do really well then yeah, it makes sense to follow on. But I guess what I've, what I found looking at the data is the survival rate from from round to round is actually, I think it was roughly the same.
So I might be a little bit off. My understanding is from, let's say Seed to series A, the drop off is 80%, A to B, 80%, B to C, 80%, C to D, 80%. And if that's the case, then. I've done the math on the follow up doesn't really, yeah, it basically, you can't predict what's going to do better than follow on following on makes less sense.
So I guess that's, yeah that, that's how I think about things. If you really knew the ones that are going to do well, like if you knew what the next can will be, of course you'd bet the proverbial house on it. But so that, that's the question that I'm still grappling with. It's a sense of yeah when the next round comes up.
Do I have a better sense than, yeah, can I beat the averages basically, like in the drop off rates going to be 80%? Can I do better than that? And if so, it makes sense to invest more. If not, then yeah.
So then is there a tendency then to just reserve the dry powder for just new investments in new companies? And then more of a case by case into follow ons?
Yeah, that's, over time that's how I look at it now. So in the case when you're investing in equity markets, I think concentration, I'm been a Warren Buffett follower for a long time.
So concentration makes a lot of sense. If you can have conviction on a particular stock, whether it's so much information, along history in startup land, I think the opposite is the right approach, the more I look into it. So because at the early stage, it's hard to really say with any conviction which ones are going to do well out of the whole universe that you're investing and I think diversification makes sense. So that's the side I'm erring towards is the sort of a larger number of smaller diversified investments rather than. Yeah, yeah, bending too much conviction on, on, on one particular, one particular startup.
Is there anything else about your angel investing strategy or approach that changed over time?
I think so. So when I started with there was, I guess the major shift is to essentially have a way to manage pipelines. When I started, I thought I'll just individually speak to founders, do the due diligence go through. And if that's the sort of strategy you have then.
Yeah, it makes sense to put in larger investments for each startup. But then at the time yeah, I realised, yeah, individually, it's hard to get, unless you're very well connected, it's hard to get access to good deal flow. So I think what it makes sense to go through funds or syndicate to our connect.
And then over time, because the deal flow increases the universe of investable startup increase, then the, I guess the strategy is more off to just, you're having, a more disciplined approach, which startup in terms of amounts of investment frequency. Got it.
What does your DD process look like today now that you're not speaking to as many companies one on one?
I guess the good thing about a syndicate like TEN13 can do the heavy lifting DD. What I've found really makes a difference is understanding the size of the opportunity.
So if you do some rough math, if you've got, let's say a portfolio of 10 companies. And you want a 20 percent internal rate of return over let's say 10 years, as it turns out, if nine, let's say nine died, the one that works, you need to go 60 times to get that 20 percent internal return over 10 years.
With dilution and so forth, you might be looking at a hundred X return. So I think the thing that will make or break a strategy is whether the winner is going to get to a hundred X mark or more. And for that, I do supplement yeah, the DD that someone like TEN13 will do with independent reports.
So I just cross check the size of the market and so forth. This and the competitors, so you want to know that if they win, they'll win big. Yeah. That's what I'll make or break the strategy.
What did you want to assess when you meet a founding team?
Yeah it's an interesting one. And I found that in terms of predictive, predictive ability of the founders, it's a tough one.
So this is not maybe in line with what everyone else does. But if I bring up a specific example, I know that someone else operating in our space, for example they have a huge kind of distribution network with access to with access to 10,000 potential customers on a mailing list.
And when they launch their product, they're getting really good traction out of the gate. And if I was doing DD about that, there's nothing in particular about. The founders in terms of their, the background, the skill sets, the knowledge that kind of has led to that. Initially, it's the case of yeah just the distribution network which other founders might not have.
So founders who might be better on paper might not be as successful for that reason. And so I guess the way I look at it, traction kind of smooths, a lot of. Things out, like it doesn't matter, yeah, degrees or educational background or network. If you can get to, a particular revenue milestone within a period of time, take into account, whether the motion is an e commerce play or B2B or B2C, I think the ones who do well there that's what I look for in the ones who have done that, whether it's, even if it's good fortune, even if you just happen to know someone at a Fortune 500 company, and that's rocketed you off to initial success.I think that's a pretty good, that's a better indicator than your degree or network or working history in terms of what the future will look like.
Is there a pet peeve that sort of is an immediate deal breaker for you when you meet founders?
If they're not forthright. If there's a difference between what they say and what the DD comes out that's an issue.
So that's something I carry over from traditional investing. You want a good management team. You want them to be upfront. You want them to be, honest when things are not going well, you don't want them to cut, try and smooth over the warts. And so it's not necessarily a deal breaker, but it's, yeah, certainly a red flag if, because, all the investors know it's a high risk investment where, we don't need to be buttered over. It's just better to be truthful on both sides so that we, both sides go in essentially with the right expectations for the relationship. Eyes wide open and right expectations.
How are you thinking about AI, Don?
It's an interesting area.
So the way I see it is I read an interesting post over the weekend, someone working at OpenAI and they just said I guess the starting point, I think there was an internal paper from Google saying nobody has an emote because OpenAI open source is going to come at everyone's lunch. So from a technical point of view as, as interesting as a technology like chat GPT is it's based on a model called the transformer, which everyone knows about.
I think of a time there's not going to be a competitive edge from a tech front. Similarly with, yeah, I was reading someone in OpenAI said trying a lot of models and essentially the results just converge to what the data looks like. So the, or, the technical aspects of the same, depending how you train it, that sort of that influences what the model will look like.
Yeah, I guess one, proprietary data sources. I think that'll be an interesting source of competitive advantage, but going forward, it probably might've already happened, but going forward, AI will look a little bit like, an Amazon server or an Azure server, where it's undifferentiated heavy lifting.
It's the ones who can build something interesting over the top of it, make it more user friendly. So it's I guess that's how I see AI, if I had to boil it down, it's a little bit like an Amazon server. Over time, everyone's going to have one. It's not going to be a source of competitive advantage.
It's more of like a building block upon which you can build a, an application specific use case and so forth. So I think companies who do that will, yeah, potentially have a competitive advantage, but it's, yeah it's going to be a pretty, it's going to be a pretty thin moat.
So anyone who says AI I'd look at with a healthy dose of skepticism. I think it's an interesting tool that can be used in different ways, but everyone, I think everyone's going to have access to the same tech before too long.
How are you guys leveraging AI within Aerofiler?
In the contract space, lots of interest in use cases, everything from, drafting to reviewing contracts. And so we started primarily as a sort of contract repository to store, extract, analyse data. And that's where, I guess that's where we're focusing AI at the moment.
In an investment context, for example, someone gets a term sheet in, they might be interested in a whole bunch of things like your drag along rights, tag along rights. Traditionally, as a lawyer, you'd have to sit there with a spreadsheet, read through the auction beginning to end, type them all in, get them reviewed.
So we're essentially looking at automating that extraction piece as the first cap off the rank. Yeah,
Going back to angel investing what's What's the best pitch that you've seen recently or the best set of founding team members that got you to a yes?
I think probably going a while back and during the peak Treinta had a really, impressive, in, in terms of traction, I think one of the, one of the fast growing companies that a lot of people had seen.
And so something like that is impressive where you can see even with a company like Facebook that didn't have monetisation strategy for a long time, like just fast, fast traction is what really grows my, Mr. Yum's another one who just, they just grow explosive growth out of the block.
So when I see companies like that, when I see companies that have just incredible they've got month to month growth that for any other company would be good from a growth perspective and that really catches the eye. So yeah, there are a few there where it's not it's certainly not a Slam dunk in the sense that yeah Every startup's got a challenge to solve but I think you can grow that fast you've got a lot of good if it's in your dna it's you've got a lot of good building blocks in place and then once you solve the other problems things will follow through.
So if, people have a stunning product, for example, but if you've only got a couple of customers and yeah, then back in my mind of, gee, you could solve this product, which is already a really hard problem, but then you've got to, if it's not going to get out there, it's not distributed enough, it's still going to be messy problems.
Whereas. If you're fast, especially if you're fast, it's almost, especially if you've got a bad product, it almost makes it better. You're fast growing with it, with a mediocre product.
So in some respects, the proofs in the traction and the execution, but then how does a very early stage company catch your attention?
It becomes a lot harder. A company that I know about that would be interesting in that regard is one called Morse Micro where you're, they're building something that literally I don't think anyone else has or can be doing.
So high level summary, not that I'm invested in Morse Micro by the way, but they're essentially building, as I understand, long, long range wifi long range wifi. Yeah, at an early stage company, it'd have to be deep tech, something like that, or quantum computing. If it was an early stage, SAS play, especially with, and I guess that's the other area where AI comes into it.
That the barriers that they just continually getting, the barriers to launching a product, they're continually getting lowered. So if it's, if I look at something and I think. And, in the back of my mind, I think, gosh, could you do an early prototype in a week kind of thing that it's harder, I think, to get me interested at an early stage, because, even if they pull everything off but the distribution, the problem solved, you've got competitors coming in.
And yeah, once you have raised a certain dollar value, it's proof that you've solved some of those problems, but with generally stage SaaS play, with no kind of traction in terms of users or revenue. They're ones that, that to get excited about.
And how have you thought about geographical diversification?
It’s a deliberate part of my thinking in the sense that one of the things you learn in the first thing you learn in finance is they've got this thing called an efficient market theory, which says that all, stocks are perfectly priced all the time.
Second thing you learn if you do any trading is you realise it's complete rubbish. There's no such thing as a perfectly priced market. So in companies in regions like the US yes, you've got companies like open AI that can grow explosively, but at the same time, if you're doing, seed rounds at raising 10 million at a 40 million valuation, that's pretty efficiently priced, like it's hard to really squeeze a lot of good gains out of there.
So if I look at yeah other geographies like Latin America, like Africa, my thesis is that there'll be more, from a pricing perspective, they'll just be the market to be less efficient. There'll be there'll be opportunities to do rounds at a much lower val than what maybe the US, Australia and Europe will have.
And that's what's going to get you a hundred X return, right? Because if you sit down and do the math massive difference between a 10x return and and and a 100x, self evidently, yes, but if you break it down into internal rates of return, the difference between breaking even over 10 years.
In which case you're really backwards after inflation versus a 20 percent return, in which case, you know, a very effective way to building meaningful amounts of wealth.
If we cast our minds of 10 years forward done and we think about your angel investing portfolio, what would be the markers of success?
The main reason I get into it, and I imagine most people get into it is cash on cash returns or anything, if you look at successful private equity funds, anything that doubles cash over 10 years, I think that's up until real estate was the target.
Anything better than that will be a good outcome. And I guess along the way, if there's a chance to participate in some ways, I guess the good thing about TEN13 is there's a network there. There's the opportunity to meet people, to liaise with founders it would be, yeah, it would be.
I guess the cherry on the cake to be able to, at least add in areas where I've got domain expertise, if I can help founders in some way, whether or not I'm directly invested in that would be nice. Cause part of the reason I got out of trading was financially lucrative, but I thought, gosh, fast, fast forward 50, 60 years, is this what I want my legacy to be algorithmic trading, right?
Robots to take money off robots that were a little bit less efficient. That's not really where I want to be. So from yeah from an angel cash is you know, cash and cash is head and shoulder number one, but certainly, not my primary motivator overall in life. If I can help, I'd very much love to do that as well.
If we just leave one question, like what's something that you're working on that or involved in life that gets you excited?
Aerofiler just takes up the overwhelming majority of my time.
We're trying to crack that, to get it to a state we're happy with. The idea with open language or with large language models, everyone's going through the idea of prompt engineering at the moment. They're saying maybe just write the right prompt and you can get it to do anything, which is true.
But in the real world, we've found that quite difficult. So one thing we're trying to do is just get large language. Models working in the background without any prompting. So essentially we're just building an interface where you want to do something, you just point and click, highlight a few things and then you can scale that across all your documents.
Getting some good early results. And then over time, if we get into a enough, a good enough level of accuracy across a wide enough range of documents, that's, yeah, that's something we'd really try to check off and say good piece of work done on our side.
So if I'm an organisation or a company, and I'm interested in learning about Aerofiler, where can I find out more?
Yep Aerofiler.com. It's got a booking link to our catalyst. So happy to for people to just jump on, book a meeting. We can have a chat and explore how Aerofile can potentially help.