In this conversation, Dan and Daniel explore the origins and evolution of Equidam, a platform designed to assist entrepreneurs in understanding startup valuation. They discuss the changing dynamics of the investor-founder relationship, the importance of financial knowledge for founders, and the challenges of measuring growth in early-stage companies. The conversation also touches on the impact of market dynamics, the hype surrounding AI and Web3, and the significance of using a robust methodology in valuation practices. In this conversation, Dan and Daniel explore the complexities of startup valuation methodologies, emphasizing the balance between qualitative and quantitative approaches. They discuss the importance of financial projections, the role of externalities in valuation, and the impact of government regulation on business models. The dialogue highlights the need for a nuanced understanding of valuation that accommodates both potential and sustainability, while also considering the broader implications of investment decisions.
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Takeaways
Equidam started as a blog to help entrepreneurs with finance.
Valuation was previously a big question with no clear answers.
Founders are now more prepared with their own valuation visions.
The theory and practice of valuation have become more widespread.
Founders need to develop financial savvy in their businesses.
Understanding growth rates is a major challenge for startups.
The market is now more competitive, requiring defensible ideas.
AI has the potential to revolutionize various industries.
Valuation is uncertain, so it’s wise to invest in reliable methods.
Combining multiple valuation methods can reduce uncertainty. Valuation methodologies can be crude or sophisticated depending on the approach taken.
Qualitative methods can provide more insight than fine-tuning quantitative parameters.
The evolution of a startup influences the weight of qualitative versus financial methods in valuation.
Financial projections should reflect both optimistic and realistic scenarios.
Understanding externalities is crucial for accurate valuation.
Government regulation can play a significant role in shaping business models and valuations.
Investors should consider the sustainability of a company based on its financial projections.
The uniqueness of each startup should be reflected in its valuation.
Blockchain technology could facilitate the ownership of externalities.
Valuation discussions should empower a broader understanding of potential and risk.
Chapters
00:00 Origins of Equidam: A Journey into Startup Valuation
03:11 The Evolution of Valuation Practices
06:04 Understanding Growth Factors in Startups
09:10 The Impact of Market Dynamics on Valuation
12:03 Navigating the AI and Web3 Hype
14:53 The Role of Methodology in Valuation
18:01 The Balance of Data and Methodology in Valuation
27:30 Understanding Valuation Methodologies
30:09 Qualitative vs. Quantitative Approaches
33:18 The Evolution of Startup Valuation
35:37 Financial Projections: Optimism vs. Realism
39:46 Externalities in Valuation
51:58 The Role of Government and Regulation
Transcript
Dan (00:00)
So we’re back. It’s our eighth episode. We’re almost in double digits. We haven’t quite managed the monthly schedule, but we’re getting there. Today, Daniel, I thought maybe we could go back a little bit to start with and talk about the origins of Equidam and eventually get into the methodology. So what drove you to start Equidam? What kind of problem were you looking to solve 10 years ago?
Daniel (00:26)
Well, yeah, 10 years ago already.
The point back in the day was, well, at the beginning, we really started with a blog to help entrepreneurs and private companies make the most out of finance because a lot of entrepreneurs are really from all sorts of backgrounds, but normally not finance, and they don’t interact with finance a lot. And I always believed like with a little bit of knowledge, they could have done so much more, right, with finance. And then we started writing articles. And then of course, a large part of startup.
finance is funding and a big question on funding was valuation that really had no answers back then. Even now, it’s still a very difficult question, but back then especially was either… And there was also a lot less data, right? So it was complete out of the blue guessing or high expense for a professional and an advisory project done. So
Yeah, I had my background in programming and making websites and games, and then in finance, and then we thought, okay, this is pretty doable to put into a tool that at least can bring a lot of speed, a lot of convenience, and especially a lot of data into that conversation and remove a lot of the waking up in the morning estimation of what the value of a startup is.
and hopefully a lot of science behind it. So that’s how it started. And yeah, it resonated fairly well with the problem from the get-go. We haven’t really changed much of that core proposition really that we have. So yeah, it went well.
Dan (02:22)
I see, you know, if you look over the last 10 years, perhaps there has been kind of two competing forces. So you have like 10 years ago, the industry was maybe a bit more conservative in thinking perhaps more driven by, you know, doing proper modeling and using more established valuation methods.
dynamics of the investor-founder relationship was that the investors would do the valuation, would lead that conversation, and that was their responsibility. Then today, the way that’s changing is, I think, this is at least my perception of it. Investors have, at least in the last few years, because of interest rate environment and so on and so on, used quicker shorthand methods of valuation.
But also at the same time, founders are more and more often coming kind of prepared with their own vision evaluation too. So it’s almost like the diligence on that is being more shared between the two. Kind of.
Daniel (03:25)
Yeah.
Yeah. I mean, I think the main change has been the refinement on both sides. The theory has gotten a lot more widespread, the practice, the data. I think back in the days, the investors had all the knowledge, let’s say, or the vast majority of the knowledge, and founders were not knowledgeable on this side of
But still, also from the investor side, the knowledge that they had back then and the data that they had was much less than what there is today. So valuations were a lot more criticized, because of course, a lot of the background these investors had was maybe in traditional companies, in corporate, in real estate, and there wasn’t anything that resembled the growth and the cash flow generating potential of software.
And it wasn’t believed, especially SAS and lower risk sort of subscription models. It wasn’t understood at all. So those valuations were not believed. And that created a lot of animosity, because also because there was just no knowledge underneath. There was no way to come together, because it was really based on wide assumptions. Yeah, right now, it’s a totally different environment on that.
Dan (05:01)
I think, I mean, not to get too far away from the topic, but that spreads elsewhere as well in that I think it’s more and more important that founders have financial savvy in other parts of the business as well. There’s been a lot of stories about, you know, revenue fraud or misreporting of revenue. And it’s perhaps because founders haven’t felt the need to develop a real brain for the kind of entrepreneurial finance part.
And that this could, you know, a part of understanding valuation and the drivers of valuation, it’s all, all kind of relates and links to at least having someone on the team with a bit of a finance brain.
Daniel (05:44)
Yeah, yeah, yeah. And I think we evolve the tools on that side a lot, right? If you have, if you’re using Shopify, you have your dashboard with your numbers, real time. If you think about pre-internet type of, well, pre-internet, pre-SaaS, like these type of numbers were coming from, maybe from your account. And if you didn’t have that number background, right? A lot of numbers were coming from your account. Like…
on a yearly basis and with like three to six months delay. So yeah, yeah. There is a lot more refined knowledge on founders, entrepreneurs and startups right now.
Dan (06:27)
The main challenge, I guess, is the thing that’s been addressed as best as possible over the last decade is this challenge of how to accommodate for the factors that influence the growth rate of early stage companies. Things that historically with traditional businesses, you wouldn’t have had to worry about because those rates were pretty well established and you could project more reliably. Whereas now you have businesses doing…
crazy new things with tech enabled growth that can grow way faster than before, depending on whether or not they get product market fit or depending on the market or the technology, whatever it may be. So understanding all of this and how that relates to, you know, future potential, finding ways to measure that, to understand it and to measure it is the challenge. So then you get into like the kind of qualitative side of, of valuation as well.
Daniel (07:21)
Yeah, yeah. Yeah, I think we are now in a much more similar situation in terms of these businesses and how they have to compete, how they have to project, how they have to forecast revenue and profit. It’s a lot more similar to like 10 or 20 years ago because now we have competition. I think we had 10 years where this really big digital SaaS revolution.
had this incredible opportunity, almost like empty space, where there was the, just pick off an idea from Craigslist and make a startup out of that, and that was an empty space, right? Right now, that’s not the case anymore, maybe the case in AI or like in specific sectors, but that was on the whole digital thing. And it’s gonna happen again, like I’m sure, but right now we are…
back at competing, trying to find modes, trying to find ideas that can be defended, that can generate cash flow, that need to pay back a certain cost of capital. All those questions are now a lot more relevant compared to, let’s say, five years ago or so where there was so much steam behind this idea that every tech company could become a world leader, that all those things were not even looked into.
sort of details that now are not details, like cost of capital, were not even looked into.
Dan (08:55)
Mm-hmm. Yeah, that relates to a topic we talked about, I think, in episode three about the kind of growing potential of mid-risk companies now that there’s no longer such huge potential for new market leaders. Maybe there’s a, yeah, that’s an interesting topic in its own right as well.
Daniel (09:12)
Yeah, for sure. And I think people still expect that, right? That’s the lag that you have in also in venture investing. People still expect that incredible growth out of companies that maybe have a more niche target. And you see these like people trying to stick a new business model or a new product to something that is already working, but already reached its own.
sort of natural potential. Yeah, let’s see what can survive of that type of thinking and model.
Dan (09:50)
Or yeah, perhaps in an even more exaggerated case, you know, my, my perspective on, on some of the recent trends has been that they have been trying to recreate exactly that. So you had web three, where the idea was essentially that you could create kind of mirror industries for everything in the physical world in the metaverse and suddenly have this huge potential for growth. And then you have now AI where the argument kind of is that it
could revolutionize industries across the board. So again, suddenly there’s that like dot-com era potential for incredible growth. Like whether that’s true in either of those cases, I think we could safely say like for Web3, it’s not at least not yet. And for AI, it’s still kind of a maybe.
Daniel (10:36)
Yeah, yeah, for sure. I think, yeah, it’s interesting to see. I don’t think it’s going to… AI, the nice thing for now is that it’s competitive, right? It doesn’t seem like one single company is capturing all the value, like, for example, social media.
Sorry, I think I have something at the door. Sorry.
Dan (11:02)
Hehehehehehe
Daniel (11:27)
Sorry about that. It felt like a bang or something. So it’s like something fell or I don’t know. But I am waiting for a package also, which said like they’re gonna deliver between 10 and 12. Are you serious?
Dan (11:29)
That’s all good.
No worries, this is-
No worries, this is why we edit. Ha ha ha.
Daniel (11:45)
Yeah, yeah. Anyways, he wasn’t going very far, that answer. So.
Dan (11:51)
Well, yeah, with social, certainly, it seems like there have been, it’s been more of a winner takes all market. And with AI, I guess there’s a fear that it could become that, which is why there’s this hot debate about regulatory capture and the influence of actors like open AI and meta, their influence on how it’s going to be regulated, because they want to avoid exactly that, which is a fairly good point, but also…
perhaps it’s going a little bit too far in the direction of anti-regulation.
But out of the two sides to this idea of developing a better understanding of growth and potential of startups, let’s say you have the ability to understand and measure startups better. And the other side to that is we also have more available comparisons, more readily available comparisons, better comparisons through having more data, more companies, more similar business models.
Which of the two do you think the focus is more on at the moment? And like is the balance right? Could it be better?
Daniel (13:08)
That’s a good question. That is a good question.
Dan (13:11)
Well, let me frame it with a stat because I like the stat. It’s from a report that came out at the beginning of the year called the Practices of European Venture Capitalists. And this might kind of bias where I think the answers should go. But it came up with the number of European VCs that use comparables as their primary method of pricing, let’s say, is 71% versus any of the other.
approaches to valuation. Does that seem reasonable to you?
Daniel (13:46)
Well, I think I was expecting 100%, expecting that they use more than one method, but maybe that they have the top method. So do we know what the other 30% uses? OK. Now, but you’re going to use comparables, right? You’re going to use comparables, but then you’re going to have this lag in understanding, right? I think nobody.
Dan (13:50)
Hehehehe
Hmm.
Daniel (14:14)
really looked at the, or they did and they didn’t say, but this difference in intrinsic risk and potential of SaaS and like sort of that wave of tech.
And that’s because a lot of it was done on comparables, right? Instead of like trying to understand. And like at the time, the data was so little that the comparables were clearly wrong. Like a lot of people comparing real estate to SaaS, right? Where buying a new building literally takes you a million of dollars, whereas adding a new country to your product or something, if it’s a SaaS product, takes like zero, zero marginal cost versus bricks, right?
completely different story. And that I think limited a lot of returns for founders and it limited a lot of…
Well, it created a lot of disagreement, right? It created a lot of disagreement and strong arguments, which in the end, I think, limited the industry for a time. So yeah.
Dan (15:31)
That relates to something we’ve talked about before, where offline, I guess, where there was a long time where echo
Daniel (15:41)
That’s the one. It’s gonna take a while because they need to do the stairs.
Dan (15:45)
No worries.
Daniel (16:00)
I’m sorry.
sorry about that
Dan (17:37)
No worries. All good, all good.
Daniel (17:41)
I guess you have to repeat the question. I have no idea.
Dan (17:44)
What was I saying?
completely forgotten.
Oh
Daniel (17:55)
But I think the interesting thing that I sort of had in my mind was that question on the most important side, if it’s data or if it’s methodology, like the one that has the most development potential and what would be more interesting to develop. Maybe we can restart from there. I don’t know.
Dan (18:07)
Mm-hmm.
going to ask? Or what was I in the process of asking? I should have written it down. Why didn’t I write it down?
Just give me a minute.
Ha! I remember, I remember. Okay.
So that relates to something we’ve spoken about before, which is the, you know, over the 10 years that Ecuador has been around, you had a period, let’s say 2013 to 2017 or 18ish, where people had this perception that Ecuador was overvaluing companies, when actually perhaps it was more in line with the market or becoming more in line with where the market should be. And then you got into the kind of
the bubble time 2020, 2021, where there began to be this perception that maybe we were undervaluing companies, when actually, again, we were kind of charting a more rational course, let’s
Daniel (19:28)
Yeah, it’s funny in hindsight, also because nobody knew back in the day whether that was the case or not, us included. But in hindsight, it really turned out that I think that was the case. The fact that there was no scientific way to lower valuations to the level that the impression was back then, especially from the investor point of view.
turned out to be that maybe they were undervaluing companies in a way that created extra returns for investors for that period, which I mean, it’s fine. I don’t think anybody did it out of genius, like out of genius of predicting all of this or anything of the sort. But yeah, and then…
Dan (20:16)
Yeah.
Daniel (20:27)
And then of course, yeah, during the COVID hype, we saw a little bit of the opposite. And I think, yeah, we decided to really stick with this philosophy, right? To try to not, well, definitely change our ways when science requires to do so, when we clearly have some fundamentals, so some analysis of fundamentals that suggests that…
things should be different and we update our data regularly. But we try also to update our data and methodology on data and on research that is not temporary, hopefully. That is a more long lasting trend and hopefully go a little bit anti-cyclical on this boom and burst cycles that there are in the market.
Dan (21:13)
Mm-hmm.
Yeah, I think there’s a really interesting point related to that, which is even if you disagree potentially with how an organization like Equidam approaches evaluation, and even if you kind of disagree a little bit with the result, like you think it should be a little bit higher or a little bit lower, there are huge benefits to using a methodology that produces like a reliable…
transparent result that you can understand fluctuations in over time in your own investments rather than always relying so much on comparables and then the ground underneath you is constantly shifting. So how can you understand trends over time?
Daniel (22:06)
Yeah. So one of the things that we always said is, uh, if valuation is so uncertain, then you might as well pay a little for it and be ready in an hour. Right. So, and be able to do it with objective data in that way. Right. So, so that was always sort of, especially at the beginning, when, um, valuation was considered, uh, more art and science. And, and, and we always got this, this argument.
Dan (22:15)
Yeah.
Daniel (22:32)
Or like, well, OK, but at least, let’s put the science that there is. Let’s put it in a convenient package that is accessible, that can be done very quickly, and it’s cost effective. So, yeah, I completely agree with you. And we do have people that use also, they just use us as a double check. Maybe they have their own valuation, maybe done by a larger consultancy firm and they pay more for it. But then.
they have the resources to pay for us as a double check for that valuation. Great. We tried to put a lot of data together, a lot of intelligence together, and try to help people make up their minds. We’re not necessarily calculating the final number for anything.
Dan (23:16)
Yeah, the kind of efficiency of packaging this in a way that combines the different perspectives and so on and so on. But yeah, to get into the perspectives in a platform agnostic way, let’s say, you know, there are lots of approaches to valuation, I think that tends to be, you know, the conversation generally tends to favor, you know, talking about one method over the others.
never really goes to talking about using combinations or why. We’re a little bit unusual in that, you know, there’s a lot of aspects of how we approach valuation that I think have been over the years considered like eccentricities, but are now maybe becoming more understood and embraced. You know, things like using an EBITDA multiple rather than a revenue multiple, for example, now is quite popular for some reason.
Daniel (24:10)
Yeah.
And using, because we’re back to when not only revenue matters, but also actual bottom line. Yeah, no, that’s true. So that the usage of discounted cash flows for even for very early stage, despite the fact that we limit it to a very small weighting. So and the usage of multiple methods. Well, I think the usage of multiple methods is controversial, not because the theory of using multiple methods is controversial.
Dan (24:22)
Mm-hmm.
Daniel (24:42)
but because there is just no time. Like, as we just said, right, so many investors, they are at a level of usage of these mathematical tools or financial tools that is, you know, basic, like a lot more basic than just because there is no time, the knowledge is difficult to attain, the data is difficult to attain, and the uncertainty of the outcome is still very high, right? So…
spending all that effort to still get that uncertainty doesn’t make sense. Like, what I think we do is we sort of leapfrog that, right? We say, okay, just compress your efforts down to a minimum and get the maximum output in a convenient way, then there’s going to be enough value. And you’re also going to reduce the uncertainty at the end, right? So combine five methods, but in the time that it takes you to do half a method in the traditional way…
Dan (25:27)
Mm-hmm.
Daniel (25:39)
and you’re gonna get a much more useful result. So, and the interesting thing is that every method, and we saw it and we can talk about it because I think it’s super interesting, but every method looks at the startup from a very different point of view and tells you a different aspect of it, right? And it allows you to think about a different aspect of it. The same way that you can think about
the cost of a car based on the looks, based on the weight, based on the fuel efficiency or the range that it has, right? So those are all the things that enter into the judgment of the cost of engineering it, right? So if you had to price a car from scratch, like on Mars, right? Like all these things are gonna create questions that help you better understand the price of that car. So…
It’s the same for startups. I truly believe it works a lot better by combining more methods. Yeah.
Dan (26:40)
Yeah, and each method is certainly its own rabbit hole of complexity. If you look at, for example, we on, you know, in our methodology, we use two qualitative methods. They look at verifiable characteristics of the company and they benchmark against, or they adjust against the benchmark valuation. And if you look at, for example, like scorecard or burkus, whatever you want to call it, and if you look at a, an example of that,
online. I think the most common ones I’ve seen have a set of risk factors weighted more or less the same or somehow in some arbitrary way. And I think they use like a 2.5 million dollar valuation as the usual benchmark valuation. And okay, you can kind of get an idea from that. Maybe it’s useful in a very crude way. But then what happens if you start really looking
feed into the different risk vectors or how and why you can weight them in different ways or how you determine that benchmark valuation that you’re adjusting against. Suddenly it can become quite sophisticated but of course nobody has time to do that except for us.
Daniel (27:55)
Yeah, that’s true. But the other thing is that at some point, you get to a level of detail where you’re spending so much time for so little gain in final valuation uncertainty that you’d be better off spending that time on a new method. So I think that’s also what we do. So especially at the beginning, we used to get a lot of questions on betas on discounted cash flows, right? Very specific.
Dan (28:11)
Mm-hmm.
Daniel (28:24)
parameters of discounted cash flows that have, sure, they’re probably gonna have a plus minus 5% impact on the final valuation of that method, right? Even for public companies, betas are an opinion, right? Well, there are methods to derive them, and then there are some heuristics and rule of thumbs on how to adapt them to the specific company. So even…
for public companies, these things are an opinion, right? For a startup, rather than spending time fine tuning the beta, doing research specific on what to, what should the specific beta be to the second decimal point, you’re much better off spending that time adding a qualitative method, right? Adding a scorecard method, investigating the management team and comparing it to the management team of other startups. It’s gonna give you so much more information and it’s gonna…
Dan (29:10)
Mm-hmm.
Daniel (29:20)
reduce the uncertainty of the final evaluation so much compared to fine-tuning that beta. So there is a huge, I think, trade-off there in favor of doing multiple methods at maybe a slightly lower level of detail. Then, of course, what we can do and what we plan to do and want to do is try to add as much of it as possible, to add as much detail for as little effort as possible and hopefully because of…
of the numbers that we have, we can do that more and more.
Dan (29:53)
Mm-hmm. Yeah. And some of those perspectives, you know, looking at the, the qualitative methods don’t give you any understanding of the, the cash generating potential of the business, but then you have, you had the DCFs and then you can see what it looks like from like a, a cash health perspective, uh, years down the line. And then the, the VC method, of course, looking at exit potential. So that one is really kind of tailored towards the
Daniel (30:15)
Yeah
Dan (30:22)
the investors’ interests, let’s say. What are they gonna get in terms of return and how does that compare to their typical expectations?
Daniel (30:30)
Yeah. And the intuition we started with was, well, it was fairly known that you cannot only use DCFs if DCFs were the solution, then also probably angels would use DCFs and stuff. Right. So angels and early stage investors, they came up with the Berkowitz method and the Kaufman method that then we standardize, let’s say, and call scorecard and checklist.
And then we started with this intuition that obviously, as the startup develops, then we shift more weight from the qualitative methods to the financial methods. Right. That proved to be working very well and to be also something that is explainable and understandable, which is also very important because in the end, we’re empowering a discussion. Right. So we need to create things that are explainable. In hindsight, what
Turns out for me, I think true and also very interesting is that we are mimicking a philosophy of potential that becomes actual, right? So when the startup is super early stage, they are almost like a pool of potential. They can go in any direction and all pools are pretty much similar, right? They differ here and there, but they are pool of potentials. They could go in any direction. They…
they are more similar than different. And then as they develop, obviously they try to follow a plan, a plan that is modeled in their financials, right? But that plan doesn’t really contribute much to the valuation as long as they are mostly a pool of potential. As they develop, they kind of hatch in that plan more and more in stone. It becomes harder and harder for them to change. And so that potential is actually becoming
How do you say? Hatched in rock, right? So in that case, then the explanatory value, the value that projections have in explain and determine valuation becomes stronger and stronger, right? Up to a point where you have a public company which almost doesn’t have opportunity to pivot. Like they have a public mission that everybody believes the same thing. Everybody joined the company for that reason. They have a very limited chance to pivot. And…
Dan (32:31)
Mm-hmm.
Hmm.
Mm-hmm.
Daniel (32:57)
In that case, the whole value comes from the plan and how well they can execute it, how well they can follow that plan. So that was a super interesting outcome of our initial intuition that I think is a very interesting model for how a company valuation is explained over time.
Dan (33:21)
Yeah, I think that there’s even that this is like extending a bit beyond that in a way that is maybe more like personal to me perhaps, but the question of like financial projections for early stage and what these models represent and how useful they are is fairly controversial, let’s say people like don’t believe in financial models for the pre seed or seed stage companies or whatever. But there’s a quote from Eric Bahn.
of Hustle Fund, a firm we reference a lot, which I think captures this so well. It’s perfect. The quote is, in reference to how everyone should look at early stage investing, especially investors, what happens if everything goes right? That’s what you want to look at. You want to see what is the…
still rational but let’s say optimistic perspective of like if this company does what it says it wants to do or what it says it can do, what is the potential? And that involves looking at everything including financial projections to understand the scope of that potential and how that influences things like exit. And then you can go and revise against that with, okay,
can’t like based on the qualitative factors, the team, the experience, the market, is it actually achievable? Or is it likely to be achievable, let’s say. But the first thing to do is to understand that like, the biggest possible, the best possible scenario. And that is how in his perspective, early stage investing should work. And then I’m kind of applying that in a way which I don’t know that he would agree with, to like saying, there is real validity.
and value provided by projections even at the earliest stages.
Daniel (35:17)
Yeah, yeah, yeah. Well, it’s a great quote and it’s a great model for people to follow, I think. What I used to say, but I think I’m gonna switch is to present the company in the best self of the company. But I think the, yeah, so that’s a great usage of projections. The other point that…
Dan (35:33)
Mm-hmm.
Daniel (35:45)
is really important, I think, is to understand what’s the minimum viable size of the company as well. Right. So, so it’s kind of like the opposite, the opposite question. So how many things can go wrong and the company still survives, right? Is the opposite side of that question. So, um, because of course, like we make those projections, right.
Dan (36:00)
Yeah.
Daniel (36:09)
see what happens if everything goes right, and that shows us the potential. But a lot of the times, especially on funding, that’s a coin flip. So what happens if you cannot raise capital? If the company is still under its efficiency scale, it’s just going to die. It’s going to go bankrupt. It doesn’t have enough margin to cover its fixed costs.
Making financial projections allows you to understand how big does the company have to be in order to just be sustainable. And I think that’s another very interesting thing, even at the super early stage, like almost from day zero, unless you’re doing deep tech or some very exploratory product that still doesn’t have an actual audience, an actual price, an actual market.
For everybody else, I think it’s useful to do projections. And that’s why they are mandatory on Equitam.
Dan (37:17)
Yeah, and it sets expectations about venture scale exits. If an investor says that they want you to… There’s various rules of thumb about this. You look at the size of the fund, and then generally speaking, a VC is looking for an exit that will return the fund. So can your company exit at that kind of scale? And if it was going to, how would that…
shape your financial projections, so then you have like kind of growth benchmarks to look at and are they achievable or not. I mean that’s kind of going the other way around which is like fitting projections to a scenario and then seeing if you fit the projections which is weird but maybe helpful.
Daniel (38:01)
Yeah, but
it’s both, right? So let’s do the opposite case, because one of the great values of projections is that it allows you, it allows the startup to show its unique future, right? To differentiate from the futures of the other startups that somebody’s gonna see, right? So let’s say we take that away, right? Let’s say we value startups based only on their qualitative aspects right now. So…
we are removing a strong differentiation part, right? What happens is that we then make also valuations that are much tighter. We also make fundraising amounts that are much closer. Then we end up making VCs that are much more similar to each other and so on and so forth, right? So what do we do is we put a blanket over all variation, right? So let’s say there is an opportunity that generates enough
Dan (38:55)
Mm-hmm.
Daniel (38:59)
enough capital, enough return for its risk, right? But it doesn’t fit the mold, right? It’s not going to get funded. And this is like, it’s not trivial, right? We were talking in a previous podcast, or maybe it was just a call about the status of the healthcare system in the UK, right? And how can healthcare be sustainable in…
smaller villages, smaller cities, right, with the costs that there are now. So when we apply a single lens to different problems, some problems just get completely ignored, right? And there is a lot of value in, or maybe there is enough value for certain people, for certain investors in opportunities that don’t fit that mold. And I think we need to preserve
We need to preserve differences between startups so that they are reflected in differences in valuation. And those are gonna be reflected in differences of risk appeal, different investors, and hopefully get more variety in the world. Because we still don’t know, we don’t know if the model of like, let’s do six, seven funding rounds, and then IPO to make a global leader, is that the model that fits humanity the best?
Right? Maybe not, right? Maybe the model that fits humanity the best is that everybody’s allowed to express their own individuality and try to make that into a service that a few other people might enjoy. Like the famous 1000 true followers thing for influencers, right? Enough to make a living. Maybe that’s a better model. We don’t know. I’m not arguing that that’s a better model. I’m just arguing.
Dan (40:42)
Mm-hmm.
Daniel (40:50)
we should try to fit the funding to the word, not try to bucket the word for the funding.
Dan (40:59)
Yeah, yeah, that makes a lot of sense. And in a, in a, let’s say a more basic way or a more easily understandable way, if you were to look at, you could take the case of two startups that have the same valuation, the same exit potential, let’s say. And then you could look at the financial projections, assuming they were done for both. And you could see, okay, this one
based on the growth trajectory, the costs and so on, this one needs to raise three more times at a huge amount each time to get to this valuation, whereas the other one only needs to raise once and much less. That’s something you’re only gonna understand if you look at the projections, but it has a huge influence on the value of that first investment and the future dilution and so on.
Daniel (41:51)
Yeah, yeah. And of course it’s gonna be a competitive process for both companies, but it might be the case that at different valuations, they both get the funding that they need, right? So yeah, absolutely critical, I think, and useful even at early stage, yeah.
Dan (42:08)
Here’s a difficult question for you, something that I’ve been pondering on. If you look at how valuation is done, and you can use equidarm as an example or any other standard, let’s say, you have all the qualitative information that drives those qualitative methods, you have the projections which drive the financial methods. What is missing from that? What isn’t measured or accommodated for that influences
that could influence valuation, but maybe we just can’t measure it or there isn’t an adequate way to do it. But what else should be factored in there?
Daniel (42:47)
Well, the first thing that for me, this is like more of a… like personal opinion, let’s say not a scientific opinion, but we still have a huge limited understanding. So a very limited understanding of externalities and how they should be accounted for, right? I think we got to the point where even small…
companies can affect literally the whole of a resource, right? Or the whole of a… So think about Starlink, right? With the satellites and the fact that astronomers are complaining that they get satellites in their data when they look at the sky, right? And that’s, I mean, it’s not a small company, but it’s a relatively small company. And they started to throw satellites up like three, four years ago.
So we still don’t have a way not, and that’s sort of trickles down into valuation is not specifically for valuation, but I think sometimes we exploit opportunities. All entrepreneurs do is exploit opportunities, right? But sometimes those opportunities are just there because the law hasn’t caught up yet, or because we haven’t thought about the secondary effects that business model or company or source is gonna have.
And so that we are still extremely under representing in financial statements, in balance sheets, in valuations everywhere. And you could argue positive externalities as well. So the companies that do the opposite don’t have the financial rewards. Let’s say that we make sustainable tables.
we’re going to have to, or sustainable food. This is a huge thing in food, right? The incentive for food manufacturers is almost always on lowering the cost, the final price for the customer, right? Because they are competing against sort of lab grown drugs, right, like chips. Those are like literally addicting almost substances, right? And so…
Dan (45:03)
Thanks for watching!
Mm-hmm.
Daniel (45:13)
there is no incentive in making like a healthy soup that is twice the price of the bag of chips or very little incentive, not enough incentive, right? So that’s because we don’t capture the positive externalities in prices and in financial statements and in return in valuation. That’s the main thing. Yeah.
Dan (45:26)
Mm-hmm.
I guess you would count inflation, interest rate environment as an externality in this case.
Daniel (45:50)
So an externality would be like…
sort of an advantage that the company has that they don’t pay for, or the opposite, an advantage that they give away that they don’t get money for, right? Or the negative of an external. So, a disadvantage that they have that they don’t pay for and so on. So, inflation, I mean, yeah, inflation is more of an externality of government.
Dan (46:02)
Mm-hmm.
Mm-hmm.
Daniel (46:24)
hubris, right? Especially this wave of inflation. I don’t think one of the externalities of companies is deflation normally. Like actually, the fact that we had inflation so limited over so many years is by some people attributed to the decreasing price of technology because like, you know, the same TV like…
Dan (46:45)
Mm-hmm.
Daniel (46:48)
10 years ago would have cost you one and a half million and it was called the cinema, right? And right now you can get it for 50 bucks a month on Klarna or something. So yeah, so normally companies have a deflationary, especially innovation has a deflationary pressure. So yeah.
Dan (46:51)
I’m sorry.
So if you could articulate, you know, whether you’re coming from the position of a founder pitching an idea or an investor understanding the potential of a company, if you are kind of sophisticated enough to understand these externalities and articulate them well and how they might shape the future of a company, like that could potentially add a lot of value to your pitch.
It could help express something that is otherwise very difficult to understand. And maybe that’s where there’s some alpha.
Daniel (47:46)
Yeah.
Yeah, so for sure that is, right? So the issue is that these things are difficult to monetize, right? So by definition. So even if you communicate it well, but investors cannot monetize it or cannot make anything from it, then it’s still not gonna give you a huge advantage. The way that it seems, the-
The words trying to tackle this problem now is on political pressure. Political pressure turns into regulation. And now we have this ESG. We seem to have democratically agreed that ESGs are the positive things that we want for people that are not monetizable. They’re not just higher GDP or higher money per person or something.
It’s the ESG side, right? And especially now with the new regulation from next year, we have ESG accounting. So investors are going to have to try to investigate at least what kind of positive or negative impact their investments are doing. So we are tackling it softly. We’re not trying to put monetary value on it and a monetary incentive on it because it’s extremely difficult to do so.
Hopefully, next step is to do that. So we have carbon rights. Carbon rights, in my opinion, are great. Again, my personal opinion, but they make a market for something that is an externality. So fantastic. I think we can do more of that because in the end, that type of simple incentive of revenue or costs is huge.
Yeah.
Dan (49:47)
So figuring out how you can turn those externalities, how you can quantify them financially.
Daniel (49:55)
Yeah. One amazing way could be also to spread ownership of these things. Right. So if a company is making an incredible profit because they are melting the ice caps, at the very, very least, society in a broader way should benefit from melting those ice caps. And that’s why I was always a big…
fan of crypto, not really, well, not crypto, blockchain, right? The blockchain technology, the fact that you can really bring down the cost of ownership and the cost of transferring ownership to zero, like to almost zero, could allow these type of things where you can really spread ownership of externality gains to the actual people that are affected. So yeah.
Dan (50:35)
Mm-hmm.
Interesting. That’s a topic that’s going to take me a while to get my head around, but fascinating stuff.
Daniel (50:56)
Yeah, it’s…
Yeah.
Yeah, it’s not easy, but yeah, it’s very good what’s happening now with the ESG, I think it’s a good start. But it’s difficult and clunky in terms of reporting is obviously open to interpretation. So the laws now are extremely vague, again, because they have to be because the knowledge isn’t there yet. If we can manage to turn that into actual…
prices, that’s gonna be a lot better, I think.
Dan (51:38)
Unlike all things similar to the conversation about regulation and AI at the moment, as soon as it feels like government or especially activists are having any influence on technology and private markets, there’s a lot of pushback, a lot of concern. People don’t like change generally, but they certainly don’t like change that feels like it’s coming from outside their market or their industry.
Daniel (51:57)
Yeah.
Yeah. But, you know, without turning this into too much of a political episode, that is the almost the only time where this type of thing should intervene. Right. There is no, there is, there is a, like, there’s a problem that affects people, but it’s not representable in money. Right. How do we handle that? By voting and by trying to make sure that the collective
Dan (52:09)
Hehehehe
Mm-hmm.
Daniel (52:35)
That for me is the best case where a government should intervene. And they should not intervene in a lot of other cases that they actually do. But that, again, that’s the long story as well.
Dan (52:51)
Yeah, well, okay, before this gets too political, we’ll save that for X, where these debates belong. Yeah, I think it’s been very interesting. Always nice to think more about methodology. It’s something we don’t talk about enough, but we’ll do a little bit more. So stay tuned for more of that in future. Any final thoughts?
Daniel (53:12)
Yeah, I think if anybody has questions, like we’re, you know, we’re always happy to try to answer them in this podcast. So if anybody hears this and you have a specific question or curiosity about valuation methodology, send it to us somehow and we’ll get to it.
Dan (53:29)
Yeah, on Twitter, on LinkedIn, through info at Equidam. Yeah, whichever way you want to get in touch with it, I’m sure we’ll find a way to squeeze your question into the next episode. Perfect. All right, thanks a lot, Daniel. Until next time.
Daniel (53:41)
Awesome.
Thanks, Dan. Speak soon.