Episode 171: A new way to build companies with Deep Science Ventures' Founding Director, Dominic Falcão
Episode 171: A new way to build companies with Deep Science Ventures' Founding Director, Dominic Falcão
Today's guest is Dominic Falcão, Founding Director of Deep Science Ventures. I was looking forward to sitting down with Dom because DSV’s approach to company building is unique. He walks me through that approach, his climate journey, and how DSV compares to other venture funds.
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When you told me a bit about the model of Deep Science Ventures, it sounded different than anything I'd heard of before and in an area that's just so important. So, let's jump in, what is Deep Science Ventures?
So, we build companies, a lot of people say they build companies. We are outcomes-focused and means agnostics. So what that means for us is we work backwards from desired outcomes to form a team, to form intellectual property and technology and products and business model from scratch based on what has failed and what succeeded in the past. Instead of building things from university-based intellectual property or building up teams based on the idea of an individual, we're focused really on these kinds of overarching goals, and we're focused on the areas such as pharmaceuticals, agriculture, energy, and computing, and specific outcomes inside of those. My work is predominantly focused on climate and inside of that, the net-zero transition. So, we have a number of intermediate outcomes inside of that, that we're focused on and then we build companies.
And what I thought was interesting when we were chatting before recording was that you have pretty well-defined outcomes that you're looking to achieve before you set out to find a company to build. Can you talk a little bit about that process and how it works?
Yeah, absolutely. To be honest, those outcomes are really well-defined, but they're constantly in flux. So I can tell you what we think are the most important things now, but they constantly change in terms of relative importance and relative confidence.
So inside of this idea of net-zero transition for us, there are sort of five key requirements that we're currently working on. One is that we need to remediate historical emissions. Another is that we need to reduce the emissions going into the future. A third is that we need to find a way of eliminating the sunk cost in the oil and gas industry and making it easy for that huge wealth of assets to move across. A fourth is that we need to kind of oil the work so the financial sector, is such that we see huge influxes of capital into the space. And a fifth is that we need to generate energy and create energy systems that are capable of holding renewable energy successfully. So under each of those, we then have to further intermediate more defined outcomes. So inside of remediating historic emissions, for example, we think that an example of an intermediate outcome is ambient temperature, ambient pressure, direct air capture. So removing carbon dioxide from the atmosphere at ambient temperature and pressure. And we think that in order to achieve a net-zero transition over all that's one of the set technologies required going back up to that whole high-level outcome of net-zero transition.
When talking about scientific breakthroughs becoming companies, you mentioned that the conditions aren't typically set up in scenarios for trade-offs to be optimized. How do those scientific innovations typically come to market? And how do you do things differently at Deep Science Ventures?
I used to sit in the tech transfer office for Imperial, which is by all accounts, one of the best science engineering universities in the world. And what generally happens is that you know, a hundred thousand papers are written a year of those a thousand are deemed by the academic themselves to be commercially valuable. Those are submitted to the tech transfer office, which then reviews them. Of those you know, 10 or 20 are then patented. And all of those 10 or 20 that are patented, four or five are deemed worthy of spinning out because there's no existing licensee for the technology. So there's no corporation that wants to buy the technology straight away. In other words, at least in most European universities, spinning out is a thing you do once you realize you can't license your technology and it's only done with less than 1% of the technologies formed at universities and on the basis of the judgment of the academics you've performed them. That's the kind of extremely harsh pipeline you have to go through to get technology into the world today.
What you don't have is a group of people who are thinking about from the beginning, from before, this technology is devised, what would have to be true of that technology in order for it to successfully scale in the world. That's a whole other set of parameters. It often would mean, for example, starting not with a technology that's novel or new, but instead starting on the basis of technology that already exists, because that's the thing that makes the best use of the existing infrastructure and has the least technical risk because it's surrounded by things in the knowledge landscape that we already know. And then combining it with things that we also already know a lot about. A kind of engineering mindset applied to science; taking known components and combining them inside of a set of defined constraints, so as to get commercial products, that can be the basis of commercial companies. It's a different thing. It's a different kind of activity. And when we started DSV, it wasn't as a criticism of the way that translation of science exists or takes place today, but rather as an acknowledgment of the fact that we serve different ends. We create companies, not to license technologies, not to create novel technologies. To be clear, we can't do what we do unless there are people creating knowledge that we can then click together like Legos to form new products.
When you look at the traditional tech transfer process, which it sounds like, and correct me if I'm wrong, is the primary feeder of the technical innovation coming out of the lab that is then commercialized into companies. What aspects of it do you think will be most important to change in order to make the model more effective? Any thoughts on how they should be changed to bring about the most improved result?
So in general, we don't license technology from universities. So for us personally, intellectual property from universities is not the major source of innovation for us. In general, we've created intellectual property at DSV. Normally what we're doing is combining things that people know in novel ways, which is a standard way of creating intellectual property in engineering, but less standard in science in terms of how tech transfer should be changed.
I personally think the tech transfer is intrinsically hard and that it's normally not the process of tech transfer that needs to be changed, but the ecosystem around it that's required to change. So if you look at the differential between the most successful and least successful tech transfer offices, it's normally not the process of resourcing of the tech transfer office that makes the difference, but the environment culture and resources around it. So the difference between MIT and Imperial is not the quality of the research or the quality of the tech transfer office, but rather the quality of the embedded ecosystem around MIT, the set of entrepreneurs, VCs, the specialists that are able to assess and appraise the value of intellectual property generated at MIT. Whereas Imperial has a less well-developed ecosystem because it's in Europe and Europe has less well-developed ecosystems more generally than the US. So I think a lot of the discussion around incentivization in universities and resourcing of tech transfer offices is a red herring. We should instead focus on increasing the specialization of our investment ecosystems around universities so that they can do what the ecosystems around Silicon Valley universities and Boston universities can do.
You've identified one bottleneck, which is this path from academic research to commercialization. DSV has a different approach that essentially skirts that process and comes at it from a totally different way. If you look at the other steps that these companies go through on their path to wide-scale deployment, are there other bottlenecks or barriers? If so, what are they and how might they be addressed?
Specifically for climate, I think there's a huge, huge array of additional factors [that cause barriers to wide-scale deployment]. Primarily, the fact that we live and work in an economy that doesn't value the same goods that we need to value in order to have an effective transition to a net-zero or a healthy pantry system. If the economy that we invested in valued biodiversity properly and valued clean air properly, then the companies that we formed would have no problem whatsoever, but we have this kind of broader structural issues, I guess, more specifically to the kinds of companies we form. There's also a lack of effective pricing mechanisms for technologies, which are not yet at scale. So for the first kilo of green hydrogen or the first ton of carbon removed, it's gonna cost you more than for the thousandth time and that pricing exists on a curve.
One thing that I'm not working on that I'd love for someone else to be working on would be creating those pricing curves, those market-making mechanisms so that there's a really clever way of assigning a price to something which has not yet assigned a price by the market. [A price] that governments and forward-thinking companies could use.
Another structural challenge [to large-scale deployment] that we have, specifically investment in deep tech for climate, is there is not a really mature stable of entrepreneurs. From, you know, a decade of successful climate innovation, many of the people from the first climate wave have left the field entirely. A lot of the investors have been burned, and so we are having to back first-time founders. I think one of the things that we need to work together to do is to generate conviction around concepts and operating models to take the place of the previous heuristic that would otherwise have been used, which is, "has the founder started a company successfully and sold it before in this field?" which very, very few people have in climate. So structurally, I'm really enjoying the fact that it feels like the investment landscape and the investor conversations are more about what is going to work, is this already working, and less about, you know, how long did he work at Facebook.
If you could change one thing outside of the scope of your control or Deep Science Ventures' controls that would most accelerate your progress, what would you change and how would you change it?
Personally? I think the thing that is different between climate and many other sectors is that we are not building companies that alone represent an entire supply chain in climate. If you're producing energy, you're part of an energy supply chain, which you cannot be the whole of. If you're capturing carbon, you're part of a carbon commodity market or carbon offset market, which you cannot control the whole of.
So what we really need to do is create constellations of companies that work together to achieve an end that was already achieved by you know, a petrochemical giant, or a series of multinationals collaborating alongside an FMCG supply chain. In other words, we have to do something different from what typical VCs do, which is we've gotta get our companies to work together, to achieve intermediate outcomes to achieve the overall. We've gotta recreate brand new supply chains and value chains, independent of the existing highly operationally efficient, but climate apocalyptic supply chains that exist today.
So the one thing I think I would change would be, I would love to think about how we can better coordinate climate venture capital and climate R&D to make sure that there are fewer replications so that there's better-shared information. I think unlike in other sectors because we work as part of these complex embedded supply chains, the returns to us collaborating and succeeding together are much greater. There's not so much of a zero-sum game as there is in, for example, software where one company can take all. In this case, you know, If I capture carbon, I need someone else to verify I've captured it. I need someone else to monetize that. I need someone else to create a marketplace in which I can sell it.
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