Ali Tamaseb — Data Collective (DCVC)
We talk about human-computer interfaces, synthetic biology, investing in deep tech and more!
Ali Tamaseb is a Partner at DCVC, an early-stage deep-tech venture capital firm with $2B under management. Previously, Ali was a founder and CEO of a hardware startup that grew to millions in revenues and before that, he was in academia where he did research on human-computer interfaces and neuro-technology.
We talk about human-computer interfaces, synthetic biology, investing in deep tech and more!
NA: Hi Ali, thanks for joining me. I thought we could start with your journey. I know you were an academic, what was the journey from an academic to an investor?
AT: For sure. I studied biomedical engineering, trying to better understand the body and how engineering can help us better understand the body and, I’m very interested in human-computer interaction. How we can better read the body, basically the brain and how we can better give back information to the brain. This is known as brain-computer interfaces or brain-machine interfaces. It’s a cool thing now because Elon Musk started something, but 10 years ago now, it wasn’t as cool, and it was mostly an academic field which hasn’t moved that much since then.
I think the last major advances in the field happened back in 2007–2008, and no major thing has happened yet. Yeah, that was my life, writing papers, publishing, going to conferences and giving speeches on these things. Then, one of these projects turned into this idea of using, the body’s signal on their wrist, to better understand interactions control and gesture control.
That turned into an idea for a startup. I started this company and kind of made a lot of turns at twists, and it became this idea for a hardware company, a wearable technology company. How we can put a bunch of different sensors around your body and around your wrist to collect information and an act.
It could be both for consumers and for industrial applications. Selling to several industrial companies, go to a few million dollars in revenue and expanded the team to a large group and raised funding. It became a proper startup.
Then I moved to Silicon Valley next to go to Stanford to study general management. I started asking myself this question of what is it that really separates these successful billion-dollar startups from the rest? You see a lot of these stories like Mark Zuckerberg and The Social Network, and during my own post-academic or startup tenure, I’ve seen a few startups that didn’t look like these stories but were very successful.
I wanted to see what is it that really is differentiating these two groups. A lot of companies fail, but some of them are successful. I started collecting data that nobody collected before. Crunchbase and PitchBook had numbers on funding information, but nobody collected data on how many competitors this startup had when they started? How many times did they pivot their idea? What did the founder’s study in school? What was their title when they worked at a company before? How many years did they work in the same industry? How many startups have they started before this?
There’s a lot of these questions that I had about what does the background of these people look like? I started getting this data and it was not easy, I had to go on LinkedIn and ask these people and email them to get this information. I collected this data set, which is 30,000 data points, on all these factors on billion-dollar startups plus companies that did not become a billion-dollar startup.
Some of them are failures, some of them are not, so I can compare this data. Meanwhile, I was writing a bit about my different ideas of venture, if I was a venture investor what type of companies I would invest in and what are the next frontiers of deep tech.
That’s how I got connected to DCVC. They liked what I was working on and doing, so I joined DCVC three years ago and have been investing here since then. I think I’ve invested in 11 companies so far, different rounds anywhere from pre-seed to series B.
At DCVC, we love deep tech. We want the companies that are doing something that’s hard, have a lot of engineering complexity, have some scientific complexity. Obviously, the science risk we want de-risked. We want to take on engineering risk and obviously big markets and, founders who’ve done this before and who are the best people in the world to take this forward. That’s some sort of defensibility that I’m looking for.
What I like in deep tech is companies that are going towards the industries that are under-loved, there is less attention to them. Everybody likes AI, ML and natural language processing. I have invested in two companies that are doing mining, like the truly physical mining of gold and iron. In crypto, I invested in blockchain infrastructure type of companies, and in food and synthetic biology.
I love construction. I love transportation. I love logistics and food, synthetic biology and industrial chemicals. These are some the areas I really like to find things to invest in.
NA: Going back to your work with human-computer interfaces, how often do you come across, companies building in that space and what are really the trends you’re seeing there. Do you see a future where we’ll be able to kind of have that deep interaction with human-computer interfaces, or we’re still far away and it’s just Elon Musk, doing his thing with Neuralink?
AT: That’s a good question. One of the things you need to have as an investor is separate personal passion from investing. I love a lot of stuff. I love neuroscience. I love human-computer interaction, but I don’t necessarily think there are venture backable companies in these spaces right now. Maybe neuroscience in some therapeutic areas of neuroscience.
If you look at human-computer interaction, it’s not because I love it and I’ve done it before, I go after investing in this space, that’s a passion project. I’ve come across good companies, but we are still far away from a real brain-computer interface.
I think what Elon does and what a bunch of other companies do and. You want to have been doing is very medically focused. I think we are a long way to a point where normal people, people who don’t have a medical complication would use a brain-computer interface. At least in the sense that we know through an invasive procedure, where you need to put something inside their brain.
I don’t think people would use that thing, that’s sort of a brain interface for pure communication anytime soon, but I think for medical purposes, this is great. There’s a bunch of companies working on, using brain-computer interfaces. To be honest, a lot of people think they’re scientific breakthroughs, they’re not. The science for many of these things has been around for the past 10 years.
When I was in the field these types of things were being done. But it’s art taking that science and making it into an engineering product, going from 12 channels or a hundred channels, which was being done in academia to a thousand channels, which is being done by companies like Neuralink. That’s an engineering breakthrough, and I’m very happy to see super-talented people and a lot of money go towards this to make it happen.
NA: You mentioned you are quite interested in synthetic biology. When you talk about synthetic biology, it seems like quite a broad subject, what are the applications and use cases where it can really make the difference?
AT: I would go back again to industrial. I see synthetic biology as a new frontier for industrial biotech. Think of new materials, think of new chemicals, think of new polymers, think of new proteins This is a new language, biology’s not a new language with a new language we can interact with it. We can play around with it.
We used chemistry and played around a lot with chemistry and created a lot of chemical products, which is the backbone of the modern world. I think a lot of people underestimate how much happens in the industrials and industrial chemicals space.
Everything we use has some sort of chemical in it, everything we use has some sort of natural material and you rarely see innovation happening here. That’s what I’m looking for new materials in construction, new materials in food., new materials in chemicals.
Normally, because these are biologic rather than chemical, they are more sustainable. They’re better for the environment. In some cases, they may be cheaper. In some cases, you may be able to create unique and novel characteristics from these synthetically biologically made products.
I can give you examples. We have, Zymergen and DCVC are one of the largest backers in Zymergen from the earliest days. I can’t publicly talk about their products or their partnerships, but it’s just fascinating. When you look at their range of products they can make, it goes anywhere from electronics, to agriculture, to home products.
I invested in a company, that’s producing synthetic Palm Oil, again very good for the environment. It’s a synthetically built alternative to Palm Oil, which is devastating for the environment.
I invested in a company in the substitute sugar space. Again, synthetic biology, how we can take a natural feedstock and turn it into sugar. I remember I’ve seen a number of interesting companies even in New Zealand, that are using synthetic biology. I saw one that was working on recycling.
NA: Mint Innovation?
AT: Yes. I these are very interesting things. How we can engineer biology to go after something like recycling, it’s fascinating. How we can go after synthetic biology to create a chemical with characteristics that never existed before, which was too toxic before that you can now create.
NA: That’s a great explanation. Specific to the DCVC investment process, since you’re investing in deep tech companies and ideas when you’re looking at an early stage opportunity, what do you really look for? Is it just an idea or you want to see some sort of proof of concept and what are the metrics that you’re looking for before you invest?
AT: For sure. We invest from a very early stage to a little bit more mature. The answer always depends. I personally look at the team. I’m a very founder centric investor. I love to see a team that I know, if somebody can solve this challenge, it’s this team, it’s these founders.
It might be because they invented the field in a lot of these cases and oftentimes in the type of companies we invest in, these are PhDs, these are professors that are taking a leave of absence that have invented a specific field or have pushed the frontiers on a specific field.
We want to partner up with them and be a financial resource and a strategic resource to them. We like to see some sort of product. We don’t know about every industry, we don’t know about every technique, our job as an investor is to better understand.
What I like to do is to break your idea down to layers of risk that you have. The earlier stage you are the more risks you can have, but it’s very important that you, as the founder understand risks and you can tell the investors about them.
I think successful investors are those who can have an independent view on the layers of risks that can stack on top of each other. Risks are a lot more granular than can you build this or not? That’s a very high level. You have to go deeper; you have to see what can go wrong.
I believe with money and talented people, a lot of things can be built as long as you’re not changing science, but you have to understand timelines. You’ll have to understand the risk of financing on timelines. A founder may think, I can do it in two years and you as an investor may think it’s four years and they’re fundamentally different. Four years, you’re going to require three rounds of financing. Two years you’re going to require one more round of financing and that changes the faith of the company.
You’ll have to think about at Series A, what type of metrics would the investors look at? What type of validation, what type of risk reduction they’re going to look at and you have to match risk layers to financing and have a better understanding of would these timelines match together or not?
That helps you decide how much to fund the company. A lot of times to me, it’s like, if you can raise $5 million as your seed round, which is quite beefy and large. Then I can know that you can sustain yourself with two and a half years, where you can de-risk this specific problem, which might be a geothermal heating problem, which might be an electrical power consumption problem.
I think it’s, it’s very important to understand the layers of risk and to into detail in the scientific and engineering risks that you’re going to have.
NA: That’s great advice on how to invest in these deep-tech companies. What’s the latest, publicly announced investment you’ve made and why did you make it?
AT: I think it was Plotlogic, which is the mining company that I invested in. It’s a company based in Australia, kind of close to you guys. What they do is they’re trying to create the miner 2.0, they’re trying to be the Rio Tinto 2.0.
How they do it is to create a tech-first mining company. they’ve created these hyperspectral, cameras, which produces a hundred gigabytes of data per day. They can real-time predict where you are on the face of the mine and exactly which rock is ore or gold or whatever, and which rock is waste.
If you don’t know that you put the whole thing in a processing plant and you use a lot of electricity to separate them and sort them and then at the end you put the bad stuff in the landfill in this way.
In this way, before you take the excavator before you spend all the money to take it and go and cut it and send it to the processing plant. You can know what the boundaries are. Sometimes it changes within meters or even like tens of centimetres of what is waste, what is high-quality ore and what is low-quality ore.
This, this company can, can help you see that in real-time. It’s like a night vision camera for mining.
NA: Wow, that’s very interesting. Those are all the questions Ali; I really appreciate your time.
AT: It was a pleasure talking to you. Take care.