#12 David Hunter of Optimal Labs on Revolutionising Human Nutrition

By Nasos Papadopoulos, EF Head of Content

David Hunter is the CEO and Co-Founder of Optimal Labs a company that applies cutting-edge deep reinforcement learning to create intelligent autopilots for farms, improving the efficiency, reliability and quality of food production. 

High tech greenhouses can produce 10-40x yield of traditional farming and they’re universally scalable and deployable anywhere. And the implications of AI controlled farms are potentially huge – with higher quality food, produced faster and more reliably, people will be able to live better for longer.

David is one of the famous cases of someone who did EF twice – he was on EF4 and after met his Co-Founder Joao on EF7 the idea for Optimal Labs became a reality and before they knew it, they were following farmers around greenhouses in Holland and building solutions to their problems.

He studied Aeronautical Engineering at Imperial and trained as a pilot in the Royal Airforce before going into the banking industry, where he eventually ended up running Deutsche Bank’s quantitative strategies team where his algorithms traded >5% of the European stock market volume.

After going through EF4 he took some time off to earn a Masters in Deep Reinforcement Learning at Oxford before returning to join EF7.

In this episode David and I discuss:

- Why he left his well-paid job at Deutsche Bank to start a company
- What he learned from doing EF and how he applied this knowledge second time around
- The challenges he’s overcome along the way and his tips on hiring and seeking advice

This was a fascinating conversation that will not only give you an insight into the future of nutrition but will also show you the resilience and continuous learning that entrepreneurs have to apply in their lives.

EPISODE HIGHLIGHTS

Why I Went to the Edge of Science Before Building My Company 

What I Did Differently Second Time Around at EF

Why I Joined EF

EPISODE TRANSCRIPT

Nasos:  Dave, welcome to the show!

David:   Hi, how's it going?

Nasos:  [00:03:30] Not bad at all. It’s a pleasure to have you on. It was real privilege to have a quick chat with you before we got cracking on this and get a little bit deeper into what you're doing at Optimal Labs. To kick us off, give me a quick 30 second intro into what you're doing and how the world is going to change as a result.

David:   [00:04:00] The mission of our company is to increase the availability of safer, healthier food by deploying AI-controlled greenhouses outside of every city on Earth. That's our fairly unambitious mission. Starting around 60 years ago, there was this big green revolution. Ever since then, this combination of new lab made fertilizers and genetic engineering has been increasing food yields and has been saving us from starvation with this rapidly growing population that we've had. But it was at the expense of food quality and was also severely constraining our resources such as water.

[00:04:30] We want to enable the population to grow from here, but also reverse this downward trend on food quality. We see that growing in controlled indoor environments is one of the big ways to do that.

Nasos:  [00:05:00] How will this change the way that the world consumes food in a practical sense? We spoke before about the implications on yields and consumption patterns. In a very tangible way, what would it look like for someone living in an urban environment and how would their life be different? How would the quality of the produce they're eating be different?

David:   In terms of food quality, you're right in talking about an urban environment. We're getting an increased amount of urbanization and that's likely to continue. The majority of people are moving away from rural areas into urban areas, and generally the majority of urban locations around the world are poor for growing crops.

[00:05:30] Either they're in very hot or very cold climates, and certainly with increased climate change, climate conditions become more variable and cities tend not to be in locations that are good for growing. Crops tend to be grown a long distance away from where they're consumed, and what that means is that crops are really grown for transportability right now.

[00:06:00] Say you're growing a tomato in a field in the South of Spain or Mexico. It often has to be transported thousands of miles before it's consumed, especially in places like North America. What this means is you end up with produce where the nutritional quality has been sacrificed. On top of this, a lot of pesticides tend to be used to grow the produce which is only available certain times of the year.

[00:06:30] If you can imagine now, we fast forward and we have huge, large scale greenhouses and/or farms outside cities. You can pretty much control every aspect of the growing climate. So, you can really grow food and optimize it for nutritional quality. Theoretically it can be harvested and consumed within hours of it being harvested.

[00:07:00] You won’t suffer this nutritional degradation because you're growing in an enclosed environment. You can grow with minimal to zero pesticides and there's a whole host of other benefits as well, such as reduced water consumption. As a consumer, it means that you'll have access to nutritionally optimized food grown all year round and grown hyper-local to where you're consuming it.

Nasos:  [00:07:30] Let's take a step back to the beginning of your career and how you were thinking about what you wanted to do. You were working in banking, you were running towards the end of that stint, a quantitative team at Deutsche Bank. How were you thinking about your career back then and what was nudging you towards doing something different?

David:   It's funny because when I was a kid I used to get told by people, "There's no such thing as a straight forward, linear career, and everyone has switches and U-turns."

[00:08:00] I dismissed all that and now when I compare what I've been doing to some of my peers, I've realized that I've probably had the most variable career out of all of them. When I was younger I wanted to be a pilot, so I was sponsored through university by the Royal Air Force and I did part of my flying training while I was at the university.

[00:08:30] That was my childhood dream, and then towards the end of my degree I realized what it meant to actually be in the Air Force and it wasn't as glamorous as Top Gun, which is what I was hoping it would be like. In the end on the balance side of it, it wouldn't be right for me so I made this last minute decision to go into banking. I was very fortunate because I was put onto the algorithmic trading desk.

[00:09:00] That kind of trading was just starting as a thing in European equities trading and I think I was the first graduate to be hired onto an algorithmic trading desk. I just grew with that. So, I have this very fortunate right place, right time career within banking. Since I was a kid, I've been programming computers and I think I was the only trader on the floor who knew how computers really worked.

[00:09:30] I was able to bridge a gap between technology and human decisions within trading, so at a very young age I ended up running the algorithmic trading desk at Deutsche Bank. I did that for a few years but it wasn't a particularly hard decision. After doing that for seven years, most of the trading had become automated. When I started in trading, something like 30% of all trades were executed by machine and 70% were executed manually by people.

[00:10:00] By the time I left Deutsche Bank we had managed to automate all of the trading volumes. So, something like 99.5% of all the trades within Deutsche Bank were executed by our systems that my team had developed. We were only up; our servers were trading something around 5% of the European stock market every day - which just shows you the power of machines. A small team of 10 people could control all these trades of 5% of the stock market each day, but at that point there's not much room to grow from 99.5% to 100%. I knew I had to leave and so I left.

[00:11:00] I went through this very up and down journey of entrepreneurship where I was failing a lot, working on the wrong ideas, or working on the wrong ideas with some really good people and eventually I ended up with Oxford doing what I'm doing today which seems to be going well.

Nasos:  Talk to me about the decision to join EF in the first place. You're one of the rare candidates or cohort members who's actually done EF twice. So, what was the factor that pushed you towards applying for EF the first time around and how do you remember that? The initial experience on your first cohort which was the EF-4.

David:   [00:11:30] I remember looking at Y Combinator and part of the application procedure was that you had to say who you're working with as a co-founder. You had to tell them about the relationship that you and your co-founder have had, and how long you have been working on things together. There was always something a bit irritating about that because in the UK, we didn't have this natural start-up ecosystem where you could meet co-founders.

[00:12:00] I think about this a lot that, back then, Google, Facebook, and these kinds of companies weren't that big in Europe. In the US you could go and work for Google and you'd be surrounded by all these really smart people who are interested in technology and on the side, you could work on personal projects. It was a natural, organic way to think of start-up ideas and then leave and do your own thing.

[00:12:30] But in the UK, most of the people I know who did engineering and other technical subjects had goals to go and join finance. It wasn't to start companies. It wasn't really a thing. Doing start-ups in the UK wasn't a thing and so we didn't have this natural way for people to meet. I was thinking about it a quite a lot and I got introduced to Matt Clifford through a friend of mine. He had also been thinking about the same thing.

[00:13:00] So, he told me about EF and it seemed like a great fit. I joined, not really knowing what I was going to be working on, other than that it seemed like a really good place to meet other people who are also looking for co-founders and looking to start companies. That was the reason why I joined the first year of cohort.

Nasos:  [00:13:30] When you look back on that experience, it turned out that the company didn't come out. What did you learn most from the experience and didn't you also consciously try to apply some of those letters when you came back the second time around on EF-7?

David:   [00:14:00] When I joined EF-4 I knew I wanted to start a technology company, but now looking back on it I realize that saying you want to be a technology entrepreneur is a bit like saying you want to be an author. It's not enough, it's not specific enough. What is technology entrepreneurship? It's all about creating new tools that you're going to sell to satisfy some unmet demand or need.  You think about the definition as being extremely broad and if you think about technology companies, they're also extremely broad. On the one hand you have operational intensive companies like Deliveroo. On the other hand, you have deep biotechnology and AI companies.

[00:14:30] You can see that these, relatively both technology start-ups, are very different types of start-ups. When I joined EF-4, I hadn't really given much thought but my thinking was still quite basic. I ended up working on, in hindsight, what was a wrong idea for me but with an amazing co-founder.

[00:15:00] We can talk about that in a second, but I ended up working on the wrong idea with the right person. It didn't work out and it was failure. There were some lessons to be learned and when I came to EF this time around, I was very clear about what kind of company I wanted to start. I didn't come into EF-7 with optimal as an idea, but I came in with a very clear idea of the kind of company I wanted to start.

[00:15:30] That meant that I was able to find the right co-founder and do things in the right way from the start. I didn’t get distracted by these seemingly great ideas that weren't quite a personal fit for me. I think this founder idea fit is really important and it helps if you've thought about that before you come into the program.

Nasos:  Sure. How did you conceptualize the type of business that you wanted to start when you came in the second time around? What was the line? How were you thinking about it in your head specifically?

David:   [00:16:00] I went through this very convoluted thought process and what came out was very simple. Which was basically that I wanted to apply new science.  Things that are really just being discovered within science and apply that to build useful technology for people to use. I wanted to focus on this technology transfer.

[00:16:30] So, let's get to the edge of some field of science and then once we're at the edge, let's see what kind of possibilities there are to build real world products leveraging that technology. I realized that for various reasons, this was a perfect role for me. I can see myself doing this as vocation. This is a useful role for someone to fill in society.

[00:17:00] We need lots of people to do science and advance human knowledge, and we need lots of people to take that bit of advancement and turn it into useful technology.

Once I narrowed that down, it became a question of what technology? What area of science do I want to get to the edge of? I'd been interested in machine learning for a while, because when I was doing algorithmic trading we were applying a lot of machine learning tools to help us predict price, volumes and other characteristics of the stock market.

[00:17:30] When I left EF-4, after doing a bit of thinking, I decided the best next step rather than me starting a company right away is to get to the edge of this field. So, I went back to university, I did a masters in computer science and I spent most of the year doing research with the reinforcement learning and the gamble paid off. Once you get to the edge, you see possibilities and that's why I joined EF again.

Nasos:  [00:18:00] How deliberate was this process that you embarked on after EF-4? Reflecting on what you learned and then going into this specific field and then looking in the specific area after that - how strategic was it?

David:   [00:18:30] It was extremely deliberate and strategic. After reflecting on EF-4, I realized that the idea I was working on was the wrong idea, and what I really wanted to work on was technology transfer. You take things that are on the edge and new discoveries in science, and see how you can use that new knowledge to build useful technology. You can really build a company, build products, that are on the edge of technical possibility and where the risk is in technology. There's little risk in the market or having good operations.

[00:19:00] Once I made that decision, it was a deliberate process of looking into the different massive areas of science, which could potentially have high engineering leverage. There are a lot of interesting things happening in science but only a small subset of those have real world engineering leverage. 

[00:19:30] Once I'd done that, in hindsight, now it seems extremely obvious, but I went into a machine learning and I did a masters so I could get to the edge of that field. Initially, I planned on doing a PhD as well but I realized that as founder of a company you don't need to necessarily have PhD-level knowledge. You need to be thinking about how you can apply this, building new technology and solving a market. You need to have a bit more general skillset and so I decided to stop after the masters and start Optimal, but it was a very deliberate process.

Nasos:  [00:20:00] You mentioned that the first time around, you were working with a great person on the wrong idea. What were you looking for in your co-founder the second time around? When and how did you realize Joel was the guy to go ahead with you on this?

David:   [00:20:30] That's a great question. This leads me into this founder idea fit kind of thing. So, when I joined EF-7 there were a lot of people. Generally, the way a founding team comes together is that there is one person who is considered to be a bit more technical and one person who is considered to be a bit more operational. The operational person naturally falls into the CEO role and the more technical person falls into the CTO role. When I joined EF-7 I was looking to have that operational role in a very technical company, where even the CEO needs to have a high technical ability.

[00:21:00] I was looking for someone who was even more technical than me, someone who had done a PhD or had extremely deep knowledge in reinforcement and machine learning. When I joined EF-7 that's what I had in mind. But when I joined, there were people who were asking me to join them as the CTO. It just shows you that I could have been the CTO of a less technical company with more operational risk, by partnering with a McKinsey or BCG associate.

[00:21:30] I could have also been a CEO at a very technical company but I knew I wanted to be the latter. So, that made my decision relatively easy because my co-founder, Joel, was doing a PhD in robotics EPFL, had that real deep knowledge and was interested in nutrition. That was a very obvious choice.

Nasos:  [00:22:00] Before we started recording, we were chatting about all the cool projects that you've done over the last few months and the awesome progress you're making, but every entrepreneur will know that there are ups and downs to the journey. What has been one of the most difficult challenges you've had to face and how did you pick yourself up after it?

David:   [00:22:30] I can honestly say that personally speaking, the most difficult thing was, after EF-4, seeing how I had this great job in banking and this fast career path and then I left and joined EF-4. I thought, "I was greater out with my training so surely I can start a company." Reality gave me a kick in the face and I went through a good couple of years of failure before eventually going to do the masters, doing research in machine learning, then joining EF-7 and starting Optimal. It's easy to gloss over that.

[00:23:00] It was actually quite a low point, personally, but I think once you've made a decision from the first principles about what you want to do, it's very hard to be shaken from that. I think once you've convinced yourself, "This is something I want to do. This is a role that I want to play." You just keep going after it.

[00:23:30] But it wasn't clear that it was going to be successful at the beginning. With the masters, I didn't know whether I was going to find any decent ideas off the back of it but I did. What's actually quite an interesting little side note is that I said, in EF-4, I was working on the wrong idea with the right person. Well, I became great friends with the person I worked with on EF-4 as a co-founder. Last week, he accepted an offer to come and join us at Optimal.

[00:24:00] It's awesome that I get to work with him again. I read something once that said, "You should only work with people who you would work for," and that's true of him. I'm just delighted that he's going to be a part of the journey. These things have a way of working themselves out if you're persistent enough.

Nasos:  [00:24:30] How are you now thinking about the hiring process as well? You mentioned that one of the challenges you're currently facing is trying to get more people on board. Is that a heuristic that you're using of, "Would you work with this person," and bring them on board? How are you approaching the process? What kinds of things are in line now?

David:   We have this very strong view that the best people for a job are not necessarily the people with full academic credentials and so, we're hiring reinforcement learning engineers, machine learning engineers and software engineers.

[00:25:00] Our process is very skill-based and we're evaluating based on their skills and cultural fit. We're testing them, so to speak, as part of the interview process. I guess we're more merit-based and we're trying not to look too much at which universities they went to and what marks they got academically. In academia someone gives you the work. Someone says, "Here's a problem. Go and solve it."

[00:25:30] In a start-up environment you have to figure out the problem and then you have to solve it. Figuring out the right problem to solve is often actually harder than solving it. So, making sure you're working on the right thing and often just because you're good in academia, doesn't mean you're necessarily good in start-ups. We're trying to take this merit-based approach and the value of people based upon what they can do, rather than what credentials they have on their CV's.

Nasos:  [00:26:00] Sure. I'd love to spend more time going into detail as to the awesome stuff that you're doing, but in the interest of time let's finish up with a little bit of the reflection on the experience for you personally. Over the course of this journey, from leaving Deutsche, starting a company in EF-4 and then having to make adjustments, starting Optimal - obviously great progress that you're making. What have you most learned, personally, through the experience?

David:   [00:26:30] I think that starting a start-up is a lot about the search for truth. You're trying to build a product and the market will decide whether it's any good or not. So, it doesn't matter what your assumptions are. What matters is what your customers say. This search for truth is important, and that goes for the market but as well as to yourself. I think it's important to not have too many assumptions about what you're going to be good at.

[00:27:00] I've got this technical background and I've got a computer science degree, but at Optimal I'm doing more of the sales and operational role, but I've actually found that I'm good at it and I enjoy it. It's something that I didn't think I would be doing going into it, but I'm embracing it and running with it. I'm bringing in other people and my co-founders are doing the technical stuff.

[00:27:30] I think it's being radically open-minded and trying not to fight yourself too much. If you find that you are naturally drawn to some particular area of a business, then you should just go with that, hire great people to do the other things and trust them to do that job. I'd say that's the biggest thing I've learned. I'm becoming more of an operational person rather than a technical person who I thought I'd be and that's great.

Nasos:  [00:28:00] Great. Well, I think the aspect of developing oneself is something that so many entrepreneurs say is critical to success. I think it's a great note to end on. David, thanks so much for coming in. It was a pleasure chatting with you.

David:   Thanks so much.

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