In 2016, Mubhij Ahmad and Timothy Day were finishing their graduate studies at UC Berkeley and struggling to get resources to support early validation of their work: developing a novel treatment for colon cancer and other diseases that could deliver gene therapy via a pill.
At the time, Patrick Scaglia and Alic Chen were running CITRIS Foundry, which they had launched as UC Berkeley’s innovation hub. Excited by Ahmad and Day’s vision, they gave the two scientists a $5,000 grant, which enabled them to run their first experiments on pig intestines.
With proof of concept, Ahmad and Day launched DNAlite Therapeutics and went on to secure $1.25 million that same year. They’ve since raised upwards of $4 million and are preparing for their series A.
What’s noteworthy about this example is not just how a science-based “deep tech” start-up is revolutionizing medicine. It’s that its growth timeframe isn’t that different from that of a traditional software start-up.
Because, while deep tech ventures certainly face market and technology risks, these risks are often misunderstood by the global investment community.
So although the amount of capital flowing into deep tech has quadrupled – from $15 billion in 2016 to over $60 billion in 2020 – it’s still a small niche compared to the estimated $1.9 trillion in PE, VC and growth capital.
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Scaglia and Chen, who are now partners at Blue Bear Ventures think limited partners (LPs) are missing out. I spoke with them to learn more about the misperceptions associated with investing in deep tech.
Renita Kalhorn: Let’s start with deep tech is “too expensive.”
Alic Chen: Sure. Over the last two decades, the growth of investment around software development has created a focus around capital efficiency: the idea that you only need a laptop and a few hundred thousand dollars to get a company up and running.
Contrast that with a start-up developing a synthetic biology or physical hardware. You need a lab, access to tools, reagents and physical materials. So there’s this perception that a deep tech company needs millions of dollars right off the bat. But costs have dropped significantly in the last five or so years.
Twenty years ago, it was a billion dollars to run the whole genome project. Now, the cost of generation sequencing has dropped to $1,000 for a whole genome. You can now produce microfluidic chips on the scale of tens of dollars, as opposed to hundreds or thousands of dollars.
Patrick Scaglia: I would add that many of these tools — the microfluidic chip production, “clean rooms” or sequencing machines, for example — are now standard equipment at the top universities. So this has dramatically lowered barriers too.
Chen: Also, shared lab space is popping up everywhere. Here, in San Francisco, it’s $1,500 or $2,000 a month to rent a six-foot long bench. There’s also a proliferation of contract research organizations (CROs) – almost 400 in the Bay Area alone – to which you can outsource parts of your experiments.
Kalhorn: So 20 years ago, how would the scenario with DNAlite have played out?
Chen: They would have had to set up their own lab, get access to microscopy and generate their own DNA to run those experiments. That would put them in the million-dollar range, just for that first pig intestine experiment.
Scaglia: More importantly, 20 years ago, you simply couldn’t encapsulate a piece of DNA or messenger RNA, by injection or otherwise. It’s a very recent technology.
Kalhorn: Got it. What’s the second misperception?
Scaglia: That scientists don’t make good founders because they don’t know about business. In face, there are several entrepreneurial qualities that scientists develop through their research experience.
First, getting a PhD develops perseverance. It takes five, six, seven years to complete — which is a similar timeframe for raising a significant round of funding. And you have to be able to take rejection. I mean, as a PhD, you send out a lot of grant proposals and very few come back with a yes.
Second, it requires leadership skills. PhDs have to convince people to believe in their idea, get support from their advisor and work in teams — collaborate with others who might have a piece of the puzzle that they’re missing.
Third, the most successful scientists excel at learning. When I mention to LPs that we use coachability as an important criteria in determining which teams we invest in, they can relate because they’ve all had the experience of investing in a founder who wasn’t coachable.
Kalhorn: So where does the misperception come from?
Chen: I think it originates from investors’ prior experience working with a PhD or academic where they couldn’t get through, and so they concluded that they didn’t understand business.
One example of a great scientist entrepreneur is Kunwoo Lee, PhD, CEO and cofounder of GenEdit, which is developing non-viral gene therapies to treat 10,000-plus genetic diseases.
We met Kunwoo just as they were forming the company, and though he had all the traits we’ve mentioned, he didn’t necessarily scream biotech CEO material. He was coming straight out of grad school and didn’t have the typical pharma experience. But he had great way of storytelling and explaining complex technical concepts to diverse audiences.
We’ve watched as he’s grown the team from 5 to 30 people, and brought on seasoned executives along the way. They’ve since developed major partnerships with established pharma companies such as Eli Lilly and Editas. And this past year, they raised a $26 million Series A to expand their platform and push some of their therapies towards the clinic.
Kalhorn: Wow, that’s a growth mindset. And what’s the third misperception?
Scaglia: That deep tech companies are not in high-growth industries. In fact, they’re mostly disrupting high-growth industries, such as biotech or EV battery production. At first, the technologies may seem to have only narrow applications but, actually, they act as a huge lever of potential.
Chen: Exactly, an investor looking at biological manufacturing might think it’s simply going to produce a substitute product in the speciality chemical space, and base potential ROI on that specific industry.
But that industry is that size because of conventional limitations. If you’re able to develop an enzymatic process to develop a new type of fabric or plastic replacement, you’re not just creating substitutes, you’re opening up a world of new properties and applications.
Kalhorn: Amazing. Scratch beneath the surface of these common misperceptions — that deep tech is too expensive, scientists don’t make good founders and deep tech start-ups are not in high-growth industries — and you see the incredible possibility.