- Venture Capital
3 reasons biotech is at an inflection point and why early-stage tech VCs should be investing now
Here’s what we learned after analyzing 1,227 biotech startup pitches.
“I invested in biotech once, and it only took me 12 years…to lose all my money.”
As venture capitalists investing in highly technical verticals, we’ve heard tech investors make this joke multiple times over the past few years. It’s funny, but it might not be accurate anymore.
Over the last two years, we analyzed 1,227 early-stage biotech startups, and we came across something interesting: Biotech is no longer limited to just therapeutics and diagnostics – it’s being used to create products that solve problems in radically different industries.
Today there’s a new subsector in biotech that’s starting to focus on what we eat, how we take care of ourselves, and even how we manufacture products. Biotech companies are beginning to resemble traditional tech companies and building products like them. We believe that investment models traditionally applied to early-stage tech startups can now be applied to this New Biotech.
Ten years ago, investing in early-stage bio-startups with less than $5M per check was fairly risky. Back then, building these types of companies required a lot of capital, time and a unique mix of talent: scientists, investors who take a big chunk of the company, and one or more non-founder operators that would eventually ride the company to the finish line (or lack of it).
The technological advances in the past decade have made experimentation, product development, and finding product-market fit much more accessible for this new breed of biotech startups. Decreasing costs in sequencing technologies, automation, and advances in machine learning are reshaping biotech at a foundational level. The tools and infrastructure for transforming these ideas into impactful realities have never been so efficient as they are today.
However, change doesn’t happen overnight. The evolution of the biotech industry has accumulated over the last few years and is due to a combination of factors.
1. The decreased cost of DNA sequencing technology
The first human genome took $2.7 billion and almost 15 years to complete. In the last 11 years, this cost has dropped from $10 million to around $1,000 today. Reading the “code” of life of any living organism now costs less than a new iPhone. As a reference, think of Moore’s Law, the projection by Intel’s co-founder Gordon Moore that computing would increase exponentially in power while decreasing in cost, it almost looks like a flat line put next to what happened with genome sequencing cost.
2. Lab automation has made biology experiments faster, more reproducible, and cheaper
This is an oversimplification of the innovative work scientists do, but most biology experiments consist of moving small amounts of liquid from one place to another and then spending time looking at them.
A startup can now buy an Opentrons robot for just $4,500 and use a machine vision SaaS platform to asses the results. Before this technology, one would need highly trained people moving small amounts of liquid around with a pipette and then spending hours looking through microscopes to achieve similar (potentially less accurate and less efficient) outcomes. In the lab, robots are replacing hands, and machine vision is replacing eyes. Finally, highly skilled scientists can focus on value creation rather than repetitive tasks.
3. Artificial intelligence can replace our need for understanding
Biology is an incredibly complex field, and even the best scientists can only understand a limited percentage of what goes on with living organisms; the most advanced, complicated, and dynamic systems ever.
Even with the monumental technological advances in the last century, we might never know for sure the correct answers to the most prominent “Why?” questions in biology.
Artificial intelligence and machine learning not only allow us to conduct experiments faster and cheaper, but they also enable us to do something that was previously impossible. We can create systems that output results without the need for understanding all the intricacies of the rules of biology.
From infrastructure to applications
We’ve seen the same signals in the past. When costs drop from their originally prohibitive levels and new sophisticated technologies become more accessible some very interesting things start happening.
When Intel launched the 4004 in 1970, it kickstarted the entire personal computer industry. When the Internet Protocol Suite was standardized in 1982, it made the Internet a worldwide opportunity for entrepreneurs and venture capitalists. When the U.S. government discontinued the Selective Availability for GPS in the early 2000s, companies like Uber became possible.
The access to new infrastructure allows smart, hard-working founders to leverage and build innovative applications on top of it. The realm of potential applications is extremely broad and it is not limited to what can be directly foreseen. On the contrary, we can witness the creation of a very interesting category of solutions that lie at the edge of the newly opened infrastructure and that oftentimes cross industry boundaries. A great example is Uber and the GPS, though not a direct application, today’s tech giant leveraged the technology that constitutes a key layer for its product. It wasn’t easily predictable at the time, but Uber is now “one of the greatest startup investments of all time”.
We’re entering the applications era of biotech. An era where investing relatively small checks in young biotech startups is not only possible but could generate outsized returns. Lower amounts of capital can take biotech companies to inflection points that will change their risk profile. This new investment model is possible because biotech products are not just traditional ones anymore. There’s a new group of startups working on applications at the edge of biotech.
A tech investment model for the “New Biotech”
Good investors capture industry changes and capitalize on them, great investors find treasures where nobody’s looking and generate outsized returns.
Although the New Biotech startups can develop products cheaper and faster, allowing for iterations and shorter feedback cycles; it might seem like a big leap to compare tech products to biotech ones. What’s missing from this picture is that not only the product development is more agile, at the same time the products themselves are different: they are “at the edge of biotech”. They rely on an underlying biological layer, on top of which the tech is built. New Biotech products are built with an engineering approach, they tap into new industries and face not so much scientific challenges as they do tech and commercial ones. Looking a lot more like tech companies, they also require similar check sizes to reach the next inflection point.
This is a longer discussion that we’ll be exploring with a deep dive into our learnings from analyzing over 1,200 early-stage biotech startup products.
We’re at the beginning of something new, the application era of biology promises to bring a wave of new companies that tech investors can understand and add value to, without having to drastically alter their investment model.
This shouldn’t eclipse the fact that biotech still means biology, and biology is complex, slow and sometimes unpredictable. Early-stage tech investors venturing into this space should understand that patience and most importantly scientific integrity are key components of any investment and that’s extremely important when these startups are building products that will have an impact on people’s lives.