Mindsets for two emerging worlds
will bioengineering be able to adopt tech(bio) mindsets?
The assembled and the grown, the programmable and the unpredictable, the engineered and the discovered. These two worlds are no longer separate. We’ve reached the point in which Moore’s law applies to both transistors and DNA technologies, making it possible for technology and biology to exist in a convergence that some like to call “techbio”.
The hypothesis here is that, as we get better at engineering biology, we will increasingly start applying tech startup mindsets like “move fast and break things” and metrics like KPIs to life sciences startups, both in techio and pure wetlab biotech.
We thought that investors and founders alike would find it valuable to read the data-backed story of these two emerging worlds at an interesting point in time: the inflection point.
Thanks to Nodes Advisors for supporting this post. If you feel like building some techbio, don’t hesitate to reach out to them. They have special partnerships with AWS and others to help early-stage founders get started :)
For years, the supply of new biotech startups and the investment in the field had remained practically flat in comparison to software tech (A). In fact, the trend was generally pointing that biotech exits were exceeding the supply of new startups of this kind1 (B).
Already in the Great Recession, biopharma had demonstrated tenacity as a sector, with no growth but no decline either in VC investment. 2020 though, was different. As the pandemic increased overall awareness on biotech, the industry received $28.5 billion in investment which represents a 60.5% year-on-year increase: beyond resilience, anti-fragility2.
Although some report a decline in the creation of new biopharma startups in the beginning of 20203, they’re not accounting for the dozens of new synbio ventures across pre-seed accelerators like IndieBio and FiftyYears in San Francisco, Petri-Pillar in Cambridge or GridX in Argentina. In fact, Built with Synbiobeta reported $3 billion dollars of investment in the synthetic biology space just in the first half of 20204. This included startups building in agritech, foodtech, biomaterials and consumer products
Techbio, or why now
At first it may seem that ever since the discovery of DNA, biotechnology has been building upon itself, without the need of major intervention of other fields. The exponential improvement (in price and accuracy) of DNA sequencing is creating a positive feedback loop: more biological data to do more research.
That, however, is precisely the argument for techbio: we need the power of bioinformatics and biorobotics to make sense of the googles of biological data and to scale up biological processes.
If this were a chemical reaction, solutions to the world’s biggest problems would be the final product, bioengineering and politics would be substrates and technology would be the catalyzer.
We know biology is ready but it’s the engineering we can now use on it that will allow for this reaction. The ability to program biology is what makes this time so rare.
How about politics? That’s always a tricky one! We’re not policy makers here but in the best case scenario, the accelerated approval of COVID drugs and vaccines in 2020 will have paved the way for faster pipelines in other biopharma innovations too.
Then, is technology ready? Well, software has been mainstream for about 15 years now, creating a large enough talent pool of coders that biotechnology can now leverage. In deep tech, just in 2020 DeepMind increased AlphaFold’s accuracy by almost 30 percentage points5 and Riguetti increased the number of qubits in a computer by almost 70%6. Tech is ready.
As it’s well-known, YC and the Bay Area are where most tech startups want to be. The environment is all about fast iteration cycles around the product and customer acquisition. The headline: only around 3% of startups that have ever been part of YC are doing bioengineering or techbio7.
Some of the first companies to go through the accelerator were Ginkgo Bioworks and Benchling. Since both of them use software and/or hardware for biological purposes, we’ll consider them techbio companies.
To know how we can apply tech startup mindsets to the life sciences, we need to know 2 things about techbio: 1) it’s helping bioengineering iterate faster and 2) techbio startups are, at their core, a type of tech startup.
That said, the following mindsets will surely apply for techbio startups. The question is: how are bioengineering startups starting to adopt them?
1. Move fast and break things
In drug discovery, the first phase of drug development, such thing as “short iteration cycles”, hasn't been possible for a long time. Of course, the arrival of AlphaFold is likely to revolutionize that, together with new crystallography8 techniques can speed up this phase, which is usually around 5 years long.
Still, the iteration cycles are only likely to take place in this first stage of R&D. One big difference between tech and biopharma in fact, is that tech iterates until reaching product-market-fit while biotech needs to consider different bio-milestones. If a drug doesn’t work in-vivo, researchers have to start all over again9.
Bioengineering startups, those creating recombinant proteins for example, may benefit from the synthetic biology framework “Design-Build-Test-Learn” to iterate just as tech does.
That’s exciting. We just need to keep in mind that biotechs will iterate based on different metrics. Instead of focusing on the customer, bio-metrics will mostly be related to a proof of concept first (the MVP in bio) and scaling up next.
A rough example: a synbio startup that wants to produce sustainable dyes through EColi. On the bio side, they need to measure the performance of their dye against chemically-produced dyes. Can they consistently produce a dye that meets the industry standards? On the engineering side, the main questions will be around yield and cost.
2. The founder-led movement
This tale of two emerging worlds couldn’t seem more separate, when Genentech and Apple were founded at the same time yet their respective startup environments seem contrasting today. While Apple was founded and led by Steve Jobs and Steve Wozniak (both people with tech backgrounds) Genentech was founded by Robert A.Swanson (a VC) and Herb Boyer (one of the inventors of recombinant technology, who stayed in the UCSF faculty)10.
When Steve Jobs was fired out of his own company, things didn’t go quite well. Only great founders have the vision to lead their startups in the right direction. Even more importantly in bioengineering, founders have the technical insights.
The bad news is that, historically, academia culture doesn’t really encourage researchers to commercialize their technology, and things can get complicated for founders especially when negotiating IP rights.
The good news is that, as the supply of biotech investors increases and the bio-trends like the cost of sequencing improve, more biotechs are being started and led by their original founders.
In the deck of biotech founders, credentials usually range from a BS in the life sciences to a PhD and over 20 years of academic experience. This breed of founder is the expert on the innovation in case, but will require some help to adapt to the startup ecosystem and become an expert in the market too.
It’s important to emphasize “usually” for we are also starting to see bold biotech founders coming from completely different fields yet with just the right soft skills to lead a biotech in the right direction and bring the right CSO into the team. This other breed of founder may know the market better but will need help understanding the science.
The techbio founder is different. They can come from a tech background and hearing about the potential of biotech led them to applying their skills and knowledge into this field. Though that would be the ideal and universities are working on relevant programs, it’s still quite rare to see founders who have a good understanding of all the tech, the bio and the business.
3. Barrier to entry
“Do you want to sell sugar water for the rest of your life, or do you want to come with me and change the world?”—Steve Jobs to John Sculley, previous executive at Pepsi.
Over the past 20 years, the tech industry has grown an immense pool of talent. Founders like Bill Gates and Mark Zuckerberg started as 15-year-olds building video games from their bedrooms. They then paved the road so that today’s 15-year-olds can learn how to code Artificial Intelligence apps on edX or even YouTube11. Here we can clearly see a positive feedback loop where more people entering the field makes it easier for even more people to enter the field–great news for techbio!
We aren’t there yet in wetlab bioengineering. 15-year-olds doing DIYBio at home have (generally) only gone as far as to grow E.Coli that glows in the dark. The founder-led movement is still stuck at the PhD level, where biotech dropout founders are the rarest outliers and unfortunately remind us of Elizabeth Holmes.
So the bad news is we might not be ready for the biotech college dropout founder yet. The good news is we know the variables we need to adjust to get there:
Foundational technologies: inherently, physical goods won’t scale as well as software. However, keeping on reducing the cost of DNA sequencing and synthesis will definitely unlock the potential for people to build in biotech.
Lab facilities and equipment: thanks to the internet, knowledge is not a barrier for the wetlab founder. Still, starting a biotech startup anywhere outside of a university is simply unrealistic. To increase the supply of these startups, we need to expand access to labs outside of universities.
Curriculum: thousands of kids are learning the foundations of programming as early as elementary school. Doing that for synbio will exponentially increase the potential talent pool in the coming years.
4. Welcome latecomers!
As we move from discovery-only biology to engineered biology, bioengineering doesn’t have to follow the “winner-takes-all” scheme where the first researcher to discover a substance closes the doors for others to commercialize something similar.
Instead, we are seeing many different biotechs building on top of other biotechs’ innovations. Some examples include the hundreds of CAR-T cell therapy startups12, stem cell therapy biotechs, or the different lab-grown meat companies.
How can your startup win when the market starts to saturate? Perhaps the best answer is to develop platform technologies…
5. Platform > focus
“Build it and they will come”—Ray Kinsella
Although the previous mindsets do align with the way of building tech startups, it’s also important to highlight unique points of the Lean BioEng method.
Tech, including techbio, is relatively easier to execute. Yet, it comes with a higher market risk. In contrast, biotech usually takes years to develop but the market is almost guaranteed. In pharma, food, or agriculture, you just build the most efficient solution possible and they (your customers) will already have been waiting for you a long time.
That said, a strategy to de-risk and amplify the gains in biotechs is not to focus on building a single product at a time but instead adopting a “platform > focus” mindset. That is, using the same biotechnology to build as many products as possible.
Some examples include Mammoth Bioscience (CRISPR for different diseases), Synlogic (probiotics for different conditions) and GALY (different cellag-based products).
It was in the early tech days when Google was being built for the first time. There were still few people in the space and few examples of what worked and what didn’t. Today, they’ve paved the way for thousands of people to enter the tech revolution and hundreds of other startups to boost it.
We’re now in the early days of TechBio and biotech. The startups we build with both today, will be crucial to solve the world’s biggest problems. Bold scientist-founders will have the right mindsets, insights, and determination to create solutions and give rise to these two emerging worlds.
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Not saying you can become an expert in weeks but it’s possible. I’ve done it myself and there are increasingly more teenage researchers and builders in the AI field.