By Bryan Johnson | 28 October 2015
Venture capital is booming again. VCs invested a total of $48 billion last year—the highest amount in a decade. The lion’s share went to software, which attracted $19.8 billion, versus just $6 billion for biotech. There are many reasons why this is the case, and it’s worth in-depth exploration: Our ability to thrive on planet Earth depends on our ability to make continued scientific breakthroughs that will enable us to pursue the best opportunities and solve many of our most vexing challenges.
Over the years, investors in software and related industries have built not only the expertise, but also elaborate modeling schemes, to mitigate risks and optimize returns. Conversely, the majority of the world’s wealth is maintained by those without scientific expertise, and even those who do have the expertise have lacked mature investing playbooks to assist in high-quality decision analysis.
So for an investor wanting to fund more science endeavors, staring at a highly technical proposal for some promising-looking company can feel like trying to summit a daunting mountain while more accessible ascents are all around you. As an investor without a science background who recently started a $100-million fund for breakthrough technologies, I wanted to find a way to change this for both our fund and then for others.
We wanted a systematic method for evaluating science-based efforts, guided not only by instincts but also by realistic prospects of getting a desirable return. The goal was to find a way to write a new playbook, a guidebook—one that would enable others to start the journey with us or try to improve their own journey.
Take The Plunge Into Modeling
All investors want to fall in love with the endeavors they are supporting, but it’s important to me to fall in love for the right reasons. Ideally, companies will be working toward quantum-leap breakthroughs that will positively impact the world, in addition to generating profits.
In finding such companies, we realized that the most important part of the decision-making process was knowing which questions to ask. Identifying those questions involved interviews (with other VCs, government agencies, academics and others), in-depth research and literature study, in addition to the guidance of technical experts. That was the foundation of our discovery process.
To start, we focused on one area, synthetic biology, and did a deep dive on one company to extract the right questions and to identify the domain- and company-specific risks. Synthetic biology is an emerging market that uses biological engineering to design living organisms to make products, such as flavors, fragrances and probiotics. I am especially excited about the field, as it promises to rewrite the operating systems of medicine, manufacturing and engineering. But while synthetic biology continues to grow, it is still a relative unknown to VCs. Unlike software investment, synthetic biology lacks a playbook.
To change that, our team used decision-analysis modeling, a method widely used in big oil and pharmaceuticals, but still novel for emerging markets like synthetic biology. Decision-analysis tools can help us break out of our own biases and look at an investment with fresh eyes. After all, some risks do not stem from the company or the market, but may come from the investor’s own decision-making process. Using models also takes us way beyond the capacity of what we can hold in working memory to make decisions.
Modeling helps provide an early quantitative understanding of how profitable or disruptive a new technology could be. The model explores the likelihood of different outcomes from an investment, including an expected return multiple (ERM), essentially the value of the investment.
But even though the model outputs a number, we gained the most insight from building the model, diving deep into the company and the domain. For example, we learned that one of the most important risk factors in synthetic biology is in scaling the manufacturing. In identifying such risks, we were able to focus our diligence on them specifically.
The process also underscored that investment domains are not all equal. For example, software and synthetic biology have different market-typical ERMs and different market models. But the modeling process is similar—again, identifying the right questions to ask.
In addition, the process was iterative. Every time we asked questions and got answers, we re-evaluated and re-imagined the models to capture the information learned. In this way, evaluating science startups is very much like science itself, testing and re-testing what we know.
Join A Vital Community
The modeling process is just one tool in the toolbox, only one example of many types of decision-making tools the investment community is beginning to explore. Using multiple tools is good because it’s more robust, especially for complex decisions involving scientific, market and team risk, in addition to many other factors.
We are hoping our case study in synthetic biology contributes to a larger discussion in the investing community about future science and technology. We are openly sharing our methodology—the OSF Playbook—to encourage continued collaboration as we explore more emerging domains.
With our new incredibly powerful tools of creation, such as genomics, biology, computer software, robotics, artificial intelligence and virtual reality—technologies that allow us to program our existence—never has the distance between imagination and creation been so narrow, nor has our ability to author any kind of world that we can imagine been more readily accessible.
I am excited to be one of a growing number of investors on this journey to climb this seemingly daunting mountain of science investing that is critical to our collective ability to create, build and thrive. Only when we set our sights on difficult things, possibilities just out of our immediate vision, will we be able to create the world we imagine.
Reprinted with permission from the author.
— Church and State (@ChurchAndStateN) July 3, 2018
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