The OSF Playbook: A Journey to Investing in Future Sci-Tech

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Taking the Plunge With Clarity

The decision of whether to invest in a startup is complex. Some risks may not stem from the company or the market but from the investor’s own decision-making process. Investors’ assumptions, cognitive biases, and the mechanics of how a decision is made can influence the decision and potentially jeopardize investments.

Quantitative methods for evaluating startups have become more popular in recent years in VC, government, and industry, as we discovered through in-depth research, interviews, and study of the literature. Traditionally, VCs rely on a set of heuristics and threshold criteria for return on investment.1 Other fields, however, such as the oil industry2 and pharmaceutical companies, have worked with decision models for years with favorable results.  Even in domains such as major league baseball and the NBA,3 quantitative methods are being successfully used to increase the likelihood of wins. As early as 1982, the literature suggests that major areas of the U.S. government had adopted decision-analysis in some form or another,4 and agencies such as DARPA (Defense Advanced Research Projects Agency) have led the way in funding high-risk science research.  In venture capital, there appears to be an interest in better methods, but there is as yet insufficient data for a conclusive judgement about the push for data-intensive methods in VC.5 Moreover, this practice is not yet widespread for science startups.

To understand more about the possible risks and rewards of investing in a science-based company, we used decision analysis, which a few venture firms have begun to adopt. Quantitative methods are valuable because investments are inherently quantitative. Although numerical results should not be interpreted naively without other context, they can add insight when used appropriately.

We based the model’s initial structure on an existing example of decision analysis in high-tech venture capital by Clint Korver, a consultant with many years of experience applying similar models in industry.6 Korver analyzed startup company Inkling, which was developing e-textbooks for the iPad. The decision-analysis framework built a clearer picture of risks and returns, and his firm invested successfully.

Diving Deep into Synthetic Biology

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We analyzed a synthetic biology company looking to grow and access new markets. While this case is based on a real example, details have been changed for anonymity.

Synthetic biology is an emerging domain in which biological engineering is used to design living organisms to make products.7 Biological engineering has existed for decades, for example to make pharmaceuticals, but recent advances are enabling a step change in efficiency and predictability of these systems.

The synthetic biology company we analyzed is making replacements for specialty chemicals (e.g., petroleum-derived or naturally occurring plant extracts) by using microorganisms to produce the chemicals. Such a process potentially offers cost, supply chain, and product quality advantages over making those same chemicals from petroleum or extracting them from plants. Similar to competing players, the company’s business model is based on upfront co-development fees and licensing royalties on the use of their biological technologies. The company has demonstrated some early technical success by producing small quantities of desired chemicals, but has yet to prove itself in the market.

While decision analysis has been applied to later-stage biofuel plant operations,8 we are not aware of cases where decision analysis has been applied to early-stage investing in synthetic biology.

  1. Seth Levine, “Venture Outcomes are Even More Skewed Than You Think”, http://www.sethlevine.com/archives/2014/08/venture-outcomes-are-even-more-skewed-than-you-think.htm
  2. Reidar Bratvold, “Decision-Making in Oil & Gas – The Good, Bad, and the Ugly”, http://www.edpn.org/wp/wp-content/uploads/2012/10/Reidar-Bratvold.pdf
  3. Rick Maese, “Moneyball Movement Sweeps Pro Basketball”, October 25, 2013, Washington Post. http://www.washingtonpost.com/sports/wizards/nba-embraces-advanced-analytics-as-moneyball-movement-sweeps-pro-basketball/2013/10/25/1bd40e24-3d7a-11e3-b0e7-716179a2c2c7_story.html
  4. Ulvila and Brown, “Decision Analysis Comes of Age”, September 1982, Harvard Business Review. https://hbr.org/1982/09/decision-analysis-comes-of-age
  5. Christina Farr, “Venture capital picks Up the Moneyball strategy”, Venturebeat. http://venturebeat.com/2012/11/09/startup-algorithm/
  6. Clint Korver, “Applying Decision Analysis to Venture Investing”, Kauffman Foundation. http://www.kauffmanfellows.org/journal_posts/applying-decision-analysis-to-venture-investing/
  7. Synthetic Biology Project, “What is Synthetic Biology?” http://www.synbioproject.org/topics/synbio101/definition/
  8. Scott Mongeau, “A Biofuel Plant Analysis”. http://blog.palisade.com/2011/08/11/using-the-decisiontools-suite-for-a-biofuel-plant-analysis/