Where Do Unicorns Come From? Reflexive Expectations in Innovation Financing

by Kean Birch*

Illustration by Amit Sharma

Unicorns are private companies valued at over US$1 billion. The term was first used by venture capitalist Aileen Lee in 2013 and has since become a cultural trope of its own. According to the business analytics firm CB Insights, there were close to 1,000 unicorns in the world at the end of 2021, mostly headquartered in the United States and China with a smattering of across other countries. Nearly half of these unicorns are in the fintech, internet, e-commerce, and artificial intelligence sectors, although ByteDance – parent firm of TikTok – represents a significant proportion of the AI field at around US$140 billion. Although there is a diversity of investors in these unicorns – over 600 in fact – several well-known venture capital investors crop up again and again, having made multiple investments in a range of unicorns: for example, Sequoia Capital, SoftBank Group, Accel, and Andreessen Horowitz are some of these key financiers.
It’s my argument that unicorns are defined by a particular mode of valuation underpinning venture capital. In particular, venture capital is defined by the construction of future expectation manifested in stories or narratives about themselves, the startups they invest in, and their importance to the world. Of particular interest, though, is the role of reflexivity in these expectations. Venture capitalists know that their stories create expectations and they and the startups they invest in then act upon those expectations reflexively; they know they are creating expectations that might have no basis in reality but they still have to act upon them. It’s why we end up with techno-economic disasters like WeWork or Theranos or Juicero. 
A common narrative in technoscientific sectors – ranging from life sciences to digital tech – places venture capital as the financier par excellence, the fount of not only capital for startups but also of expertise, drive, and kudos. It doesn’t hurt that there’s been a huge inflow of funds into venture capital over the last decade of low interest rates, underpinned by the massive glut of easy money from quantitative easing. It’s also evident in cultural depictions of venture capital, like HBO’s Silicon Valley television series. Consequently, venture capital plays an inordinately influential role in configuring the financing and development of technoscience (Balzam & Yuran, 2022; Cooiman, 2021; Hellman, 2022; Sauter, 2020), even though it’s not the most important or even largest source of financing for hi-tech startups (Birch, 2016, 2017b; Fabozzi, 2016; Hopkins, 2012).
Part of venture capital’s influence results from what Sebastian Mallaby (2022) calls the ‘power law’. By this, he means that venture investments rarely follow a normal distribution, but rather very large returns are concentrated in a few investments. This ‘power law’ forms the basis of venture logic: invest in multiple firms but only expect one or two to provide the returns necessary for the success of the fund (Sauter, 2020). This venture logic has led to a more recent focus on scaling up startups quickly to become dominant firms in their market: first as unicorns, then as monopolies (Balzam & Yuran, 2022; Pfotenhauer et al., 2022).
And here is where the problems arise. Venture capital is represented by a series of distinct funds; for example, in 2020 there were 339 VC funds in the USA. These funds secure capital investment from wealthy individuals and families as well as from financial institutions like pension funds, mutual funds, and the like (Elder-Vass, 2021). Capital investors in VC funds are called limited partners (LPs), while the people running the fund are called general partners (GPs; Klonowski, 2018). The venture investor Elizabeth Yin provides a useful rundown of the relationship between LPs and GPs in a Twitter thread she wrote (Yin, 2021). As Yin points out, most LPs in the USA have to be ‘accredited investors’, meaning that they need to have over US$1 million in assets (outside their principal residences) and an annual salary of over US$200,000; you have to be wealthy to be an LP.
LPs commit their capital to a venture fund, they don’t just hand it over. As a result, VC funds do not have an enormous amount of capital on hand, which has the added benefit of increasing their overall rates of return (and making them look good). Yin and others (e.g. Klonowski, 2018) explain that GPs charge annual management fees – usually 2 percent per year – and expect the fund to last 10 years, which necessarily reduces the funds available to make venture investments to 80 percent of the committed capital – the other 20 percent comprises the management fees. There are other costs too, which means the eventual amount that a venture fund has available often comes to about 75 percent of the LPs’ commitment. As Yin notes, this means that any venture fund already has to do well even to break even; invest 75 percent but yield 100 percent of the LPs commitment.
This is why venture capitalist focus on ‘hot’ sectors and on particular ‘exits’; they need to ensure high returns which are more likely in sectors with lots of investor interest (whether or not they create any social benefits), or where a startup can monopolize or control a whole market. These expectations are embedded in the stories that investors tell. VC investors deliberately seek to identify and invest in startups that they think other investors will want to invest in or buy in the future; they reflexively think about how to create expectations and interest in particular sectors or startups – how they might dominate this market or that. Otherwise, they’ll end up with no ‘exits’, the point at which investors sell their investment (Shapin, 2008). Several informants in my research noted this dilemma, that they sometimes wanted to invest in startups where there was no clear exit; if they invest, though, they are less likely to hit the big returns they need. All of this creates a ‘group mentality’, focusing on particular technologies and not others; it leads to a massive waste of financial resources that could be used to invest in technologies that we desperately need but lack the expectations or stories necessary to secure high returns.
* Kean Birch is a Director of the Institute for Technoscience & Society and Associate Professor at York University. Among his books are Assetization: Turning Things into Assets in Technoscientific Capitalism (with F. Muniesa), Neoliberal Bio-economies? The Co-construction of Markets and Natures, and A Research Agenda for Neoliberalism. This blog is based on Professor Birch recent article published in Social Studies of Science.

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