A Scaling Perspective on AI Startups

Abstract

Digital startups' use of AI technologies has significantly increased in recent years, bringing to the fore specific barriers to deployment, use, and extraction of business value from AI. Utilizing a quantitative framework regarding the themes of startup growth and scaling, we examine the scaling behavior of AI, platform, and service startups. We find evidence of a sublinear scaling ratio of revenue to age-discounted employment count. The results suggest that revenueemployee growth pattern of AI startups is close to that of service startups, and less so to that of platform startups. Furthermore, we find a superlinear growth pattern of acquired funding in relation to the employment size that is largest for AI startups, possibly suggesting hype tendencies around AI startups. We discuss implications in the light of new economies of scale and scope of AI startups related to decisionmaking and prediction.

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“…A firm grows as its components-such as the number of employees and sales, assets, revenue, market share, and expenses-in relation to each other scale up. In that sense, scaling is the relation between size-related concepts that work as growth measures (Schulte-Althoff et al, 2021). Crucially, scaling is a managerial strategy underpinned by a concern for what needs to be scaled for a business firm to realize growth goals (Chandler, 1990).…”

“…If no one else owns a phone, the value for the one person with a telephone equals zero. Thus, generally, it is better to be part of a larger network than a smaller one (Shapiro et al, 1998), creating a strategic imperative to grow the network. The user base of an offering can be said to comprise a network structure: every user is a node, and the more connections that can be made, the more value is generated for each user, and, as an effect, the network grows (Henfridsson, 2020).…”

“…The increasing importance of multi-sided platform-based ecosystems, such as those associated with Sony with its PlayStation or AirBnB, has made them the source of considerable interest to academics and practitioners (Gawer, 2021;McIntyre et al, 2021;Parker et al, 2016). Unlike the firms based on pipeline economies (Parker et al, 2016)-i.e., those using the industrial paradigm of mass production-firms based on network economies (Parker et al, 2016)-i.e., platform-based ecosystems-seek growth through a new logic (Parker et al, 2016;Shapiro et al, 1998) (Henfridsson, 2020). The new logic sees firms in networked economies scaling in a qualitatively different way from the firms of pipeline economies (Henfridsson, 2020).…”

“…The differences in scaling arise from supply economies of scale in contrast to demand economies of scale (Parker et al, 2016;Shapiro et al, 1998). The rise of industrial firms is based on technological innovation on the supply side (Chandler, 1990).…”

“…In these market contexts, the iconic scaling issue is one of 'the chicken and the egg': scaling any of the market's sides requires the other side(s) to be scaled first (Parker et al, 2016;Shapiro et al, 1998). Our understanding of scaling and growing multi-sided platforms, is based on economic theories of multisided markets (Hagiu & Wright, 2015;Rochet & Tirole, 2003) and network effects (Katz & Shapiro, 1985;Shapiro et al, 1998). Network effects theory assumes that certain market structures-markets that are composed of several sides bound together in a network-shape the emergence of economic organizations.…”