Entrepreneurial action, creativity, & judgment in the age of artificial intelligence

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Highlights

  • Modal uncertainty is a fundamental obstacle to entrepreneurs in theories of entrepreneurial action.

  • Many new ventures are utilizing AI-powered decision tools to solve problems of modal uncertainty.

  • We address the implications of AI for future theory development in the field of entrepreneurship.

Abstract

The rapid advancement of computationally complex systems of artificial intelligence (AI), is the fruit of a decades-long effort to endow machines with cognitive capabilities that equal or even exceed those possessed by human actors. As the growing sophistication of AI algorithms revolutionizes entrepreneurial action in uncertain environments, these advancements raise an important set of questions for future theory-building in entrepreneurial action, creativity, and decision-making research. In this paper, we take up these critical questions by exploring how advancing AI systems provide novel solutions for resolving the fundamental challenges of modal uncertainty in entrepreneurial decision environments. And in doing so, AI algorithms create new possibilities for future forms of entrepreneurial action. We conclude the paper with a robust discussion of future research at the intersection of AI and entrepreneurship.

Introduction

The rapid emergence of computationally-complex systems of artificial intelligence (AI) is the culmination of a decades-long effort “… to create and understand intelligence as a general property of systems, rather than as a specific attribute of humans” (Russell, 1997: 57). After many fits and starts, the field is now principally organized around the goal of constructing a systems-based form of machine intelligence (Russell, 1997), yielding impressive results across an advanced range of cognitive tasks previously thought to be impossible for software systems (Shi, 2011). Andrew Ng, former director of AI initiatives at Stanford, Google, and Baidu, suggests that “if a typical person can do a mental task with less than 1 s of thought, we can probably automate it using AI” (Ng, 2016: 4).

This rapid growth in the capabilities of AI is transforming the practice of entrepreneurship. Over the past few years, venture funding for AI startups has grown exponentially. Annual expenditures to advance AI initiatives now exceed $40B, mostly in the form of strategic acquisitions by tech giants such as Google, Amazon, Microsoft, Alibaba, and Baidu, in their continuing battle to access mission critical AI technologies developed by early-stage startups (Bughin et al., 2017). Global revenues generated from AI applications/systems are expected to climb from $12B in 2017 to more than $46B by 2020. Between 2012 and 2017, annual venture funding for AI startups increased from $1.7B to more than $15B (Statista, 2018). In China alone, cumulative investment into AI technologies exceeds $300B, fueled in part by the ambitious plan to build a $1 trillion AI industry by 2030 (Barhat, 2018). A crucial part of this ambitious plan centers on incubating AI startups with more than $4.5B in funding invested in Chinese AI startups since 2012 (Lee and Triolo, 2017). Overall, this fervent interest and escalating investment in AI to mimic, augment, and even replace the cognitive labor of human actors reflects the long-term belief in the transformative potential of AI systems (Makridakis, 2017).

The rapid emergence of AI will also transform entrepreneurship theory (Mitchell et al., 2017). The most important of these transformational changes stem from the ways in which AI fundamentally alters the calculus of the actor-environment nexus, particularly as it pertains to entrepreneurial action under conditions of uncertainty (McMullen and Shepherd, 2006). In the field of entrepreneurship, entrepreneurial action, judgment, and decision-making under conditions of uncertainty are central tenets of entrepreneurship theory (Townsend et al., 2018; Packard et al., 2017; Foss and Klein, 2012; Foss & Klein, 2015; Klein, 2008; McMullen, 2015; McMullen and Shepherd, 2006; Alvarez and Barney, 2007; Sarasvathy, 2001; Venkataraman, 1997). These uncertainties range from questions about the effectiveness of new processes, the consequences of key decisions, or even the preferences of customers for novel products and services while new ventures toil with limited resources, amidst social resistance, and against competitive threats (McMullen and Shepherd, 2006). Taken in this context, the emerging wave of AI systems offers transformative technological solutions that hold the potential to mitigate key uncertainties that are central to new entrepreneurial opportunities (i.e., Alvarez and Barney, 2007). This is due in part because “… the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction … (and) better prediction reduces uncertainty” (Agrawal et al., 2018: 2). Since core theories of entrepreneurship were designed to contend with action, judgment, and decision-making under conditions of uncertainty, the advancing capabilities of AI systems offer functionally superior and low-cost means to eliminate uncertainty. This, in turn, has generated challenges to existing entrepreneurship theory but also new opportunities for entrepreneurship theory development (Van Burg and Romme, 2014).

Alert to these important developments, our study addresses the theoretical implications of advancing AI systems to theories of entrepreneurial action, judgment, and decision-making. To provide a concrete grounding for our arguments, in following sections, we focus attention on how rapidly advancing AI systems provide new tools for entrepreneurs to address key aspects of modal uncertainty, best defined as “uncertainty about what is possible” (Bradley and Drechsler, 2014: 1229), meaning the array of situation-specific outcomes and eventualities that could potentially come to pass. Modal uncertainty is endemic to the types of decision problems entrepreneurs face in the pursuit of new opportunities (Townsend et al., 2018). For this reason, AI technologies have become freshly relevant in providing powerful new tools that are capable of augmenting entrepreneurial judgment and decision-making. This, in turn, is transforming the practice of entrepreneurship (Townsend et al., 2018) and raising new questions regarding theories of entrepreneurial action, judgment, and decision-making. We conclude the paper with an overview of these questions in order to outline and motivate future research on several important issues emerging at the intersection of the fields of AI and entrepreneurship.

Section snippets

What is artificial intelligence?

The loss by world chess champion, Garry Kasparov, to IBM's Deep Blue computer in 1997, was a watershed moment for AI (Hsu, 2004). Although AI had by that time already become well-established as a central component of serial applications in manufacturing processes and productivity enhancements (Simon and Munakata, 1997), the primacy of human intelligence was still largely unquestioned (Newborn, 2000). In the wake of Kasparov's stunning loss, it became apparent that the capabilities of Deep Blue

Artificial intelligence & entrepreneurial action

Given the centrality of modal uncertainty to entrepreneurial action, AI's escalating capacity to deliver assistive, enhanced decision-making under uncertain conditions is simultaneously transformative but also problematic for extant entrepreneurship theory. On the one hand, the ability to leverage the benefits of AI into an entrepreneur's decision-making processes increases the capacity of individuals and organizations to make headway in reducing modal uncertainty (Agrawal et al., 2018).

Directions for future research

Our central premise in this paper asserts that emergence of AI will transform both theory and practice in the field of entrepreneurship, offering powerful new tools for resolving the modal uncertainties endemic to the processes of innovation and opportunity pursuit. By rendering moot many of the obstacles that thwart systematic search through seemingly infinite possibility spaces, AI systems are poised to improve the range of actions and opportunities new ventures will pursue even while driving

Conclusion

In the opening paragraphs of this paper, we asserted that unbridled progress of AI research over the past few decades is the culmination of years of work “… to create and understand intelligence as a general property of systems, rather than as a specific attribute of humans” (Russell, 1997: 57). We further asserted that the application and use of these tools within the field of entrepreneurship provides powerful new tools for resolving modal uncertainty in the emergence and identification of

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