briefly summarize this "Substitution is an important RBV construct (Peteraf & Bergen, 2003; Polidoro & Toh, 2011),
which centers on resources replacing others providing the same functionality (Levinthal &
Wu, 2010). Resources with low fungibility are specific to one domain, whereas those with high
fungibility can be applied widely. Furthermore, scale-free resources can be allocated across
domains without additional costs, while those that are not scale free have opportunity costs.
While humans' general cognitive capabilities have a high fungibility, advantage-generating
resources, such as humans' domain-specific cognitive capabilities, have a low fungibility and
are also not scale free (Helfat & Peteraf, 2015). This constrains these capabilities' use to related
domains (Montgomery & Wernerfelt, 1988) because their application for substitution in
unrelated domains would require extensive learning from experience, coming with significant
economic costs that quickly exceed any potential benefits (Helfat & Peteraf, 2003).
The adoption of AI in a competitive domain could trigger substitution. Currently, AI is used
widely to automate predictions regarding strategic decision making and problem solving tasks,
which, traditionally, only humans could do by relying on their cognitive capabilities (Choudhury
et al., 2020; Shrestha, Ben-Menahem, & von Krogh, 2019). Unlike humans, machines have quasiunlimited information processing capacity, often providing better predictions than humans do
(Raisch & Krakowski, 2021). This is evident, as AI-based machines now match or surpass physicians'
cancer diagnosis and treatment recommendations (Jiang et al., 2017), human resource experts' prediction of candidates' future job performance (Chamorro-Premuzic et al., 2019), product developers'
creation of design alternatives (Verganti, Vendraminelli, & Iansiti, 2020), and business angels' venture investments (Blohm, Antretter, Sirén, Grichnik, & Wincent, 2021). AI resources' accuracy and
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abundance are therefore likely to reduce human prediction capabilities' traditional value (Ahuja,
Coff, & Lee, 2005), or even make these capabilities obsolete (Agrawal et al., 2018, p. 80).
While this prior work offers strong foundations, several open questions remain on how substitution occurs in an AI context. First, technical limitations hinder machines from taking over
decision making or problem solving tasks entirely (Raisch & Krakowski, 2021). For example,
prediction is only one component of decisions, which also require tasks like setting objectives,
selecting data, exercising judgment, and taking action (Shrestha et al., 2019). While substitution
through AI may occur, it is unlikely to capture AI-driven dynamics exhaustively. Second, the
nature of substitution may differ when machines replace humans. For example, prior studies
described the substitution of resources between related domains (Levinthal & Wu, 2010;
Peteraf & Bergen, 2003), because humans' domain-specific cognitive capabilities are difficult to
transfer to unrelated domains. While these arguments apply to humans and their cognitive
capabilities, they may not apply to machines' corresponding capabilities, which AI scholars suggest are widely applicable across domains (Agrawal et al., 2018, p. 2) and scale free
(Brynjolfsson & McAfee, 2014, p. 31). Extant research has little to report on how substitution
might occur across previously unrelated domains, such as AI technology versus medical care,
talent recruitment, product design, and venture capital. Consequently, the first question to
explore is whether and how AI substitutes humans' advantage-generating cognitive capabilities."