2.1. VC Network Reachability and AI Startup Investments
Venture Capitalists (VCs) serve as crucial financial intermediaries, tasked with the diligent evaluation of startups to identify and channel funds towards the most promising ventures [
50]. Central to this evaluation is a thorough examination encompassing the startup’s technological innovation, the competency of the founding team, the distinctiveness of the value proposition and business model, an analysis of the market environment, and the prevailing regulatory framework, among other factors that are indicative of potential success [
11,
12,
13,
14,
15,
16,
17]. The nature of startups often embeds them with inherent uncertainties, necessitating a significant volume of information for an accurate assessment [
5,
6].
The predicament of information asymmetry significantly challenges VCs, particularly when navigating complex and rapidly evolving domains like AI [
5,
51]. Founders typically possess a deeper understanding of their ventures than external investors, creating a discernible information imbalance that hampers the VCs’ ability to identify genuinely promising startups. Despite this, VCs must meticulously evaluate the viability of the technology, the capabilities of the team, market dynamics, regulatory considerations, and other pertinent factors to ascertain the potential of novel technology-based startups to yield returns.
One viable strategy to mitigate the information asymmetry issue is to rely on prior syndication. The existing literature underscores the benefits VCs accrue from syndication networks, including enhanced deal flow access, risk mitigation, decision validation, and information sharing [
21,
50]. Sorenson and Stuart [
45] further elucidate that syndication networks foster efficient knowledge transfer between partners, as exemplified when a VC gains a superior understanding of a startup’s potential from insights shared by a syndicate partner invested in the startup. Meuleman and colleagues [
24] demonstrate how VCs utilize different types of network embeddedness, relational embeddedness (direct ties) and structural embeddedness (indirect ties) when participating in cross-border investments associated with startups located in countries with different degrees of institutional uncertainty.
Extending these insights, we posit that not only direct, ongoing ties, but also the extent of a VC’s reachability within their historical syndication network, particularly to other VCs with prior AI investments, can significantly augment the expertise available to them, thus increasing their propensity to finance AI startups. Network reachability refers to the existence of connections, both direct and indirect, between a focal venture capital firm (VC) and other VCs within the broader syndication network [
33]. It measures how readily the focal VC can access and interact with other VCs across the network. A higher degree of reachability is indicated by the number and lengths of the shortest paths connecting the focal VC to others, facilitating faster and more efficient knowledge and resource exchange across the network [
33,
34,
52].
For instance, VC A has high reachability to VC B if they have directly collaborated on deals (direct ties) or if they have a chain of other syndicate partners who can act as efficient conduits of information (indirect ties). The ability to tap into VC B’s expertise through short, low-friction network connections increases VC A’s reachability. Conversely, reachability is limited if VC A and B are not directly or indirectly linked; in such cases, knowledge from VC B is less likely to diffuse to VC A, increasing isolation. Low reachability often entails reliance on longer intermediary chains to exchange knowledge, a process that tends to be slower and less reliable.
In the context of AI startup investment, high network reachability to incumbent AI startup investors broadens the channels through which a VC can acquire nuanced intelligence, referrals, and other benefits that enhance their ability to evaluate AI deals. Such robust connectivity provides access to external AI expertise. We argue that a VC’s network reachability, especially to established AI investors through historical syndicates, is likely to increase their likelihood of investing in AI startups. These channels offer access to expertise that is critical for overcoming information barriers.
Network reachability confers several informational benefits: it provides technology alerts, validation, referrals, and clarifications about market hype, aiding VCs in navigating uncertainties when evaluating AI startups. We maintain that a VC’s network reachability to former AI investors via historical syndicates significantly increases their likelihood of engaging with and investing in AI startups. These connections reveal expertise that is vital for addressing information asymmetries. This argument is supported from two perspectives. First, multiple studies have shown that indirect, third-party connections convey invaluable tacit knowledge about startup quality and technological viability, thereby guiding investment decisions amid uncertainty [
30,
31]. Sullivan and Tang [
22] found that small-world networks, which combine local clustering with short path lengths, had a positive impact on the performance of US venture capital firms from 1995 to 2003, but this effect varied with the firms’ absorptive capacity to recognize and assimilate external information and knowledge. Schilling and Phelps [
33] highlight that shorter network path lengths enable a smoother knowledge transfer across network boundaries, offering a wealth of diverse information that spurs innovation. These factors suggest that VCs are likely to make extensive use of both direct and indirect connections of varying path lengths when evaluating innovative, unproven ventures where information asymmetries are substantial.
The significance of network reachability is particularly pronounced in the uncertain and complex realm of AI, characterized by pronounced information asymmetries. The dynamics of network connections and the subsequent information flow are vital in enhancing investors’ abilities to assess quality and mitigate risks, especially in uncertain environments. This reasoning supports our theory that the web of relationships, enabling potential access to seasoned AI investors through a VC’s historical syndication network, is likely to act as a source of insights and expertise on the AI landscape, even without direct syndication in a specific deal.
For example, interactions with veteran AI investors, either directly or through shortened network paths, yield nuanced intelligence that is crucial for precise evaluations. The expanded range of information provided by syndicate network connectivity could significantly alter perceptions of AI investment potential. Positive insights could alleviate the skepticism often associated with previous cycles of AI hype. Assertions of significant progress in AI technologies, like deep learning with unmatched predictive accuracy, might prompt a reassessment of the AI sector. Moreover, seasoned AI investors might facilitate access to prospective deals and entrepreneur referrals, thus ensuring a steady flow of attractive, under-the-radar AI startups. Additionally, experienced partners may validate a VC colleague’s initial interest in a particular AI deal.
In essence, network reachability to established AI startup investors offers numerous informational benefits—insights into technology, referral streams, validation, and the mitigation of hype—all of which collectively heighten the propensity to pursue AI deals. These relationships, even in the absence of actual syndication, act as a pivotal mechanism to overcome information barriers while assessing unfamiliar AI ventures, conveying nuanced intelligence that enables accurate evaluations under conditions of uncertainty. We, therefore, hypothesize the following:
Hypothesis 1 (H1): VCs with higher network reachability to syndicate partners with a history of investing in AI startups are more inclined to invest in AI startups themselves.
2.2. VC Network Brokerage and AI Startup Investments
We contend that a VC with elevated betweenness centrality is predisposed towards investing in AI startups. Rooted in social network theory, betweenness centrality measures the extent to which a node acts as a bridge or intermediary among other nodes in a network [
28]. In the VC syndication network milieu, a VC’s betweenness centrality reflects their strategic positioning in connecting disparate investor cohorts or entrepreneurial ventures. This positional advantage not only broadens a VC’s access to diverse and non-redundant information but also enhances their control over resource flow, including financial capital and expertise, across the network [
29].
Central to our hypothesis is the notion that a VC’s structural position within the syndication network profoundly influences their aptitude and willingness to engage with emerging technological realms, notably AI startups. The theory of structural holes postulates that entities (here, VCs) bridging gaps in a network—structural holes—gain unique informational advantages [
29]. They are privy to a wider spectrum of information and innovations traversing the network, enriching their situational awareness and enabling them to seize emerging investment opportunities. This aspect gains prominence in the AI domain, known for its rapid technological advancements and marked market uncertainty [
53]. VCs with higher betweenness centrality, by bridging structural holes, are positioned to grasp evolving AI trends and technologies earlier, rendering the AI domain more salient to them.
Our assertion that VC betweenness centrality will foster AI startup investments is undergirded by theoretical insights into the strategic boons of network brokerage. Specifically, the bridging of structural holes avails VCs of expansive informational access, expediting the discovery of innovative AI ventures and timely knowledge acquisition regarding AI’s evolving technological readiness. Moreover, the broker’s selective exposure capacity can magnify positive narratives surrounding AI, overshadowing outdated, negative perceptions among skeptical investors. The recombinative potential inherent in bridging enables VCs to discern synergies between AI startups and incumbents positioned to leverage AI capabilities. Furthermore, ecosystem cultivation denotes VCs utilizing brokerage to interlink fragmented activity pockets around AI entrepreneurs, researchers, customers, and other stakeholders. This system-building role diminishes coordination costs, fostering AI experimentation. Lastly, the reputational merits of betweenness centrality may increase the AI deal flow to broker VCs while bolstering the credibility of their upbeat AI assessments among peers. Such bridging positions also hone pattern recognition faculties concerning AI’s burgeoning ubiquity. Collectively, these multidimensional boons of brokerage provide information, vision, referral pipelines, combination potential, ecosystem orchestration, and cognitive advantages, enabling VCs to avidly pursue and secure promising AI startup deals.
Venturing into domains like AI is laden with uncertainties stemming from technological volatility, market unpredictability, and regulatory ambiguity. Nonetheless, network positioning, as depicted by betweenness centrality, can significantly allay these uncertainties [
54]. A high betweenness centrality emboldens VCs to form connections among previously unconnected or loosely connected entities intrigued by the new domain. By fostering these connections, VCs cultivate a conducive ecosystem for knowledge exchange, risk sharing, and the collaborative exploration of emerging opportunities in the AI domain. This, in turn, curtails the coordination costs and information asymmetries, usually characterizing entrepreneurial entry into emerging technological domains.
From the outlined theoretical framework, we derive several rationales supporting our assertion that a VC with higher betweenness centrality is more likely to invest in AI startups. First, the informational advantages associated with high betweenness centrality enhance the VC’s insight into the AI domain, bridging the perceptual gap between their existing knowledge base and this novel domain. This narrowed perceptual distance is crucial in overcoming the cognitive biases and inertia that typically deter investors from venturing into unfamiliar technological domains.
Second, the ability to establish connections and facilitate exchanges of knowledge and resources across the network enhances the VC’s ability to navigate the uncertainties and complexities inherent in the AI domain. This not only strengthens their risk assessment and decision-making processes but also boosts their capacity to identify, evaluate, and engage with promising AI startups.
Third, the strategic advantages of a high betweenness centrality extend beyond the individual VC to benefit the broader venture capital ecosystem [
21]. By serving as conduits, VCs with high betweenness centrality foster a conducive environment for the cross-pollination of ideas, resources, and expertise across the network. This triggers a collective learning process, where the insights and experiences garnered from engaging with AI startups are shared across the network, reducing collective uncertainty and elevating the collective propensity to invest in AI startups.
Fourth, the bridging role of VCs with high betweenness centrality aids in aggregating the complementary resources and expertise essential for nurturing and accelerating the growth of AI startups. This not only heightens the likelihood of successful entrepreneurial outcomes but also amplifies the positive externalities for the broader innovation ecosystem. Taken together, these factors create a conducive milieu for VCs with high betweenness centrality to actively engage with and invest in AI startups, thereby contributing to the vibrancy and evolution of the AI entrepreneurial ecosystem. Thus, we hypothesize the following:
Hypothesis 2 (H2): A VC endowed with higher betweenness centrality is more inclined to invest in AI startups.