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# Metaverse and AI diffusion: an empirical assessment from a financial perspective

Gabriel Tailleur, Morgane Ramis

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## To cite this version:

Gabriel Tailleur, Morgane Ramis. Metaverse and AI diffusion: an empirical assessment from a financial perspective. IEEE Engineering Management Review, In press, 53 (3), pp.1-13. 10.1109/EMR.2025.3565995. hal-05082630

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Submitted on 2 Jun 2025

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# Metaverse and AI diffusion: an empirical assessment from a financial perspective

Gabriel Tailleur, PcEn Chair (Paris 1 Panthéon-Sorbonne) & LEFMI (University Picardie Jules Verne)

Morgane Ramis, LEFMI (University Picardie Jules Verne)

**Abstract**—The metaverse has gained popularity in recent years but remains technically nascent. Theoretical literature highlights the key role of AI in metaverse development, as AI is expected to bolster the creator economy, a key pillar of these platform's success. However, no empirical work confirms this expectation yet. To address this gap, we constructed two proxies – proto-metaverses and traditional video games – to isolate financial transactions specific to the metaverse towards AI from general trends in the gaming sector. We present empirical evidence from Crunchbase data on acquisitions and investments, confirming AI's role as a catalyst for metaverse development. Our analysis reveals that proto-metaverses are more likely than traditional video games to invest in and acquire AI startups controlling for several factors. Additionally, we examine the specific types of AI targeted in these financial transactions and integrate the findings into a theoretical framework that highlight the structural specificities of AI applications in metaverses, distinguishing them from those in traditional video games. Lastly, the results give credit to proto-metaverses as a relevant proxy for metaverse evolution, with a replicable categorization. This article contributes an empirical foundation to the theoretical literature on the subject, explaining it through an analytical framework that connects the economics of innovation and network effects. Moreover, we highlight specific implications for practitioners and policymakers in the metaverse industry.

**Index Terms**—Metaverse, Artificial Intelligence, Crunchbase, Network effects, Innovation

## I. INTRODUCTION

The Metaverse is a widely discussed yet ambiguous concept, difficult to grasp due to the absence of tangible or unified manifestation. Inspired by cultural artifacts like the novel *Snowcrash* [1] or the movie Ready Player One, the metaverse is often defined as an interoperable virtual world leveraging new immersive technologies to bridge the virtual and physical realms [2]. In this context, competing visions among developers highlight virtual reality (VR) [3] or augmented reality (AR) [4] based metaverses depending on their industrial comparative advantages. Alternative approaches advocate for a decentralized blockchain-based infrastructure fueling an open metaverse in a less immersive setup [5], [6]. However, the technological foundations for true immersion and interoperability remain underdeveloped [7], [8]. As such virtual worlds do not yet seem to exist, a core tension emerged in the literature to seize metaverse's specificities, adoption drivers, and implications for both users and practitioners.

To address this problem, scholars have moved beyond futurist narratives to examine the specific characteristics that distinguish the metaverse within the global virtual world (gaming) industry. Lee et al [9] describe it as “*a perpetual, shared, concurrent, and 3D virtual space concatenating into a perceived virtual universe*”. Park and Kim [10] emphasize the role of socialization and institutions, defining it as “*a three-dimensional world where avatars engage in political, economic, social and cultural activities*”. Within this framework, platforms like *Roblox*, *Decentraland* and *Second Life* – which embody many metaverse characteristics [11]–, have been labeled as proto-metaverses (PMs) [8] or antecedents of the metaverse [6]. In this context, we define PMs as shared virtual worlds, intermediated by avatars, fostering the development of social, economic and creative institutions within user communities. In contrast, linear video games (LVG), while immersive, lack persistence and derive their utility from gameplay rather than social, economic, or cultural interactions.

In this article, we examine artificial intelligence (AI) as a main driver of (proto)metaverse growth. We argue that this influence arises not merely from AI's ubiquity and broad applicability across the gaming sector but from the structure and unique dynamics of PMs, including their reliance on network effects (NE) and the central role of the creator economy. We first propose a theoretical framework to examine how and why developers leverage AI models to drive PM attraction and growth by reducing user-generated content production costs, and enhancing user immersion, the latter being a shared application in both LVGs and PMs. Then, using Crunchbase data, we empirically investigate the hypothesis that AI acts as a growth-enhancing catalyst for PMs by analyzing acquisitions (M&A) and investments in AI-startups. To isolate metaverse-specific trends, we use PMs as a proxy for “metaverse in the making”. We compare PMs to LVGs financial transactions to understand if metaverse builders internalized the AI adoption incentives that we theoretically display to sustain their platform's network effect. We provide insights into the synergies and developers incentives that underpin PMs, setting them apart from LVGs. Additionally, we connect the specific AI subcategories targeted in financial transactions with AI applications in virtual worlds such as AI-driven non-playable characters (AI-NPCs) and personalization, shedding light on strategic trade-offs developers face.

Our findings are threefold. First, we confirm AI's role as a catalyst for metaverse development [12], [13], [14], [7], [15], with PM platforms showing significantly higher AI adoption
signals compared to LVGs, alongside a stronger focus on machine learning-based IA such as computer vision and generative models. Second, we explore various AI applications in the gaming sector, examining the game-design trade-offs they entail. We integrate these findings into a broader PM-specific theoretical framework, highlighting AI's critical role in amplifying network effects, scaling UGC, and fostering the creator economy. This framework may explain why PMs emerge as comparatively more innovative platforms in adopting AI than LVGs. Indeed, PM executives might strategically anticipate AI's beneficial impacts, leveraging structural attributes inherent to metaverse platforms that amplify these effects. Lastly, our results reinforce the validity of employing PMs as relevant proxies for metaverse evolution, with a replicable categorization using Crunchbase data.

The paper is organized as follows: Section II presents a comprehensive review of the existing literature on the metaverse and its potential synergies with AI. In section III, we delineate the specific implications of network effects in (proto)metaverses platforms and discuss a theoretical framework with specific AI-related incentives among PM developers. Section IV presents the data and the overall research process, including the construction of our proxy for the metaverse. Section V presents the results, while section VI discusses these findings in relation to the research questions, highlighting the theoretical contributions and implications for practitioners. Section VII concludes by outlining the limitations of our study and future research directions.

## II. LITERATURE REVIEW

### A. Proto-metaverses as proxies to empirically assessing metaverse dynamics

The metaverse is an evolving concept whose diffusion remains difficult to quantify, explaining why most discussions on its technological developments are theoretical or proof-of-concepts. To avoid the limitations of overly restrictive definitions, we focus on proto-metaverse (PM) platforms as a metaverse proxy, which are more widely adopted. In this section, we present several reasons supporting our arguments on this matter, before discussing AI applications in virtual worlds.

#### 1) A proximity between proto-metaverses and future metaverses

Proto-metaverses share core characteristics with (anticipated) metaverses. PM platforms such as Roblox or Second Life are indeed persistent 3D virtual worlds, online, with avatar-mediated interactions. They rely on user-driven creativity and institutional dynamics with socialization, community gathering, cooperation and market mechanisms. However, as we explained previously, they lack two defining features of what could be the metaverse due to technological immaturity: (i) interoperability, enabling seamless transfer of data and assets

across virtual worlds, and (ii) advanced immersion, as current platforms primarily rely on 3D screens.

#### 2) Metaverses are natural extension of online social video games

Metaverses can be seen as a natural evolution of online video games, progressively transitioning toward more immersive and realistic environments that reflect the social and institutional dynamics of the physical world. This perspective has led Mohammed et al. [16] to characterize MMOs as “quasi-metaverse experiences,” while platforms such as Roblox and Minecraft embody a *prosumption* model wherein player creativity actively shapes the shared virtual environment. Indeed, while the immersive dimension of the metaverse still requires significant technological advancements, proto-metaverses already exhibit essential social, economic, and cultural characteristics. Users in PMs can create virtual spaces or games, and establish economic institutions to coordinate their activities [17]. These elements exemplify how PMs are laying the groundwork for the metaverse’s eventual realization. In this context, platforms like Fortnite and The Sandbox have explicitly adopted the term “metaverse”, positioning themselves as precursors in the sector’s evolution [18].

#### 3) Skills specialization and the development of virtual worlds

Considering the technological prerequisites for metaverse development and the specific expertise required to construct immersive virtual worlds, we posit that the creators of today's virtual worlds are the most likely builders of future metaverses. Indeed, the skills necessary to design virtual worlds, make them engaging, and populate them with narrative content are deeply rooted in the gaming industry. In this context, AI applications that enhance user immersion often build upon knowledge, algorithms and tools already widely used in video game development. This is the case with procedural generation of 3D content that helps developers create vast, diverse and high-quality 3D environments [19], or AI-driven NPCs which could refine current algorithmic models to simulate human-like behavior in more realistic ways [20].

Second, several major companies aiming to construct metaverses have strategically acquired leading gaming studios to absorb the specialized knowledge and expertise of the sector. For instance, Microsoft acquired Mojang, the studio behind the popular metaverse platform Minecraft, in 2014, and in 2023 purchased Activision-Blizzard, the producer of World of Warcraft, for $68.7 billion. These acquisitions underscore the critical role of development expertise, game design and knowledge in shaping dynamic metaverse platforms.

#### 4) Companies advertising in the “metaverse” head for proto-metaverse platforms

Although ultra-technological metaverses are not widely adopted, businesses are already integrating metaverse strategies by leveraging proto-metaverse platforms. Giang Barrera and Shah [21] observed that companies are particularly active in
popular virtual world for advertising and commercial purposes. For instance, luxury brand Gucci purchased land on the Sandbox to showcase its content, while Nike has developed games, skins and digital artifacts representing its brand on the platform Roblox. Simultaneously, major corporations like Meta, Microsoft, and Nivida are increasingly investing in metaverse development, positioning themselves as potential key players in shaping its future [8].

## B. AI and Metaverse

Meta's CEO, Mark Zuckerberg, has explicitly endorsed the potential synergy between AI and the metaverse. He stated: 'the two technological waves that we're riding are AI in the near term and the metaverse over the long term. [...] In many ways, the two areas are overlapping and complementary' [22]. Since then, the potential applications of AI in metaverses have been widely documented. The literature identifies three main families of AI outcomes that might drive metaverse growth and development: (1) AI non playable characters (AI-NPCs) designed to be more realistic agents, capable of personalizing their interactions based on context, previous interactions, and unanticipated player strategies; (2) Personalization of the user experience, with AI models using behavioral data generated within the metaverse to recommend content, tailor user experiences, or automatically detect prohibited textual patterns; (3) Sophisticated user-generated content (UGC) tools based on generative models and computer visions that aim to lower the cost of content creation, enabling users to produce high-quality virtual assets more efficiently.

In this section, we present the long-standing connection between AI and video games, followed by a discussion of the three primary applications of AI in virtual worlds: NPCs, personalization, and UGC tools. We then analyze why the local adoption of AI innovations in PMs differs from LVGs with the AI-UGC example, and how it could theoretically drive a PM-specific growth by sustaining network effects and the creator economy (sections B.4 and III).

### 1) What AI for which virtual worlds? The local adaptation of a ubiquitous innovation

Artificial intelligence is often described as a *General Purpose Technology* (GPT) [23]. GPTs are transformative innovations that impact productivity, production processes, and labor markets across multiple industries [24]. However, foundational AI advancements alone are insufficient for transformative applications in the metaverse. For AI to have a growth-generating effect, innovations must be adapted locally to the unique characteristics of virtual worlds. We argue that the first two applications – AI-NPCs and personalization- are broadly beneficial to most virtual environments, whether in proto-metaverses (PMs) or linear video games (LVGs). In contrast, the ability to reduce content generation costs through generative or vision-based tools is mainly significant for PMs given their reliance on UGCs and social dynamics. This section focuses on the specific synergy between AI and PMs, hypothesizing the incentives for developers to locally implement AI innovations

in these environments according to the specific structure of the platform and its success vectors.

To focus solely on AI complementary innovations in virtual worlds, the adoption of our proto-metaverse proxy leads us to overlook one of the potential applications of AI in metaverses, namely, digital twins. These are digital counterparts of our current universe, whose purpose is to automatically replicate actions in the virtual world in the real world [21]. They are employed primarily for industrial applications [25], which fall outside the scope of our current field of study. Indeed, the use of the metaverse within the framework of digital twins is largely instrumental. It does not allow for engagement in a social and collective dynamic within a virtual world.

### 2) AI and video games: an old story centered on NPCs

Virtual worlds have served as valuable environments for evaluating and benchmarking AI performance. Notably, AI models have been trained to compete against human players in structured games such as Go, and more recently, in StarCraft 2, where the AI model *AlphaStar* employs neural networks trained on extensive professional player gameplay data [26]. Beyond performance, the gaming industry moved toward an objective of behavioral credibility and realism of NPCs [27]. NPCs are non-playable characters in virtual environments that players interact with, whether for narrative purposes assistance, companionship and global immersion. Their behavior is traditionally determined by complex rule-based models, such as pathfinding algorithms and Finite State Machines, that govern an NPC response to environment dynamic variables and player actions [28], [20]. Before recent advancements in deep learning, the focus of AI research in gaming has thus been mainly NPC-oriented [29], [30], as the realism of their behavior was widely regarded as critical for player engagement and scenario immersion [31].

AI-powered NPCs mark the transition from rule-based to adaptive agents capable of learning and adjusting their behavior based on interactions with users [9]. In order for agents to imitate intelligent behaviors and adapt realistically to the language and behavioral inputs provided by the user, this type of innovation calls from deep learning [32] to build cost-efficient generative and vision models. For instance, one challenge is enabling NPCs to adapt dynamically to conversation history, using a sophisticated chatbot systems such as ChatGPT, but tailored for the game context and NPC personality design. The mod *Mantella* in the Skyrim LVG is a concrete example of AI-NPC, based on generative AI and computer vision to allow the game characters to engage in natural spoken interactions with the player. These interactions are context-aware, shaped by the agent's personality and conversation history. Regarding VR immersion, Dobre et al. [13] trained a model detecting human social attitudes and drawing information from them to enable a more humanized conversation. Such innovations are pregnant for player immersion in LVGs, but also in PMs where AI-NPCs could play a crucial parasocial role, acting as guides or interactive characters within vast virtual worlds [8].
### 3) Exploiting the data generated in the metaverse

The third level for deploying AI in the metaverse is more of a systemic nature, rooted in the vast amount of data generated by users' online behavior and preferences. Data can be processed using deep learning (DL) recommendation models to propose user-specific content [33] or through regulation and general management of the virtual world [9]. For example, Sanghvi et al [34] propose a model for detecting prohibited textual content, such as hate speech or toxic online behavior.

### 4) AI User-Generated Content (UGC)

The term “user-generated content” (UGC) refers to any type of content created by players within persistent and shared virtual world, such as virtual spaces and environments, games, or digital artifacts like skins and items. UGC plays a pivotal role in PMs or metaverse platforms, as their popularity is deeply tied to the social and cultural dynamics they foster – dynamics that are, in turn, linked to the variety of activities and assets available in these virtual worlds. While virtual worlds in PMs like Roblox or Horizon Worlds rely heavily on user-creation and social dynamics, this is not the case for LVGs that typically offer pre-designed scripted content, are not persistent, and provides generally limited opportunities for players to modify the virtual environment. The singular role of individual creations in PMs allows us to highlight the importance of strong network effects, along with the developers' incentives to attract creators and high-quality creations, as it will be discussed in the next section.

In this context, AI emerges as a powerful accelerator of creativity. Creating virtual objects, games and environments can be costly for users both in terms of time and accessibility. Indeed, the toolkits provided by developers often require technical expertise, such as coding skills or the ability to navigate through complex design interfaces to create and import a creation inside a virtual world. In this context, AI is expected to help dramatically reduce these barriers and the technical threshold by simplifying the creation process, allowing users to produce virtual environments, game, items and other 3D outputs more efficiently. Consequently, AI-driven content generation (AIGC) tools can enhance the productivity of PMs platforms by lowering the cost of entry to creation, thus fostering a greater asset and activity diversity in a given virtual world [7].

The AI applications of generative AI and computer vision for UGC are varied and still evolving. For instance, computer vision models enable the generation of three-dimensional replicas of photographed objects or environments [32]. Generative AI can also bridge the gap between physical and virtual worlds by creating 3D avatars that represent players [9], as demonstrated by MetaHumans, a company backed by Fortnite creator Epic Games, which offers 3D replicas of scanned individuals. Most importantly, AI allows for the creation and modification of virtual worlds, artifacts, NPCs behavior and games by interpreting natural language written or spoken prompts. For instance, Roblox introduced in 2023 a

generative AI tool enabling creators to generate and modify 3D content using written instructions [35].

This incorporation of deep learning-based AI models into virtual worlds is a relatively recent phenomenon, observed in a few platforms. The developer incentive behind this innovation is to reduce the cost of creation, thereby increasing the platform's attractiveness and strengthening its network effects. Our study seeks to determine whether such AI diffusion trends are unique to PMs or reflect a broader trend within the gaming industry. To support this hypothesis, the next section explores the theoretical narrative of PM success drivers, focusing on their reliance on the creator economy and the local adaptation of AI applications to enhance UGCs.

### III. WHY METAVERSE GROWTH DEPENDS ON AI-DRIVEN UGC: A THEORETICAL ARGUMENT FOR DEVELOPER INCENTIVES.

The synergy between AI and metaverse reflects a specific characteristic that is seldom discussed in the literature but is essential to metaverses or proto-metaverses platforms: network effects (NE). Network effects were first conceptualized from an industrial perspective. They refer to a situation in which "*the value of a unit of a good increases with the number of units sold*" [36]. While this perspective has since been widely transposed to the digital economy, its application to metaverses is intuitive. The utility a player derives from a virtual world is a function of the number of users already in it. The more a virtual world is structured around well-established communities that drive social, cultural, and economic dynamics, the more attractive it is to a new entrant.

Rong et al. [37] applied this NE perspective to massively multiplayer online role-playing games (MMORPG, e.g., World of Warcraft). An MMORPG, in contrast to a more traditional or linear video game, is of limited interest if it is not populated (and played) by other users. The authors identified two main determinants of network effects based on interviews with MMORPGs developers and players: (1) gameplay design and (2) social interaction mechanisms. These two features intersect in the context of a metaverse, where accessibility to virtual worlds and co-creation underpin the structures from which communities of users interact and socialize.

In addition to fostering community-driven social interaction, the significance of user-generated content is specifically amplified on PMs. The economic appeal of UGC lies in the decentralization of productive activities and outputs, as a dynamic virtual world would be more cost-effective and varied when it is not developed and operated vertically by a single company [11]. Instead, it relies on a creator-driven economy, where users are incentivized to co-construct the virtual environments in which they participate. Thus, user-generated content can be viewed as a form of “*immaterial labor*” [38] that simultaneously benefits platform owners – who see their platforms enhanced at a reduced cost – and other users, who gain access to richer and more dynamic experiences. This aligns with the principles of a traditional platform economy, wherein the platform's role involves attracting creators by lowering
barriers to entry or proposing financial incentives, thereby
expanding overall demand among both active creators and
casual users. In this context, creators act as "social enablers"
[39] of the metaverse. Recognizing the centrality of creators to
platform dynamism, metaverse developers could be
incentivized to provide the supply-side (i.e. the creators) with
sophisticated, user-friendly production toolkits based on AI.

We argue that AI has the potential to serve as a stronger catalyst for the growth and attractiveness of proto-metaverses compared to linear video games. Unlike LVGs, which rely primarily on vertically developed content by professional game designers and derive limited benefits from a large user base, PMs thrive on co-creation and user interaction, driven by NE that AI-powered applications can amplify. Since these dynamics are largely dependent on UGCs, enhanced by generative AI and computer vision models, we expect PM developer to locally adapt AI innovations to this specific UGC domain. In an intensely competitive sector where platforms vie for market share and player engagement, the localized application of such innovations can serve as a strategic tool for PM developers to amplify NE and increase platform popularity driven by the dynamism of co-creation.

The following sections empirically examine and discuss
whether PMs exhibit stronger signals of AI innovation
compared to LVGs and identify the specific types of AI
involved in relation with their application in virtual worlds.

IV. DATA AND METHODS

A. *Overall Research Framework*

To analyze the role of AI in proto-metaverses compared to
linear video games, we rely on company and financial
transaction data from Crunchbase. This dataset enables us to
systemize our identification strategy by distinguishing PMs
platforms from LVGs producers (Section IV.B). Crunchbase is
a structured, crowd-sourced database that contains multiple
information on technological companies and startups, including
their financial transactions. It is a reliable data source frequently
utilized in economics and management research [40], [41].
Crunchbase provides information on company investments,
fundraising and mergers and acquisitions, as well as firm-level
information. The data are organized in multiple datasets, which
can be integrated using the unique identifier ("uuid") of the
company or transaction. Table 1 lists the main variables
employed in this study. Among these, the most crucial variables
are the company's creation date, its sectors of activity, its
description, and the financial transactions in which it is
involved.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Overall research process. The standard view reflects the predicted interaction between the metaverse and AI as depicted in the literature. We employ proxies of the metaverse and AI diffusion to estimate the interaction.</figcaption></figure>

Figure 1 depicts the overall research framework of this study. First, data for M&A and investment transaction records, along with a set of key company information variables, are collected from the Crunchbase API. We matched and preprocessed the Crunchbase datasets to isolate proto-metaverses from linear video games, while also ensuring that only companies and not investment funds or incubators are gathered. Additionally, we filtered the data to retain only transactions from the gaming industry to the AI sector forming the basis for our subsequent empirical strategy.

In the next section, we compare AI diffusion signals measured
by collected investment and M&A data, between our metaverse
proxy and linear video games. This analysis includes
informative descriptive statistics and is complemented by a
logistic regression model controlling for potential bias such as
company size, founding date, and the distribution of company
categories in the Crunchbase database. Additionally, we link
the AI subcategories prioritized in PM and LVG investments to
the theoretical incentives outlined in Section III. Finally, we
present our findings and discuss their theoretical and industrial
implications for practitioners and policymakers.
<table><caption>TABLE I
VARIABLES USED AND SOURCES</caption><thead><tr><th>Variable</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td>Organisation name</td><td>The name of the company</td><td>Crunchbase</td></tr><tr><td>Founded on</td><td>The establishment year of the company</td><td>Crunchbase</td></tr><tr><td>Category groups</td><td>Primary classification comprising 168 labels (e.g., "Gaming")</td><td>Crunchbase</td></tr><tr><td>Category groups list</td><td>Secondary classification comprising 801 labels (e.g., "Metaverse", "MMORPG")</td><td>Crunchbase</td></tr><tr><td>Short description</td><td>Short description of each company</td><td>Crunchbase</td></tr><tr><td>Long description</td><td>Long description of each company</td><td>Crunchbase</td></tr><tr><td>Acquirer (acquiree) name</td><td>Acquirer and acquiree company names</td><td>Crunchbase</td></tr><tr><td>Investor (invested) name</td><td>Investor and invested company names</td><td>Crunchbase</td></tr><tr><td>Proto-metaverse</td><td>Metaverse proxy</td><td>Calculation from category_groups_list (see section III.B)</td></tr><tr><td>Traditional Video Games</td><td>Non-metaverse video game proxy</td><td>Calculation from category_groups_list (see section III.B)</td></tr><tr><td>Number of investments</td><td>Number of investments towards AI</td><td>Calculation from the investment dataset</td></tr><tr><td>Number of acquisitions</td><td>Number of acquisitions towards AI</td><td>Calculation from the acquisitions dataset</td></tr><tr><td>Number of operating companies</td><td>Number of operating company for each year</td><td>Calculation from the "status" and the "founded on" variables</td></tr></tbody></table>

## B. Proto-metaverses: a proxy for metaverses using Crunchbase classification labels

Our empirical strategy is predicated on the distinction between proto-metaverses and linear video games. The former is a proxy for metaverses in the making and refers to online 3D persistent and shared virtual worlds based on community interactions and displaying social and economic institutions (i.e., Roblox or Fortnite). The latter category encompasses linear video games without world persistence, continuity, and user-based institutions (e.g., FIFA or Call of Duty). This distinction is crucial to validate the theoretical literature perspectives on synergies between AI and metaverses. It allows us to assess whether PM developers have internalized AI-innovation incentives, and identify afterwards the specific types of AI involved. Importantly, we measure these signals relatively to LVGs to ensure that they do not simply reflect a general trend across the entire video game sector, as we hypothesize that synergies with AI would be particularly salient in virtual worlds that correspond to the characteristics of metaverses discussed in Section II and III.

To distinguish proto-metaverses from other video games, we employ a pre-processing methodology based on the companies' respective sectors of activity. Crunchbase classifies each company into two categories that contains at least one categorical label (e.g., "Metaverse" or/and "Virtual Word"). The "category_groups" variable comprises 168 labels of distinct business sectors and is by nature generic. We utilize the second categorical variable, "category_groups_list", which allows for more detailed data processing, encompassing 801 unique outputs that represent a company's specialization or sub-sector of activity. This choice enables us to analyze companies with greater granularity. For instance, while video game producers Linden Lab (creator of the Second Life Metaverse) and Harmonix (creator of the Rockband and Guitar Hero series, i.e., linear video games) both fall into the first "Gaming" category, the second classification variable highlights their differences. Linden Lab is considered a "virtual world", while Harmonix only holds the "Video Games" and "Music" outputs. To automate the identification of proto-metaverses, we create filters according to the categories set up by Crunchbase. Table 2 lists the labels that have been applied to the dataset in order to create two new categorical variables. One corresponds to proto-metaverses, while the other identify linear or traditional video games.

In accordance with the distinction between PMs and LVGs displayed in previous sections, we consider the categories "metaverse", "virtual reality", "virtual worlds", "online games" and "MMO games" as proxies for metaverses. The initial three categories are straightforward, as they are connected to immersive and social dynamics in an open world. The last two labels are intended to reflect the importance of online interactions in a scalable virtual world "antecedent" to the metaverse. The categories defining the linear video game variable are more general. It is important to note that to prevent any overlap, linear video games must not include any of the categories defined as belonging to the metaverse. Subsequently, our identification methodology has been based on an exclusion criterion. For example, a company that has been classified as both "Video Games" and "Console Games" labels is considered to belong to a linear video game. However, if the company also holds the "Metaverse" or "MMORPG" label, it will be considered a proto-metaverse only.

Finally, to define companies whose sector of activity is related to artificial intelligence, we do not only rely on the general category "Artificial Intelligence" but rather utilize the subcategories mentioned in Table 2. This enables us to make a finer selection of companies based on the fields of AI defined by the *Elelectronic Frontier Foundation* [42]. Indeed, certain fundamental streams of AI described by EFF (e.g., "computer vision", or "facial recognition") are not included in the general "Artificial Intelligence" label defined by Crunchbase in the primary classification. Consequently, we rely on more granular AI categories, as exemplified by the categorical labels "computer vision", "speech recognition", which indisputably belong to the artificial intelligence industry.
<table><caption>TABLE II
VARIABLE CREATIONS FROM CRUNCHBASE CLASSIFICATION
LABELS</caption><thead><tr><th>Variable creations</th><th>Crunchbase categorical label</th></tr></thead><tbody><tr><td>Proto-metaverse</td><td>MMO Games; Virtual World;<br>Online Games; Metaverse<br>Virtual Reality</td></tr><tr><td>Standard video game</td><td>Video Games; Playstation<br>Xbox; Nintendo; Gaming<br>Console Games; PC Games</td></tr><tr><td>Artificial Intelligence</td><td>Artificial Intelligence<br>Machine Learning; NLP<br>Virtual Assistant; Chatbot<br>Generative AI; Compute Vision<br>Human Computer Interaction<br>Image/Facial/Speech Recognition<br>Predictive Analytics<br>Data collection and Labelling</td></tr></tbody></table>

### C. Acquisitions and investments: signals of AI diffusion in the gaming industry

The adoption of AI-related innovations in virtual worlds remains limited, posing challenges for empirical research and often necessitating a reliance on theoretical approaches. This difficulty is compounded by the lack of consistent firm-level data, which renders the measurement of AI adoption inherently imperfect [43]. As a result, empirical research frequently relies on proxies to capture AI-related activities. For example, the use of factory robots often serves as a proxy for automation [44], [45]. Alternative methods to access AI adoption signals include analyzing labor market data or the skills composition of enterprises by collecting job advertisements or conducting comprehensive company surveys. However, these approaches are costly and time intensive.

In this article, we examine both strong signals (mergers and acquisitions, or M&A) and weak signals (investments and minority stakes) from companies in the video game sector directed towards AI startups. Acquisitions provide explicit evidence of AI adoption and diffusion within the production process. Indeed, it is a common strategy for companies to acquire innovative firms in order to gain access to specialized knowledge outputs, particularly in sectors where smaller, younger firms tend to drive more radical innovations [46]. In the highly competitive metaverse industry, where AI is essential for complementary innovations, acquisitions serve as a direct strategy for enhancing market power and technological capabilities. Beyond acquisitions, we also analyze investments made by gaming companies in AI startups, as these might convey innovation signals. Minority shareholdings often enable knowledge sharing, partnerships, and collaborative projects based on the technologies developed by the invested startups.

While these investments are less explicit than acquisitions, they still provide insights into the potential adoption and integration of AI innovations.

To ensure that our analysis reflects genuine innovation strategies rather than speculative operations, we excluded venture capitalists (VCs), investment firms, incubators, and accelerators from our dataset to analyze only gaming studios. To this end, we initially limited our dataset to the "companies" outputs in Crunchbase (the two other categories are "investors" and "schools"). The selection process is further refined by excluding observations that fall into the "venture capital" category. In addition, as some investment funds are only categorized by their sector of specialization (i.e., a Web 3.0 venture capital could be labeled "Metaverse"), all observations containing the strings "VC", "venture capital" or "investment firm" in the company's long and short description variables were excluded. This preprocessing step is essential to isolate a signal conveying an innovation incentive specific to the video game industry.

## V. RESULTS

### A. Descriptive statistics

<table><caption>TABLE III
NUMBER OF INVESTMENTS AND ACQUISITIONS BY COMPANY
CATEGORY TOWARDS AI STARTUPS</caption><thead><tr><th></th><th>Proto-metaverses</th><th>Traditional video games</th><th>Total</th></tr></thead><tbody><tr><th>Number of investments</th><td>168</td><td>60</td><td>228</td></tr><tr><th>Number of acquisitions</th><td>52</td><td>35</td><td>87</td></tr></tbody></table>

Table 3 shows the number of investments and M&A transactions in AI by company category. The two types of financial transactions on which we rely—mergers & acquisitions and investments—are complementary. They convey innovation signals towards the AI sector. Naturally, we have less data on M&A compared to investments, which leads us to focus mainly on investment analysis. However, while it is not possible to draw conclusions solely from M&A data, its analysis enables us to confirm our intuitions, and the trends observed in investments regarding the specific relationship between PMs (conversely, LVGs) and AI. In this section, we analyze comparatively the differences in interest in AI between proto-metaverses and linear video games. Based on M&A and investments indicators, our findings confirm the theoretical literature expectations, and the PM-developer incentive intuition displayed in Section III.

Figures 2, 4, and 5 depict the trends in financial transactions (M&A and investments) from proto-metaverse and traditional video game categories towards AI-related startups. Figures 4 and 5 refine these results by normalizing them according to the
total number of companies within each category in gaming industry. The upper sections of these figures illustrate investments and minority stakes, while the lower sections display M&A transactions. We then estimate a logistic regression model (Section V.B) to confirm the results from the descriptive statistics. Finally, Figure 6 illustrates the AI domains of startups invested in and acquired by PMs and LVGs (Section V.C).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Evolution of investments (acquisitions) by year based on the investor (acquirer) category.</figcaption></figure>

Figure 2 illustrates, in absolute terms, the trend in the number of investments and M&A deals from the video game industry to the AI sector. An increasing trend in financial transactions is observed over time, with a notable slowdown in 2017 for proto-metaverse investments. Mergers and acquisitions of AI-related companies show a sharp rise from 2018 onwards. Two factors may explain this increase: (i) the development of the Crunchbase platform, which has become increasingly comprehensive as it gains reputation, and (ii) a growing interest in AI-related companies due to the maturation of the technology and its industrial applications. Figure 2 reveals that PMs are engaging in a greater number of investments compared to LVGs, while the M&A trends are very similar, except for a peak between 2018 and 2020 for proto-metaverses. For instance, in 2020, 15 companies categorized as PMs invested in an AI-related company, compared to 6 companies classified as LVG. While this figure is informative, it does not account for the total distribution of the number of companies categorized as proto-metaverses or linear video game producers.

Therefore, it is necessary to put the results into perspective, by considering the total number of companies in the Crunchbase database belonging to each category, irrespective of their financial operations. As shown in Figure 3, the number of companies classified under PM and LVG is not evenly distributed. Although high growth is observed in both categories since 2012, LVGs consistently outnumber proto-metaverses. In 2020, 8333 companies are classified as linear video games, against 2844 proto-metaverses. Notably, from 2020 onwards, the number of companies defined as proto-metaverses has been increasing. This trend can be attributed to the proliferation of the term 'Metaverse' during the years 2020-2021, following Facebook's rebranding to 'Meta' on October 28, 2021. This phenomenon may thus be explained by an

endogenous effect resulting from the re-categorization of companies under the 'Metaverse' label."

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Evolution of the number of companies in the proto-metaverse and linear video game categories.</figcaption></figure>

To account for the specific distribution of PMs and LVGs categories in the Crunchbase database, we calculated the average number of investments per company category, reasoning in terms of proportion rather than absolute value. Figures 4 and 5 contextualize the results of Figure 2 by considering the total number of companies in the Crunchbase database. Figure 4 demonstrates that there are significantly more investments and M&A transactions per company for proto-metaverses than for linear video games in the AI sector. In 2020, there were 6 investments per 1000 PMs company versus 0.7 per 1000 LVGs, indicating 8 times more investments from proto-metaverses. On average, over the period studied, each PM company made 0.006 AI-directed investments compared to 0.0006 for LVG development studios.

Furthermore, since a company can engage in multiple transactions and potentially skew our results, Figure 5 shows the percentage of companies investing or completing an M&A oriented towards the AI sector. Except for 2017, the proportion of proto-metaverse companies investing in AI is higher than that of linear video games. For instance, in 2020, 0.22% of PM companies made an investment compared to 0.05% of LVGs companies. On average, 0.25% of proto-metaverses invested in an AI startup, versus 0.06% for linear video games.

Regarding acquisitions, PMs consistently perform more M&A transactions proportionally than LVGs throughout the studied period, with a particularly marked difference between 2018 and 2020, which narrows from 2021 onwards.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Number of investments (acquisitions) per company based on the investor (acquirer) category.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Percentage of companies that invested (acquired) in AI based on the investor (acquirer) category.</figcaption></figure>

## B. Empirical analysis – a logistic regression model

In the previous section, we observed that so-called "proto-metaverse" companies tend to invest more frequently in AI-related firms. To confirm this finding, we perform a regression analysis using a logit model to examine the probability of a gaming company investing in AI. The dependent variable in our analysis is binary, equal to 1 if the company has invested in AI, and 0 otherwise. This analysis focuses exclusively on the likelihood of investment rather than acquisition. While there are 87 recorded acquisitions, they were carried out by only 16 distinct companies, which is insufficient to meaningfully analyse statistical variability.

We estimate the following model:

$$Y_i = \beta_0 + \beta_1 pm + \beta_2 \text{employee\_count} + \beta_3 \text{founded\_on} + \varepsilon \quad (1)$$

<table><caption>**TABLE IV**
LOGISTIC REGRESSION OF THE LIKELIHOOD OF GAMING COMPANIES INVESTING IN AI</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Proto-metaverse</td><td>0.5761<br>(0.2955)</td><td>0.6736*<br>(0.3105)</td><td>0.6325*<br>(0.3111)</td></tr><tr><td>employee_count</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>founded_on</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Number of<br>observations</td><td>12,604</td><td>12,604</td><td>12,604</td></tr></tbody></table>

Table 4 present the result of the estimation of the model (1). In the initial baseline regression, which includes only the proto-metaverse variable, the coefficient is positive but not statistically significant at the 95% confidence level. To ensure that this lack of significance is not driven by differences in company characteristics—such as size, measured here by the number of employees—we incorporated several control variables. After accounting for company size, the proto-metaverse variable remains positive and becomes statistically significant at the 95% confidence level.

Similarly, we introduced the founding year of the company as an additional control variable to account for the possibility that proto-metaverse companies are more recently founded. Indeed, by contagion effect, it could be assumed that more recent technological companies would be more incentivized to adopt high-tech AI applications. Even after controlling for company size and founding year, the proto-metaverse variable remains positive and significant, suggesting that being classified as a proto-metaverse substantially increases the likelihood of investing in AI. Specifically, as shown in Model (3), proto-metaverse companies exhibit an 88% higher likelihood of investing in AI relative to their non-PM counterparts, when holding these control variables constant.

## C. Domains of AI innovation: A comparison between PMs and LVGs

These signals of interest, captured by financial transactions, reflect an internalization of the perceived benefits associated with AI integration, contingent upon the type of virtual world – PMs or LVGs. In this context, examining emerging trends regarding the specific AI categories targeted by these transactions is insightful. Figure 6 illustrates the distribution of investments and acquisitions in AI startups across sectors, normalized by the number of investing and acquiring firms from LVGs and PMs. Bold subcategories indicate areas where PM firms are more active than LVGs, eventually reflecting their differing strategies for implementing AI innovations. Regarding acquisitions, PM firms show stronger activity in AI startups specializing in augmented reality (AR), computer vision (CV) models, and machine learning (ML). In contrast,
LVGs focus more than PMs on acquiring companies related to big data, chatbots and fraud detection technologies. A similar pattern emerges with investments: PM firms are particularly active towards generative AI startups, simulation technologies, and machine learning. In contrast, LVGs allocate relatively more resources to predictive analytics, big data, and fraud detections. Both PM and LVG demonstrate comparable interest in AR and CV-oriented investments. While this figure provides only an impression of the types of AI connected to the adoption signal, it remains informative as it demonstrates a stronger interest among PMs to deep learning-based AI models relative to LVGs. Thus the theoretical framework derived from platform economics helps explain the observed empirical divergence between the two types of actors –PMs and LVGs– given that the success of PMs fundamentally relies on robust network effects and a dynamic creator economy.

<figure><img src="image_9.png" /><figcaption>**Fig.6.** Proportional distribution of AI investment and acquisition sectors between linear video games and proto-metaverses (in %).</figcaption></figure>

VI. DISCUSSION

A. Summary of the findings: evidence and implications of a
strong AI adoption signal among proto-metaverses platforms

Our results confirm the hypotheses formulated in section II by
the theoretical literature regarding the synergies between the
metaverse and AI. Specifically, we found that metaverse-
related companies engage significantly more in AI-oriented
investments and acquisitions compared to linear video game
companies. These signals of complementary innovation toward
AI can be attributed to the singularity of PMs, and more

specifically on the drivers of user activity and platform value.
In this context, game producers are incentivized to adopt AI
tools that stimulate network effects by reducing the costs
associated with asset, game, and environment creation, or by
improving immersion, which is a feature also important to
LVGs. Indeed, while AI-NPCs could enhance user immersion
in both PMs and LVGs [27], we argue that generative AI and
vision models may serve as key driver specifically for PM
platforms growth. AI models oriented toward UGC allows to
externalize content production to players, while the
generalization of high-quality AI models for generating texts
dedicated to NPC discourse could present very large economies
of scale and qualitatively increase user immersion at a low
marginal cost. Moreover, drawing on the findings of Rong et al.
[37] for MMORPGs, AI-based innovations could also
significantly increase the social ties that bind communities of
players together and the lock-in effect within a PM.

These developer incentives are further evidenced by the
comparison of AI application types between PMs and LVGs.
PMs allocate resources to recent AI applications such as
machine learning, generative models and computer vision,
reflecting their dependence on user-generated content (UGCs)
tools and immersive experiences. Conversely, LVGs
demonstrate greater interest in more traditional AI-connected
domains such as augmented reality, big data and predictive
analytics. Thus, PMs are not only more active in AI-related
financial transactions but also tend to prioritize specific
advanced AI technologies, particularly related to deep learning
[32], [9], and generative AI [12], which the recent research
highlights as a key drivers of metaverse development.

Finally, the distribution of companies classified as PMs versus
LVGs, as shown in Figure 3, further validates our metaverse
proxy “PM”. Indeed, we expect PMs to be governed by a
‘winner-takes-all’ effect typical of platforms with strong
network effects. Indeed, virtual worlds rely heavily on social
and cultural dynamics [17], [11] which depend on large and
active user base. As a platform grows, the entry cost for a
newcomer decreases, as we expect user engagement to be
correlated with the number of actives users contributing to the
platforms’ dynamism. Therefore, by their very nature, proto-
metaverses are subject to a higher concentration process. Unlike
the scripted and time-limited nature of LVGs, PMs allow for
potentially unlimited playtime as they rely on social interaction
co-creation. Consequently, competition between PM platforms
is expected to be far more intense as they rely on a constant base
of active users. PMs are therefore fewer, but engaged in a
potentially more intense innovation race than LVGs in order to
strengthen their network effects and foster user engagement.
The considerable interest demonstrated by certain proto-
metaverses virtual world producers in 3D content generating AI
models [22], [35] can be interpreted in this context.

B. Theoretical Contributions

Our contribution to the metaverse literature is threefold. First,
our findings corroborate the literature's hypothesis of a major
synergy between AI deep learning models and metaverses. It
appears that the creators of these virtual worlds have adopted
strategies where AI is regarded as a key driver in the advancement and diffusion of metaverse, as described by Luy [12]. Second, following Park's [10] findings indicating a close relationship between the metaverse and certain contemporary virtual worlds, we show that it is feasible to utilize proto-metaverses as relevant proxies for the metaverses that may emerge in the future. We explain the strategy we employed using Crunchbase data to distinguish them from other types of video games, so that our empirical classification remains replicable. Finally, we extend the work of Rong et al. [37] on proto-metaverses to demonstrate the specificity of these platforms, whose attractiveness and value rely on the existence of significant network effects.

### C. Implications and trade-offs for developers and policymakers

The findings of our study convey significant implications for practitioners, highlighting the pivotal role of a deep learning-based AI models for PMs virtual worlds. However, such AI applications in virtual worlds also come with trade-offs, or significant compromises that can affect the gameplay experience or the player directly. These decisions, which influence the incentives to implement or develop specific AI technologies in PMs or LVGs, ultimately translate into fundamental game design choices.

First, significant trade-offs emerge regarding the personalization of the user experience. For instance, personalization models are often effective only if they can collect and analyze user behavior. This creates a persistent tension between two competing goals: maintaining a high level of user privacy on the one hand, and offering tailored, engaging recommendations in a highly competitive gaming market to sustain player engagement. These trade-offs extend to automated content moderation, where developers must navigate the delicate balance between ensuring freedom of expression within games and preventing toxic behavior [47].

Second, both LVGs and PMs developers face a trade-off when implementing AI-powered NPCs. These decisions are deeply rooted in game design, as integrating AI into NPCs may undermine the artistic homogeneous direction of a carefully crafted script. For instance, narrative-driven games that rely on meticulously designed NPC behaviors and dialogues may not align with the varied unconstrained interactions offered by user-agent AI systems. In this context, AI-NPCs may be better suited for chatbots or agents designed to assist user in navigating and engaging with the modalities of virtual worlds, a feature predominantly associated with PMs.

Finally, while UGC AI tools may not face significant game design trade-offs as they aim to lower the barriers to content creation, they encounter other challenges such as the potential rise of proprietary models consecutive to a competitive race for AI innovations that regulators might address. As discussed in Section II, metaverses generate vast behavioral and 3D data. However, these 3D generative models are currently limited in their capabilities and less sophisticated than text-based models, due to the scarcity of high-quality 3D training data. A potential

competitive avenue in the metaverse industry could therefore be the development of a qualitative and economically viable model of 3D-content generation integrated into a virtual world. Therefore, companies that gain a comparative advantage in collecting and leveraging these data to train a cost-efficient model may access market dominance. This highlights the need for data access regulations, and AI generative and vision open models to prevent anti-competitive behavior and ensure fair competition between platforms.

## VII. LIMITATIONS AND FUTURE RESEARCH

The objective of this study is to provide an empirical entry point into the theoretical literature on the links between AI and metaverses. However, our findings suffer from natural limitations inherent of the field of study. First, metaverses lack a tangible, material, widely distributed embodiment. To circumvent this issue, we utilize a proxy for the metaverse to investigate empirical trends at work and theoretically explain its evolution toward innovations linked to artificial intelligence. While the use of our proxy is informative and appears relevant, this measure is imperfect because it relies on normative (but replicable) choices in the definition of the metaverse. Our approach is based on a definition that emphasizes social interactions between users and their creativity within economic and social institutions that reflect the physical world. In contrast, other authors may define the future metaverse as relying exclusively on technologies that do not yet exist, which does not fit well with our choice of proto-metaverses as a proxy.

Secondly, the diffusion or adoption of artificial intelligence is also an extremely complex phenomena to measure. We rely on the analysis of both strong and weak signals of interest in AI-related startups. This measure captures only a part of the behaviors and strategies originating from the gaming sector. An alternative approach to measuring this phenomenon would be to collect job advertisements at regular intervals or conduct in-depth qualitative interview with metaverse industry actors [48].

Third, our research endeavors to quantify and compare the signals of interest in AI between proto-metaverses and traditional video games. However, only the occurrence of mergers & acquisitions and investments is quantified, not their financial amount. Indeed, it is not possible to access a company's individual investment via Crunchbase, but only the total amount raised by a startup. Finally, to establish a causal relationship between PMs and AI-oriented investments, our logistic regression would require the inclusion of more extensive data on company characteristics and other investments they may have undertaken, ensuring more robust and reliable results.

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