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

Author

Listed:
  • Gabriel Tailleur
  • Morgane Ramis

    (LEFMI - Laboratoire d’Économie, Finance, Management et Innovation - UR UPJV 4286 - UPJV - Université de 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 platforms' 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 highlights 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.

Suggested Citation

  • Gabriel Tailleur & Morgane Ramis, 2025. "Metaverse and AI diffusion: an empirical assessment from a financial perspective," Post-Print hal-05082630, HAL.
  • Handle: RePEc:hal:journl:hal-05082630
    DOI: 10.1109/EMR.2025.3565995
    as

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