AI AND PREDICTION MARKETS
variables, similar to certain profiles within the financial sector, where data processing capabilities and analytics may partially reduce uncertainty. Nevertheless, it is essential to distinguish prediction markets from sports betting based on the nature of the underlying event. In prediction markets, the object of prediction is generally linked to structural, economic, political or social variables whose evolution may be modelled through aggregated information and rational analysis, allowing uncertainty to be progressively and cumulatively reduced. By contrast, sporting events contain an irreducible element of randomness associated with inherently unpredictable factors, including real-time human decisions, errors, physical conditions, refereeing decisions and unforeseen events, all of which limit the scope of any predictive system. For this reason, although artificial intelligence may significantly improve accuracy within sports betting, we believe it is still incapable of eliminating chance in these events. It may therefore become necessary to propose a regulatory test based on at least three variables for classification purposes, namely (i) the degree to which chance may be reduced, (ii) the role of information and predictive capability, and (iii) the possibility of actively managing positions. Another regulatory alternative may lie not necessarily in the creation of entirely new legal categories, but rather in the reinterpretation or adaptation of existing industry models, such as certain exchange betting schemes whose operational logic increasingly resembles dynamics commonly associated with prediction markets. This may represent only the beginning of AI’s impact on the gambling industry. As previously noted, the evolution of these technologies is occurring at remarkable speed, raising the possibility that gambling environments may soon involve artificial intelligence agents with a far more active role in digital interaction through the autonomous execution of tasks based on pre-established prompts. In this context, artificial intelligence is already beginning to generate new asymmetries between users with access to
advanced predictive processing tools and those who continue to participate solely through traditional human capabilities 8 . The discussion therefore moves beyond automation itself and towards the possibility that certain participants may develop structurally superior analytical advantages within prediction markets and gambling verticals, potentially disrupting the balance between users. At the same time, advances in algorithmic personalisation models are transforming the way users interact with digital betting platforms. The article “AI Personalization and Its Influence on Online Gamblers’ Behavior” 9 notes that modern machine learning systems are already capable of dynamically adapting content, recommendations and digital experiences based on each user’s individual behavior, thereby creating highly personalised environments in real time. This raises the possibility of environments where bettors are no longer simple artificial intelligence agents, as is already the case today, but highly specialised and continuously trained systems capable of developing predictive and analytical advantages within increasingly personalised betting markets. This demonstrates that the impact of machine learning within gambling is no longer limited to operational, commercial or responsible gambling processes. It is progressively transforming structural dynamics within the sector, as well as the traditional understanding of the gambling consumer itself. The growing ability of these systems to process vast amounts of information and generate predictive advantages is already having a direct impact on highly data-intensive verticals such as sports betting. This phenomenon also appears to impacting the profile of users within certain gambling verticals. The traditional consumer, historically driven mainly by intuition, entertainment or chance, is beginning to coexist with participants using advanced analytical tools, data processing systems and predictive models to optimise decision-making. It may even mark the beginning of competitive dynamics between human users and AI agents capable of directly interacting with betting platforms. The accelerated evolution of machine learning, together with
8 Tshilidzi Marwala and Evan Hurwitz “Artificial Intelligence and Asymmetric Information Theory” , University of Johannesburg 9 Mihai Florin et al. “AI Personalization and Its Influence on Online Gamblers’ Behavior” . Review Behavioral Sciences”. (2025)
IMGL MAGAZINE | JUNE 2026
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