AI AND PREDICTION MARKETS
of this discussion continues to develop on the assumption that these markets remain fundamentally dependent on chance. Within this debate, one element has still received limited attention, namely the impact of artificial intelligence, particularly machine learning. As these technologies evolve, predictive systems are becoming capable of processing vast amounts of information, identifying complex patterns and developing increasingly sophisticated probabilistic models. Several recent studies on machine learning applied to sports betting and prediction markets show that these systems not only improve the accuracy of certain predictions but also identify inefficiencies within markets. This raises an important question as to what happens when uncertainty no longer depends solely on human intuition or basic analytics but instead becomes increasingly influenced by advanced computational capabilities. This article argues that artificial intelligence does not entirely eliminate chance. However, due to its rapid evolution, it may progressively transform the way uncertainty is distributed within certain gambling verticals. In environments driven by large volumes of data and real-time analytics, competitive advantage may begin shifting away from traditional chance and towards predictive dynamics, information processing and the analysis of algorithmic asymmetries. From this perspective, the evolution of AI could reopen, at least from a technical and regulatory standpoint, the discussion as to whether certain gambling verticals are gradually moving closer to structures commonly associated with prediction markets under a financial market logic. The predictive power of AI Those defending prediction markets from a financial perspective argue that these products share important similarities with certain dynamics commonly found in trading activities and derivatives markets. One of the main arguments supporting this position is that, unlike traditional gambling, participants may buy, sell or close positions before the final event occurs, much like certain financial instruments. This ability to manage positions in real time, while generating economic benefits, has become one of the main elements used to distinguish these models from traditional concepts of chance. 2 Standford University, “Artificial Intelligence Index Report 2025”.
Within this logic, information plays a central role. In many trading environments, participants seek advantages through the analysis of data, trends, news, market behaviour, political developments and future projections, among other factors. A similar dynamic can be observed in certain prediction markets, where participants no longer act solely on intuition or entertainment, but rather on the interpretation of information and probabilities. As noted in NEXT’s article “The Great Divide” , one of the most common arguments advanced by supporters of these models is that their users behave more like traders or specialized analysts than traditional gambling consumers. Closely linked to the role of data and the way it is processed within these markets, another element emerges which could substantially transform this discussion, namely the accelerated evolution of artificial intelligence, particularly through systems based on machine learning. According to Stanford University’s 2025 AI Index Report, the growth of these models over the last decade has been exponential. The report states that “parameter counts have risen sharply since the early 2010s, reflecting the growing complexity of their architecture, greater availability of data, improvements in hardware, and proven efficacy of larger models” 2 . The AI Index Report also highlights significant improvements in benchmarks designed to measure complex reasoning, programming and advanced problem-solving capabilities. Between 2023 and 2024, results achieved by AI models increased considerably across these types of tests. In certain scenarios, language model-based agents even outperformed humans in specific programming tasks under limited time constraints. It is also important to highlight the increasingly sophisticated predictive capabilities being developed in real-world scenarios. The same report refers to models such as “Aurora” and “GenCast”, designed for advanced climate forecasting and capable of generating highly accurate predictions relating to cyclone trajectories, air quality, ocean waves and complex weather phenomena at significantly lower computational costs than traditional systems. In other words, modern machine learning systems are no longer limited to automating tasks or processing information, but are increasingly capable of
IMGL MAGAZINE | JUNE 2026
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