#CommunityAMA The growing sophistication of artificial intelligence in Forex trading has enabled rapid decision-making, pattern recognition, and portfolio optimization at unprecedented scales. However, a troubling risk is emerging: the potential for collusion via shared AI strategies. As financial institutions, hedge funds, and proprietary trading firms increasingly deploy models trained on overlapping datasets and similar machine learning architectures, these systems may begin to converge toward analogous trading behaviors. While not explicitly coordinated by humans, this convergence can mimic collusive behavior, especially when firms license the same third-party AI engines or rely on centralized model marketplaces. The danger arises when these AI systems, interacting in the same market environments, respond identically to specific signals—creating synchronized trades that can move markets artificially. Such coordinated action, even if unintentional, can distort price discovery, amplify volatility, and disadvantage retail traders who cannot access or predict these shared AI patterns. Moreover, the opaque nature of black-box algorithms makes it difficult to detect whether similar strategies are the result of independent optimization or engineered alignment between institutions. Regulatory frameworks are not yet fully equipped to address this form of indirect collusion. Traditional anti-cartel laws rely on evidence of human intent or communication, which AI-driven strategy overlap can easily circumvent. This raises critical questions about accountability and surveillance. Should algorithmic similarity be grounds for investigation? How do we balance innovation with fairness? To mitigate risks, exchanges and regulators may need to monitor strategy clustering patterns, analyze correlations across trades at high resolution, and require disclosure when firms employ shared AI vendors. Without safeguards, the Forex market could unintentionally drift into a state where a handful of AI models dominate flows, reducing diversity in market behavior and increasing systemic fragility. Collusion via shared AI is not merely theoretical—it’s a subtle form of algorithmic alignment that could undermine market integrity if left unchecked.