AI gains outpace profits as iGaming adoption gaps widen
Artificial intelligence performance is advancing faster than the revenues it generates, a gap that is beginning to shape strategy decisions across industries, including iGaming. According to Moody’s 2026 outlook, concerns about a possible AI investment bubble are growing as capital spending on computing power and infrastructure far outpaces the revenue being generated by AI applications. The rating agency warned that while adoption is broadening, the value capture will become increasingly uneven.
For iGaming operators, the mismatch between technical progress and monetisation is already visible on the ground. In an exclusive interview with SiGMA News, Martins Lielbardis, Founder of iGaming Centre, said the clearest returns today come from areas that are data-heavy and operationally defined.
“I think the real returns are in the amount of data that operators are able to sort and reduce the risks associated with fraud and abuse, and also the time it takes to bring ideas to real products,” he said.
This assessment aligns with Moody’s view that AI continues to deliver substantial benefits in routine, document-centric, or customer-facing tasks, but complex workflows still face frictions. In iGaming, fraud detection, risk scoring, customer support automation, and product testing fall squarely into those categories, allowing operators to deploy AI without reengineering entire organisations.
However, Lielbardis cautions that not every use case justifies the investment. “The hype might be similar to other industries, that companies try to chase AI, even where it might not be that necessary,” he said. This mirrors Moody’s outlook, which said “the benefits of AI have been mostly concentrated in task-level automation rather than delivering systemic efficiency gains at the firm level.”
Sorting data versus chasing hype
The divergence between practical deployment and marketing-led experimentation is becoming clearer as AI models grow more capable. Moody’s noted that “AI capabilities are progressing fast, led by a handful of U.S. players whose latest models are both multimodal and agentic and continue to set global benchmarks.” Yet the agency stressed that “deploying AI into an enterprise’s operations requires the redesign of full processes to deliver substantial value.”
For iGaming operators, the challenge lies in translating model performance into regulated, live environments. Fraud prevention systems can be trained on historical transaction data and adjusted within existing compliance frameworks. Player-facing personalisation, by contrast, often touches responsible gaming rules, data protection obligations, and licensing conditions.
As a result, many operators remain selective. AI is being used to shorten development cycles and automate decisions that were previously manual, but broader ambitions around fully autonomous marketing or dynamic pricing remain constrained. Moody’s expectation that productivity gains will gradually increase but remain highly uneven both across and within sectors reflects this cautious approach.
“I believe that the overall adoption speed for the AI will be the main factor, but player engagement tactics might probably be the main driver of the gap, so you can better predict and understand player behaviour.”
– Martins Lielbardis, Founder, iGaming Centre
Compute costs and the mid-tier squeeze
Rising infrastructure costs are emerging as a defining fault line between large operators and the rest of the market. Moody’s stated that “AI infrastructure is a critical bottleneck,” warning that “demand for computing power will exceed supply, giving pricing power to infrastructure owners.”
Lielbardis sees this pressure clearly in the iGaming sector. “With larger capital, you have larger capabilities and customisation, you can already see prices rising for what used to be customer-grade hardware, in some cases 2-3 times,” he said. He added that when firms cannot invest directly in infrastructure or specialist teams, trade-offs become unavoidable. “If a company can’t afford its own data centres or specialists, you have to outsource, and you would not be able to tailor it to your precise needs.”
Moody’s echoed this concern at a broader level, noting that market share consolidation among a small number of cloud service providers is pushing prices higher and widening the adoption gap between well-capitalised firms and cost-constrained peers. Access to high-performance computing increasingly requires long-term commitments, a model that favours groups with strong balance sheets.
Uneven access reshapes competition
In its outlook, Moody’s predicted that in some sectors, AI will contribute to a “winners-take-most” effect, where scale and early adoption reinforce each other. Lielbardis told SiGMA News, this pattern is likely to play out in iGaming, though not evenly across all functions.
“I believe that the overall adoption speed for the AI will be the main factor, but player engagement tactics might probably be the main driver of the gap, so you can better predict and understand player behaviour,” he said.
In its assessment of cross-sector impacts, the agency noted that leaders are embedding AI “across multiple stages of their activity,” while smaller players struggle to fund transformation programmes. In financial services, for example, “leaders are leveraging AI to defend margins by offering services at a lower cost or running key processes more cheaply,” while others fall behind.
“We are now reaching levels where the tech and rules for AI can change in a month and new regulations can come up very quickly.”
– Martins Lielbardis, Founder, iGaming Centre
Regulation and fragmented deployment
Beyond cost and capability, regulatory divergence is adding complexity to AI deployment. Moody’s highlighted that diverging regulatory regimes, from the European Union’s AI Act to China’s licensing framework, will further raise compliance costs and complicate global deployment.
Lielbardis described a fast-moving environment that leaves little room for long-term certainty. “We are now reaching levels where the tech and rules for AI can change in a month and new regulations can come up very quickly,” he said.
Open-source promise and operational risk
One potential counterbalance to rising costs is the rapid evolution of open-source AI models. Moody’s noted that “open-source systems, which are free for developers to download and modify, have evolved rapidly, with several model families closing the gap with leading proprietary systems.”
As AI becomes more embedded in workflows, Moody’s warned that cybersecurity and AI model-related risks are expanding, including vulnerabilities such as prompt injection and model poisoning.
While AI performance continues to accelerate, the path to consistent returns remains narrow. As Moody’s said, “for now, capital spending far outpaces the revenue generated by AI applications.”
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