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Particularly concerning to Kesitilwe is the amount of gambling advertising in sports-related content.

“At a break or at half time you see 10 to 20 [gambling] adverts,” he says. “It’s come to a point that oftentimes I even hear my kids singing [gambling adverts]. They’ll just keep asking, ‘But Daddy, what is this? What does this mean?’

“Our position is unequivocal. Betting is an adult activity. Operators must maintain effective age verification, marketing must not target or appeal to children and regulators; parents, schools and communities must reinforce the same message.”

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AI is used, but only towards the end of the process. First, the system gathers objective indicators; AI is then used to assess the overall pattern and probability of a connection. “We only provide evidence-based findings,” Madsen says. “We don’t say that something is a mirror site because we suspect it is. It is only reported when we are 100% confident there is a match.”

The timing is significant. The illegal gambling market is attracting growing attention from regulators and the wider industry, while estimates of its size vary considerably depending on definitions and methodology.

A recent Fincord Intelligence report highlighted by the Betting and Gaming Council estimated that illegal online gambling generated around $50 billion in gross revenue globally in 2025. It estimated that around 5,000 operator structures were using more than 15,000 websites and apps.

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Now, the focus is on what the company does with the additional capacity AI has created. Six months ago, Cubeia’s experiment was essentially about replacing human-written code with AI-generated code.

Since then, it has evolved into something broader: a different development pipeline, a different role for developers and quality assurance (QA), a different way of organising teams and, increasingly, a different relationship with customers.

Cubeia’s first phase was an open approach to AI. Developers could use it whenever they wanted. Phase two brought structure, with everyone using the same agents and working through the same AI-driven pipeline. That required Cubeia to solve questions around quality, reliability and how agents could work together, while getting employees comfortable with the new way of working. Grenstad believes that work has largely been completed.