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Internally, the board has also been hard at work revising and overhauling several sets of regulations, including rules for gaming salons, AML reporting and now technical standards. Dreitzer represents the fifth NGCB chair to take office since 2019, and the partial term he inherited runs through January 2027.
He told iGB earlier this year he’d “certainly be interested” in a full four-year term after the current one expires. With a background in suppliers and testing labs, Dreitzer is acutely aware of the technical challenges facing the state.
“When I started here, I had multiple conversations with various licensees who operate across multiple jurisdictions, and the consistent commentary I heard was that they would go to Nevada last, if not never at all, because there was concern about the time it would take, the lack of regulatory consistency, the lack of clarity,” he told iGB in January. “So when I came in, in view of the mandate from the governor and the work began by Chair Hendrick, I felt I needed to do something.”
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During a session of the House Audit Committee last Thursday, members of parliament (MPs), the Data Protection Commissioner and representatives from the Welfare Benefits Administration Service (WBAS), the Gaming & Casino Supervision Commission and the National Betting Authority (NBA) convened to discuss ways to identify and restrict gambling activity among GMI beneficiaries, as reported by Cyprus Mail.
While the intention is to protect vulnerable households and ensure welfare is spent on essential needs, proposed measures are being complicated by legal, technical and privacy obstacles.
Harris Tsangarides, executive director of the Gaming & Casino Supervision Commission, said his department had noticed spikes in gambling activity coinciding with GMI payment dates, prompting the regulator to consider “technical solution” to cross-reference GMI beneficiary lists with casino membership records.
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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.
