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Five years from now, Curwen expects the development landscape – and the studios operating within it – to look markedly different.
“I don’t think the successful studio of the future necessarily has hundreds of people,” he says. “It could be a relatively small group of extremely talented people with great ideas, strong brands and a very good understanding of players, supported by technology that does much of the heavy lifting.”
That would also shift the basis on which studios compete. If sophisticated development capabilities become accessible to a wider range of creators, scale alone becomes less of an advantage.
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The charter does not create new regulatory obligations and was designed to complement legislation including the EU AI Act and GDPR.
Instead, it proposed a risk-based approach. It suggested lighter controls on use cases such as coding assistants, but strong checks on customer-facing functions, particularly those involving fraud and responsible gambling.
Kenneth Brincat, CEO of the MDIA, said companies needed “practical ways” to better understand where AI fits within their businesses.
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Strip away the language of contracts, order books and event trading and Bernard Marantelli has a blunt description of what is happening inside US sports prediction markets. “Kalshi is a sportsbook that’s just not allowed to have an in-house risk team,” says the founder of White Swan Data, one of the specialist firms now making markets on prediction exchanges.
The difference is all about the risk function. A sportsbook employs traders to price bets and manage its exposure. An exchange provides an API through which firms compete to quote prices and supply liquidity.
“Here’s an API. Bernard and 88 other people can market-make all these request-for-quotes (RFQs),” Marantelli says. “Some people might come in and just do esports because they’re esports experts. Others do everything. Some focus on same-game parlays. But it’s a sportsbook.”
