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Yet the presence of professional counterparties complicates the customer-facing idea that prediction markets merely allow users to trade opinions with one another. As Kendrick puts it, two ordinary customers are not going to place $10 million or $20 million behind the Philadelphia Eagles. Markets at that scale require institutions.
For Marantelli, the exchange format could also cause some customers to lose money faster than they would with a conventional sportsbook. The ability to enter and exit positions creates a perception of flexibility, but that optionality can encourage users to commit more of their bankroll.
A customer might buy a team at 55 or 56 cents expecting the price to rise to 58 or 59 cents, he explains. If it falls to 45 cents instead, the trader may refuse to accept the loss and continue holding the position.
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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.
“During the hybrid period in Q1, Cubeia solved 259 issues. Once it moved to the AI-driven process, that figure rose to 421 – a 62% increase. Larger projects increased from 17 to 58. So the answer is yes: moving to AI-driven development has increased our output tremendously,” Grenstad says.