Amazon’s AI Chief Peter DeSantis Sees Quantum Computer Launch In Five-Seven Years: Report - Amazon AI executive Peter DeSantis told CNBC that in five-to-seven years, “we’re going to start to see the first commercially useful small-scale quantum computers.” - DeSantis leads Amazon Artificial General Intelligence (AGI) which is focused on AI and quantum computing. - Quantum computing proponents claim the technology will be able to solve problems that current computers can’t. An Amazon artificial intelligence executive predicted on Wednesday that the tech industry will deliver its first commercially practical, error-corrected quantum computers within the next five to seven years. The timeline highlights an intensifying, highly competitive race among American technology giants, including Amazon Web Services (AWS), Microsoft, Google, and IBM, to transition quantum computing from a theoretical research field into a viable business tool. AMZN Executive Predicts Quantum’s Moore’s Law Moment Peter DeSantis told CNBC that the (quantum) technology will then grow in a similar way to the advancement of semiconductor capabilities. “I actually do believe, over the next five-to-seven years, we’re going to start to see the first commercially useful small-scale quantum computers,” DeSantis told CNBC. “From there, we’re going to see something that looks a lot like Moore’s Law, where they’re going to get bigger and bigger every year, and they’re going to be able to tackle more and more interesting problems,” he added. Tech providers are currently betting on fundamentally divergent engineering approaches to achieve stability. While companies like IBM and Google have heavily focused on superconducting circuits that must be deep-frozen to near absolute zero temperatures, Amazon is dividing its strategy between specialized internal hardware research and cloud platform partnerships leveraging neutral-atom technologies. The five-to-seven-year operational window aligns with broader industry forecasts to shift these high-performance machines out of academic laboratories and into mainstream cloud data