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“With expanded context windows, enterprises can potentially accelerate their development and debugging at scale,” mentioned Neil Shah, vice chairman for analysis and associate at Counterpoint Research. “Over time, as models become more proficient in generating, validating, and refining boilerplate code, enterprise-grade quality output would be the north star. This gives the enterprise time-to-optimize and time-to-market advantage.”
These efficiency features might additionally change the very nature of a developer’s position, in accordance with Oishi Mazumder, senior analyst at Everest Group.
“Long-context AI moves development from piecemeal assistance to holistic collaboration, turning developers into code orchestrators who direct end-to-end changes across entire systems,” Mazumder mentioned. “This restructuring enables smaller, specialized teams to deliver enterprise-scale projects faster, with gains in onboarding speed, code quality, and delivery pace. The biggest staffing shift will be toward AI-augmented engineers and governance roles, as repetitive coding tasks increasingly move to the AI.”