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AI coding tools are automating more implementation work, while software teams face growing demands for specification, review, testing and verification. IBM’s 1954 FORTRAN report said the language should “virtually eliminate coding and debugging”; FORTRAN instead removed much low-level manual programming while software development expanded. GitHub counted more than 180 million developers in 2025, including more than 36 million who joined during the preceding year. Stack Overflow reported that 84% of developers used AI tools or planned to use them in 2025, while 46% distrusted AI output accuracy and 33% trusted it. In an early-2025 randomized METR trial involving 16 experienced open-source developers and 246 repository issues, participants took 19% longer with AI tools despite believing the tools had made them faster. METR’s task-time series found that Claude Opus 4.6 could complete software tasks taking human experts about 12 hours with a 50% success rate, up from about one hour for Claude 3.7 Sonnet a year earlier. Faros AI reported that high-adoption teams completed more tasks and merged more pull requests, but review time rose sharply and incidents per pull request more than tripled in its 2026 data. Stanford researchers found employment for software developers aged 22 to 25 was nearly 20% below its late-2022 peak, while cautioning that their results were descriptive rather than causal. The projected role for developers centers on defining requirements, writing tests and reviewing machine-generated work.