Generative AI stormed into software development promising a coding revolution, but so far, it’s making developers only marginally faster—and in some cases, slower. Bain & Company’s Technology Report 2025 argues that real productivity gains will require a wholesale reinvention of the entire development lifecycle, not just sprinkling AI on coding tasks.

AI generated summary of: AI coding hype overblown, Bain shrugs.

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Key Points

• Developer productivity gains from generative AI average just 10–15% at most, often offset by the time spent checking AI’s mistakes.
• METR research found AI coding tools can actually make developers slower, as error correction eats into potential time savings.
• Writing and testing code is only ~25–35% of the dev lifecycle, so speeding it up alone doesn’t reduce time-to-market.
• Bain recommends an “AI-native” reinvention of the full software lifecycle, integrating AI into discovery, planning, design, testing, and maintenance.
• Agentic AI—autonomous coding agents—has high failure rates, with projects like Devin struggling and 70% of multi-step office tasks failing in Carnegie Mellon tests.
• More than 40% of agentic AI projects are projected to fail by 2027, per Gartner.
• Cultural resistance, lack of training, and absent KPIs are the biggest barriers to adoption, not the tech itself.
• Companies that paired full process transformation with GenAI saw 25–30% gains, suggesting piecemeal AI efforts won’t cut it.

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Summary

Generative AI entered software development with promises of exponential efficiency, but Bain’s new report suggests the industry’s expectations were wildly optimistic. While roughly two-thirds of firms have rolled out tools such as AI code copilots, actual developer adoption and measurable returns remain tepid. Many early pilots saw productivity bumps of just 10 to 15 percent, with the time “saved” often consumed by error correction or stuck in processes that weren’t reimagined to leverage AI’s potential. A separate METR study even found that some AI coding initiatives slowed developers down, puncturing the narrative of a frictionless AI coding future.

A core problem is that software development is far more than coding. Bain points out that writing and testing code accounts for only a third of the lifecycle; design, requirements gathering, deployment, and maintenance dominate the rest. Simply speeding up code generation won’t meaningfully change delivery timelines or business outcomes. This is why Bain advocates an “AI-native” approach, which implies embedding generative AI across every lifecycle phase. That means automating planning, integrating AI into testing and deployment pipelines, and rethinking workflows so AI-driven work can move seamlessly to production. Without this comprehensive shift, most pilot projects will plateau, yielding disappointing returns that are hard to justify to CFOs.

The report also casts doubt on the next big buzzword: agentic AI. Tools like Cognition’s Devin promised autonomous software engineering, but in practice, struggles abound. Devin completed just 3 of 20 tasks in early tests, often producing unusable solutions or getting stuck. Carnegie Mellon’s benchmark study found AI agents fail 70% of multi-step office tasks, while Gartner forecasts 40% of these projects will be abandoned by 2027. Cultural and organizational hurdles may be just as challenging: engineers resist AI adoption, firms skimp on training, and executives fail to define clear KPIs. Bain’s takeaway is blunt—without bold, top-down leadership and a total process rethink, generative AI in software development risks being a costly sideshow rather than a transformative tool.

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• AI coding hype overblown, Bain shrugs - Bain’s 2025 report finds that generative AI in software development has delivered minimal productivity gains, and real ROI will require a full lifecycle reinvention, not just faster coding.

Summarized by ChatGPT on Sep 24, 2025 at 8:28 AM.

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