Introduction
Senior developers and AI code are reshaping release velocity: Fastly’s 2025 survey highlights a clear gap versus juniors.
Seniors ship more AI code and report larger speed gains, even as they spend more time fixing it.
Fastly’s July 2025 survey (791 developers) finds that about one third of 10+ year veterans say over half of their shipped code is AI-generated—nearly 2.5× the junior rate (13%). Seniors also invest more in fixing AI output: just under 30% say edits offset most time savings, versus 17% of juniors. Even so, 59% of seniors feel faster overall with tools like Copilot, Gemini, or Claude, compared with 49% of juniors. There is a paradox: a cited RCT on experienced open-source developers found tasks took 19% longer with AI. The pattern suggests early momentum from autocomplete is often followed by editing and testing cycles that erode gains. Sustainability and green coding awareness are also rising across the field.
Context
Fastly surveyed 791 professional developers in the US from July 10–14, 2025.
All respondents write or review code as a core job function. The panel was quality-controlled, though self-reported data can include bias. Concerns about "vibe coding" and potential vulnerabilities are growing.
Senior developers and AI code: what changes
Seniors ship more AI to production and report higher speed-ups, thanks to faster error detection and correction.
Among juniors, just 13% say over half their shipped code is AI-generated; among seniors, 32% say the same. This points to more aggressive and confident AI use in production by experienced engineers. On speed, 26% of seniors say AI makes them "a lot faster," double the junior share (13%). Still, nearly a third of seniors edit AI output enough to wipe out most time savings—evidence that quality requires skilled oversight.
"AI will bench test code and find errors much faster than a human, repairing them seamlessly. This has been the case many times."
Senior developer, Fastly 2025 survey
"It’s always hard when AI assumes what I’m doing and that’s not the case, so I have to go back and redo it myself."
Junior developer, Fastly 2025 survey
Perception vs. reality
AI often feels fast, but downstream edits and tests can stretch timelines.
Nearly 28% frequently fix AI code enough to offset most savings; only 14% rarely change it. An RCT cited in the findings saw experienced developers take 19% longer with AI. The likely driver: early momentum from autocomplete followed by costly rework. Yet nearly 80% say AI makes coding more enjoyable, a morale boost amid burnout and backlogs.
"GitHub Copilot greatly helps by suggesting snippets and even functions, but it once produced a subtle bug that took hours to debug."
Developer, Fastly 2025 survey
Hidden costs and sustainability
Green coding rises with experience, and the energy cost of AI tools is widely recognized.
Green-coding consideration climbs with seniority: just over 56% of juniors actively consider energy use, rising to nearly 80% among mid and senior engineers. Roughly two-thirds across levels know AI tools can carry a significant carbon footprint; fewer than 8% of the most junior are unaware. Sustainability appears embedded in developer culture.
Conclusion
The line is clear: experienced engineers ship more AI code and report bigger speed gains, but pay with oversight and QA time. Teams should balance momentum and verification—especially for business-critical code—and invest in green coding practices to curb the energy cost of AI tools.
FAQ
- What is the gap for senior developers and AI code in production?
About 32% of seniors say over half their shipped code is AI, versus 13% of juniors. - Why do seniors gain more speed from AI coding?
They better spot and fix mistakes, using AI efficiently even on high-stakes tasks. - Do AI tools always improve efficiency in AI search and development?
No: a cited RCT saw tasks take 19% longer with AI; perceived speed can be misleading. - What risks does "vibe coding" pose with AI models?
It can introduce vulnerabilities and inefficiencies, requiring rework that erodes initial gains. - How does green coding intersect with AI tools?
Most developers recognize energy costs; sustainability awareness grows with experience. - When should senior developers and AI code be limited on critical paths?
When verification and debugging costs outweigh early autocomplete gains.