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👋 Hi, this is Gergely with a subscriber-only issue of the Pragmatic Engineer Newsletter. In every issue, I cover challenges at Big Tech and startups through the lens of engineering managers and senior engineers. If you’ve been forwarded this email, you can subscribe here.
AI’s impact on software engineers in 2026: key trends, Part 2
Tradeoffs of AI tooling, why adopting AI at company-level is hard, what’s changed in two years, and more. The third and final part of a series analyzing our 2026 AI survey results
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Earlier this year, we asked The Pragmatic Engineer subscribers about the AI tools you use, how you use them, and, crucially, what you think of them. We received more than 900 responses, and with this article we conclude our analysis of that data, covering:
Tradeoffs of AI tooling. Positive and negative sentiments, like how AI tools often mean less time spent on tedious, repetitive work, but that it often leads to unrealistic business expectations.
Adopting AI at scale is hard. Companies are struggling to achieve adoption that feels productive at the team and org levels. Our survey finds the benefits of AI heavily depend on the engineering culture that was in place before.
Impact on codebase quality. Codebase quality seems to be decreasing, but management at most places does not care. Meanwhile, maintenance duty is falling upon a shrinking number of engineers who still understand increasingly complex codebases.
Less experienced engineers and AI. For these folks, AI seems to be less helpful and they rack up higher AI token bills. Maybe they need more space to learn, mentorship, and support.
AI tooling ‘addiction’. Using AI agents “feels like a slot machine” encouraging “just one more prompt”-type behavior, while some folks think that the pricing of plans is built in a way to “lure” them to prompt more and more.
Changes since 2024. Fewer devs are negative about AI, but there’s not all that much more positivity. The models have become much higher quality, and better tooling improves trust.
Where are we headed? The concept of code ownership seems to be eroding, and collaboration within teams becomes less important.
Previously in this series, we covered:
AI tooling for software engineers in 2026: a detailed summary of survey responses, covering the most-used AI tools, trends, AI agent usage, company size and usage, and tools engineers love.
The impact of AI on software engineers in 2026: key trends. Part 1. Concerns about mounting AI costs, more engineers hitting usage limits, and AI tools having uneven effects upon different types of engineers
Full subscribers can access a more detailed report.
The bottom of this article could be cut off in some email clients. Read the full article uninterrupted, online.
1. AI tooling tradeoffs
What can be said about the impact of AI tooling on engineers this year? Well, based on the responses that readers sent in to our survey, there are some common patterns:
Code and output tradeoffs
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Productivity tradeoffs
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