Gergely Orosz · 12 min read · Productivity
AI’s impact on software engineers in 2026: key trends, Part 2
Read the original on newsletter.pragmaticengineer.com →
The Pragmatic Engineer analyzes 900+ responses on AI tooling in 2026, highlighting productivity tradeoffs, adoption challenges, and cultural factors that shape outcomes. It notes that benefits depend heavily on preexisting engineering culture, while codebase quality may degrade and maintenance burdens concentrate on fewer experienced engineers. The piece also discusses AI addiction risks, uneven impact on less experienced engineers, and shifts in code ownership and collaboration.
Key Takeaways
- AI tools can boost productivity for some by enabling flow and reducing tediously repetitive work, but may increase context switching for others.
- Adopting AI at scale remains hard due to costs, inconsistent usage, and organizational culture barriers.
- Codebase quality appears to be deteriorating, with maintenance increasingly handled by a shrinking group of engineers who understand the codebase.
- Less experienced engineers tend to benefit less from AI and face higher token costs, underscoring the need for mentorship and learning space.
- AI usage can become addictive, with a 'slot machine' feel and pricing potentially encouraging more prompting.
Excerpt
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…