Building AI-Powered Engineering Teams

AI is changing team dynamics in ways I didn't expect. Code reviews and pair programming are becoming more important, not less—because teams now need to collectively evaluate AI-generated changes.

The value shows up in non-coding applications first. Building dashboards, generating better PRs, writing documentation. Meeting transcripts that turn into engineering tasks. These use cases carry minimal risk and deliver immediate benefit.

The line between planning and execution is blurring. AI helps translate ideas into code more fluidly than before.

Cross-functional collaboration is changing too

Engineers can now generate polished documentation. Product managers can prototype features. Designers can suggest implementation details. AI understands enough across disciplines to enable contributions beyond traditional boundaries.

This isn't about replacing expertise. It's about making collaboration smoother when people need to work outside their primary domain.

Different experience levels, different impacts

Junior engineers

AI provides answers but still requires guidance for effective integration. Mentorship becomes even more critical because AI can generate working code without conveying the underlying principles.

Junior engineers who learn to prompt effectively and validate outputs gain real leverage. But they still need senior oversight to ensure best practices. The code works, but does it work well?

Mid-level and senior engineers

This is where I see the biggest gains. AI significantly reduces tedious tasks—boilerplate, repetitive patterns, documentation updates—while freeing these engineers to focus on architecture and design.

Mid-level and senior engineers extract the most value because they spend significant time coding and have the experience to evaluate whether AI outputs are actually good. They can also use AI to navigate unfamiliar technologies quickly, expanding their technical range.

Staff+ engineers

AI becomes a research assistant and knowledge amplifier. In familiar domains, the productivity gains are modest. In unfamiliar territories, AI dramatically accelerates learning and exploration.

At this level, AI's impact on day-to-day coding tasks may be less dramatic. But its ability to quickly synthesize information across different domains becomes invaluable for strategic technical decision-making.

Every engineer will become an AI-powered engineer

The distinction between "AI engineers" and "software engineers" is already starting to blur. AI is becoming as fundamental as version control or automated testing—just part of every engineer's standard toolkit.

I don't think "AI engineer" will be a separate role for long. It'll just be what engineers do.

The next stage: collective intelligence

The most forward-thinking organizations are evolving beyond viewing AI as sophisticated tools. The next step involves agentic workflows where AI systems become active participants in the development cycle rather than passive assistants waiting to be prompted.

Some areas to watch:

Identify one end-to-end workflow in your development process that could benefit from an agentic approach. Don't try to do everything—pick one.

Build guardrails that ensure human oversight at critical decision points. AI should help, not replace judgment.

Create metrics that measure quality outcomes, not just speed improvements. Faster isn't better if the code is worse.

Develop a framework for engineers to effectively collaborate with AI agents. This is a skill that needs intentional development.

The question isn't whether your engineers will work alongside AI agents. It's how deliberately you'll design those partnerships to amplify your team's unique strengths.