Most engineering leaders are rethinking workflows because of AI. But not enough are rethinking team structure itself.
The math has changed.
Ten senior engineers with the right AI leverage can now outperform thirty engineers operating with traditional processes. That reality requires a reset in how we think about roles, hiring, and what a “small but mighty” team can achieve.
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The 80% Rule in Practice
At my company, we’re a small engineering team—just ten people writing code, including myself and our CTO. On paper, we should be outmatched by competitors with two or three times our headcount.
But we’re keeping pace—and in some areas pulling ahead—because of how aggressively we lean into AI.
Our philosophy: let AI get the foundational work to 80%, then focus human intelligence on the remaining 20% that drives real business value.
Here’s what that looks like in practice:
- API documentation: AI now generates comprehensive docs that are better than most teams produce manually. It’s no longer a grind.
- Solution planning: Instead of staring at whiteboards, we feed requirements into MCPs and get back multiple starting approaches. We spend our time refining, not inventing from scratch.
- Testing & optimization: AI agents generate test plans, write coverage, summarize merge requests, and surface performance improvements. Humans stay focused on architecture and user experience.
The result? Our small team operates at a level that feels disproportionate to our size.
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Rethinking Roles and Hiring
What I’m seeing is a fundamental reset of what a team can accomplish.
Ten senior engineers today can deliver more than fifteen could just a year ago. That doesn’t mean you stop hiring—it means your expectations shift.
- You can be more selective about who you bring on.
- You can shape roles around value creation, not task completion.
- You can hire for AI fluency and judgment, not just raw technical skill.
Every contributor gets to operate at a higher level because the baseline work is handled by agents.
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The Compound Effect
The pace of change is accelerating.
Three months ago, there were AI tasks we had to double-check. Today, they run autonomously. Next quarter, the baseline will rise again.
This creates a compound effect:
- AI-native teams keep accelerating.
- Traditional teams stagnate.
- The gap between them widens exponentially.
It’s not just about faster delivery—it’s about solving harder problems with higher quality outcomes.
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What Leaders Should Do
If you’re structuring teams like it’s 2022, you’re already behind.
Here’s how leaders can adapt:
- Redesign roles around outcomes. Let AI handle tasks, let humans drive strategy.
- Hire for AI fluency. The best engineers will be those who can direct and shape AI tools.
- Measure impact, not activity. Traditional productivity metrics lose meaning when AI automates execution.
- Evolve continuously. What worked last quarter might be obsolete now. Stay fluid.
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The Bottom Line
AI isn’t just changing workflows—it’s changing what’s possible with the people you already have.
A small, senior-level team that’s deeply fluent with AI will consistently outpace larger, traditional teams.
The tide is rising fast. The leaders who learn to surf it will build the next generation of high-leverage engineering organizations. Those who don’t will get left behind.
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✦ This piece is part of my newsletter Beyond the Commit, where I write about building resilient, high-performing engineering teams in the age of AI.
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