The Leadership Gap

Who Needs to Own This

You can hand every developer on your team the best AI tools available. Without technical leadership to wire it all together (the architecture, the workflows, the code quality infrastructure, the review processes), you'll get a 20% improvement instead of a 200% one.

Gabe

The teams investing in AI infrastructure today (the tests, the types, the CLAUDE.md files, the review processes) are building a compounding advantage. Every quarter, agent capabilities take a leap. The teams with the infrastructure absorb each leap immediately. The teams without it start over every time. I've watched this pattern play out across three technology shifts in my career. The winners weren't the ones who adopted first. They were the ones who built the foundation to absorb what came after. That's the real argument for starting now: be ready for what these tools become in 18 months.

Tools Without Strategy Is Just Expensive Copilot

The teams seeing the biggest productivity gains aren’t the ones with the best tools. They’re the ones with technical leaders who redesigned their entire development workflow around AI capabilities. They restructured code review, rethought sprint planning, invested in test infrastructure, and created AI-specific conventions. This is CTO-level work.

Programmers Aren't Going Anywhere

The idea that programmers are going away is a fantasy. Until we have AGI, we’re going to keep programming. Non-technical people can now build simple tools, and that’s real. But large, complex systems still need experienced engineers who know what questions to ask. What IS real: AI agents are becoming more autonomous every quarter, and the teams that build the infrastructure now (the tests, the types, the conventions) will be positioned to absorb each new leap. Agentic coding is a new programming language, a higher-level one. The engineers who learn it now will have a massive advantage.

The Opportunity Is Now

Right now, adopting agentic coding is a genuine competitive advantage, but it won’t stay that way. Within 12-18 months, this will be table stakes. The teams that start now get to learn, iterate, and build their AI infrastructure while the stakes are low. The real advantage is compounding your team’s skills so they’re ready for whatever comes next, not just shipping faster today. I’ve been through enough technology shifts to know that early movers don’t win because they rush. They win because they learn first.

## AI-Powered Development: Leadership Brief

### The Opportunity
AI coding agents now let one engineer do the work of three.
Our team can adopt this paradigm and unlock significant
capacity, but it requires intentional technical leadership.

### The Shift
Agentic AI coding (Claude Code, Cursor, GitHub Copilot agents)
has moved beyond assisted mode. AI agents now write entire
features, run tests, and handle 70-80% of implementation.

### The Evidence
- Anthropic writes 70-90% of its code with Claude Code (Boris Cherny, Jan 2026)
- 25% of YC startups have 95% AI-written code (Garry Tan, YC)
- Experienced devs see 2-5x productivity gains (Simon Willison)
- Google engineer replicated a year of work in one hour (Jaana Dogan)

### What We Need
1. Technical leadership to architect AI-ready workflows
2. Codebase improvements: test coverage, typing, CI speed
3. 90-day phased rollout (audit → pilot → full team)
4. Redesigned code review process for AI-generated output

### The Ask
Approve a 90-day pilot: audit our codebase for AI readiness,
run a small-team pilot with measured baselines, then expand
based on results. Estimated setup cost is minimal — the
tooling is already available, the investment is in workflow
and infrastructure changes.

### Expected ROI
- Features that took sprints now ship in days
- Reduced time-to-market on backlog items
- Team upskilled on AI-augmented development
- Infrastructure improvements that compound over time