Leading AI-First Engineering Teams: Lessons From Scaling 5 to 45 Engineers
Four years ago the studio I co-built had five engineers. Today it has 45. Half of those hires happened after AI-assisted development went mainstream, and that shift changed both what we needed to hire for and how we led. Here is what I learned.
What does AI change about engineering leadership?
It changes hiring criteria (judgement over typing speed), code review focus (intent over style), documentation value (docs are now velocity infrastructure for AI agents), and team rituals (async updates replace standups, retros become more strategic). The leader\'s attention moves up the stack, away from execution detail.
1. Hiring criteria. I used to screen for "writes clean code fast". Now I screen for "defines the problem clearly, reviews AI output critically, ships reliably". The first is a skill. The second is a judgement cluster that is harder to train and more important than ever.
2. Code reviews. In 2022 a PR review was mostly about style and subtle bugs. In 2026 it is mostly about intent, was this the right thing to build? Did we solve the actual problem? Style is largely auto-fixed, obvious bugs are largely auto-caught, so the human attention moves up the stack.
3. Documentation. Used to be a chore. Now it is a competitive advantage: well-documented codebases are dramatically easier to work with for AI agents, which means your team ships faster, which means your docs pay for themselves in velocity. Document religiously.
4. Rituals. Daily standups are largely replaced with async updates + an AI-summarised daily. Sprint planning shrank. Retros got more strategic, we talk less about "what went wrong this sprint" and more about "where is our prompt library drifting".
What does AI not change about leadership?
The need for clear vision, team psychology (trust, autonomy, safety), the stakes of senior hiring (a bad senior still costs you a quarter), and the value of outcome ownership. AI amplifies whatever leadership you already have, in whichever direction it is already pointed.
1. The need for a clear vision. AI makes weak leaders look weaker. If you cannot articulate what good looks like, the team will ship beautiful implementations of the wrong thing.
2. Team psychology. Trust, autonomy, safety, belonging. Untouched by AI, still the foundation.
3. The stakes of hiring. One bad senior hire still costs you a quarter. AI amplifies a good engineer\'s output and also amplifies a bad engineer\'s output, in the wrong direction.
4. The value of ownership. Engineers who own outcomes ship outcomes. Engineers who own tickets ship tickets. No model changes that.
What is the playbook for scaling AI-first engineering from 5 to 50?
Invest early in a prompt library and internal AI tooling (compounding asset). Hire for judgement, train for tools. Keep senior-to-junior ratios honest, AI needs more senior eyes, not fewer. Write the culture down. Measure cycle time, not lines of code. The numbers will move quietly, then loudly.
- Invest in a prompt library and internal AI tooling. It is your compounding asset. A good one saves every new hire a month.
- Hire for judgement, train for tools. AI tools change every quarter. Judgement does not.
- Keep senior-to-junior ratios honest. AI does not let you run 10 juniors with 1 senior. If anything, it shifts the ratio the other way, you need more senior eyes on the output.
- Write your culture down. Values, engineering principles, review standards, how-we-ship docs. New hires onboard on text, not on osmosis.
- Measure cycle time, not LOC. In an AI-assisted team, lines of code is meaningless. Cycle time from idea → production is the number.
What is the hardest part of scaling an engineering team?
The 15-to-30 crucible. You need middle management but cannot yet afford senior managers, so your best ICs become first-time managers and half hate it. 30-to-45 is a different company entirely, systems, HR, hiring pipelines, legal. Most technical leads fold here on people, not tech.
If you are in the middle of this curve and want to compare notes, reach out. I work with UK and European founders scaling AI-first engineering orgs, remotely and in GMT/BST-friendly hours.