How engineering effectiveness looks at 10 vs 1000 engineers

Scaling engineering teams involves navigating different challenges at various stages of growth, from maintaining focus in small startups to managing coordination in larger enterprises. At the startup scale (10-30 engineers), the key is to maintain focus by saying no to non-essential tasks, leveraging the direct connection between engineers and business outcomes, and establishing lightweight processes to track productivity. As companies scale up (30-150 engineers), the challenge shifts to managing cross-team dependencies and maintaining alignment without stifling speed, requiring clear ownership, architectural patterns, and systematic visibility. At enterprise scale (150+ engineers), the focus is on creating autonomous teams within clear boundaries and improving developer productivity through marginal gains, while treating developer experience as an internal product. Successful AI adoption across these stages follows a deliberate path of experimentation, adoption, measuring impact, and optimizing costs, with different risks and metrics relevant at each scale. Throughout all stages, the fundamental approach is to maintain visibility, preserve team autonomy, and keep engineers connected to the impact of their work while adapting the strategy to the organization’s current scale and challenges.