AI Didn't Make Engineering Teams Faster. It Forced Them to Grow Up (medium.com)

The emergence of AI tools has significantly transformed how engineering teams operate, moving beyond mere speed in code development to reshaping their entire functioning. While teams can now produce results faster, the more profound impact lies in the collapse of traditional linear lifecycles. Teams are transitioning to a model where thinking and building happen simultaneously, creating a dynamic workflow marked by continuous architectural judgement and responsive feedback loops. This shift highlights the need for teams to rethink their structures; when execution costs diminish, the real expense is misalignment and delayed decisions, which can accrue technical debt quickly. As software becomes a shared substrate rather than a competitive differentiator, successful teams are those that embrace intentional collaboration and adapt their operational models. They favor smaller, end-to-end accountable groups that rely on shared mental models, reducing reliance on formal processes and meetings. Automation is emerging as a central organizational system, necessitating shared responsibility in removing low-judgment tasks. Ultimately, AI has not simplified engineering; it has revealed and amplified existing structural weaknesses in team dynamics, underscoring the importance of coordination and decision-making in achieving successful outcomes.