AI in SDLC = the End of Outsourcing? (blog.flurdy.com)

A recent analysis argues that AI agents integrated into the software development lifecycle (SDLC) could erode many of the traditional reasons companies outsource work—capacity, access to specialized skills, flexibility, cost and risk transfer—because AI can act like scalable “junior developers.” When developers use AI as delegates, they shift into architect/manager roles, enabling teams to horizontally scale output, switch languages/frameworks quickly, and potentially reduce headcount or outsource needs. The piece compares trade-offs: AI brings visible, adjustable costs and up-to-date capabilities, while outsourcing carries hidden coordination costs, cultural friction, knowledge churn and intermediary overheads—especially for offshore teams. However, AI also introduces risks like feature bloat (YAGNI), developer skill atrophy, “AI slop” and maintenance/quality issues that require human supervision. For the AI/ML community the takeaway is pragmatic: AI in SDLC is not an automatic replacement for outsourcing but a disruptive enabler. Technical implications include new emphasis on tooling and workflows for AI delegation, rigorous supervision/validation pipelines to avoid degraded code, and continued investment in developer-in-the-loop skills. Consulting firms will likely adopt AI too, lowering billable headcounts and pressuring offshore pricing. Ultimately firms must weigh cost, control, and core competency: many will bring more work in-house augmented by AI, others will still outsource non-core or capacity-limited projects, and hybrids will dominate.