According to reports, AI can complete up to 80 percent of a task with impressive speed, but this creates a trap: the result appears almost finished, yet the final 20 percent is critical and often requires reworking the earlier portion. Developers must understand what the AI has built beyond its surface appearance, as responsibility for the final product lies with them. They need to assess limitations, shortcuts, scalability issues, and security gaps.
Contrary to expectations, AI does not simply reduce workload; it shifts it. Reports indicate that as speed and capability increase, so do complexity and stakeholder expectations. Consultants at Mpya Digital note that AI now produces large parts of code, but the scope of systems developers must consider grows. Many expect instant solutions and are disappointed when the last 20 percent still requires deep craftsmanship. The specific AI models and quantitative productivity gains remain unclear, as does the extent to which these challenges apply across all development domains.