Assessing AI tool value is challenging due to flawed metrics like lines of code or developer surveys. Historically, automation hasn't eliminated professions; instead, it has transformed job roles, making forecasting AI's impact on work extremely difficult. While closed AI models currently lead innovation, open models are rapidly catching up, with capability gaps shrinking quickly. A significant issue with AI is hallucinated citations, which can poison the internet's knowledge pool with misinformation. Despite concerns about AI-generated code being used for attacks, it's also proving effective for defending software by finding bugs preemptively. Technical debt in codebases is amplified when AI models use existing code as context, leading to compounded "generative debt." The internet is increasingly filled with AI-generated content, creating a "Zombie Internet" where discerning human interaction from AI is problematic. Spawning AI agents can feel like parallel processing, but human attention remains the critical serial resource that cannot be duplicated. While AI tools have reduced the cost of building solutions, organizational alignment and coordination have become new bottlenecks. Productivity gains from AI are often captured as increased output volume rather than improved quality, eroding time for crucial strategic thinking and mentoring.