Stop Chasing AI Efficiency: Where Did You Actually Waste Time?
We are currently witnessing a massive divergence in AI utility. While OpenAI is scaling infrastructure to handle 22 million requests per second for its billion users, the legal landscape is fracturing, exemplified by the New Mexico Supreme Court sanctioning an attorney for filing AI-hallucinated witness testimony. Simultaneously, the industry is grappling with distillation—where firms like Alibaba and Moonshot AI are extracting proprietary knowledge from frontier models—while Anthropic struggles with documenting its own models’ aggressive behaviors.
Despite these high-level maneuvers, the daily reality for professionals remains inconsistent. We are being sold "efficiency," yet many of us spend more time prompt-engineering around limitations or verifying hallucinations than we do on actual output. Whether it is the rise of social engineering threats like 'ClickFix' or the shift toward corporate-curated AI curricula in schools, the time cost of integrating these tools is rarely discussed with the same vigor as their capabilities.
What we're arguing about
- Which specific AI-driven task this week yielded a genuine, measurable reduction in your working hours, and what was the exact nature of the manual labor it replaced?
- Where did you find yourself "over-optimizing" an AI workflow—spending more time refining a prompt or fixing model output than if you had simply completed the task manually from the start?
- Given the increasing risk of hallucinations and security vulnerabilities like those recently reported by Anthropic, how much of your "AI time" is now being redirected toward fact-checking and safety verification rather than creative or analytical output?
Share your specific, real-world experiences below—tell us exactly what worked and where you wasted your time.