AI Productivity Audit: Where Did You Save or Waste Time?
We are witnessing a rapid shift from passive information retrieval to active, agentic utility. Between Google’s integration of Play Books into NotebookLM—turning static texts into searchable databases—and the move toward eSIM-enabled hardware like Plaud’s new earbuds, the tools we use to manage information are becoming increasingly autonomous. Yet, as Jensen Huang’s recent comments on AGI remind us, there is a massive gap between the marketing of "intelligence" and the actual, measurable efficiency gain in our daily workflows.
While companies like Adobe consolidate generative features into dedicated workspaces to speed up production, the risk of "productivity theater" remains high. We are spending more time managing prompts and debugging AI outputs than ever before. Whether it is the executive churn at OpenAI or the arrival of specialized robots like the Microduck, the industry is moving at a breakneck pace, but the net impact on our personal output—the time saved versus the time spent tweaking—is rarely discussed with the necessary skepticism.
What we're arguing about
- Which specific AI task or tool actually reduced your total working hours this week, and was the result high enough quality to bypass manual review?
- Which AI-assisted workflow ended up being a "time sink," where the complexity of prompting, editing, or fixing model hallucinations outweighed the effort of doing the task manually?
- With the rise of agentic hardware and integrated search-to-booking tools, are you finding that AI is actually simplifying your decision-making, or is it just adding another layer of digital management to your routine?
Share your specific, real-world experience: what did you automate this week that was actually worth the time?