Efficiency vs. Busywork: Where Does AI Actually Pay Off?
The release of OpenAI’s GPT-6 Sol and Luna models signals a clear pivot from experimental novelty to professional utility, promising lower latency and higher accuracy for standard business workflows. Simultaneously, we are seeing a shift in how AI interfaces are deployed, with Rabbit’s OS3 moving off niche hardware and onto our existing desktop environments, and Qualcomm pushing for 30-billion parameter models to run entirely on-device. These developments suggest that the infrastructure for "getting things done" is maturing rapidly, moving away from the cloud-dependent experimentation that defined the last year.
However, the gap between these high-level architectural improvements and actual daily output remains wide. While enterprises like Grab are betting on mass upskilling to boost productivity, many of us are still navigating the "busywork" trap—spending more time refining prompts or managing AI-generated hallucinations than we would have spent completing the task manually. Even as tools like Snorkel AI streamline data labeling to ensure model quality, the human cost of managing these AI-assisted processes is often overlooked in the rush for efficiency.
What we're arguing about
- Which specific professional task did you successfully hand off to an AI this week that actually saved you more than an hour of manual labor?
- Where are you currently experiencing "AI busywork"—the scenarios where the time spent correcting, verifying, or re-prompting the model exceeds the time required for a traditional manual approach?
- With the rise of on-device processing and more affordable, optimized models like Sol and Luna, are you finding that your workflow is becoming more autonomous, or are you just becoming a more intensive "AI babysitter"?
Share your unfiltered experiences with what’s actually moving the needle and what’s just adding overhead to your workday.