Human-AI Intersectionr/artificial

Why Hasn’t AI Made Work Easier?

Monday, March 23, 2026Read original

ai-productivity-paradoxshallow-work-trapdeep-work-declineai-adoption-consequences

“AI users spent 100%+ more time on email/messaging and 9% less time on focused work—we're working faster on the wrong things”

Key takeaways

Large-scale study (164K workers, 180-day tracking) shows AI adoption doubled time spent on email/messaging/chat and increased business software use by 94%, but reduced focused work time by 9%
This represents a 'productivity paradox'—AI accelerates shallow, context-switching work while cannibalizing the deep work that drives actual value creation
Pattern repeats historical technology adoption cycles (email, mobile, video-conferencing) where efficiency tools paradoxically increased busyness without proportional output gains
The methodology is particularly strong: individual tracking before/after AI adoption with control group comparison, eliminating confounding variables
Represents emerging contrarian narrative against uncritical AI adoption—organizations need intentional frameworks to prevent AI from becoming another busyness multiplier

Why this matters for operators: Critical for companies implementing AI tools—need frameworks to prevent shallow work proliferation

I cover AI×GTM intelligence like this every Wednesday.

Human-AI IntersectionThought LeadershipAI WeeklyVictor's pick

now this is truly thought-provking

human-in-the-loop designautomation philosophyintentional friction

Personal Productivity & AI-Augmented WorkTechCrunch AI

Cursor's new coding model is built on Chinese AI company Moonshot AI's Kimi foundation model
This represents a significant supply chain transparency issue in a widely-adopted developer tool
Geopolitical tensions around Chinese AI models create regulatory and compliance risk for enterprises using Cursor

ai-coding-toolscursor-vs-copilotregulatory-impact

Enterprise AIThe Verge AI

Grammarly/Superhuman shipped 'Expert Review' feature that cloned real journalists and experts as AI advisors without permission, triggering class action lawsuit
Company response evolved from email opt-out to killing feature entirely after backlash - demonstrates reactive vs proactive approach to AI ethics
Case illustrates emerging regulatory/legal risk category: AI companies using real people's professional identities and expertise as training data or product features without consent

ai-policyregulatory-impactai-ethics-backlash

This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.