Organizations across private and public sectors are pursuing AI initiatives with little evidence of success, driven by executives and boards who face career risk for questioning the strategy. The author's team observed zero successful AI projects over 18 months and found that most announced productivity gains are false, with common failures including internal chatbots that nobody uses and customer-facing systems that don't deliver promised results. Employees now face pressure to use AI tools regardless of whether they're appropriate, leading to performative adoption, fabricated metrics, and workers lying about AI usage to keep their jobs.
Developers using LLMs for coding find it simultaneously productive and destabilizing, as the satisfying parts of programming get automated while supervision and review create new fatigue. The constant availability of AI parallelizes task creation but bottlenecks on human judgment, replacing dopamine hits from solving problems with the exhaustion of quality-checking machine output. The core skill of engineering judgment becomes more valuable, not less, but the isolated human-in-the-loop work lacks the collaborative rewards that previously sustained motivation.
AI-native startups are operating with smaller staffs, higher proportions of engineers, and flatter hierarchies compared to traditional companies. These companies employ fewer total workers while maintaining engineering-focused teams where managers also contribute directly to projects. Their organizational models provide examples that larger corporations are studying as they invest in AI and restructure their own operations.
AI chatbots have become more capable but have not seen explosive growth in adoption. Users find that while AI excels at ideation, most real-world problems require actual implementation and validation rather than conceptual work alone. This suggests intelligence alone is insufficient for widespread impact without demonstrated practical utility.
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