After dealing with yet another AI tool malfunction at work, I investigated why so many companies are quietly regretting their AI investments.
Key findings: - The "hallucination tax" phenomenon where employees spend more time fixing AI errors than they saved - 55% of companies regret replacing humans with AI - Major providers spending $40B/year, generating $20B - Striking parallels to dot-com bubble economics
The core issue is that current LLMs don't know what they don't know - they're prediction engines, not knowledge systems. This works great for creative tasks but fails catastrophically in high-stakes, accuracy-dependent applications.
Happy to discuss the research and answer questions about the economics, technical challenges, or potential solutions.