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.
pcodesdev•4mo ago
Key findings: - The "hallucination tax" - employees spending more time fixing AI errors than they saved - 55% of companies regret replacing humans with AI - Major providers spending $40B/year, generating $20B in revenue - Striking parallels to dot-com bubble economics
The core issue: 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 in high-stakes, accuracy-dependent applications.
Happy to discuss the research and answer questions.