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.
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" - 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.
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.
Curious to know how you conducted the investigation? What was the sample size etc?
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" - 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.