AI FAILS CONSISTENCY TEST ON CARB COUNTING

The experiment revealed significant inconsistency in AI's ability to perform a straightforward nutritional calculation. Each query produced varying results despite identical inputs, making the system unsuitable for diabetes management or other medical applications where accuracy is essential. The finding highlights a fundamental limitation of current AI models: they generate probabilistic responses rather than deterministic ones. While this approach works well for open-ended tasks like writing or brainstorming, it creates problems for domains requiring consistent, accurate outputs. The test gained traction on Hacker News with 255 comments and 200 points, sparking discussion about AI's role in healthcare. The results suggest users should not rely on AI for precision medical calculations without additional verification systems. This case underscores the need for AI developers to implement consistency checks before deploying systems in health-sensitive contexts.