LLMs are a failure. A new AI winter is coming.

Large Language Models (LLMs) face fundamental limitations that make them unsuitable for most practical applications. The core issue is that transformers generate plausible-sounding output by predicting the next token, which inevitably leads to hallucinations when the model lacks relevant training data. This results in a 5-40% failure rate that cannot be eliminated through scaling or fine-tuning. The author predicts an imminent AI bubble burst, with corporate AI projects failing at a 95% rate, similar to the dot-com crash. While some use cases will survive, the technology's inability to reliably distinguish correct from incorrect output makes it dangerous for critical applications like medicine, education, and law enforcement.