LLMs have their limits.
The underlying technology behind most of the widely available artificial intelligence models is large language models, a form of machine learning and language processing. The bet that most AI companies are making is that LLMs, if fed enough data, will achieve something like full autonomy to think and function in ways similar to humans—but with even more collective knowledge. It turns out, betting on infinite growth might not have great odds of paying off. A new study claims to show mathematical proof that “LLMs are incapable of carrying out computational and agentic tasks beyond a certain complexity.”
The paper, published by father and son researchers Vishal Sikka and Varin Sikka and surfaced recently by Wired after its initial publication flew under the radar, has a pretty simple conclusion, though there’s quite a bit of complicated math to reach it. Distilled as simply as possible, it reasons that certain prompts or tasks provided to an LLM will require a more complex computation than what the model is capable of processing, and when that happens, the model will either fail to complete the requested action or will incorrectly carry out the task.
