The Unreliability of LLMs and What Lies Ahead

Thoughts on Investing and Starting Up It’s been a big week in AI. Google, OpenAI, and Anthropic all had major releases, and one clear throughline was the push toward increasingly autonomous coding agents. So we figured this was the perfect moment to talk about how unreliable Large Language Models (LLMs) are as a base technology, and what that means for builders trying to work with them. Unreliability is the core bottleneck to unlocking the full power of LLMs. For all the deserved excitement around LLMs, most users still engage with them only occasionally. Daily active use remains comparatively low. You could read that as limited utility or slow dispersion, but we think a major contributor is unreliability. When a system can’t be trusted to work consistently, its real-world utility collapses. It’s no coincidence that the clearest value from LLMs so far has come from code generation, where outputs are not only useful, but highly verifiable. You can run the code, test it, compile it. It ... Read full article.