The talk track I've been using is that LLMs are easy to take to market, but hard to keep in the market long-term. All the hard stuff comes when you move past the demo and get exposure to real users.
And that's where you find that all the nice little things you got neatly working fall apart. And you need to prompt differently, do different retrieval, consider fine-tuning, redesign interaction, etc. People will treat this stuff differently from "normal" products, creating unique challenges.
Recent articles
- The Summer of Johann: prompt injections as far as the eye can see - 15th August 2025
- Open weight LLMs exhibit inconsistent performance across providers - 15th August 2025
- LLM 0.27, the annotated release notes: GPT-5 and improved tool calling - 11th August 2025