A viral debate on Hacker News recently challenged the popular idea that AI models are merely 'next-token predictors.' Commenters highlighted that once you factor in reinforcement learning, post-training, tool integration, and agentic frameworks, treating these models as simple text-completion engines is wildly outdated.
For early-stage founders, shifting this mental model changes how you build. If you treat LLMs as simple text generators, you will end up building thin, easily copied wrappers. But if you design around recursive execution, tool use, and specialized agentic loops, you can turn base models into defensible products that solve complex, multi-step problems.
For backers putting £500 into early rounds, this distinction is a crucial signal. Ignore pitch decks relying on simple prompt engineering, and back the teams building deep integration layers and workflow architecture around these evolving systems.