Tagged Anthropic · show all articles
Constitutional AI keeps RLHF's reward-model-and-reinforcement-learning core intact and swaps the source of one training dataset, a document standing in for a person's judgment. That swap is narrower, and its limits more specific, than the name suggests.
A deployed Claude model is a pretrained prior, a post-training pass partly shaped by written principles, a handful of per-request settings, and a documented evaluation gate — four separable things, not one object, and each changes the answer differently.
Two questions, not one, decide how Anthropic and the Claude line look in 2035: does its safety-training method stay on its current path, and does its scaling policy become everyone's floor or stay its own. Four scenarios follow, each with a falsifier.
Anthropic's own dated record — a 2021 founding, a Constitutional AI paper that preceded its first product, a Responsible Scaling Policy that has since restricted a real model's deployment — is more precise than the branding built on top of it.
The consequential differences are in trust boundaries, configuration, state, hooks, subagents, and workflow—not whichever model topped a changing leaderboard this week.
OpenAI, Anthropic, and Google DeepMind are converging on the same bet: the next phase of AI competition will be won by whoever builds the most reliable autonomous agents, not the biggest chatbot.