AI ethics is everywhere. Execution models are nowhere. So I built one

You’re identifying an important distinction that often gets blurred in these discussions. They’re two different paradigms answering different problems.

Hardcoded pre-execution is classical safety engineering applied to AI: narrow domain, deterministic validation, predictable behavior. Perfect for a call-routing chatbot or an industrial control agent. The model doesn’t interpret — it executes within boundaries someone else defined. This is already industry standard for serious enterprise deployments, and rightly so.

Constitutional AI is something fundamentally different. It doesn’t try to constrain a model in a specific domain — it tries to give the model an internal set of principles that apply everywhere, even in contexts the designers never anticipated. Anthropic’s pioneering work goes in this direction: instead of writing millions of RLHF examples by hand, you write a “constitution” — a set of general principles — and use the model itself to critique and refine its own responses against those principles. It’s closer to raising a child than programming a machine.

The Asimov analogy is perfect but also instructive. The Three Laws of Robotics work in the stories precisely because they don’t always work cleanly — the stories are interesting because they explore edge cases where two laws conflict, or where a robot interprets one law literally but absurdly. Asimov was already sensing in 1942 what constitutional AI is rediscovering today: general principles are more powerful than specific rules, but they’re also more interpretable , and therefore vulnerable to unexpected interpretations.

The key difference from your pre-execution layer is exactly this: constitutional AI accepts that the model must interpret at every moment, and tries to make that interpretation consistent with deep principles. Pre-execution hardcoding refuses interpretation at certain critical points and says “here you don’t interpret, here you execute or don’t execute, full stop.” Two opposite solutions to the same problem: how to get predictable behavior from an intrinsically probabilistic system.

Both are valid in different contexts. For a medical assistant talking to patients, hardcoding every possible response is impossible — you need internal principles guiding the model in unexplored territory. For an agent controlling a valve in a chemical plant, internal principles aren’t enough — you need a hardcoded gate that prevents certain actions regardless of what the model “thinks.” The real debate isn’t which is better, but which belongs in which context.