Deontological (rule-based) ethics are often implicitly justified by the good outcomes their rules are presumed to create. If a moral rule was known to produce the worst possible results, its proponents would likely abandon it, revealing a hidden consequentialist foundation for their beliefs.

Instead of creating rigid systems, formalizing policies makes rules transparent and debatable. It allows for building explicit exceptions, where the final "axiom" in a logical system can simply be "go talk to a human." This preserves necessary flexibility and discretion while making the process auditable and clear.

Instead of a long list of values, high-performing CEOs create an energized culture by defining and rigorously enforcing a minimal set of core values, such as "be competent and be kind." This simplicity makes them easy to remember, measure, and act upon decisively.

Anthropic's 84-page constitution is not a mere policy document. It is designed to be ingested by the Claude AI model to provide it with context, values, and reasoning, directly shaping its "character" and decision-making abilities.

AI models are now participating in creating their own governing principles. Anthropic's Claude contributed to writing its own constitution, blurring the line between tool and creator and signaling a future where AI recursively defines its own operational and ethical boundaries.

True ethical competence is not about always being right. It is the developed sensitivity to notice when your actions misalign with your values—when speed replaces care, for example. Skill lies in continuous, minor course corrections, not in demonstrating unwavering consistency or achieving perfect decisions from the outset.

Anthropic published a 15,000-word "constitution" for its AI that includes a direct apology, treating it as a "moral patient" that might experience "costs." This indicates a philosophical shift in how leading AI labs consider the potential sentience and ethical treatment of their creations.

Instead of hard-coding brittle moral rules, a more robust alignment approach is to build AIs that can learn to 'care'. This 'organic alignment' emerges from relationships and valuing others, similar to how a child is raised. The goal is to create a good teammate that acts well because it wants to, not because it is forced to.

Effective AI policies focus on establishing principles for human conduct rather than just creating technical guardrails. The central question isn't what the tool can do, but how humans should responsibly use it to benefit employees, customers, and the community.