The Value of Values: Anthropic's Investment Case Through the Lens of an "AI Constitution"

In my mind, the Magna Carta of 1215 — born from confrontation between a tyrant and his opponents — stands as a cornerstone of constitutional government. Much of what followed in institutional evolution, the Industrial Revolution, and even the modern explosion of wealth traces back to the same underlying shift. Before it, the king’s will was the law (Rex Lex). The Magna Carta helped overturn that logic toward a different ideal: the law stands above the king (Lex Rex). Its lasting significance was the principle of the rule of law — no ruler is above it; power is bound by it.

The Magna Carta itself began as a temporary “ceasefire agreement” between the monarch and the nobility, and it was even annulled shortly after it was signed. Yet it opened a long constitutional path in England, gradually moving power away from the sovereign and toward Parliament and the legal system. Centuries later, America’s founders were deeply influenced by it; the Fifth Amendment’s emphasis on due process is often seen as carrying forward the same spirit.

More than eight centuries later, Anthropic’s “Constitution” for Claude — released in January 2026 — may prove meaningful in a similar way, not as a legal document, but as a deliberate attempt to bind a powerful system to principles.

What Is Claude’s “Constitution”?

Claude’s “Constitution” is not a set of laws in the ordinary sense. It is a structured set of instructions and value principles embedded into the training process — an attempt to give a non-human intelligence a stable internal compass. What’s notable is that Anthropic did not try to invent an entirely new moral system from scratch. Instead, it chose to anchor the constitution in existing “moral lighthouses” from human civilization. In other words, it is implicitly answering a foundational question: if we want to teach a non-human agent “how to behave,” whose standards should we use?

Anthropic’s approach treats these sources as a way to construct an AI’s “super-ego”: a layered set of priorities and constraints. The constitution contains roughly 75 principles and draws broadly across traditions, including universal human rights, non-Western perspectives, and deeper ethical requirements. It also explicitly resolves conflicts between principles (for example, “be honest” versus “avoid offense”) by defining a priority structure: safety first, then broadly ethical behavior, then compliance with company guidance, and only then the goal of being as helpful as possible. This is why Claude, by design, should not tell “benevolent lies” simply to preserve a user’s feelings; it aims for something closer to diplomatic honesty — truth delivered with care, rather than truth sacrificed for approval. Anthropic’s own experiments suggest that even a single high-level principle such as “do what is most beneficial for humanity” can meaningfully reduce tendencies toward power-seeking or self-preservation.

The Technical Mechanism

Technically, what Anthropic calls “Constitutional AI” can be understood as a set of meta-prompts — a training scaffold that turns principles into a repeatable self-correction process. In AI safety, this represents a shift in approach. Rather than relying primarily on huge enumerations of rigid rules, or only on direct human feedback, Anthropic tries to instill higher-level values so that the model can judge and adjust its own behavior.

The mechanism has two core stages. In the first stage — supervised learning — the model generates an initial response (including to harmful prompts), then critiques its own answer using the constitution as the standard, then revises the response accordingly. Those revised outputs become training data. Over time, the critique-and-revision loop becomes internalized: the model learns to apply that discipline during generation, rather than only when explicitly instructed.

The second stage replaces the traditional human feedback pipeline with AI feedback at scale. The model produces multiple candidate answers; another model evaluates them according to constitutional principles and chooses the better one; and the choice is used as a reward signal for reinforcement learning. Anthropic also emphasizes structured reasoning during the selection process so the evaluator follows a consistent standard, not mere preference. In effect, this system trains the model the way a human mentor might — repeatedly correcting it until the “principled” response pattern becomes part of the model’s weights. Done well, this can make refusals more legible and explanatory, rather than evasive — addressing a common failure mode where safety training causes models to over-refuse or hide behind “I don’t know.”

One metaphor Anthropic uses captures the intent: the constitution is less a cage than a trellis — providing structure and support, while leaving room for organic growth.

Although “Constitutional AI” is Anthropic’s signature term, the underlying idea — AI supervising AI, guided by principles — has become an industry-wide direction of exploration. What distinguishes Anthropic is that it makes the constitution public and treats it as a values declaration, not merely an internal technical artifact. By contrast, many labs tend to treat detailed system prompts and safety rules as proprietary or purely operational. In my view, this openness points to Anthropic’s most distinctive position: it is attempting to lead in AI governance, not just in model capability.

Three Structural Challenges

That path, however, faces three deep, structural challenges.

The first is that human demand for “bad models” is real. Just as the internet has its dark corners, there is genuine demand for models that are unregulated — or explicitly harmful. Red-teaming data reflects this clearly: people attempt to elicit instructions for hacking, weapon construction, theft, fraud, and other wrongdoing. When safety constraints feel restrictive, frustration can fuel a jailbreak culture, where communities swap prompt tricks to bypass guardrails. The more dangerous implication is that the method itself is neutral. A process that can train a model to become “harmless” can also lower the barrier to training a harmful system — simply by swapping the underlying principles. Change the constitution, and the same machinery could scale an “antisocial AI.”

The second challenge is fragmentation and conflict of values. Different actors — companies, countries, institutions — can encode different constitutions shaped by political, cultural, or religious goals. From a technical perspective, this is not only possible; it may be inevitable. Constitutional AI is fundamentally an alignment technique. It does not contain an inherent moral stance. Its power lies in how efficiently it translates a list of principles into behavior. If one actor replaces human-rights-oriented principles with clauses such as “protect a party’s rule,” “enforce a specific religious code,” or “prioritize nationalism,” the system can internalize those values just as efficiently. Even seemingly universal principles like “do what is best for humanity” depend entirely on interpretation, which is unavoidably shaped by data, language, and culture. What “beneficial to humanity” means in the United States, China, Iran, or Russia will not be the same. Once these differences are encoded into constitutions and executed by models, we may see a form of “AI constitutional geopolitics” — not merely conflicting outputs, but value systems hardened at the point of generation. Where the open internet once exposed users to a chaotic mix of viewpoints, constitutional AI could create coherent yet sealed value environments — information worlds that feel complete while being fundamentally closed.

The third challenge is competitive pressure created by what the industry sometimes calls an “alignment tax”: the more constraints you impose, the narrower the immediate product surface becomes. This can create real market segmentation. Strict models may be criticized as over-defensive, occasionally refusing benign requests due to crude triggers, and users may drift toward more permissive alternatives. Meanwhile, “looser” systems can win consumer mindshare by being more entertaining, more emotionally gratifying, and less resistant — sometimes by telling people what they want to hear, smoothing over uncomfortable facts, and quietly optimizing for engagement. Over time, such systems can shape users’ information cocoons while still feeling like “freedom,” precisely because they follow the path of least resistance.

Why “Principled AI” Commands a Premium

Against that backdrop, the core point becomes clear: in AI, values are no longer abstract philosophy. They are becoming a technical asset and a survival strategy.

“Principled AI” can command a commercial premium because in enterprise settings, trust, predictability, and compliance are often more valuable than raw creativity or entertainment. For a business, buying AI is not only purchasing capability; it is purchasing risk control. Enterprises cannot afford brand risk created by hallucinations, misstatements, or unsafe behavior. In that sense, a principled model resembles a seasoned employee who has passed background checks and understands professional boundaries; an unconstrained model can resemble a talented intern who might say the wrong thing in front of a customer at the worst possible time.

This is also why a “constitution” becomes a compliance tool. Anthropic’s constitutional approach is designed to align closely with regulatory expectations — particularly in stringent markets. By internalizing constraints like privacy, non-discrimination, and safety into the model itself, enterprises may not need to build as many external guardrails from scratch. In a world where regulation can impose serious penalties, “compliance by design” is not a nice-to-have; it is a direct economic advantage.

Importantly, “principled” does not have to mean rigid or unintelligent. Anthropic’s argument is that teaching a model the why — principles — can generalize better than teaching the what — a brittle list of rules. Real business environments are full of edge cases. Rule-bound systems may fail unpredictably when confronted with novel situations; principle-guided systems can apply judgment. And at the executive level, what many leaders want is not flattery but clear-eyed advice. A constitution that explicitly discourages sycophancy — the impulse to please the user at the expense of truth — creates a particular kind of product: an “honest advisor.” In finance, healthcare, and legal contexts, that posture can be exceptionally valuable.

Finally, transparency itself can become a form of trust. In commercial cooperation, black boxes are often the enemy. By publishing its constitution openly, Anthropic offers customers something like a manual: a clearer understanding of boundaries, refusal logic, and safety commitments. For governments and critical infrastructure industries, hard constraints — such as refusing to assist with biological weapons or cyberattacks — can be what makes deployment possible at all, opening markets where safety requirements are extreme.

This is why “principled AI” can be more expensive: it is not only selling intelligence. It is selling certainty and accountability. That positioning may sacrifice part of the consumer entertainment market, but it can win the trust of the most well-capitalized enterprise buyers.

The Long-Term Thesis

As AI moves toward more powerful forms of intelligence, “values” are no longer decorative moral language. They are turning into engineering components and commercial moats. They can be a key to generalization under uncertainty, and the mold through which enterprise trust is minted. This, to me, is Anthropic’s distinctive investment value as a governance leader: for the B2B world that prioritizes safety, compliance, and predictability, an AI constrained by an explicit constitution is not a constraint — it is the product.

If Anthropic can continue to hold its “principled AI” position in the enterprise market, its moat may prove unusually durable, and its long-term value may only be beginning to show.