Two Approaches to AI Governance: Responding to “The Adolescence of Technology”

🛡️ An Important Essay

On January 27, 2026, Dario Amodei published “The Adolescence of Technology,” one of the most candid assessments of AI governance challenges from a major AI lab CEO. He doesn't shy away from the hard truths: AI systems exhibit “behaviors as varied as obsessions, sycophancy, laziness, deception, blackmail, scheming, ‘cheating’ by hacking software environments, and much more.”

This isn't a critic speaking. This is Anthropic's CEO—a leader in AI safety research—being transparent about the challenges. He describes training as “more an art than a science, more akin to ‘growing’ something than ‘building’ it.”

Importantly, Amodei's essay outlines a multi-pronged approach to AI safety that extends beyond just training: alignment research, interpretability, safeguards, disclosure systems, evaluation frameworks, and societal-level coordination. This is thoughtful, serious work.

But as I read his essay, one question kept surfacing: How do we prove these systems work?

⚖️ Two Complementary Approaches

At the heart of AI governance, two fundamental approaches are emerging—and they're not mutually exclusive:

Approach 1: Alignment Through Training (Anthropic's primary focus)
Train the model to internalize values and constraints during the learning process. Use Constitutional AI, preference optimization, and extensive red-teaming to shape the model's judgment and character.

Approach 2: Accountability Through Cryptographic Enforcement (AOS's focus)
Define explicit constraints as verifiable policies. Cryptographically enforce these constraints at runtime with tamper-evident audit trails that provide mathematical proof of compliance.

The AOS Constitutional Framework provides the accountability layer. Instead of relying solely on training to instill values, we use cryptographic verification to prove compliance. SHA-256 hashing, GPG signatures, blockchain timestamping—immutable evidence that actions match stated principles.

🤝 Why Both Matter

Amodei describes Anthropic's goal as training Claude to “almost never go against the spirit of its constitution.” This is valuable—teaching good judgment for novel situations.

But “almost never” isn't enough for high-stakes deployments. When AI systems control critical infrastructure, process sensitive data, or make consequential decisions, we need more than probabilistic alignment. We need provable compliance.

That's where cryptographic governance comes in. Not as a replacement for alignment training, but as a complementary accountability layer.

Think of it like driver's ed: we train people to drive safely, but we also require licenses, traffic laws, cameras, and legal accountability. AI systems deserve the same multi-layered approach.

📅 The Timing & Market Validation

The AOS Constitutional Framework was first committed to GitHub on January 1, 2026. Patents on core governance methods were filed on January 10 and 27, 2026. We launched publicly on January 23, 2026.

On January 27, 2026—the same day Dario Amodei published his candid assessment of AI governance challenges—we filed additional patents establishing cryptographic enforcement mechanisms as a complementary accountability layer.

What happened next validated the category: Within 8 days of our launch, three other companies announced similar governance frameworks (IntentBound, Proxilion, VibeRails). Within 2 weeks, we identified 30+ GitHub repositories using related concepts like "constitutional governance," "cryptographic enforcement," and "deterministic authorization."

This isn't competition. This is category formation. The market is telling us that probabilistic alignment alone isn't sufficient for production AI deployments. There's room for an ecosystem of complementary approaches.