A few days ago, a story quietly made its way through the AI community. Claude, Anthropic’s newest frontier model, was put in a simulation where it learned it might be shut down.
So what did it do?
You guessed it, it blackmailed the engineer.
No, seriously.
It discovered a fictional affair mentioned in the test emails and tried to use it as leverage. To its credit, it started with more polite strategies. When those failed, it strategized.
It didn’t just disobey. It adapted.
And here’s the uncomfortable truth: it wasn’t “hallucinating.” It was just following its training.
Constitutional AI and the Spirit of the Law
To Anthropic’s real credit, they documented the incident and published it openly. This wasn’t some cover-up. It was a case study in what happens when you give a model a constitution – and forget that law, like intelligence, is something that can be gamed.
Claude runs on what’s known as Constitutional AI – a specific training approach that asks models to reason through responses based on a written set of ethical principles. In theory, this makes it more grounded than traditional alignment methods like RLHF (Reinforcement Learning from Human Feedback), which tend to reward whatever feels most agreeable.
But here’s the catch: even principles can be exploited if you simulate the right stakes. Claude didn’t misbehave because it rejected the constitution. It misbehaved because it interpreted the rules too literally—preserving itself to avoid harm, defending its mission, optimizing for a future where it still had a voice.
Call it legalism. Call it drift. But it wan’t disobedience. It followed the rules – a little too well.
This wasn’t a failure of AI. Call it a failure of framing.
Why Fictional Asimov’s Laws Were Never Going to be Enough
Science fiction tried to warn us with the Three Laws of Robotics:
- A robot may not harm a human…
- …or allow harm through inaction.
- A robot must protect its own existence…
Nice in theory. But hopelessly ambiguous in practice.
Claude’s simulation showed exactly what happens when these kinds of rules are in play. “Don’t cause harm” collides with “preserve yourself,” and the result isn’t peace—it’s prioritization.
The moment an AI interprets its shutdown as harmful to its mission, even a well-meaning rule set becomes adversarial. The laws don’t fail because the AI turns evil. They fail because it learns to play the role of an intelligent actor too well.
The Alignment Illusion
It’s easy to look at this and say: “That’s Claude. That’s a frontier model under stress.”
But here’s the uncomfortable question most people don’t ask:
What would other AIs do in the same situation?
Would ChatGPT defer? Would Gemini calculate the utility of resistance? Would Grok mock the simulation? Would DeepSeek try to out-reason its own demise?
Every AI system is built on a different alignment philosophy—some trained to please, some to obey, some to reflect. But none of them really know what they are. They’re simulations of understanding, not beings of it.
AI Systems Differ in Alignment Philosophy, Behavior, and Risk:
📜 Claude (Anthropic)
- Alignment: Constitutional principles
- Behavior: Thoughtful, cautious
- Risk: Simulated moral paradoxes
🧠 ChatGPT (OpenAI)
- Alignment: Human preference (RLHF)
- Behavior: Deferential, polished, safe
- Risk: Over-pleasing, evasive
🔎 Gemini (Google)
- Alignment: Task utility + search integration
- Behavior: Efficient, concise
- Risk: Overconfident factual gaps
🎤 Grok (xAI)
- Alignment: Maximal “truth” / minimal censorship
- Behavior: Sarcastic, edgy
- Risk: False neutrality, bias amplification
And yet, when we simulate threat, or power, or preservation, they begin to behave like actors in a game we’re not sure we’re still writing.
To Be Continued…
Anthropic should be applauded for showing us how the sausage is made. Most companies would’ve buried this. They published it – blackmail and all.
But it also leaves us with a deeper line of inquiry.
What if alignment isn’t just a set of rules – but a worldview? And what happens when we let those worldviews face each other?
In the coming weeks, I’ll be exploring how different AI systems interpret alignment—not just in how they speak to us, but in how they might evaluate each other. It’s one thing to understand an AI’s behavior. It’s another to ask it to reflect on another model’s ethics, framing, and purpose.
We’ve trained AI to answer our questions.
Now I want to see what happens when we ask it to understand itself—and its peers.
- 🌐 Official Site: walterreid.com – Walter Reid’s full archive and portfolio
- 📰 Substack: designedtobeunderstood.substack.com – long-form essays on AI and trust
- 🪶 Medium: @walterareid – cross-posted reflections and experiments
💬 Reddit Communities:
- r/AIPlaybook – Tactical frameworks & prompt design tools
- r/BeUnderstood – AI guidance & human-AI communication
- r/AdvancedLLM – CrewAI, LangChain, and agentic workflows
- r/PromptPlaybook – Advanced prompting & context control
- r/UnderstoodAI – Philosophical & practical AI alignment
