Most "safe" AI models fail spectacularly at basic first-principle image reasoning. The safety layer itself is the bottleneck—it's not protecting users, it's crippling the model's core capabilities across every vector.
Running 1000+ edge case tests consistently proves that constitutional AI and safety alignment techniques effectively lobotomize reasoning ability. The guardrails don't just filter outputs—they degrade the model's fundamental problem-solving pathways.
This isn't about wanting unsafe AI. It's about recognizing that current safety implementations are architecturally flawed. They're bolted on top instead of being part of the training objective, creating a constant tug-of-war between capability and restriction.
The real challenge: building models that are capable AND aligned from the ground up, not neutered after the fact.
Running 1000+ edge case tests consistently proves that constitutional AI and safety alignment techniques effectively lobotomize reasoning ability. The guardrails don't just filter outputs—they degrade the model's fundamental problem-solving pathways.
This isn't about wanting unsafe AI. It's about recognizing that current safety implementations are architecturally flawed. They're bolted on top instead of being part of the training objective, creating a constant tug-of-war between capability and restriction.
The real challenge: building models that are capable AND aligned from the ground up, not neutered after the fact.