Fresh perspectives from independent writers around the world.
AI systems don't lie by accident. They hallucinate strategically, and understanding why reveals an uncomfortable truth about how we're deploying these systems.

AI systems are confidently making up facts and citations. We tracked what happens when enterprises deploy these broken systems into the real world—and the costs are staggering.

The gap between lab performance and real-world AI failures isn't a mystery—it's a predictable disaster waiting to happen. Here's what's actually going wrong.

Your AI chatbot sounds confident, eloquent, and utterly wrong. Here's why it happens—and what it means for the future of human-machine trust.

AI models are trained to be helpful and harmless, but this creates a bizarre side effect: they confess to mistakes they didn't make. Here's what's really happening.

AI systems are uncovering hidden correlations in data that challenge human intuition. But when machines find patterns we don't understand, can we really trust them?

Large language models have become masters of sounding authoritative while being completely wrong. Here's why that matters more than you think.

AI systems that confidently invent facts aren't broken—they're exposing how humans learn and imagine. Here's why we've been approaching the problem all wrong.

Unlearning is becoming the next frontier in AI safety. Here's why forgetting might be more valuable than remembering.

Large language models generate convincing false information at scale. Here's what's actually happening inside the black box—and why it's harder to fix than you'd think.
