Fresh perspectives from independent writers around the world.
Language models have mastered the art of sounding authoritative while being fundamentally uncertain. Here's what's really happening inside their black box.

Language models don't just make mistakes—they make them with absolute certainty. Here's what's actually happening inside their neural networks, and why it's harder to fix than you'd think.

Large language models are getting better at sounding certain while being fundamentally wrong. We explored why—and what it means for the future of AI.

A New York attorney discovered his AI assistant fabricated legal precedents. This isn't a minor glitch—it's exposing a fundamental flaw in how we're deploying large language models in high-stakes professions.

Large language models generate confident-sounding falsehoods with alarming frequency. Here's what's actually happening inside the black box when your AI assistant invents facts.

As AI systems scale up, they're developing surprising blind spots. We investigated why bigger models sometimes fail at basic tasks their smaller predecessors handled with ease.

Most AI assistants sound robotic and stiff because they're trained on data, not on how humans actually talk. Here's why personality in AI is harder than it looks.

Despite passing every benchmark, AI systems hilariously botch sarcasm. Here's why this seemingly simple problem exposes the real limits of machine learning.

AI systems aren't broken when they hallucinate—they're doing exactly what we trained them to do. Understanding why machines invent facts reveals uncomfortable truths about how we build intelligence.

As language models grow larger, their tendency to confidently fabricate information is skyrocketing. Here's why scaling up might be scaling up the lies.
