We'll be in touch. But first — meet AgentTwin.

For the most critical enterprise workflows, models are already intelligent enough. What's blocking scale isn't intelligence — it's reliability. We went back to first principles and asked a different question: what would it take to build a model that gets the right answer every single time? The answer required rethinking the architecture entirely — and a series of breakthroughs that took years to develop.

Large language models have erased the surface signals that used to tell us when information was wrong — and that's a bigger problem than the hallucinations themselves.

Scaled Cognition co-founder and CTO Dan Klein joined the Gradient Dissent podcast to explain why large language models are plausibility engines, not truth engines — why the hallucinations you catch are only a fraction of the real problem, and why building reliable AI requires rethinking the architecture from the ground up, not layering more models on top of broken ones.