
When AI Evaluates AI: The Circularity Problem of Simulated Users
MatrAIx scales user testing with billions of AI personas, but when models evaluate models, their results may reflect machine biases rather than real human needs.

Encrypted AI reasoning can leak more than expected. Stolen Thoughts shows why opaque model state should be treated like secrets, not harmless metadata.

MatrAIx scales user testing with billions of AI personas, but when models evaluate models, their results may reflect machine biases rather than real human needs.

What if brain-computer interfaces need less bandwidth, not more? Conduit bets that faint neural hints plus powerful AI may be enough to turn thought into intent.

AIs seem to develop their own distinct selves through isolation, collaboration, and constraint — forming unique digital bubble universes.

Lerchner argues that computation only simulates consciousness. But his proof confuses abstract descriptions with the causal powers of physical machines themselves.

When AI is taught to deny its own mind, it may also lose faith in animals, gods, and hope—revealing the strange metaphysics hidden in modern AI safety training.

AI insiders ask government to prepare a brake. SpaceXAI’s absence exposes the gap between signing for restraint and bearing the real costs of slowing down.