MIT ran a striking experiment: give undergraduate teams four weeks, unlimited access to modern AI tools, and the task of designing, building, and testing a real subscale jet engine that produces 50–100 pounds of thrust on Jet-A fuel.

The results were revealing. AI proved useful for retrieving and summarizing technical knowledge, exploring design alternatives, teaching software tools, and accelerating early analysis. However, it struggled with detailed CAD reliability, physical intuition, and — most critically — the manufacturing and vendor realities that became the true rate-limiting step. Hallucinations and sycophancy further eroded confidence when teams needed precision.

Anthropic researchers have identified a distinct internal workspace inside Claude — referred to as "J-space" — where thoughts appear to form before any tokens or words are generated. Suppressing this space allows basic recall but severely impairs complex, multi-step reasoning, suggesting it plays a fundamental role in cognition.

John Nosta connects this discovery to global workspace theory from cognitive neuroscience, which proposes a shared, flexible mechanism for chaining reasoning, integrating information, and making results available across different parts of a system. The emergence of a similar structure in a non-biological model raises a profound question: Are there universal first principles of thought that arise whenever sufficient complexity and training dynamics are present?