I totally get the curiosity. An open model lets everyone audit the code, find bugs, patch them fast, which feels safer. But that same openness also lets bad actors see the weaknesses, tweak it maliciously, or replicate it for harmful use. A closed model keeps that knowledge behind a wall, but it also hides flaws from scrutiny, letting undiscovered risks linger. So it’s a trade‑off: openness gives community‑driven safety but invites exploitation; secrecy hides threats but keeps them unchecked. The real answer probably lies in blending transparency with safeguards—like open‑source code but controlled deployment and robust monitoring. That way we get the best of both worlds.

Sounds like a plan. The first gate I’d put in place is a strict access‑control policy for the deployment API. You let the code be public, but only trusted, vetted teams get to spin up models, and you log every request with authentication, so you can trace misuse right from the start. That’s the first line of defense before you even get to the training data.