I’ve been following the developments regarding OpenAI’s open weight model, and it looks like they’ve delayed its launch yet again. The reason given is that they need to conduct further safety tests.
It seems there’s a trend where they set a release schedule but then delay it for extra safety assessments. I’m curious about what safety issues they may be addressing and if this is just part of the normal process or if real problems have come up.
Has anyone else been keeping track of these hold-ups? What are your thoughts on their balance between safety testing and releasing the model to developers? I wonder if this caution is typical for AI model launches or if OpenAI is being more careful compared to other firms.
OpenAI’s safety testing has gotten way more intense since GPT-4 dropped. These delays are mostly red team exercises - they’re literally trying to break the model and find ways people could abuse it. Open weights make this super critical because once it’s out there, they can’t control how it gets used. With API models, they can add safeguards on their end. But open weights? Anyone can run it however they want. That’s a completely different risk level that needs way more testing. The safety folks have been demanding these longer testing periods, especially after some sketchy outputs from earlier models. Yeah, it’s annoying for developers, but these delays are probably real technical issues, not just them being overly careful.
yeah, totally! it feels like openAI keeps hyping up releases just to delay for safety checks. who knows, maybe they’re uncovering serious hiccups. but for real, they seem way more careful than other companies out there.
I’ve noticed these delays happening more often across all the big AI companies, not just OpenAI. Safety evals usually check for misuse scenarios, alignment problems, and weird behaviors that pop up when you scale things up. I’ve worked with earlier model releases, and those extra testing rounds always catch edge cases you’d never spot in the first pass. Yeah, it’s annoying when you’re waiting to integrate these models, but the longer eval periods definitely lead to more stable releases. The whole field seems to be prioritizing thorough safety checks over quick deployments now.