"Open weight" only means a model's trained numerical parameters are published; it does not disclose the training process or data and is a separate concept from open-source licensing.
CJ clarifies that 'open weight' is distinct from 'open source' — releasing weights only exposes the billions of trained numbers, not how the model was trained or what data was used. ✦ AI generated
CJ Reynolds · Syntax · 2026-07-20 · original ↗
starts at this moment · 31:00
“Can you speak on this comment? Someone says open weight does not equal open source.”
That at the end of the day results in a file that has billions of numbers inside of it. And so when we say open weight, they mean you can look at the weights. But the thing about neural networks is just looking at the weights doesn't tell you how it was trained or what they did special with it. You just can see the numbers. So that's what we say when we mean open weight.
verbatim transcript · starts at 31:00
30:40training a model is long and intense, right? They have to they have to get all of the training data. They have to run it on GPUs and they run >> they have to distill all of anthropics models. Exactly that. We're going to talk about that next. But yeah, so basically there's this whole big upfront process that they do >> that at the end of the day results in a
31:00file that has billions of numbers inside of it. And so when we say open weight, they mean you can look at the weights. But the thing about neural networks is just looking at the weights doesn't tell you how it was trained or what they did special with it. You just can see the numbers. So that's what we say when we mean o open weight. Um, and then on top
31:18of that, these models also have various licenses like yes, you can run this for profit or you you can only you you can run this model, but you uh can't charge people for it. So yeah, that's a good distinction to make. We can't see into how they trained this thing, but we can see the numbers that the training process produced. Um, so something to think about. >> And like I'm curious like like you
31:40obviously wouldn't be able to take this model and then start charging people or I'm not sure what the license actually is, but like what about like if you were to like slap this into your product that uses AI, you know, there's the rules around that as well. >> Exactly. You'd Yeah, I'm I'm not a lawyer. You'd have to look at their their terms of service and licenses for
31:58each of these models, but that that's the kind of thing. The other cool thing you can do with these openw weightight models is fine-tune them. Uh and so like there's a whole ecosystem of uh taking an openweight model and then further training it on specific types of data to get specific outputs. So >> that's the other cool thing about openw weight is like we could take a a
32:18specific model and then train it really really good on all of the the syntax podcast transcripts and then it would be like and this is as opposed to something like retrieval augmented generation where you just like paste in the transcripts. You could literally fine-tune the weights to know more about how Scott and Wes talk and what topics they talked about in what episode. Um, or if you work at an enterprise, you
32:42could take all of your internal documentation and and everything else and fine-tune a model on that. So instead of having to wait for API calls, you have a model that knows your company uh inside and out, that kind of thing. >> Yeah. >> Remember everybody remember that was what everybody wanted to do. They're like, I can't wait till I can train my own model. Um, and that doesn't seem to