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Why do you feel that creating the LLM from scratch is sufficient to trust it? Are you suggesting that you personally would read all 15 trillion tokens (plus every single agentic trade used in RL, along with its relative advantage in the batch) and personally guarantee that gradient descent would train a model which would not exfiltrate your corporate data?

Or that perhaps you have a perfect alignment algorithm which you are unwilling to share with the broader research community (evil)?



> Why do you feel that creating the LLM from scratch is sufficient to trust it

If you're doing the training yourself, you at least have a verifiable supply chain and an audit trail. Today, we have no idea if a black-box model handed to us is coded to recognize specific domains or patterns and back door an application in a sneakily targeted way.

Black box is a black box. Many open-weight models clearly haven't been trained or created in the manner their creators claim---which raises the obvious question: if they lied about the recipe, what else did they lie about? Putting those models in a production capacity scares the living crap out of me.

That said, building from scratch isn't about achieving mathematical perfection or manually auditing 15 trillion tokens---that's impossible. It's about eliminating third-party supply chain risk and having actual governance over the pipeline.

Of course, that doesn't mean we can magically guarantee gradient descent won't produce weird emergent behaviors, or that we can blindly trust OpenAI not to backdoor things. But at least with the latter, you're making a calculated operational decision rather than blindly trusting an opaque black box of entirely unknown provenance.


Why do you think you've prevented third party supply train risk when you're training on a 30+ Terabyte blob that you have little understanding of its contents. It's essentially entirely composed of unfiltered user input.




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