I'm outside the AI industry, outside of academia, and no easy contacts into relevant areas. My background lends itself to exploring AI & reasonable level of care checking results. I was focusing on building practical useful things for a portfolio to help change industries mid-career, but that has become something a little different now.

The exploration has led to an analytical framework that appears useful more broadly for looking at neural representational models. (It's not a model architecture or training method, benchmark, or prompt engineering.) It is a way of analyzing internal representations through the mapping of structure-preserving connections, to transport them to a frame of reference outside the model, exposing some stable relationships, while not being model-specific or require training something else.

It has survived attempts at invalidation, generated useful predictions and functioning interventions, and has continued to reveal additional applications the more I do. There could still be errors or misinterpretations and limitations I haven't explored, there's some limitations I know about already, but I do not believe that any new ones found would nullify all of the utility. I'm trying to be careful in not conveying over broad claims I don't intend without endless detail, so I apologize for vagueness.

My difficulty now is in moving forward. The apparent value is not confined to research curiosity, but apart from potential in faster & less cumbersome model analysis and control, improvements, or production workflows, reachability, there are clear dual use considerations, as well with the tangible toolkits and implementations I have. Lack of contact with industry means I don't know all the things I don't know in these considerations, and learning what I can about them along the way has seen some capabilities referenced more often in the area of red teaming and other sensitive work. I'd like more than my own judgment on security concerns that aren't my daily life.

As a result, I don't see straightforward path that doesn't steer hard in the direction of either 1) resigning myself to potentially little or no control or ability to realize value potential for what I've done, if it proves valuable in the ways it seems to be. or 2) Retaining some control and agency at the cost of life disruption, which I don't actually have the resources to support anyway, just to make a solid attempt at the effort to do so.

The questions of open sourcing versus patents or commercialized aren't the ones I'm struggling with. Generally I think all of them have appropriate places, and opinions aren't unwelcome here either, but my difficulty is something else. It's in trying to understand how people who have navigated research commercialization, startups, technology transfer, or frontier-lab recruitment would reason about these tradeoffs before making an irreversible decision, and how from outside of the industry can go about that to begin.