I see Open-weight AI models as a form of digital soft power. When an entity releases their models for free public use, they aren’t doing this operating under an altruistic motive; they’re aiming at shaping global perception: users get to run the latest (or the best for their hardware) model for free, but in return, they accept all the biases embedded in the model. For sure one could argue that the same holds true for search engines and even encyclopaedias, but encyclopaedias don’t hallucinate, the search results usually will point you to many competing analyses on what you’re looking for (otherwise SEO would not have been a thing) and users tend to accept answers easier due to the anthropomorphization.

This makes the open weight models a new strategic battlefied in the soft power arena. You do not have to run any long play PSYOP or even a tourist advertising campaign. You can have the free model say the thing and concentrate your efforts into making the users run the thing on their machine. Running it locally feels more “free” both in cost and dependency.

I foresee a near future with shareware models. The same companies giving us open weight models for free, are going to give us the new ones for a fee (training costs real money) and you will either subscribe to this service to get the latest model, or use the free one that will be two versions older. Which for most cases is going to be okish for the end user. Special purpose models, for writing code for example, will have the single user and the enterprize version, with maybe the enterprize version being fluent in more languages, or being aware of more libraries for your agents to work in an enterprise environment, as opposed to the indie game that you’re trying to push out.

We’ve seen the pattern: open-access allowing for an ecosystem to arise (see Kubernetes as the latest example), proprietary extensions capturing value. But with GenAI models, the issue is greater: The biases aren’t just in code; they’re in how the model serves truth, morality, and reality. Leading you to accept the producer’s perception one token at a time.