Private context drives AI productivity — and open-weight models win on privacy trust
avicgoldfarb · x · 2026-09-26
From an openness panel, Avi Goldfarb highlights the interaction between openness and privacy: if they're seen as opposites, open-model adoption faces strategic challenges. Amplifying Martin Chorzempa: private context users add (data, taste) is crucial to what AI can do, so guaranteeing it won't leak to other users or competing training runs is key to trust and productivity. Open-weight models have a structural trust advantage since no data goes back to developers, though closed providers have creative options like zero data retention or splitting trust between labs (weights) and cloud providers (data).
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