Data Privacy at Scale: EU Rules, LinkedIn Data Training, and the Open Web Dilemma

LinkedIn will train AI models with data from European users, and the move is fueling a broader debate about privacy, consent, and who gets to train on what data. The conversation now pivots to openness versus gatekeeping in AI data access. ^[1]

LinkedIn says it will train AI models with data from European users. This ties privacy concerns to consent regimes, with an opt-out in the data-for-ai-improvement settings. “You can opt out here,” the post notes, underscoring how consent mechanics are playing out in practice ^[1].

Open Internet vs Gatekeeping — The debate echoes a push toward openness, even as data access remains tightly controlled. In a call for less central choke points, the piece stresses how discovery and data access shape AI progress. The stance highlights the tension between broad access and security, privacy, and governance concerns. Cloudflare weighs in with a provocative line: “Let us host stuff at home.” The argument is for more distributed, self-hosted options to reduce platform-dependent risk ^[2]. The reference cloudflared is cited as a tool to connect private networks to the broader Cloudflare stack, illustrating a path to more user-controlled data access while acknowledging the tradeoffs of security and scale ^[2].

Regulation and consent regimes are steering which data can be used for training, nudging companies to rethink data workflows and business models. The tension between openness and privacy is a live thread in LLM development as EU rules and corporate strategies collide ^[1][2].

Closing thought: the next few years will reveal whether openness can coexist with stronger privacy safeguards as models grow bigger and data flows harder to control.

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