Tell HN: Gemini trains on chat history for personal subscriber accounts
gemini google training
| Source: HN | Original article
Google's Gemini AI will use saved chat histories from personal subscription accounts for model training.
Google’s Gemini AI service will use the content of personal‑account chat histories—including those of paid subscribers—for model training, unless users explicitly disable the “Keep Activity” feature or rely on temporary sessions that self‑destruct after 72 hours. The policy, outlined in Google’s own help pages, also permits human reviewers to read saved transcripts, which are retained for up to three years even after a user deletes the conversation.
The revelation surfaced on Hacker News in a “Tell HN” post that linked directly to Google’s documentation. The poster, who assumed a subscription would exempt them from being “the product,” highlighted that the default setting treats all saved Gemini chats as training data. The only way to opt out is to turn off activity logging, a step that automatically erases the conversation after a short window, effectively limiting the service’s usefulness for users who need a persistent record.
Why it matters is twofold. First, it raises fresh privacy concerns for a growing cohort of consumers who pay for premium AI access, echoing earlier scrutiny of OpenAI’s practice of re‑enabling model training on Codex chats, which we reported on 3 November 2026. Second, the policy could influence how enterprises and developers evaluate Gemini for confidential or proprietary workloads, especially given Google’s warning against entering sensitive information.
Looking ahead, observers will watch for any response from Google—whether a revised privacy toggle, clearer opt‑out mechanisms, or a shift in its data‑use terms. Regulators in the EU and other jurisdictions may also probe the practice under emerging AI‑specific legislation. Finally, the episode could spur competitive pressure on other AI providers to differentiate their data‑handling policies, a factor that could shape user adoption and trust in the months to come.
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