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LinkedIn’s updated generative AI policy has prompted plenty of questions, and for good reason. As of November 3, 2025, the popular social media network began using certain member data to train the generative-AI models that support many of the platform’s features.
For lawyers who rely on LinkedIn to share thought leadership, build visibility, or stay connected to their professional networks, the shift marks a significant change in how the platform interprets and organizes information. By training its systems on the actual activity happening across the network, we believe LinkedIn is positioning itself to deliver a more accurate, more useful, and more meaningful experience for its users.
What Changed on November 3, 2025?
LinkedIn’s updated data-use terms explain the core shift:
“We can use some data from members in these regions to train content-generating AI models that enhance your experience and better connect our members to opportunities.”
— LinkedIn Help, “Update to our Terms and data use” (A8059228)
So what exactly has changed?
A Broader Set of Member Data Use to Train Gen-AI Models
The most significant update is LinkedIn’s expanded use of member data to train its generative-AI models. This includes:
- LinkedIn profile details (job titles, skills, education, location).
- Public posts, comments, articles, and poll responses.
- Activity in Linkedn groups and feedback provided on AI features.
- Job-related data, such as resumes and answers to screening questions (used in aggregate).
If you use LinkedIn’s built-in AI or content-creation tools (for example post-drafting, message suggestions, profile prompts), any data you input or interact with may also feed into those models.
Certain Categories of Data Remain Excluded
Several types of member data are specifically excluded from generative-AI training, including private messages and sensitive data like login credentials and payment information.
Data Sharing with Affiliates
Member data and content can be shared with LinkedIn affiliates (including Microsoft Corporation and subsidiaries) to train their own Gen-AI models and facilitate “more personalized and relevant ads” and professional matches.
Expanded Regional Scope
The new gen-AI policy covers regions that may have previously been excluded or treated differently, including the UK, EU/EEA, Switzerland, Canada, Hong Kong, and other jurisdictions
Data Sharing is the Default
LinkedIn will use your data unless you actively take steps to opt-out. While we believe this new policy will ultimately enhance the LinkedIn experience for all members, should you prefer your data not be used for Gen AI training, you can opt out by navigating to: Settings & Privacy → Data Privacy → Data for Generative AI Improvement. However, be aware that this action only prevents the use of future data. Any data already collected by LinkedIn and integrated into its AI training systems will remain within those systems.
Why LinkedIn Updated Its Gen-AI Policy
So what motivated LinkedIn to make this move?
According to the company, the update is intended to strengthen the generative-AI features that support many of the platform’s tools. Training these models on real professional activity gives LinkedIn the context needed to deliver more accurate suggestions, smarter prompts, and more reliable outputs across the LinkedIn features you use every day.
The new policy will also improve how LinkedIn interprets and organizes professional information. With a broader training dataset, the platform can better understand what content is relevant, how topics relate to one another, and which conversations or opportunities may matter most to individual users.
Finally, the update aligns with LinkedIn’s ongoing investment in AI. As more of the platform is powered by generative models, having access to richer, real-world data helps those systems perform more consistently and at scale.
Putting the LinkedIn Gen-AI Update in Perspective
With this update, LinkedIn is moving toward a platform that will better understand what users share and search for, which should translate into a more meaningful experience for everyone. As the underlying AI models continue to learn from genuine professional activity, many features you rely on will become more intuitive and better aligned with the conversations happening across the network.
