This clause establishes the operational scope of data usage for model development purposes and creates a mechanism by which users can control participation in training activities. The provision distinguishes between default data usage practices and user-elected opt-out procedures.
OpenAI
· OpenAI Privacy Policy
The clause establishes a default data usage practice where user content contributes to model development operations, with an opt-out mechanism available rather than an opt-in requirement. This structure makes content training participation the operational default unless affirmatively disabled.
The clause establishes a data utilization practice that differentiates service tiers: model training on user interactions occurs only for free-tier users who have not exercised an opt-out mechanism. This operational distinction creates separate data handling pathways based on subscription status and user election.
PayPal
· PayPal Privacy Statement
The clause establishes PayPal's operational authority to incorporate user data into AI model development and to apply automated decision systems for security and fraud mitigation functions. This defines the scope of permissible data uses beyond transaction processing and direct service provision.
The clause establishes the operational scope of data use for model development purposes and creates an opt-out mechanism that allows users to restrict a specific category of data processing while other uses of personal data may continue under the terms.
Suno
· Suno Privacy Policy
This means content you create or upload, including music prompts and generated songs, may feed back into Suno's AI training pipeline without requiring your explicit, specific consent, which is a materially different standard than opt-in consent.
The clause establishes a default operational practice where conversation data contributes to model training unless affirmatively disabled, creating a distinct data processing pathway separate from service delivery. This structure places the burden of opting out on users rather than requiring affirmative consent to training use.
This authorization establishes the operational basis for incorporating user search activity and interaction patterns into the company's model development pipeline. The provision defines the scope of permitted data uses beyond real-time query processing to include systematic model training and performance iteration.
This clause establishes the default data practice for model training and specifies the mechanism by which users can opt out of this use. The provision clarifies that conversation data serves a dual function: both to provide the service and to improve Google's AI infrastructure.
Users engaging in potentially personal or sensitive conversations with AI characters may not fully appreciate that their messages and voice inputs can become training material for commercial AI models.
The clause establishes a differentiated data practice based on customer tier: standard users' code may be retained for model training purposes, while Teams and Enterprise customers receive default non-retention of code post-processing, with retention available only through affirmative configuration. This structure creates distinct operational scopes for model development across customer segments.
OpenAI
· OpenAI Privacy Policy
This provision is operationally significant because it means that conversational inputs, which may include personal, professional, or sensitive information, may be incorporated into AI model training unless the user actively disables the setting.
The provision establishes that conversation data serves multiple institutional purposes—product improvement, service development, and AI model training—and clarifies that the standard privacy control mechanism (disabling activity logging) does not restrict this particular data usage practice.
This provision establishes that conversational input submitted by users during ordinary platform use may be incorporated into AI model training workflows. The opt-out mechanism's operational scope, accessibility, and technical implementation are material to compliance under GDPR and CCPA, particularly regarding whether opt-out requests are honored prospectively or also retroactively.
Zoom
· Zoom Privacy Statement
This clause establishes a consent requirement for a specific data use practice, creating an operational framework where AI model training using customer communication content requires affirmative customer authorization rather than occurring by default.
This provision means that even users who opt out of training cannot fully prevent their conversation data from being used in AI model development under certain circumstances, which has implications for personal data shared in conversations.
The clause establishes the operational basis for incorporating user-generated query data into model development processes. This practice affects the scope of authorized data uses beyond providing individual search results.
This clause establishes the operational scope of how user-generated content may be applied within Synthesia's service development and model optimization processes. The provision includes an opt-out mechanism that allows users to restrict this specific use of their uploaded materials.
Midjourney
· Midjourney Data Retention & Privacy FAQ
The policy states that prompts, uploaded images, and generated images may be used for AI model training, and the terms assert a license to use this content for that purpose, which may affect users who submit personal, sensitive, or proprietary material through the platform.
The clause establishes that user-generated content serves as training data for the company's AI model development. This creates an operational practice where conversations and inputs become part of the model improvement pipeline.
The clause establishes that user inputs and outputs become part of the training dataset for model development. This allocation of content rights affects how the service operates and the scope of data used in model improvement cycles.
Replit
· Replit Privacy Policy
The clause establishes a broad grant of rights to Replit permitting incorporation of user content into model development and product improvement workflows. This authorization operates without requiring separate consent for each use instance or content category.
This clause establishes the company's operational authority to incorporate user-submitted content into its model development pipeline. The provision defines the scope of permitted uses for content generated through service interaction and establishes that such use is incorporated as part of the service delivery framework.
This provision establishes a default-on data practice in which user-submitted creative prompts and generated outputs are authorized for use in AI model training; the opt-out mechanism places the procedural burden on users to contact the company rather than providing an in-platform toggle.
This means your queries, including potentially sensitive ones about health, finances, or personal matters, could become part of the data used to build Perplexity's AI models.
This clause establishes the operational basis for incorporating user-generated content into model development workflows. The authorization applies to all information submitted through the service, with data minimization practices specified as a procedural requirement rather than a categorical restriction.
The clause establishes a data use authorization tied to model development and allows users to restrict this use through an opt-out mechanism, creating a procedural choice point for how personal data is processed by the service provider.
Most people do not expect that the details they share in a private conversation could be retained and used as training data; this is especially significant if you have shared sensitive personal, health, financial, or emotional information with the AI.
The provision establishes a dual-consent framework where users may restrict training use through opt-out but retain defined carve-outs that permit training use for safety-related flagging and user-initiated reporting. This structure maintains model improvement capabilities for specified institutional purposes while providing users discretionary control over routine training use.
The provision establishes a default data usage framework for model training while preserving carve-outs for safety-critical and user-initiated use cases. This structure allows the entity to conduct ongoing model improvement while maintaining defined boundaries around safety review and explicitly reported content.