AI Platform Terms of Service: Business Use FAQ

The three major AI platforms have distinct terms of service that businesses should carefully compare.

OpenAI (ChatGPT, GPT-4, DALL-E):

• Assigns output ownership to users
• Permits commercial use of outputs
• Uses inputs/outputs for model training by default (can opt out)
• Offers enterprise tier with enhanced privacy and no training on data
• Includes broad liability disclaimers and indemnification requirements

Anthropic (Claude):

• Similar output ownership assignment to users
• Strong emphasis on responsible use policies
• Does not train on customer data by default for API users
• Offers enterprise agreements with custom terms
• More transparent about data handling practices

Google (Gemini):

• Terms have evolved from restrictive to more permissive
• Generally permits commercial use of outputs
• Retains broader rights to use data for service improvement
• Enterprise agreements through Google Cloud offer enhanced protections

Key Differences: Training data policies vary significantly. Enterprise terms differ substantially from consumer terms. Indemnification and liability caps are negotiable for large customers.

Enterprise AI agreements offer significantly different terms than standard consumer or small business terms of service.

Key Enterprise Benefits:

• Data Isolation: Enterprise data is typically segregated and not used for model training
• Custom Security: SOC 2 compliance, SSO, audit logs, and custom data retention policies
• Negotiable Terms: Liability caps, indemnification, SLAs, and termination provisions can be customized
• Dedicated Support: Priority support, dedicated account management, and custom integration assistance
• Compliance Features: HIPAA BAAs, GDPR DPAs, and industry-specific compliance certifications

Negotiating Enterprise Terms:

• Start negotiations early, as enterprise agreements can take months to finalize
• Identify your must-have terms: data training opt-out, liability caps, indemnification, audit rights
• Understand what is negotiable versus standard
• Consider volume commitments in exchange for better terms
• Request SLA guarantees with financial remedies for downtime

Cost Considerations: Enterprise tiers typically require annual commitments of $50,000-$500,000+ depending on provider.

API usage terms contain critical restrictions that affect commercial AI applications.

Common API Restrictions:

• Rate Limits: All providers impose request limits that may affect high-volume applications
• Usage Policies: Prohibited use cases include weapons, illegal activities, harassment, and deception
• Output Restrictions: Some outputs may not be used for certain purposes (e.g., training competing models)
• Attribution Requirements: Some uses may require disclosure of AI involvement

OpenAI API Specific Terms:

• Outputs can be used commercially without attribution (for most use cases)
• Cannot use outputs to train competing AI models
• Must implement content filtering for user-facing applications
• Specific restrictions on political content, medical advice, and legal advice without disclaimers

Anthropic API Specific Terms:

• Strong emphasis on Constitutional AI principles
• Clearer restrictions on high-risk applications
• Explicit prohibitions on autonomous weapons and mass surveillance

Output ownership for business AI applications depends on platform terms, the nature of the output, and applicable law.

Platform Terms on Output Ownership:

• OpenAI: Assigns "all right, title, and interest" in outputs to users, subject to compliance with terms. This is a contractual assignment, not a copyright grant.
• Anthropic: Similarly assigns output rights to users. Clear language that Anthropic claims no ownership of outputs.
• Google: Generally permits user ownership of outputs. Terms have evolved to be more permissive.

Important Caveats:

• Copyright Status: Pure AI outputs may not be copyrightable under U.S. law
• Non-Exclusive: Multiple users may receive identical or similar outputs
• License Back: Some platforms retain rights to use outputs for service improvement
• Third-Party Rights: Outputs may potentially infringe third-party copyrights

Protecting Your Outputs: Add substantial human creative elements to strengthen copyright claims. Use outputs as starting points rather than final products. Consider trade secret protection for proprietary prompts.

AI platforms include extensive liability disclaimers that significantly limit their responsibility for AI outputs and service issues.

Common Liability Provisions:

Warranty Disclaimers:

• Services provided "as is" without warranties of accuracy, reliability, or fitness for purpose
• No guarantee that outputs are error-free, complete, or suitable for any particular use
• Disclaimers of implied warranties to the maximum extent permitted by law

Limitation of Liability:

• Caps on total liability, often limited to fees paid in the preceding 12 months
• Exclusion of consequential, incidental, and indirect damages
• Exclusion of liability for lost profits, data loss, and business interruption

Indemnification:

• Users typically must indemnify platforms for claims arising from user's use
• May include obligation to defend platform against third-party claims
• Some enterprise agreements provide mutual or limited indemnification

Practical Implications: Businesses bear virtually all risk for AI-related harms. Insurance coverage for AI-related liability should be considered.

Content policies significantly constrain how businesses can deploy AI platforms, with violations potentially resulting in account termination.

Universal Prohibitions:

• Illegal Activities: Content facilitating illegal acts, fraud, or harm
• Weapons: Assistance with weapons of mass destruction, illegal weapons
• CSAM: Any child sexual abuse material
• Malware: Creating malicious code or facilitating cyberattacks
• Harassment: Content targeting individuals for harassment or abuse

Platform-Specific Restrictions:

• OpenAI: Detailed use case policies for political content, medical/legal advice, and adult content
• Anthropic: Emphasis on avoiding "Constitutional AI" violations; stronger guardrails on potentially harmful applications
• Google: Integration with Google's broader content policies; specific restrictions on election-related content

Business Application Considerations: Financial services may require additional compliance measures. Healthcare applications generally prohibit AI for medical diagnosis without proper oversight. Legal AI-generated content typically requires attorney review and disclaimers.

Data privacy and security terms are critical for businesses handling sensitive information or operating in regulated industries.

Key Privacy Terms to Evaluate:

Data Processing:

• How is input data processed and stored?
• What data retention periods apply?
• Is data encrypted in transit and at rest?

Model Training:

• Is your data used to train models? (Critical difference between consumer and enterprise tiers)
• Can you opt out of training data use?
• Is opt-out retroactive or prospective only?

Security Standards:

• SOC 2 Type II certification
• ISO 27001 compliance
• Penetration testing and security audits
• Incident response procedures

Regulatory Compliance:

• GDPR: Ensure Data Processing Agreements are available
• HIPAA: Check if Business Associate Agreements are offered
• CCPA: Understand how California privacy rights are handled

Intellectual property provisions in AI platform terms affect both what you contribute and what you receive from the platform.

Input IP Provisions:

• License Grants: You typically grant platforms a license to process your inputs. Scope varies: some are limited to providing the service, others are broader.
• Ownership: You generally retain ownership of your input content. Platforms disclaim acquiring any IP rights through your use.
• Confidentiality: Enterprise terms may include confidentiality protections. Consumer terms typically don't guarantee confidentiality of inputs.

Output IP Provisions:

• Assignment: Platforms typically assign output rights to users (contractual, not copyright grant)
• Non-Exclusivity: Others may generate identical outputs
• Third-Party IP Risks: Platforms disclaim responsibility for outputs infringing third-party IP

Platform IP:

• Model and Technology: Platform retains all rights to underlying AI models and technology
• Feedback: Any feedback you provide may become platform IP

Dispute resolution provisions in AI platform terms significantly affect your legal rights and remedies.

Arbitration Clauses:

• Mandatory Arbitration: Most consumer terms require binding arbitration
• Class Action Waivers: Typically included, limiting collective legal action
• Arbitration Rules: Usually AAA or JAMS rules apply
• Enterprise Exceptions: Enterprise agreements may permit litigation and more favorable dispute resolution

Governing Law and Venue:

• OpenAI: California law, San Francisco courts
• Anthropic: California law, San Francisco courts
• Google: California law (or local law depending on entity)

Terms Changes:

• Unilateral Modification: Platforms can typically change terms with notice
• Continued Use: Using service after changes constitutes acceptance

Protecting Your Interests: Negotiate dispute resolution terms in enterprise agreements. Seek carve-outs for injunctive relief and IP disputes. Document communications and potential disputes contemporaneously.

Businesses must navigate various compliance and regulatory requirements when deploying AI platforms.

Industry-Specific Regulations:

Financial Services:

• SEC and FINRA guidance on AI in investment advice
• Bank regulatory guidance on AI in lending decisions
• Model risk management requirements

Healthcare:

• HIPAA requirements for protected health information
• FDA considerations for AI in medical devices or clinical decision support
• Informed consent considerations

Legal Services:

• State bar ethics rules on AI use in legal practice
• Duty of competence regarding AI tools
• Client confidentiality with cloud-based AI services

Emerging AI-Specific Regulations:

• EU AI Act: Risk-based regulatory framework with transparency requirements
• Colorado AI Act: Requirements for high-risk AI decisions
• California: AI regulations under development

Compliance Best Practices: Conduct AI risk assessments before deployment. Implement human oversight for high-stakes decisions. Document AI governance policies and procedures. Train employees on responsible AI use.