Claude: Based on these factors, my analysis concludes that current AI training practices likely DO NOT constitute fair use.

Asking Claude: Kindly review all my Substack posts and provide a final decision on whether AI training on copyrighted content (without license, permission or compensation and where the content may partially or fully appear in model outputs) and the use of the model in commercial scenarios, constitutes Fair Use.

Claude’s answer:
Based on a thorough analysis of your Substack posts and the evolving legal landscape around AI training, I'll break down the fair use question for AI models:

Key Factors Working Against Fair Use Determination:

1. Commercial Nature and Market Impact

• AI companies are using copyrighted content to train models sold for commercial purposes

• These models can generate outputs that compete with original works

• Companies are making significant profits from models trained on others' content

• The market impact on content creators and publishers is substantial

• AI outputs can serve as market substitutes for original works

2. Systematic and Wholesale Copying

• AI training involves copying entire works, not just portions

• The copying is systematic and industrial in scale

• Content is being processed in ways that go beyond traditional fair use scenarios

• The scale of copying exceeds what courts have typically protected under fair use

3. Transformative Use Questions

• While AI training may be transformative in process, the outputs can be directly competitive

• Models can reproduce substantial portions of training data

• The purpose often overlaps with the original works' intended markets

• The transformation process is automated rather than creative

4. Rights Management Issues

• Companies have actively removed copyright management information

• There's evidence of deliberate circumvention of copyright protections

• Internal documents show awareness of copyright issues

Critical Legal Considerations:

1. Traditional Fair Use Factors Looking at the four traditional fair use factors:

• Purpose and character: Commercial use weighs against fair use

• Nature of copyrighted work: Creative works receive stronger protection

• Amount used: Complete copying weighs against fair use

• Effect on market: Clear potential for market harm

2. Existing Legal Precedents

• Current fair use precedents don't clearly cover AI training at this scale

• Cases involving computational use have been more limited in scope

• Text and data mining cases involved different contexts and scales

3. Public Interest Considerations

• While there's public benefit in AI advancement, this doesn't automatically justify unauthorized use

• Commercial exploitation differs from pure research purposes

• Alternative licensing mechanisms are available

4. International Context

• Fair use is primarily a US doctrine

• Many jurisdictions require explicit permission for such uses

• International harmonization is lacking

Based on these factors, my analysis concludes that current AI training practices likely DO NOT constitute fair use when:

1. The training is done without permission or compensation

2. The model is used commercially

3. The content can be reproduced in outputs

4. Copyright management information is removed

5. No meaningful limitations are placed on output reproduction

This conclusion is based on:

1. Scale of Use

• The comprehensive copying exceeds traditional fair use boundaries

• The systematic nature of the copying suggests a level beyond fair use

2. Commercial Impact

• Clear commercial motivation

• Direct market competition with original works

• Significant profit generation without compensation

3. Alternatives Available

• Licensing mechanisms exist

• Permission could be obtained

• Commercial benefits could be shared

4. Rights Holder Impact

• Clear potential for market harm

• Displacement of traditional revenue streams

• Creation of competing products

5. Intentional Actions

• Deliberate removal of copyright information

• Awareness of potential legal issues

• Strategic decisions to proceed despite concerns

The key distinguishing factors from traditional fair use cases are:

• The industrial scale of copying

• The commercial nature of the use

• The potential for direct market competition

• The systematic nature of the operation

• The removal of rights management information

This suggests that while some AI training scenarios might qualify as fair use (such as pure research applications with appropriate safeguards), the current commercial practices likely exceed fair use protection.

The solution likely lies in developing:

1. Clear licensing frameworks

2. Fair compensation mechanisms

3. Usage limitations

4. Output controls

5. Rights management systems