Can Blockchain Protect Creators in the Age of GenAI?

19

September

2025

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Generative AI is revolutionizing creativity, but it also exposes deep cracks in how ownership and intellectual property (IP) are managed. Most models are trained on vast datasets scraped from the internet, raising questions about whether creators gave consent, and how they can be recognized or compensated when their work is reused (Balan et al., 2023).

One promising answer lies in blockchain whose immutable, decentralized ledger, can record provenance, encode usage rights, and automate payments. The DECORAIT project shows how this could work: it allows creators to opt in or opt out of AI training, embeds provenance metadata through the Coalition for Content Provenance and Authenticity, and uses smart contracts to distribute rewards whenever a creator’s content contributes to a synthetic output (Balan et al., 2023).

Blockchain can underpin decentralized data marketplaces, allowing creators to monetize AI training data directly, while NFTs serve as tamper-proof certificates of authenticity and enable secondary royalties (Telles, 2025). Consulting leaders argue that blockchain may become the first mainstream safeguard against GenAI-driven IP risks. Encoding corporate knowledge assets (such as contracts, white papers, and presentations) into NFTs with embedded permissions, organizations could control how AI systems access and reuse their data (KPMG, 2023). Such mechanisms not only create new revenue models and reduce reliance on intermediaries but could also lower the risk of costly lawsuits, like the Getty Images case against Stability AI over alleged copyright misuse.

While challenges like blockchain scalability or regulatory uncertainty remain, the direction is clear. As GenAI blurs the boundary between human and machine creativity, blockchain provides a foundation for consent, attribution, and compensation which ensures that creators remain empowered in the AI economy.

Balan, K., Black, A., Jenni, S., Gilbert, A., Parsons, A., & Collomosse, J. (2023, September 25). DECORAIT — DECentralized Opt-in/out Registry for AI Training. arXiv.org. https://arxiv.org/abs/2309.14400v1

Blockchain and generative AI: A perfect pairing? (2023). KPMG. https://kpmg.com/us/en/articles/2023/blockchain-artificial-intelligence.html

Telles, Y. (2025, March 6). Generative AI and blockchain. Ventionteams. Retrieved September 19, 2025, from https://ventionteams.com/blog/generative-ai-blockchain

Popple, L. (2025, July 29). Getty Images v Stability AI: where are we after the trial – copyright? Taylor Wessinghttps://www.taylorwessing.com/en/insights-and-events/insights/2025/07/getty-v-stability

How it works – Content Authenticity Initiative. (n.d.). https://contentauthenticity.org/how-it-works/

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Generative AI – friend or foe?

9

October

2024

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Tools like GitHub Copilot assisting you with coding, ChatGPT serving as a tutor or DALL-E boosting creative projects (actually the pictures for this blog post were created using AI) can often be invaluable. Even though I have some experience using Gen AI tools, I would not call myself an expert as there are still many useful ways of implementing this technology that I have not yet discovered. Gen AI often proved to be a great brainstorming partner or a companion helping explore uncharted territories of new information whether at work or university. However, there are significant challenges I came across, with intellectual property (IP) and accuracy.

Gen AI is an amazing tool if used wisely. Nonetheless, I came across a fair deal of issues when implementing Gen AI into my tasks. The biggest difficulty I had was dealing with intellectual property such as legacy code. Do not get me wrong, Gen AI proves to be a great aid with most coding assignments, however, there are points at which it is close to useless. Developing code that is working based on legacy code is one of these examples. This is one of the biggest concerns in adopting AI at businesses, the concerns about intellectual property. In a case like this one cannot just simply copy and paste the legacy code into an AI assistant due to the obvious breach of confidentiality. In addition, AI models can reuse said code for other users increasing the number of questions around sharing IP with these models. 

This image was created using ChatGPT.
This image was created using ChatGPT.

I also sourced Gen AI for help with university tasks. I often asked ChatGPT to help explain a difficult topic or to help work on new ideas, however, oftentimes I run into issues when asking Chat to provide sources for the information it’s giving me. I thought it would be a clever workaround to fact-check the information provided by the model, however, it turns out that these models have a tendency to fabricate citations. I was a bit shellshocked when I found out but again this was a lesson on the need to be incredibly cautious and aware while using the assistance of AI. 

In conclusion, GenAI is a force changing the world at a rapid pace and it would be unwise to not use it. However, the limitations and reliability issues must be kept in mind. I do believe that this situation will only improve, and I am looking forward to broadening the range of AI products I am familiar with but for now, I will stick to using AI as a supplementary tool to the work I do.

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