Honestly, the way DATs combine usage, ownership, and revenue sharing might just be what AI data was missing. Anyone else thinking of building around this?
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It’s a powerful idea, no doubtbut we need to interrogate it before rushing to build.
Yes, DATs promise usage-based revenue and ownership, but there are some non-trivial challenges baked into this vision:
- Quality Control vs. Open Contribution
If anyone can contribute data and expect compensation, what safeguards exist to prevent low-quality, adversarial, or even toxic datasets from flooding the network? Decentralized doesn’t mean unfiltered. - Attribution is a Nightmare
How do we accurately track which datasets directly influenced a model’s output especially in multi-source training scenarios? Without granular attribution, revenue-sharing quickly becomes speculative or unfair. - Gaming the System
Tokenized rewards always open the door to manipulation. Think botnets uploading synthetic data or feedback loops designed solely to trigger payout thresholds. This needs aggressive prevention design. - Legal & Ethical Landmines
If a user uploads copyrighted or sensitive data, who’s liable? The protocol? The contributors? The downstream AI project? Decentralization doesn’t erase legal responsibility. - Revenue Distribution Models Are Still Vague
How exactly will revenue be split between thousands of contributors? Based on access? Utility? Frequency of use? These formulas will need to be not only transparent but also defensible under scrutiny.h
So yes DATs might offer a new design space, but turning them into a reliable, abuse-resistant, and value-aligned mechanism for AI is far from solved.
Builders should proceed but with eyes wide open.