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.
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Do you mind we mumble ideas together?
With the highlighted problems if solved we could build something truely exemptional. What are your niche?
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Hehehe… sounds good. No particular preference anything cool and unique will do.
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i have thought about building an Ai that would help sports betting. but the problem mostly is the data that would help make this decisions a lil more accurately. there is where i see proper DATs usage and leveraging would coming handy. what do u think?
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That’s a solid use case! Sports betting AI would definitely benefit from DATs, you could access real-time injury reports, fan insights, weather data, social sentiment, and local news that traditional expensive data feeds miss. The tricky part is ensuring data quality since bad info costs real money in betting, plus you’d need lightning-fast verification for time-sensitive updates like last-minute player changes. The financial incentives could also create serious gaming risks with fake reports or manipulated sentiment. But if you can solve the quality control and real-time verification challenges, you’d have access to way more comprehensive data than what’s currently available. What specific data gaps are you seeing that existing sources don’t cover well?
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Totally agree. A lot of current feeds miss that real-time edge like last-minute player moods, coach interviews, or travel delays fans pick up on fast. If DATs can help capture and verify that kind of info at speed, it’s a game changer.
But yeah, filtering noise from real signals will be the real test.
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Is a thought that just popped in, and I don’t see it’ been resolved properly. And now I will like to pursue it taking your key highlights into consideration.
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Exactly, how does it work creating a forum for live updates for sports. Dedicated to sports alone football, soccer, and the likes.
That will be the first edge before solving the betting Ai.
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