HUD launches DataVendor data marketplace, where AI labs can buy private data at listed prices
HUD releases the public beta of DataVendor, an online marketplace that lets companies sell private data to AI labs; labs can browse data, view automated quality results, try samples, and purchase at listed prices. The marketplace does not include or price public code repositories, because frontier models have already been trained on that code; buyers can see quality results and a desensitized file tree, and can randomly run 1 task from the task set within their own HUD workspace. Previously, labs procuring private data for RL environments typically had to go through intermediaries, NDAs, and weeks of testing.
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The original text explains the shift in the data trading process from private intermediaries to public listings, allowing readers to understand a new channel for procuring RL training data.
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View original postWe're announcing the public beta of https://t.co/YgMb6QF79k We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data. Data is the fundamental bottleneck for AI progress, and DataVendor will be the infra that solves it.
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We're announcing the public beta of https://t.co/YgMb6QF79k We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data. Data is the fundamental bottleneck for AI progress, and DataVendor will be the infra that solves it.
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RL engineers, you’ll want this one.
AI labs used to buy private training data for RL environments through brokers, NDAs and weeks of testing.
HUD just released DataVendor, an online marketplace where companies sell their private data to AI labs for training AI models. Here a lab browses, checks automated quality results, tries a sample and buys at a listed price.
The marketplace won’t list or price public repositories, since frontier models have already trained on that code. Buyers see quality results and redacted file trees, and can run 1 random task from a taskset inside their own HUD workspace.
Another reminder that in the AI economy, proprietary data is one of the most valuable assets there is.