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AI needs data. Venture capital is being deployed into data buying

A few weeks ago I read about a company in NYC that will clean your home for free. What’s the catch? The person cleaning is wearing data and wearables collecting as much training data as possible to likely sell this data and insights to humanoids that will eventually clean your home.

Now there are companies that want to buy all the internal data your company produces. If you are a services firm or a company with over 30 employees using Slack and JIRA and doing manual human tasks, that data is valuable. Micro1 is offering from $100,000 to as much as $2,000,000 for this type of data. Why? They are taking this data and then selling it into data labs that are training on this information. Make the LLMs smarter, package the data for humanoids, and combine the data with dozens of other datasets. Who is going to sell to them? Likely companies are looking for new revenue streams. Companies that are going out of business I would imagine. Companies that are struggling with growth or have been around for years with steady income, but would love to pick up a few hundred grand or a million+ for access to their data.

We see in every new technology boom and every cycle, VC money plowing into things that aren’t likely sustainable, but at the same time are necessary to advance and move faster. Do I think this data is worth up to $2m? Maybe. For the right data, for the right project, I imagine it will lead the buyer to multiples of income on what they spend. Finding the data sellers is likely hard. But who knows, I can think of a dozen companies that would likely be interested.

You already have a take on which AI lab ships next.

Claude or Gemini? OpenAI or Anthropic? GPT-7 before year-end or not? If you read tech newsletters, you've already formed opinions on all of it.

Kalshi has real-money markets on which AI model leads benchmarks this week, which lab ships AGI first, when Anthropic releases Mythos, whether OpenAI raises ChatGPT pricing, and which company has the best coding model at year-end. These aren't abstract questions — they're live markets with real money on both sides, moving as labs ship, benchmarks drop, and announcements land.

The edge belongs to whoever actually follows this space. Not the casual observer — the person who reads model cards, tracks evals, and notices when a new release outperforms the field before the mainstream press catches up.

That person has a genuine edge. If that's you, Kalshi lets you act on it.

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