Every major LLM is drinking from the same data trough—Reddit, Wikipedia, Stack Exchange, but the platform owners have begun to catch onto the value of their data, and are making scraping harder and harder.

The result is a shrinking public internet, and a greater proportion of AI slop in what remains. We will not be able to train AGI on the 2025 web. Not only is it too small, the vast amounts of synthetic data skew the distribution of the training set. This will lead to more beige, average answers, and finally to model collapse.

This is the future? A beige slurry of average? Nah.

The real unlock is decentralized data. Not just for privacy, not just for provenance—but also for signal.

To source high-quality, high-entrop data for future training it will be necessary to fine-tune AI models on sovereign, user-owned data vaults.

Models get trained on the weird, the wild, the real. Subcultures. Local languages. Outlier behavior.

These edge cases don’t break the model—they make the model.

What a model knows matters more than how it's built, especially as LLMs commoditize. Data is the new differentiator, and the most valuable data won’t come from the public web—it’ll come from the edges.

Where data is owned, permissioned, and alive.

And here's the kicker—centralized AI models are allergic to messiness. They’re optimized for compliance, not curiosity.

But messiness is where meaning lives. A model trained on DAO governance forums, fringe science subreddits, or voice notes from rural WhatsApp groups understands the world differently. It doesn’t just autocomplete—it contextualizes to produce deeper perspective.

If you're building AI without thinking about where the data comes from, or who controls it, you’re not building intelligence. You’re merely scaling consensus.