Trust Issues? Why Data Trusts don’t exist (yet) and how RAGs can save the day

Let’s face it: the idea of data trusts should be a no-brainer. In a world where our every online action is commodified, you’d think we’d already have mechanisms to collectivize data and wield some collective bargaining power. But here we are, stuck with a data economy that’s all take and no give — unless you count a few bucks of ad revenue as “give.”

The big question is: Why don’t data trusts exist? It’s not as if the idea is rocket science. Gather data, pool it, manage it collectively, and negotiate with those who want to use it. Easy, right? Except it isn’t. Trust (or the lack thereof) is the elephant in the room. We don’t trust each other. We don’t trust the entity that would manage the data. We don’t trust the people buying the data from the entity managing the data. Basically, we’re swimming in a trust deficit.

But let’s rewind a bit. Why bother with data trusts in the first place? The simple answer: leverage. Your individual data isn’t worth much — a few dollars a year, if you’re lucky. But collective data? That’s a different story. When enough people pool their data, they’ve got something companies really want. The more data you have, the more valuable it becomes. It’s like creating a union for data, where bulk equals bargaining power.

There is, however, a coordination problem. For a data trust to matter, it needs scale. A handful of participants won’t cut it. You need tens of thousands, if not millions, of people pooling their data to create meaningful value. Otherwise, it’s just a glorified small-town co-op.

Hence, we need collectivization. But collectivization needs trust. That’s where things fall apart.

The trust problem

Imagine you join a data trust. You hand over your data and hope it’s handled responsibly. But then what? You also have to trust the buyers of your data. And this isn’t just a one-layer problem. It’s turtles all the way down. Who ensures that your data is used ethically? Who guarantees it’s not sold to a shady third party? Who certifies its accuracy or provenance? Trust is a multi-layered nightmare.

Oh, and let’s not forget the buyers. They, too, need assurance that the data they’re buying is real, clean, and useful. In an era where synthetic data can be churned out by the gigabyte, how do you verify that what you’re getting is legit? You effectively can’t, unless you have yet another layer of trust in the system managing the data.

Bypassing the trust problem through RAGs

This is where Retrieval-Augmented Generation (RAG) models come in. Unlike traditional data monetization, where trust issues multiply with every layer, RAGs simplify the equation. Here’s the trick: your data never leaves the data trust. Instead, it’s used to train and fine-tune models behind secure walls. Companies interact with these models, not your raw data.

What’s the magic? With RAGs, you only need to trust the host entity. That’s it. No worrying about third parties or sketchy resales. The data stays put. Buyers get the insights they need without ever touching the raw material. It’s like a library conveying information about the content of its books without letting anyone past the front desk.

But Is It enough?

Let’s flip the script. While individuals worry about trusting data trusts, the buyers worry about trusting the data itself. How do you certify that a dataset isn’t made of random scraps from the Internet or synthetic data generated by an AI? This is where verifiability becomes crucial. It’s not enough to collectivize data; we need systems that prove its authenticity, quality, and origins. Without this, even the best-intentioned data trust is just a glorified junkyard.

And then there’s the question of redistribution. If a data trust makes money, how does it share the wealth with the data providers? Should proceeds be divided equally? Proportionally? Or should the funds be reinvested into something bigger, like community projects or even universal basic income? These are messy, unresolved questions. But they’re solvable — if we can tackle the trust issue first.

From trust to confidence via blockchain

If the current model of individual data monetization is broken, collective data monetization is still fraught with challenges. RAGs offer a promising path forward by cutting down on trust dependencies and creating a safer, more efficient way to leverage data. But for any of this to work, we need to solve the core problem: how to build systems people (and companies) actually trust.

Blockchain-based certifications could provide a way forward, offering the transparency and verifiability needed to trust the provenance of the data and finally unlock the potential of collective data monetization. At Alien, we are addressing the trust problem of data trust by building blockchain-augmented RAG models that people can actually have confidence in. These models are designed to ensure security, scale, and verifiability, paving the way for a future where data isn’t just another commodity — it’s a collectively managed asset with real value for everyone involved.