We give data to train AI models and get nothing in return

5 days ago 1

I’m less worried about being replaced by AI and more frustrated that companies are stealing our data to train AI models they profit from with potential to make us less valuable over time.

Whether you’re:

- A coder writing clean, reusable functions or internal tooling,

- A UGC creator making tutorials or product demos,

- A data labeller doing precise annotations...

…all of that labor creates intellectual property that ends up training AI models.

But here’s the problem: we don’t own any of it, even though it wouldn’t exist without us.

They take our data—by hook or by crook—train a model, and extract massive value from it, while paying us nothing or, at best, a small one-time fee.

Yes, companies do play a valuable role. But they are using our work to replace us or devalue our work. So we have every right to ask for more.

If you really think about it, data mining is much like mineral mining — just as companies extract valuable resources like gold or diamonds from the earth, often exploiting labor and poorly governed regions, data mining extracts value from a poorly managed pool of people and their data, frequently without their full knowledge or consent regarding how it will be used.

I think now is the right time to build fairer systems around data for everyone—royalties? data unions? open ownership of internal contributions within companies?

This business model isn't new—some data sourcing and collection companies charge not only a one-time fee but also a usage-based fee each time the data is used.

Doing this is not only necessary to make the data supply chain fair, but also to improve AI. We all know that AI performance scales with compute, and the best way to leverage increasing compute is by applying it to new data. So, if we want AI to continue improving, we need a proper data supply chain. And if we want high-quality data for more complex tasks, we must ensure that everyone is paid fairly.

Would love to hear your thoughts on this.

Read Entire Article