Can We Prevent AI From Becoming Another Web2-style Monopoly?

For all its promises, AI today is facing a deepening crisis of trust.

The tools we now use daily, from ChatGPT and Midjourney to AI-powered medical assistants and financial copilots, are trained on data collected largely without consent, built behind closed doors, and governed by entities with little transparency and no obligation to share rewards. Trust in these systems is rapidly eroding, and rightly so.

According to Edelman's 2024 Trust Barometer, global trust in AI companies has fallen to 53%, down from 61% just five years ago. In the U.S., it’s plummeted to 35%. A June Reuters survey showed that over half of U.S. respondents don't trust AI-generated news. And yet, the technology barrels forward, embedding itself into everything from legal advice and education to content moderation and healthcare.

The problem isn’t AI itself, but the extractive system behind it.

The real problem

At the heart of the AI trust problem is a missing economic layer that tracks who contributes to these models, who benefits, and how decisions are made.

Traditional AI companies operate on opaque pipelines. Public data is ingested silently, human labor is hidden, and model outcomes are treated as black-box results. Billions of dollars are made off the backs of contributors who never see a cent.

We see the consequences with lawsuits against OpenAI and Google for unauthorized training data; the New York Times’ legal battle for scraping content; growing concerns about misinformation and bias; and the unchecked centralization of power in a few AI labs.

Blockchain can do what AI alone can't

AI's rapid ascent has brought immense capabilities, but also glaring gaps in how value is attributed, how decisions are audited, and how contributors are rewarded.

This is where blockchain can act as a true corrective force, with an idea of Payable AI, a new framework that embeds attribution, accountability, and rewards directly into the AI development lifecycle. Whether someone is labeling data, or fine-tuning outputs, they can be recognized and compensated transparently through smart contracts and on-chain proofs.

A key innovation enabling this is Proof of Attribution, a method that verifiably traces each step in a model's evolution back to its source. Every dataset, every tweak, every contribution becomes part of a transparent, auditable ledger.

Think of it as an open-source version of Scale AI, except instead of fueling proprietary systems for Big Tech, it unlocks public data pipelines where value flows to the many, not the few.

Why now

As AI agents grow more autonomous, embed themselves in consumer apps like Telegram (via xAI and TON), and start generating revenue, we must ask: who gets paid?

Currently, there's no equivalent of AWS for data. No SaaS model that enables individuals to upload, attribute, and monetize their contributions to AI. That's the missing layer.

And in this moment of rising economic pressure and growing distrust in tech, the need for infrastructure that bakes in fairness is urgent.

The web3 alternative to closed AI

Companies like Meta pour billions into centralized pipelines, controlling everything from raw data to model deployment.Meta's $15B isn't just a bet on labeling, but also on controlling the entire AI value chain. The future demands systems where value flows back to those who create it.

If we don’t act now, AI will follow the same trajectory as Web2 with a handful of giants extracting disproportionate value while everyone else watches from the sidelines.

AI is no longer experimental, it's powering the systems we rely on daily. And yet, the foundational layers remain closed, biased, and opaque.

Blockchain can fix that, introducing verifiability, traceability and built-in economic fairness AI systems inherently lack.

The next phase of AI will be about credibility, and that starts by building trust into the infrastructure of intelligence itself.