Problem

The current system governing our data is fundamentally flawed. Individuals, the very source of this valuable resource, have little to no control over how it's collected, used, or profited from. They lack the tools to reclaim ownership and participate in its monetization. Businesses, on the other hand, grapple with the high cost and often poor quality of data obtained through traditional methods. They face data silos, sluggish intermediaries, and wasted resources due to fraudulent activity. Developers spend a significant portion of their time wrangling messy datasets, hindering innovation. Even tech entrepreneurs struggle to connect with their user base and reward them for their contributions.

This intricate problem demands a revolutionary solution. Fortunately, advancements in Web 3 and decentralized technologies present a path forward. By leveraging smart contracts, micropayments, and trustless governance, we can usher in an "Internet of Ownership." Here, individuals have the power to monetize their data, while businesses gain access to a reliable and transparent source of information.

The Data Desert: A Barrier to AI Progress and Innovation

The field of Artificial Intelligence (AI) is experiencing explosive growth, but it's facing a hidden roadblock: a data desert. AI companies traditionally rely on vast troves of internet information to train their powerful models. However, as these models become ever more sophisticated, their hunger for data intensifies.

The current internet landscape, once thought boundless, is proving finite. Companies like OpenAI and Google are confronting a stark reality: high-quality data is becoming increasingly scarce. The sheer volume of information needed is staggering. Consider OpenAI's GPT-4 model, trained on a staggering 12 million tokens (roughly 9 million words). To maintain this growth trajectory, its GPT-5 successor would require a mind-boggling 60 to 100 trillion tokens (45 to 75 trillion words) – and that's even after exhausting all readily available high-quality internet data!

This data scarcity poses a significant hurdle for AI development. Companies are actively seeking alternative data sources to quench their models' thirst and ensure continued progress in the field. Despite obtaining the data at last, they encounter two persistent challenges. Firstly, there's the issue of quality; not all internet content proves suitable for training LLMs. Filtering out misinformation and poorly written material results in a reduced pool of options for companies. Secondly, there's the ethical dimension; scraping internet data raises significant ethical concerns. Companies must carefully balance data accessibility with responsible usage.