Is ‘Decentralized Data Contributor’ the Next Big Role in the AI Economy?

The rapid pace of AI advancement is truly a sight to behold. But what’s the last time you thought about where the data that is fueling this evolution is coming from?

Artificial intelligence is data-hungry, and despite tons of publicly available sources, shadow datasets from brokers, and corporate data, the threat of a shortage is looming. Why?

High-quality data is a finite asset, and it’s only a matter of time until this precious resource runs dry.

That is, unless decentralized data contributors become more mainstream. These individuals could become essential quite soon if AI development continues on its established trajectory. Yet, they’re still a niche group.

What Decentralized Data Contributors Do

Decentralized data contributors are individuals who share device or sensor data with decentralized networks in exchange for rewards.

This is a stark contrast to traditional data sharing. We already share extraordinary amounts of information with centralized companies like Google, OpenAI, or Meta, but the difference is that no one explicitly opts into this practice.

Moreover, with the centralized approach, users aren’t compensated for the data itself. The company pretty much takes full control of the information it sources and is free to profit from it by selling it to third parties, using it for targeted ads, or feeding AI models.

Decentralized data collectors, in addition to opting in to share their data with a particular network, also retain more control and benefit from a privacy-by-design approach, where everything they contribute is shared in anonymized form.

The Dire Need For Decentralized Contributors

Training AI models requires real-world, high-quality, and diverse data. The problem is that the astronomical demand is slowly outpacing the available sources. Take public datasets as an example. Not only is this data overused, but it’s often restricted to avoid privacy or legal concerns.

There’s also a huge issue with geographic or spatial data gaps where the information is incomplete regarding specific regions, which can and will lead to inaccuracies or biases with AI models.

Decentralized contributors can help bust these challenges.

For starters, everyone can participate, which will lead to wider behavioral and geographic limitations. Additionally, decentralized data collection ensures resilience and redundancy in data pipelines. Since distributed contributors provide the data (no central source), even if one of the cogs fails, the data will continue flowing through the pipeline with no interruptions.

It’s also worth mentioning that moving toward decentralization in AI drastically reduces the risk of monopolies by large corporations. This not only provides smaller organizations a fighting chance in the AI race, but could also actively elevate the rate of innovation.

Real-World Examples

The landscape of decentralized data collection is ripe and ready to explode onto the scene, since there are plenty of companies allowing users to contribute to the development of AI as we speak.

A good example is OORT DataHub, a data platform by OORT, the data cloud for decentralized AI. OORT lowers the barriers of entry by allowing users to contribute data through a simple app. Completing tasks like photographing everyday objects for inclusion in high-quality datasets earns rewards. It’s a practice that has proven effective, considering that one of OORT’s community-sourced datasets even managed to hit the number one spot in multiple Kaggle categories.

With OORT, user-friendliness is the name of the game. This applies to the frictionless approach, as well as the privacy-first architecture that makes sure everything users share stays private, both of which are imperative if decentralized data collection is to hit mainstream.

OORT’s CEO and founder, Dr. Max Li, clarified: “AI development has entered a phase where scraping and harvesting data is no longer enough to support the growth. Real-world data shared by global contributors is one of the key approaches to solve the data shortage.” Dr. Max Li continued: “With community-sourced data, it’s possible not only to solve data shortages, but also to strengthen AI pipelines, diversify datasets, and fill any geographic gaps, ultimately ensuring the future foundation of AI development is as robust as possible.”

Other decentralized projects echo this sentiment, providing users with the opportunity to share different types of data. For instance, WeatherXM is a community-powered weather network where individuals can set up a weather station to share hyper-local data, or they can sell their unused bandwidth for rewards with Grass.

The Question of Privacy

Even though a large part of the world’s population has no problem with passively sharing data when browsing the web, due to the relative infancy of decentralized systems, active data contribution may seem to many like a bridge too far.

Anonymized data isn’t 100% safe. Determined threat actor parties can sometimes re-identify individuals from unnamed datasets. The concern is valid, which is why decentralized projects working in the field must adopt privacy-by-design architectures where privacy is a core part of the system instead of being layered on top after the fact.

Zero-knowledge proofs is another technique that can reduce privacy risks by allowing contributors to prove the validity of the data without exposing any information. For example, demonstrating their identity meets set criteria without divulging anything identifiable.

Along with solid privacy practices, transparency is also important, especially in terms of clearly explaining how user data is used. This extends to the opt-in mechanism that needs to be genuine, allowing users to opt out of the platform at any time.

Will This Become a Real Job Category?

Non-standard digital jobs often start small and evolve into actual roles as the particular industry develops and gains widespread acceptance. Digital content creation, ride-sharing, and e-commerce all started as fads until they led to full-time occupations.

In a sense, decentralized data contributors are set up for success. Data is in such high demand, and individuals filling this role don’t need to rely on a middleman and can monetize the data directly.

At the moment, decentralized data solutions offer long-tail participation. Put differently, the entire community collectively generates massive datasets with individuals providing a piece of data. Through such a model, individuals receive micro-earnings for their effort, meaning that the low effort combined with the segmented collection is more conducive to a strong source of passive income.

While it’s an amazing side gig, only time (and the further developments) will tell if digital data contributors will become a viable full-time occupation.

A Sleeper Role in AI’s Future

Without enough data, the development of AI will be in the doldrums. This is exactly why the people feeding AI algorithms are an integral piece of this increasingly more complex puzzle.

Along with supporting further growth, the new decentralized model can also democratize AI development and infrastructures, which is more than welcome, as large corporations are already disproportionately represented.

The decentralized AI field is still new, so it’s worth jumping in early. The micro-earnings will add up over time, and the financial benefits could become more substantial as decentralized AI platforms mature in the near future. Whatever happens, you certainly don’t want to miss it.