Does Blockchain Need AI?

It’s interesting to see how both blockchain and AI have evolved rapidly in the last decade, moving from theory and rudimentary technologies to integral parts of our daily lives. Both had been described as possible, at least theoretically, decades earlier, but had to wait for technology to catch up in order to really build something useful. And while each industry has mostly stayed in its lane, as more and more use cases for both blockchain and AI develop, it’s unavoidable to ask the question: What could we accomplish by combining them?

Before we can combine them, however, we need to first ask what problems AI and blockchain are designed to solve. For AI, we can use it to make predictions, identify things, make suggestions, and pull insights out of large and messy piles of data. For blockchain we can create decentralized and trustless environments, we can create equal communities of decision makers, we can share data securely and privately, and we can use the power of many individuals to create stronger consensus.

So what can we do when we mix the best of both worlds? It turns out, quite a bit. Web3 has been working hard to develop unique and powerful use cases that result in true innovation, and the use cases are growing every day. Let’s look at some of the key applications of bringing AI on chain.

Data Indexing

Before search engines, the internet was not incredibly useful. However, as web indexing became a central focus with the likes of Google, the internet became the most powerful informational tool in history. Web3 is no different, and thanks to the complexities of blockchain, data indexing has not been easy. However, platforms like The Graph are using AI to collect, index, classify, and serve up data from across Web3. This allows for decentralized queries for dApps, along with many different API applications. AI cannot function without data, and data indexing creates a strong foundation for blockchain platforms to apply AI within their own platforms, making decisions, identifying trends or anomalies, and predicting key events using data-driven models.

Model Deployment

As the data is collected, various Web3 platforms can develop AI models and train them using these massive datasets. Creating AI models that can solve multiple problems is still a technological challenge, but creating basic models that can be re-trained and deployed for various uses is growing in popularity. Platforms like have become leading drivers for models that can be deployed for various reasons. Focusing on the area of digital twins, these models can solve problems such as optimizing transportations systems, create efficient DeFi trading services, and develop models for systems to create autonomous tasks.

Advanced AI Models

One of the strongest uses that leans on what AI does best, combined with what blockchain does best, is creating advanced AI models that leverage decentralized consensus methods. This is what Flare does, and it has already demonstrated with its research that its unique Consensus Learning can outperform traditional AI models. There is a field of AI called ensemble learning, where an AI model is built using a collection of different techniques, then using them all to solve a problem and taking the most accurate answer as an output. While there are different ways to do this, consensus learning vastly widens the use cases by developing this multi-layered AI model over a blockchain architecture. Users each create an AI model to solve a specific task, then use blockchain-style consensus to take the predictions from each model as input, use a method called the Communication Phase to determine the most accurate answer, then provide a much better result than any individual model could. Further, by leaning on blockchain-specific advantages, the training data can be provided to individuals through a smart contract, or they can utilize their own data and so that everyone’s data remains completely private, with only outputs being submitted for consensus. The maximum use of decentralization, security, and data privacy allow this architecture to be used where AI applications have been completely infeasible up to now.

Data Marketplace

The last use case, though it will likely be the most replicated, is that of a data marketplace. As mentioned before, AI lives and dies by the data it uses to train models, and this data has become the much sought-after currency of the AI industry. Blockchain platforms such as Ocean Protocol have developed methods to package up data sets for sale, along with ways for different parties to bring their own data to the marketplace. There are many ways that blockchain can use rewards structures for people who willingly provide their data to be used in these data sets, and these marketplaces can scrub data to ensure anonymity before the data is sold, creating a much better environment for users whose data is currently scraped and sold by data brokers who don’t offer compensation, don’t ask permission, and who often don’t scrub to remove privacy issues.

Looking Ahead

While the applications of blockchain platforms using AI are impressive, this is still the beginning of what we can expect as both industries continue to grow, flourish, and become larger parts of our daily lives. Only time will tell what use cases we might see in a year or two, but the possibilities seem endless.