In Cryptocurrency and Blockchain, AI and machine learning has the potential to bring about a true paradigm shift, making blockchains more secure and more efficient. In this article we will be exploring this paradigm shift, and why machine learning should be at the core of every blockchain.

What makes blockchain an incredibly useful technology is the fact that, every wallet and every transaction is recorded on a public ledger. Every move, no matter how small, is being logged and recorded onto the blockchain. That’s how Applications such as Metamask are able to accurately determine the Gas fee for a transaction. This also creates a sea of information and therefore resource, which currently remains untapped. There are only a handful of companies that provide chain analytics, but even even that use-case itself remains quite limited. There is only so much you can accomplish by analyzing on chain-data, but not be able to take a course of action based on this analysis.

AI as a Blockchain “Ghost in the Shell”

The approach that I’m proposing in this article is to have an AI or deep neural network as an integrated part of the blockchain. In a sense, this could be looked at as an intelligent entity that contains the awareness of the blockchain. This integration would make the chain aware of itself. A self-aware system with a certain degree of agency and the correct permissions will be able to repair, heal and raise its own efficiency in a way that a traditional decentralized network is unable to.

Practically Bulletproof Security

The majority of blockchain exploits and many of the hacks that we have witnessed in 2022 had one thing in common. They rely on the fact that once an entry point has been discovered for an exploit, there is no way that the chain, the validator nodes or anyone else can react quickly enough in order to revert a fraudulent transaction.

This is especially true with some of the cross-bridge hacks that took place in late 2022, such as the Nomad Hack, which was also dubbed as the first crowd looting event on the Blockchain. In essence, people noticed the hack as it started unfolding, and most were just left watching, while others figured out that it’s exceedingly simple to replicate the hack and started siphoning crypto themselves.

Introducing neural networks and ensuring the blockchain is aware of all transactions at all times, would make such exploits a thing of the past. Here’s practical example of how this may work:

The above would still be vulnerable to a 51% attack, as it still relies on a majority to agree on a decision, but it keeps the network decentralized.

The second option would be for the AI to be given agency. Instead of simply reporting to validator nodes, the Intelligent layer of the blockchain would be able to act in the chain’s best interest all by itself. This would be a lot quicker and arguably a lot more secure too but in this paradigm shift we’re no longer talking centralized-decentralized. This is now a self aware organism, tasked with preserving itself and the rules of the chain. Validator nodes will still play an important role as decentralized computing, and will still play a role in decision making, but transaction validation can now be offloaded onto the neural network.

Network Optimization

A blockchain network’s performance can be impacted by several factors, including the number of nodes, the distribution of transactions across the network, and the efficiency of consensus algorithms. These factors can result in bottlenecks that slow down transaction processing times.

An AI can be used to analyze the blockchain network’s performance and identify these bottlenecks. Based on on-chain data, the Neural Network can suggest ways to optimize the network, reducing the time it takes to process transactions. For instance, AI algorithms can analyze the distribution of transactions across the network and suggest ways to balance the load, reducing the burden on any one node.

AI can also be used to predict potential issues before they occur, allowing the network to proactively address them. This can reduce downtime and improve the overall speed of transactions.

Better Consensus Algorithms

Consensus algorithms are crucial for the secure and efficient functioning of any blockchain. They determine how transactions are validated and added to the blockchain. A Neural network can be used to improve the efficiency of these algorithms, which can result in faster transaction speeds.

An AI will be able to dynamically suggest new and improved versions of current consensus algorithms based on the state of the chain. Adding or reducing the number of validations required to process a transaction based on network congestion, or even more advanced practices such as updating the consensus algorithm entirely, once enough on-chain data has been analyzed in order to make a decision.

Predictive Maintentance

In a blockchain network, various components such as nodes, consensus algorithms, and data storage systems need to be maintained and monitored to ensure their smooth functioning. This maintenance can be time-consuming and requires significant resources, leading to downtime and slower transaction speeds. Some blockchains (Solana) are particularly bad at this, with multiple offline maintenances recorded in the past 2 years.

AI can be used to predict potential issues before they occur, allowing the network to proactively address them. For example, our neural network can monitor the performance of nodes and predict when they may fail, allowing the network to take action before the failure occurs.

These predictions can also apply to the consensus layer as well as the transaction layer discussed above. Predictive maintenance not only reduces downtime, but it also helps to improve the overall efficiency of the network, freeing up resources that can be used to process transactions faster. In addition, it helps to reduce the costs associated with maintenance and repairs, making the network more sustainable in the long run.

Closing thoughts

The more autonomy the AI has, the more efficient the chain will become, that’s why it all circles back to the blockchain being aware and capable of acting in its own best interest, based of course on the parameters that are given to it. From that perspective, this represents a true paradigm shift from the current validator-controlled networks, to a new breed of self-aware, self-regulating network. This, of course brings into discussion decentralization and its scope.

Here are some questions to consider, let me know what you think in the comments —

Should the validator nodes be able to influence the AI’s decision making?

If so, how do we ensure that the validators themselves are competent enough to fiddle with the AI’s logic? Is decentralization even relevant anymore in such a paradigm?