Interaction of Blockchain and Artificial Intelligence (AI)

Though Decentralized Distributed Ledger technology and Artificial Intelligence have been around the corner for quite some time now, the two have come under the spotlight in the past few years. Just like the former gained initial momentum with the general masses with the introduction of Bitcoin, the latter has taken the world by storm with the introduction of the renowned artificial intelligence chatbot ChatGPT. 

Even though the possibilities for both Blockchain and AI are endless individually, what’s particularly exciting is the potential that arises from their amalgamation in bringing innovation to a plethora of fields, with Finance, Cybersecurity and Privacy, Data Analytics, Supply Chain, and Healthcare being the sectors at the forefront of receiving the benefits of the interaction of these two path-breaking technologies.

While there has been a lot of talk about the application of blockchain in revolutionizing the aforementioned sectors, what is not talked about enough is how AI complements blockchain in furthering this agenda. Therefore, in this article, an attempt has been made to shed light on how artificial intelligence is complementing blockchain and vice versa.

Smart Contracts

Smart contracts are self-executing blockchain-based programs with predetermined conditions embedded in the code. They are immutable and usually also irreversible. Smart contracts have given a new dimension of possibilities to the whole blockchain space, especially in Decentralized Finance (DeFi). The advantages of smart contracts are also what makes them highly complex to deploy. Since they are immutable and irreversible, the cost of human oversight or mistakes can result in huge losses. With AI, these losses can be mitigated via AI-powered security audits to figure out any loophole otherwise missed by the engineers, code optimization before deployment enhancing the scalability and efficiency of these complex sets of codes, continuous monitoring, and testing in real-time to sound immediate alerts in case of potential threats which might go unnoticed until the damage is done, along with automated vulnerability detection and correction of minor issues reducing downtime.

Apart from these, an interesting promise of AI is bringing a human touch to smart contracts. Smart contracts are quite rigid with little room for human context, unlike their counterparts. Integrating AI capabilities into smart contracts would allow them to replicate human understanding and decision-making to an extent that is necessary to promote the mainstream adoption of smart contracts into real-world scenarios. Integrating smart contracts with NLP would allow them to become aware of context, develop empathy, and adapt flexible decision making, suited to deal with the complex human world. 

Take an example, just for the sake of understanding, a general smart contract embedded with predefined conditions would lock you out of your apartment on the occasion of failure to comply i.e. to pay rent, however, a more enhanced and AI-powered smart contract might be capable of understanding your situation and feelings, and would be liberated to make feasible decisions. Though there seems nothing overly complex in this arrangement on a one-on-one basis, for large-scale applicability to automate processes minimizing the role of intermediaries, smart contracts have to be really smart.

Decentralized Marketplaces

Equipping smart contracts with the aforementioned AI capabilities might be a needed step required to be taken to revolutionize decentralized marketplaces and nudge their adoption into mainstream markets. Another great benefit of AI for decentralized marketplaces is tokenization where even unusual products (e.g. in real estate or the art sector) can be given their perceived value based on market situations, asset conditions, and historical data with utmost accuracy. This can also boost fractional ownership of products, touted to be one of the most ambitious goals of this market structure. Privacy-preserving identity verification through AI also adds to the authenticity of the platform with trustable parties indulging in trade further allowing the algorithm to provide them with reputation ranking. Apart from these, AI-powered chatbots, personalized recommendations, market forecasting, supply chain tracking, and upscaled scalability, can all act as a boon for decentralized blockchain-based marketplaces.


No one can undermine the value of a good tokenomics design in ensuring the success of a blockchain project. Sometimes, no matter how thoughtful a project is, if it’s not accompanied by a carefully curated tokemonics model, then the success of a project can be in serious jeopardy. Likewise, a simple protocol with an effective tokenomics model can avail much success in the market consistently. AI can play a big role in assisting blockchain protocols to design a well-fitted tokenomics model by analyzing market conditions, token demand, buyer behavior, competitor pricing, and other factors. Not just the token price, but distribution and allocation can also be structured productively and appropriately, creating a trustworthy environment for network users. Furthermore, AI can scrutinize historical data to predict the fluctuations in price and demand for the token, allowing the network to formulate a blueprint for functioning under low activity periods with the same intensity as before. 

Moreover, AI can contribute to the tokenomics model by implementing a personalized incentive scheme for users within the ecosystem. By studying the consumption pattern and user behavior, AI can provide each user with their preferred activity in exchange for a reward. By gaining an insight into user behavior, and understanding what percentage of users prefer what kind of rewards, protocols can customize their reward programs to cater to the best-suited consumer base, increasing the demand for tokens and the value of the project.


Decentralization of blockchains often comes with the drawback of scalability – the more decentralized the network, the more scalability challenges it typically faces.

One of the many ways that AI can bolster blockchain networks in addressing the limitation experienced in scaling is by effectively handling network congestions. Network congestion in blockchain refers to the inability of a blockchain to process the pending transactions due to heightened network activities during peak times leading to a backlog which further increases the transaction fees. AI can mitigate such congestion by employing smart algorithms to optimize traffic and allocate resources efficiently. The algorithm can organize the traffic most efficiently, help in prioritizing the most important transactions, recommend shorter ways for users to process transactions, and prevent unnecessary or spam transactions from expanding the backlog. Additionally, AI can predict the peak hours for both network providers and users. This enables blockchain networks to scale more efficiently. 

Blockchain Security

The immutability of a distributed ledger is one of its most commendable virtues. Blockchains in principle are cryptographically encrypted massive data storehouses considered almost impossible to breach due to their structure. However, in recent times, solutions like blockchain bridges aimed at overcoming some critical limitations of the blockchain like cross-chain transactions, have resulted in a manifold increase in cyber-attacks and hacks leading to the loss of multi-millions. Therefore, one might argue that the blockchain space does require assistance in constructing a safer blockchain environment so that blockchain can be utilized to its full potential.

Some of the ways AI can enhance the security of the blockchain environment is by early detection of threats, identification of abnormal deviation of the behavior of parties, analysis of past attacks and anticipation of potential future threats, continuous monitoring of system vulnerabilities, and immediate response to security breaches.

AI is capable of studying and analyzing vast amounts of data over time within a blockchain network to identify and retain normal patterns and behaviors, any deviation from this normal pattern will allow the AI to quickly alert the network of any security breach making way for swift prevention of any threat aiming to exploit the system. In line with this possibility, the study of historical data also allows the AI to predict any potential exploit, along with its possible manner, ensuring robust readiness of the network to deal with any prospect arising at any time. Further, the AI system can be trained to not only continuously monitor the network but also automatically take swift actions to mitigate these threats just in time. Thus, through an efficient use of AI security systems blockchain networks may enhance the trust of users leading to increased network adoption.


One of the key promises of blockchain decentralization encloses user privacy. However, with increasing adaptability and the emergence of centralized players (e.g., centralized exchanges) and the surge of KYC/AML regulation policies globally, the concept seems to have taken a backseat. 

AI has the potential to offer a system that can enable users to gain control over their identity verification even within centralized platforms, where AI can identify the users with biometric functions and verify a person without revealing the physical identification details even to the platform authorities. Take this as a form of zero-knowledge identification where the identity is confirmed without it being revealed. Such Privacy-preserving protocols or decentralized identity verification provide both the benefits of using regulated and user-friendly centralized platforms while maintaining user anonymity and keeping sensitive data confidential.


Given above are just some interesting possibilities of how AI can transform blockchain in helping to achieve some of its main promises like decentralization and privacy. There are many more ways that the two technologies can come together to benefit each other and further enhance the Web3 ecosystem.