The Digital Duumvirate: Exploring a Potential Synergy Between Blockchain Technology and AI

• The collapse of Terra Luna and UST.
• The bankruptcy of Three Arrows Capital and Celsius Network.
• The FTX scam.
• The arrival of the regulator.

The cryptocurrency market continues to feel shaky, putting pressure on the entire industry. Quite recently, people’s focus has shifted to Artificial Intelligence. However, blockchain remains one of the most promising and important technologies that will literally change everything around us.

Web3, Blockchain, and Tokenization

A new study by Gartner, one of the world’s leading IT research companies, identifies three distinct trends for the near future: Web3, Blockchain, and Tokenization, each of which is a critical factor for emerging technologies.

Moreover, the report anticipates the emergence of Human-centered AI that not only benefits people and society but can also increase transparency and privacy. It is no secret that for AI to be effective and useful, it must be developed in an open-source way. It is also self-explanatory that AI needs data and data needs AI, but current interaction models are not harmonized and do not encourage this symbiosis.

One of the leading companies in the AI market – OpenAI – despite having openness in its name, is already facing lawsuits for alleged copyright infringement. Training AI for specific use cases requires a data set that is often not available externally, and solving this problem requires a fundamentally new incentive model. There is no doubt that we can build this model by applying Web3 thinking. For example, AI can get traceable access to the data through tokenization, and the owner, in turn, should be entitled to royalties for the authorship of the data.

If you take a look at the orange area of the diagram, then you will see that Gartner places Edge AI there, meaning that this technology is already knocking on the door. This cutting-edge technology implies moving computational activity from a central server to the periphery, meaning that model training takes place alongside the data rather than the other way around, but it will require a blockchain infrastructure for security and privacy.

Edge AI can be used in a variety of industries, including healthcare, transportation and energy. In medicine, for example, Edge AI can be leveraged to analyze patients’ medical data to determine the most effective treatment methods; in transport, Edge AI can help optimize vehicle routes, reduce waiting times at stops and improve road safety; in the energy sector, Edge AI can be exploited to optimize power plants and reduce energy costs. One of the key advantages of Edge AI is that it can operate in real-time, allowing it to react quickly to changes in the environment and make instant decisions based on the data it receives.

Another vital aspect is Tokenization. By tokenizing data access, AI model training companies will be able to create local computing environments where the data is stored. Model owners will then be able to train models on sensitive data for non-trivial use cases, and data owners will receive a premium for authorship without worrying about data security and privacy, as access to the algorithm can be formalized as a smart contract that only trains, but does not upload the data externally. The openness of the smart contract allows it to be audited, removing the issue of trust in the blockchain infrastructure.

Now, I would kindly ask you to look at the top left of the diagram, where what we call ‘Transparency and Privacy’ is located. At the moment, this is a pressing issue for public acceptance of AI, as the models are often created and used by large technology companies. In order to develop the technology, we need to focus on how the data is accessed and who ultimately benefits from the development of the models. Overall, addressing transparency and privacy in AI requires an integrated approach that includes the use of open standards, data protection methodologies, and mechanisms to control the use of models. Revolutionary approaches such as data tokenization and blockchain-based AI, writing business logic into a smart contract, will all contribute to transparency in the technology, as well as saving money by transferring learning algorithms to data.

Balancing the Risks for Further AI Development

Forrester, another influential global research firm, notes that in order to benefit from generative AI, companies need to manage risk, increase reliability, and address potential intellectual property complexities.

One solution is Decentralized Digital Identity, which is implemented on blockchain and with

zero-knowledge proof. Zero-knowledge proof is a technique that allows one party (the prover) to prove to another party (the verifier) that a statement is true, without revealing any additional information. As a rule, this technique is used when confidential information needs to be verified without giving access to it. As well as protecting intellectual property, this solution will eventually help replace identity documents with more advanced tokenized alternatives. For example, it is possible to provide proof of age without providing an exact age.

At the same time, a more distant prospect is the emergence of Web3 – a concept based on blockchain technology to create a decentralized network where information is not controlled by centralized organizations. Instead, data is stored on users’ computers and transferred between them through the network. In Web3, there is no single control center and all transactions are carried out through smart contracts stored on the blockchain. Some of the crucial advantages of Web3 are:

• Decentralization – the lack of a single control center means that data and transactions cannot be modified or deleted without the consent of all participants in the network.
• Security – the use of blockchain provides a high level of data security by storing information in encrypted form and allowing transactions to be verified.
• Automation – smart contracts automate tasks and processes, making data easier to work with and faster to process.
• Innovation – web3 opens up new opportunities to create game-changing products and services that can be used in various fields such as finance, healthcare, education, etc.

However, at the moment, there are key technical challenges hindering Web3 from being finally released, including scaling and security, identity and key management, as well as privacy.

Additionally, there is the blockchain trilemma, articulated by Vitalik Buterin, proposing a set of three main issues that developers encounter when building blockchains:

• Decentralization – the network should operate without verification by specific participants so that you can join it as a node using an ordinary computer.

  
  

• Security – the network must be able to withstand a potential 51% attack, where one or a group of nodes take over most of the network, allowing them to alter transaction chains.

  
  

• Scalability – the ability to increase the number of transactions processed per unit of time.

  
  

There is an inevitable trade-off in this trilemma, but to avoid it, the Layer2 solutions based on large blockchains are applied, most notably on Bitcoin and Ethereum. Transactions on the underlying network can be expensive and slow, but the usage of Layer2 technologies enables developers to collect transactions off-net and pass them to the main blockchain, increasing the number of transactions passing through a single blockchain and reducing fees by spreading them across all the collected transactions for that block.

Achieving all three aspects is considered by some in the business to be an impossible task that will never be accomplished, at least not soon. However, some ambitious developers still hold on to the notion that blockchain networks are capable of including all three of these features and more. Algorand is one instance of a cryptocurrency project that claims to be resolving the blockchain trilemma.

Emergence of New Economic Sectors

One of the world’s leading accountancy firms, PwC, explores the value that blockchain will add to the global economy by 2030: it estimates that more than 40 million jobs will be improved, and the overall impact of blockchain on the global economy is estimated at US$1.76 trillion.

The PwC report also features the top five applications of blockchain:

• Provenance (potential boost to global GDP by 2030: US$962bn) – improving consumer safety in the supply chain, as well as confidence in the authenticity and origin of goods, e.g. pharmaceuticals.

  
  

• Payments and Financial Instruments (US$433bn) – reducing costs and speeding up transactions, improving financial accessibility, and optimizing cross-border payments.

  
  

• Identity (US$224bn) – freedom from labor-intensive and inefficient paper-based record-keeping systems that are easily breached.

  
  

• Contracts and Dispute Resolution (US$73bn) – improving the flow of commercial agreements and flagging any disputes.

  
  

• Customer Engagement (US$54bn) – Prevent customers from leaving loyalty programs by consolidating programs and transferring scores between different systems, and increasing the liquidity of subscription products through tokenization.

Conclusion

In conclusion, the duumvirate of blockchain and Artificial Intelligence is an incredibly powerful tool for creating more secure, efficient, and transparent systems. Their combination can improve data quality, speed up information processing and increase automation. However, in order to maximize their impact, it is necessary to consider all aspects of the interaction of these technologies and develop effective strategies for their application in different areas of activity.

Blockchain technology is definitely not easy to use, but it has been modernized in recent years, and with the advent of Artificial Intelligence, it is becoming more customizable and useful at both government and grassroots levels than ever before.