Blockchain is already used to store and trade financial instruments such as cryptocurrencies and security tokens (cryptographic tokens backed by underlying assets). However, this is a nascent market that is only a few years old. Security tokens themselves are even more nascent— by one measure, the total security token market cap for January 2020 was a mere $52.7 million.
AI will create and trade digital investment assets over high-speed private blockchains
Clearly there is not enough activity (and data) to apply AI to financial products traded over blockchain yet. However, as data volumes going through blockchains increases, AI can glean insights from data, help create financial products, and even trade these products autonomously.
- Stage I: Blockchain proof of concepts
- Stage II: Tokenization of assets on blockchain
- Stage III: Digital investment assets traded on blockchains, powered by machine learning
- Stage IV: AI as economic agents that trade digital investment assets
We are in the second stage where assets can be tokenized and traded on blockchains. Tokens can represent underlying securities, physical assets, rights to cash flow, or utilities. Tokenizing and trading assets on blockchains reduces transaction costs and settlement time while improving auditability. AI and machine learning become applicable for pattern detection and predictive algorithms. However, we don’t have enough on-chain activity to apply AI yet.
The third stage will see the introduction of native digital assets. Tokens can go from representing an underlying asset to becoming the underlying asset. While this concept is hard to digest now, it will be helped by the future explosion of complex blockchain data. This kind of financial engineering will create new revenue sources for financial firms. Applying AI and machine learning will create a competitive advantage.
To be sure, these native digital assets will be highly exotic products. They will exist on blockchains and have their own economic behavior and unique cash flows. They will be created by either human or AI-driven financial engineering. Their risk, predictive and pricing models will be AI-driven because they might be too complex for humans.
These exotic and complex financial products might bring back memories of the Asset-Backed Securities, CDOs, and Credit Default Swaps that led to the 2008 financial crisis. Yes, the downside risk is there and these products must be regulated. Still, native digital assets are likely the next evolution of financial engineering and we will see them eventually.
The fourth and final stage will see AI become economic agents. AI algorithms will actively trade digital investment assets over a blockchain-powered tech stack. Evolutionary (genetic) algorithms could generate, test and trade multiple strategies, kill off under-performing strategies, and continually tweak the winning strategies to maximize trading profit. All with minimal human supervision.
In this new world, AI will create and trade digital investment assets over high-speed private blockchains. Institutional investors will buy these assets because they trust the ability of the issuing firms. This means that incumbent firms will have a huge advantage.
This future might seem hard to fathom, and the details are necessarily vague because nobody has done this yet. However, the underlying blockchain technology and AI methods already exist. We simply need increased blockchain activity, improved AI capabilities, and corporate adoption.
Remember, if you had told people in 2009 that everyone would be talking about magical internet money called Bitcoin within 10 years, you would have been laughed out of the room.
Positioning your Organization for Blockchain and AI Convergence
Specific use cases for combining blockchain and AI will depend on company needs but the underlying theme will be data. Blockchain will ensure that data is secure, private and trustworthy. AI models will use this data to become more effective.
Companies can prepare themselves to develop combined AI and blockchain solutions by improving their digital and data capabilities
Executives must first determine the specific business needs and determine whether blockchain and AI can address these needs. If they already have AI initiatives in place, they can explore how blockchain could improve them. Alternatively, companies sitting on valuable data could monetize it by joining a blockchain ecosystem and sharing data with people building AI models.
For instance, an autonomous car company could store data collected by its cars on a blockchain. When self-driving cars go mainstream, they will collect huge amounts of driving data from on-board cameras and sensors. This data is used to improve the neural networks powering self-driving functions.
Storing this data securely and maintaining driver privacy is a business need. Storing data on blockchains can anonymize driver information, ensuring driver privacy. The car company can still use the data to improve its self-driving neural nets.
From a monetization perspective, the car company could share aggregated and anonymized driving data with insurance companies. Insurers can use the data to price self-driving car insurance more intelligently since self-driving cars have a different risk profile than regular cars. In the end, driver privacy is protected, the car company improves its self-driving capabilities, and the driver may get insurance at a better price.
The reason we haven’t yet seen many examples of joint adoption of blockchain and AI is that implementation at scale is challenging. Many businesses are still in the early stages of implementing blockchain and AI in isolation. Companies are still figuring out how to structure their organizations and modify business processes for blockchain and AI.
Companies can prepare themselves to develop combined AI and blockchain solutions by improving their digital and data capabilities. Digital transformation is a precursor to AI and blockchain adoption. Managing data and business processes using digital systems provides AI initiatives with firm-wide data, enabling AI implementation at scale.
Executives must also understand how to upgrade current data infrastructure to enable future AI and blockchain adoption. They must understand what kind of data needs to be collected and where the current gaps are. Building these core capabilities is like laying the foundation for a house — it greatly improves the chances of building successful blockchain and AI solutions.