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Amazon, Airbnb, and Uber are all examples of disintermediation (innovation that undermines established or incumbent structures of a market, organization, or industry sector). Moody’s went with the recent headline that the “Convergence of AI and blockchain could recode multiple industries.” The notion of the “recoding” of an industry sector is compelling – as it suggests a more nuanced, code-level transformation of an industry sector (as opposed to just the disruption that accompanies disintermediation). We explore the nuances of the Moody’s report.
The transformative potential of AI and blockchain technologies in the financial markets will not be merely incremental improvements but represent foundational shifts in how financial transactions are conducted, how markets are structured, and how trust is established and maintained within the ecosystem:
As CoinDesk notes: “Recent innovations have increased the transformative potential of technologies like artificial intelligence (AI) and distributed ledger technology (DLT) when applied to financial markets…while AI could potentially reduce operating expenses for financial institutions by automating manual tasks, DLT could ‘gradually lower financing expenses, especially for smaller issuers,’ according to {Moody’s].
‘DLT could improve financial market efficiency, modernize the payment system, and foster financial inclusion,’ Vincent Gusdorf, head of DeFi and digital asset analytics, said in a press statement shared with CoinDesk. ‘The overall economic and financial effects of technological changes, including the policy and strategic changes they prompt, are likely to be positive.’
Digital or tokenized bonds, which are becoming popular in global markets, could lower transaction expenses and make capital markets more accessible by letting organizations bypass intermediaries like banks and by increasing liquidity on the secondary market, the report said. Hong Kong’s central bank arrived at similar conclusions following a successful $100 million tokenized bond issue from earlier this year. DLT could also enable some businesses to capture untapped revenue opportunities and enter new markets.”
AI and blockchain are also paving the way for a more inclusive financial system. By lowering the barriers to entry, they enable broader access to financial services, particularly for unbanked and underbanked populations worldwide. This democratization of finance could spur economic empowerment and growth, especially in emerging markets.
However, integrating AI and blockchain in financial markets is not without challenges. Regulatory frameworks must evolve to address these technologies’ unique characteristics and potential risks. The adoption of AI and blockchain requires significant investment in technology infrastructure and skills development.
Now, on to Moody’s “recoding” of other industry sectors by AI and blockchain technologies.
The Mood’ys “Sector in Depth memo on integrating Generative Artificial Intelligence (GAI) with blockchain technology addresses challenges such as scalability, security, privacy, and interoperability. It introduces various GAI techniques and their applications in blockchain networks, including a case study on blockchain design using a Generative Diffusion Model (GDM) to optimize network performance, which showed improvements in throughput and latency over traditional AI approaches. Additionally, the document touches on the convergence of AI and blockchain as a transformative force in multiple industries.
AI and blockchain, linked by data, can be mutually beneficial when combined. Data is a vital link between AI and blockchain. Both technologies rely on vast data sets, and in combination, each technology enhances its strengths and mitigates its weaknesses. AI can help address the limitations of smart contracts – self-executing agreements coded into a blockchain – including potential coding errors or security flaws. Blockchain, in turn, can improve AI’s data integrity and transparency. The combination of AI and blockchain is already being tested in sectors and activities ranging from supply chain tracking to medical record
storage and management to trading within decentralized finance.
Risks arise from the combination of AI and blockchain. The numerous risks that stem from the two technologies’ convergence can be grouped into six categories: data-related risks, including safeguarding privacy, data accuracy and data protection; regulatory risks, operational risks, technology risks, governance risks, and social risks, such as job losses arising from automation.
Convergence of AI and DLT could foster new solutions for regulation. Regulating each of these transformative technologies on its own is difficult, because responsibility for undesired outcomes is either opaque (AI) or decentralized, with no single entity to hold accountable (blockchain). Together, though, the technologies could yield regulatory and enforcement solutions. The development of Decentralized Digital Identity (DID) based on distributed ledger networks, for example, could give AI an immutable trail that increases transparency.
Source: Moody’s Investors Service
AI and blockchain technologies, when combined, can be mutually beneficial due to their reliance on large data sets. AI can analyze and generate solutions from centrally stored data, while blockchain provides an immutable data trail that enhances transparency and data integrity. The convergence of these technologies can lead to more efficient operations and potentially reshape various sectors, including supply chain management and digital finance markets. AI’s pattern recognition can improve blockchain security, and blockchain can ensure the integrity of AI’s data.
AI can enhance the safety and effectiveness of smart contracts by addressing their inherent limitations, such as coding errors, security flaws, and inflexibility. AI-driven tools can improve cybersecurity by identifying specific weaknesses in the code of smart contracts. Additionally, AI can ensure that the terms and operations of smart contracts are in compliance with relevant laws by analyzing legal texts. Machine learning algorithms have the potential to make smart contracts more adaptable to changing circumstances even after deployment, although this adaptability must be balanced with the need for consistency and consensus on the blockchain.
Exhibit 2
AI can improve smart contract functionality and security
Source: Moody’s Investors Service
The convergence of AI and blockchain is already being experimented with and could significantly change a range of business activities and operations. Examples of applications include enhancements in supply chain tracking, medical record storage and management, and trading on decentralized finance (DeFi) platforms. AI and blockchain, as individual technologies, are each groundbreaking, but their integration could lead to even more substantial changes. The introduction of large language models (LLMs) in AI, which can be queried without coding, represents a leap forward, although the data underlying these models is often not openly accessible. Decentralized identity (DID) systems, enabled by blockchain, could further allow AI to scale while maintaining transparency.
Exhibit 3
Multiple industries and companies are combining AI and blockchain, to explore diverse concepts.
Most are in the pilot phase, while some are already operational
Source: Moody’s Investors Service
By storing AI models on distributed ledgers, blockchain creates an audit trail that maintains AI’s data integrity. This immutable nature of blockchain supports transparent data management and can provide a rich source of datasets for AI, facilitating better data management and model distribution. Additionally, blockchain’s cryptographic methods and consensus mechanisms contribute to self-governance, security, and efficiency in systems where it is applied. The synergy between AI and blockchain is being explored in various sectors, with blockchain ensuring the immutability and transparency of data used by AI systems.
Exhibit 4
AI can be integrated with DLT in three layers
Source: Moody’s Investors Service
The Moody’s report nailed this strategic insight, pointing to the “recoding” of industry sectors at the computational hardware architecture layer on the technology stack:
“Meanwhile, decentralized platforms built on distributed ledgers such as blockchain could help AI manage computationally intensive tasks such as model training. Conventional cloud-based solutions, which are centralized, often bring high expenses, when not appropriately managed, and potential risks to data privacy. Decentralized network technologies provide a remedy by enabling distributed computing, which can be implemented on top of cloud infrastructure or even integrated into a decentralized cloud model, blending the characteristics of both paradigms. This not only cuts costs but also bolsters privacy and taps into globally underused computational resources. Emerging companies are now constructing decentralized infrastructures that aggregate processing resources– both graphics processing units (GPUs)10 and central processing units (CPUs)11 – providing them to organizations in need of AI model
training (Exhibit 5).
EXHIBIT 5
AI and blockchain will have mutual benefits in several broad areas
Source: IBM, Moody’s Investors Service
As we stand on the cusp of this transformation, stakeholders across the financial ecosystem must engage proactively and strategically with these technologies. Moody’s points to engagement with the regulatory environment moving forward:
From a regulatory standpoint, AI and blockchain pose a very similar challenge to regulators, in that both technologies challenge the very notion of accountability. In the realm of AI, it can be difficult to trace inputs and outputs generated by the algorithms, and the decision-making process of AI models is often opaque and inaccessible, obscuring the lines of responsibility when an AI system’s actions lead to undesired outcomes. It is also not always possible to be certain whether a specific output has been produced by an AI agent or a human. Regulatory frameworks are meant to address the accountability dilemma, and regulators are trying to develop appropriate rules for the supervision of AI applications. For instance, in the EU, the AI Act proposed to subject certain AI systems such as foundational models to strict governance rules to minimize data traceability issues and associated risks, although tech and legal experts have expressed concerns about potential flaws in this regulatory approach.
On 30 October 2023, President Biden issued an executive order that aims to promote the safe, secure, and trustworthy development and use of AI. This comprehensive order represents a significant step in enhancing accountability in AI development and deployment across various sectors. As companies assess the impact of this order, they must consider not only their own AI usage but also the extent to which third-party vendors’ AI capabilities are integrated into their products and services.
Decentralized finance (DeFi) protocols often raise accountability issues, but from another angle: in the absence of a (centralized) legal person or entity to be held accountable in case of malfunction, legal systems struggle to enforce rules. DeFi often therefore challenges legal and regulatory norms. However, in that regard as well, the convergence of AI applications and digital finance could hold immense potential. The development of decentralized digital identity (DID) based on distributed ledger networks could provide AI a robust and immutable trail thatincreases transparency and makes AI applications more trustworthy. In the context of interoperable and interconnected systems, DID could enable a growing number of interactions among different platforms that allow AI to scale without compromising its transparency.
In thinking about the adoption of disruptive technologies, the best mental model is not one that layers these technologies on our existing stacks, but rather rethinks the whole of the system from first principles and seeks to displace and replace with new approaches.
The ability to adapt and also revert to first principles will be a necessity of governance as well. First principles, the fundamental concepts or assumptions at the heart of any system, serve as the bedrock upon which the future is built. In government, this approach necessitates a return to the core values and constitutional tenets that define a nation’s identity and purpose. It’s about stripping down complex policy issues to their most basic elements and rebuilding them in a way that is both innovative and cognizant of the historical context.
When it comes to economics and money, a first principles mindset could lead to a reevaluation of foundational economic theories, potentially fostering new forms of currency or novel financial instruments that could reshape markets. This is evident in the emergence of digital currencies and the underlying blockchain technology, which challenge traditional banking paradigms and redefine value exchange.
In the realm of engineering, applying first principles thinking often results in breakthrough innovations. By focusing on the fundamental physics of materials and processes, engineers can invent solutions that leapfrog over incremental improvements, much like how the aerospace industry has evolved with the advent of composite materials and computer-aided design. These disciplines, when underpinned by first principles, are not just adapting to change; they are the architects of the future, sculpting the landscape of what is to come.
If there was to be one guiding first principle over the next 5 years, it would emphasize the role that computation plays across everything we are and will be the underpinning over everything we do. In engineering, computation serves as the bedrock upon which structures both tangible and conceptual are built; it is the mechanism by which we translate the laws of physics into the marvels of modern infrastructure. Within finance, computation is the pulse that courses through the veins of markets, embodying the algorithms that drive trading strategies and the quantitative models that shape economic forecasting. As for nature, computation can be seen in the intricate dance of evolutionary processes, the patterns of genetic code, and the emergent complexity of ecosystems—a testament to the universal language of mathematics that governs all.
Through this lens, computation emerges not merely as a tool but as a fundamental principle that underpins the complexity and beauty of the world we navigate. It is a thread that weaves together the fabric of human ingenuity with the tapestry of the cosmos. Comprehending the world through a lens of computation will be the ultimate re-orientation.
Regular readers of the OODA Loop know that we cover exponential technologies on a daily basis and we expect disproportionately disruption where these technologies start to converge. For example, AI + Bio Tech or Robotics + AI. We will be tracking and continue re-orienting you to developments in the following areas:
For more OODA Loop News Briefs and Original Analysis, see OODA Loop | Generative AI OODA Loop | LLM
Corporate Board Accountability for Cyber Risks: With a combination of market forces, regulatory changes, and strategic shifts, corporate boards and directors are now accountable for cyber risks in their firms. See: Corporate Directors and Risk
Geopolitical-Cyber Risk Nexus: The interconnectivity brought by the Internet has caused regional issues that affect global cyberspace. Now, every significant event has cyber implications, making it imperative for leaders to recognize and act upon the symbiosis between geopolitical and cyber risks. See The Cyber Threat
Ransomware’s Rapid Evolution: Ransomware technology and its associated criminal business models have seen significant advancements. This has culminated in a heightened threat level, resembling a pandemic’s reach and impact. Yet, there are strategies available for threat mitigation. See: Ransomware, and update.
Challenges in Cyber “Net Assessment”: While leaders have long tried to gauge both cyber risk and security, actionable metrics remain elusive. Current metrics mainly determine if a system can be compromised without guaranteeing its invulnerability. It’s imperative not just to develop action plans against risks but to contextualize the state of cybersecurity concerning cyber threats. Despite its importance, achieving a reliable net assessment is increasingly challenging due to the pervasive nature of modern technology. See: Cyber Threat
Decision Intelligence for Optimal Choices: Numerous disruptions complicate situational awareness and can inhibit effective decision-making. Every enterprise should evaluate its data collection methods, assessment, and decision-making processes for more insights: Decision Intelligence.
Proactive Mitigation of Cyber Threats: The relentless nature of cyber adversaries, whether they are criminals or nation-states, necessitates proactive measures. It’s crucial to remember that cybersecurity isn’t solely the IT department’s or the CISO’s responsibility – it’s a collective effort involving the entire leadership. Relying solely on governmental actions isn’t advised given its inconsistent approach towards aiding industries in risk reduction. See: Cyber Defenses
The Necessity of Continuous Vigilance in Cybersecurity: The consistent warnings from the FBI and CISA concerning cybersecurity signal potential large-scale threats. Cybersecurity demands 24/7 attention, even on holidays. Ensuring team endurance and preventing burnout by allocating rest periods are imperative. See: Continuous Vigilance
Embracing Corporate Intelligence and Scenario Planning in an Uncertain Age: Apart from traditional competitive challenges, businesses also confront unpredictable external threats. This environment amplifies the significance of Scenario Planning. It enables leaders to envision varied futures, thereby identifying potential risks and opportunities. Regardless of their size, all organizations should allocate time to refine their understanding of the current risk landscape and adapt their strategies. See: Scenario Planning
About the Author
Daniel Pereira is research director at OODA. He is a foresight strategist, creative technologist, and an information communication technology (ICT) and digital media researcher with 20+ years of experience directing public/private partnerships and strategic innovation initiatives.
Informing your decisions with actionable intelligence
Informing your decisions with actionable intelligence