New Report Reveals AI × Web3 as the Next Trust Stack for the Digital Economy

A new peer-reviewed report has identified the convergence of artificial intelligence (AI) and Web3 as the emerging “trust stack” for the digital economy, signaling a major shift from hype to infrastructure. The study highlights how the fusion of intelligence from AI and verifiability from blockchain is creating the foundation for a more secure, transparent, and scalable digital financial ecosystem.

The research reveals that as AI startups raised $100.4 billion in 2024 and Web3 investments rebounded to nearly $11.5 billion, the intersection of the two technologies is evolving into a critical layer of enterprise-grade infrastructure. It projects that the synergy between institutional tokenization, on-chain risk intelligence, and compute-to-data markets could unlock $15–20 billion in economic value by 2030.

“AI gives Web3 a brain; Web3 gives AI a memory,” said Vwakpor Efuetanu, CEO of Elite Global Intelligence Technologies. “When intelligence meets verifiability, institutional adoption follows, first in security and compliance, then in agents and data markets. This is how we move from promise to proof in the digital economy.”

The report identifies security, compliance, and emerging market infrastructure as the three core vectors driving the AI × Web3 convergence. It notes that security is the first frontier as enterprises embrace smart-contract assurance and AI-assisted audits to detect vulnerabilities and ensure faster, safer tokenization. AI models trained on blockchain data are already being used to analyze millions of smart contracts within minutes, helping institutions reduce risk while accelerating adoption. According to the study, this area alone represents a $1–3 billion software and services opportunity by the end of the decade.

Efuetanu explained that institutional trust in Web3 will not be built by regulation alone but by automation that guarantees transparency and compliance. “The institutions entering this space need real-time proof that code behaves as it should,” he said. “AI allows us to monitor, test, and verify at scale something that was previously impossible.”

The second key trend outlined in the report is the shift toward continuous compliance, where on-chain anti-money laundering (AML), know-your-customer (KYC), and fraud analytics systems are powered by AI to monitor transactions dynamically. The study projects this segment to reach a $5–8 billion total addressable market by 2030 as global regulators operationalize principles from the Financial Stability Board (FSB), Bank for International Settlements (BIS), and Organisation for Economic Co-operation and Development (OECD) into programmable financial systems.

In this model, AI algorithms analyze blockchain activity in real time, spotting irregular wallet behavior, suspicious liquidity flows, and potential sanction breaches long before traditional systems can. “Programmable finance is only as safe as its rules,” Efuetanu said. “When policy can be translated into code and monitored continuously by AI, compliance becomes proactive, not reactive.”

The third area of focus is the emergence of AI × Web3 infrastructure in developing economies, where digital payment rails and mobile adoption are creating fertile ground for innovation. Africa, for instance, processed 62 billion mobile-money transactions worth $919 billion in 2023, surpassing several developed markets in digital transaction volume. The report highlights how this ecosystem provides the foundation for deploying AI-powered credit scoring, decentralized identity systems, and cross-border payment corridors that are more inclusive and efficient.

The report argues that this convergence is happening at a pivotal moment for global policy and technology. Regulatory clarity has improved, with international institutions defining the frameworks for tokenization, digital assets, and responsible AI. Meanwhile, enterprises are increasingly focused on deploying AI models that are transparent, explainable, and auditable precisely the characteristics blockchain infrastructure is designed to ensure.

At the same time, advances in compute-to-data markets are solving one of the most complex challenges in AI adoption: how to train models on sensitive data without breaching privacy. In this setup, algorithms move to the data rather than transferring the data itself, maintaining security and compliance while enabling collaboration. Blockchain technology underpins this architecture by recording permissions, usage, and results immutably.

The report describes this evolving system as “the trust stack for the intelligence economy” a framework where data, compute, and governance work together to create verifiable and transparent intelligence networks. It envisions a future in which financial institutions, regulators, and startups build upon a shared foundation of trust, powered by AI’s cognitive capacity and blockchain’s immutable logic.

In a statement accompanying the report, Efuetanu emphasized the strategic importance of collaboration across sectors. “No single company or country can build the next trust stack alone,” he said. “We need coordinated pilots, shared standards, and open infrastructure to ensure this transformation benefits everyone not just the early adopters.”

The research team is inviting investors, enterprise partners, and policymakers to participate in pilot programs focused on smart-contract assurance, on-chain regulatory technology, and compute-to-data platforms. These initiatives aim to accelerate adoption, strengthen compliance, and demonstrate scalable use cases across finance, identity, and digital trade.