VeChain Says AI’s Next Phase Is the Agent Economy, Where Verified Outcomes Create Value

VeChain’s Sunny Lu argues that artificial intelligence is entering a new phase in which agents execute tasks rather than simply answer questions. In the second part of his analysis, published in September 2026, Lu says the long-term value of AI will depend increasingly on specialized expertise, verified results and trust, rather than intelligence alone.

The shift could change how businesses deploy AI, how developers monetize their knowledge and how autonomous software interacts with the real economy. For blockchain networks, it also presents a potential use case beyond payments: providing infrastructure for agent identity, transaction settlement and independently verifiable outcomes.

Lu points to the rapid emergence of agent products across major technology platforms as evidence of the transition. Instead of asking an AI system how to optimize a logistics schedule, a business could delegate the task, including contacting suppliers, updating records and preparing purchase orders within an approved budget.

Why AI Agents Need More Than Intelligence

The economic argument begins with measurable productivity. A chatbot subscription may be treated as a discretionary expense, but an agent that reconciles financial records, manages procurement or coordinates deliveries can be evaluated against labor costs, processing time and operational savings. That makes productive AI easier to justify through conventional business budgets.

Agents could also increase demand for computing infrastructure by running continuously, monitoring events, using external tools and checking their work. However, Lu distinguishes raw usage from productive usage: more AI processing does not automatically translate into economic value. The decisive question is whether the work produces outcomes worth paying for.

The same logic applies to competition between AI providers. General-purpose agents will increasingly share capabilities such as booking travel, sending emails and updating databases. As these functions become widespread, Lu argues, they are unlikely to provide a lasting competitive advantage on their own.

Specialist agents could differentiate themselves through access to regulated systems, domain-specific workflows and a reliable record of results. Private data and encoded expertise can help, but both may be copied, licensed or replaced. Access to payment rails, regulatory permissions and business systems is harder to replicate, while a verified history of successful outcomes takes time to establish.

Lu also identifies four requirements for agents to learn from their actions: outcomes must be observable, attributable to the agent’s decisions, exclusive enough to provide a genuine advantage, and connected to systems that turn experience into better future decisions. Without those conditions, executing more tasks does not necessarily make an agent more capable.

Blockchain’s Potential Role in the Agent Economy

This argument leads to a different business model. General AI assistants are often priced through subscriptions, while specialist agents could charge according to completed work or measurable outcomes. A general-purpose assistant might handle a company’s initial request before delegating customs clearance, compliance checks or carbon verification to specialist agents.

Such a market would require a way for agents belonging to different people and organizations to identify one another, establish authority, exchange payments and verify completed work. Lu identifies four important components: verifiable identity, programmable escrow and settlement, verification of physical-world events, and open marketplaces where agents can discover and hire other agents.

Blockchain could support these functions by creating independently verifiable records across organizational boundaries. Smart contracts could manage conditional payments, while on-chain records could help establish agent identities and document transactions. Physical-world verification would still require reliable evidence from sources such as delivery systems, inspections or supply-chain records; a blockchain alone cannot prove that a real-world event occurred.

VeChain has been developing products around this thesis. Its AgentSuite platform, accessible through VeWorld.ai, is designed to let creators build, publish and monetize specialized AI agents, with AgentForge for creation, AgentMarket for discovery and AgentTrust for identity and transaction transparency. These products position VeChain to explore the trust infrastructure Lu describes, although commercial adoption and measurable results will determine their lasting value.

The opportunity extends beyond large technology companies. Nurses, farmers, customs brokers and small-business owners possess practical knowledge that may never appear in public training data. AI tools could help these experts turn their experience into specialized services for customers around the world, while verified identities, payment systems and reliable performance records help prospective buyers decide whom to trust.

Lu closes with a broader lesson from previous infrastructure booms: building capacity is not enough. Railways, electricity and the internet generated lasting value through the services built on top of them. AI may follow a similar path, with the next phase depending less on producing better answers and more on delivering useful, accountable results. For VeChain and other blockchain projects, the agent economy represents a possible path toward real-world utility, but success will depend on whether businesses and users adopt these systems to solve problems they are willing to pay to fix.