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Agentic AI Could Push Blockchain Demand, Franklin Templeton Says

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Agentic AI Could Push Blockchain Demand, Franklin Templeton Says

2026-07-22 13:32 UTCBy AlphaScala Research DeskSource reporting: CoindeskEditorial standards

Agentic AI Could Push Blockchain Demand, Franklin Templeton Says

Franklin Templeton's Sandy Kaul says autonomous AI agents need blockchain rails for micro-transactions, pointing to TRON's $89B USDT supply as proof of infrastructure demand.

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Artificial intelligence has fueled a giant investment theme. Franklin Templeton's head of digital assets and innovation, Sandy Kaul, says investors may need to look beyond AI chipmakers and cloud companies for the next place to put money.

In a recent post, Kaul argued the next AI wave could benefit blockchain networks and crypto assets as autonomous AI agents start transacting with one another. Institutional investors have poured money into semiconductor companies, hyperscale cloud providers and data centers. She said they are overlooking the infrastructure that could power machine-to-machine commerce.

Her thesis centers on agentic AI. Unlike generative AI, which creates text, images or code in response to prompts, agentic AI is built to complete tasks with little human input. An AI agent could book travel, compare prices, buy computing power, retrieve data or manage software workflows on a user's behalf.

That shift is already beginning. Robinhood launched AI-powered investing tools in May that let agents trade stocks and make purchases for users. CEO Vlad Tenev has said AI agents will eventually rival the capabilities of human traders. OpenAI and Anthropic are racing to build increasingly autonomous systems that can navigate software and complete complex tasks.

For Kaul, those agents introduce a problem today's payment systems were not built to solve.

Many transactions between AI agents could be worth fractions of a cent, such as paying for an API call, a second of computing power or access to a dataset. Traditional payment networks become expensive when fees cost more than the transaction itself.

She argued public blockchain networks are better suited to machine-to-machine payments because they offer programmable transactions, cryptographic identity and near-instant settlement. Instead of relying on banks or card networks, AI agents could hold digital assets and pay one another directly over blockchain rails.

If that happens at scale, demand for blockchain networks could grow alongside AI adoption. Since agents would need native cryptocurrencies to pay network fees, Kaul argued rising transaction volumes could increase demand for those tokens while generating more revenue for developer incentives, network security and decentralized applications.

Her argument echoes a broader vision put forward by Circle CEO Jeremy Allaire, who said the rise of agentic AI and blockchain represents a single technological shift rather than two separate ones.

In a recent paper, Allaire said AI is driving the cost of knowledge work toward zero while blockchain and programmable digital money are doing the same for payments, settlement and coordination. As businesses rely more heavily on specialized AI agents, he argued those agents will become economic actors that buy services, hire other agents and exchange value autonomously. Blockchain networks, digital identities and programmable money would provide the infrastructure needed to support those interactions at internet scale.

That vision extends beyond payments. Allaire argued AI-native companies could increasingly operate on-chain, with tokens representing ownership and governance. Software pricing could shift from monthly subscriptions to pay-per-task models as AI agents become both the buyers and sellers of digital services.

For investors, Kaul said the implication is straightforward. AI portfolios have largely focused on public technology companies building models and infrastructure. If autonomous AI agents become a meaningful part of the economy, she argues, blockchain networks and the cryptocurrencies that power them could emerge as another way to gain exposure to AI's next stage of growth.

In Q2, TRON's stablecoin dominance rose to 28.7%. USDT supply on TRON hit an $89 billion all-time high. The network generated $89 million in protocol fees, second only to Hyperliquid. TRX traded up 3%. The network is deepening institutional and agentic reach.