Franklin Templeton: Intelligent AI is the "killer application" of blockchain

Author: Sandy Kaul (Head of Digital Assets and Innovation at Franklin Templeton)

Compiled by: Jiahua, ChainCatcher

The Evolution of AI is Leading the Investment Narrative

AI has been evolving. Around the 2010s, early capabilities such as machine learning, natural language processing, and predictive analytics ignited the "big data" era, enabling people to process structured and unstructured data at speeds and volumes previously unimaginable. At that time, AI was more like a tool assisting human work.

By the early 2020s, generative AI emerged, taking its uses and roles to a new level. AI transformed from a helper into a "co-creator," capable of generating content, responding to various inquiries, and even completing parts of tasks for people. The potential of generative AI has not yet been fully realized; products are becoming stronger and penetrating more corners of daily life.

With its growing influence, AI's status as a central investment theme is now indisputable.

On July 14, 2026, IBM's stock price plummeted by 25.2% in a single day. Prior to this, it had warned that corporate technology budgets were increasingly being directed towards AI infrastructure, while spending on traditional software and IT projects was being postponed or cut.

Today's S&P 500 index concentration is at its highest point since the tech bubble of the late 1990s. The ten largest stocks by market capitalization are all AI-related, collectively accounting for nearly 40% of the total market capitalization of the index. In contrast, during the internet bubble, this figure was only 25%, and in 1980 it was just 15%.

Institutional investors especially view AI as a structural mega-trend, heavily betting on AI infrastructure, data centers, and semiconductor stocks.

However, such positioning may not be enough to capture the dividends of AI's next evolutionary phase.

The Rise of Agent AI

Generative AI marked a leap in capabilities, effects, and application scenarios compared to early AI tools. Now, agent AI is maturing and being widely adopted, potentially impacting daily life as much as, or even more than, its predecessor.

Agent AI advances the interaction model from a passive, responsive chatbot to an autonomous system that can perceive its environment, formulate its own plans, and execute multi-step tasks to achieve higher-level goals without continuous human oversight.

Agents can directly interact with external software systems and code repositories, thereby redefining the role of AI. 38% of institutions indicate that by 2028, agents will become team members like human colleagues, enhancing productivity and driving innovation together.

Following this trend, the tasks assigned to AI will become increasingly complex. Generative AI excels at gathering knowledge and organizing content; agents will increasingly take on "transaction" type work, initiating, tracking, completing tasks, and managing final outcomes.

Predictions suggest that by 2030, the scale of the agent business could reach $3 trillion to $5 trillion.

For institutions, these transactions will mostly occur within enterprise software. Predictions show that by 2028, 33% of enterprise software will be embedded with agent AI, with up to 15% of daily decisions being handled by these agents.

Software will pay small fees to each other for computing power, API calls, data usage, and various services. This is a brand-new interaction method that can account and settle on a per-task basis.

Protocols for "Software Paying Software" are Emerging

Protocols supporting this "machine-to-machine" transaction are gradually emerging. Stripe and Visa have already launched a Machine Payment Protocol (MPP).

Open-source solutions are also gaining traction. In the early 1990s, the designers of the World Wide Web specifically reserved a response code numbered "402," labeled as "Payment Required," when establishing communication rules between browsers and servers.

Coinbase built an "x402" protocol based on this, allowing agents to initiate and complete such payment instructions, and subsequently handed over the related intellectual property to the Linux Foundation to become an open industry standard.

Today, major credit card networks, Web2 giants like Stripe, Shopify, Google, and Amazon Web Services (AWS), as well as an increasing number of Web3 service providers, have integrated this payment standard. The goal is to enable "software to pay software" without human intervention.

In the coming years, agent payments are likely to reshape consumer interaction methods. Predictions suggest that by 2030, agents will contribute 15% to 25% of U.S. e-commerce sales.

Currently, ChatGPT handles 2.5 billion inquiries daily, with 53 million of those being shopping-related queries initiated through AI platforms. OpenAI is also integrating checkout processes into third-party ChatGPT applications, such as Target, DoorDash, and Instacart.

Blockchain: How to Support Transactions Between Machines

To support these "machine-to-machine" transactions, a secure, autonomous, verifiable, and high-throughput accounting system is needed.

Traditional credit card and banking systems are not suitable for the small payments of agents due to their fee structures. A standard credit card transaction typically incurs a fee of 2% to 3%, plus about $0.30 in fixed costs; whereas an agent might spend only $0.001 for 1 second of computing power or a data query.

For agent AI to operate effectively, it will likely rely on cryptographic technology and blockchain, as this underlying framework is inherently suitable for such scenarios. In fact, due to the following characteristics, blockchain and cryptographic technology are expected to become the foundational support for these transactions.

Automatic Generation and Execution of Contracts. Payment agents will generate tokens to complete purchases and settlements. Each token will contain a set of transaction rules: which merchants can accept the token, the maximum amount that can be spent per transaction, and the token's validity period. Once a purchase is completed, this one-time token will automatically become invalid. The blockchain can hold, send, and receive these tokens, executing them strictly according to the rules written in the token, much like executing smart contracts.

Decentralized Identity Verification. Each agent has a unique, cryptographically verifiable identity. Each token it generates carries its own credentials, which are necessary for signing blockchain transactions. The blockchain will verify these credentials when validating transactions; if an identity is deemed invalid, the consensus mechanism will block the transaction.

Full Auditability. Every decision, transaction, and data exchange made by agents on the blockchain can be recorded on an immutable ledger, accessible to anyone via blockchain explorers, ensuring accountability and transparency.

Access to Decentralized Computing Power and Data. Through blockchain, agents can tap into distributed computing resources (like GPU networks) and data, reducing reliance on centralized, private cloud infrastructure, and helping to lower the operational costs of high-frequency trading models.

Speed and Settlement. Bitcoin can only process about 7 transactions per second, Ethereum about 75, but newer high-speed public chains have significantly increased these peaks: Aptos can reach up to 12,933 transactions per second (TPS), Solana at 6,284 TPS, and BNB Chain at 3,252 TPS.

This speed is comparable to the Visa network, which processes between 1,700 and 10,000 transactions per second during normal operation. However, even with this comparison, it still underestimates on-chain systems. The blockchain records and settles transactions within that TPS time window; whereas Visa only records the transaction, with actual settlement taking 1 to 3 business days.

With these characteristics, blockchain will play a key role in the consumer-level transactions of agent AI. Conversely, the growth of agent AI may also become the "killer application" that drives the widespread adoption of blockchain.

How to Invest in the Opportunity of Agent AI

Currently, investors looking to capture the dividends of AI growth typically buy stocks of AI concept companies and related industry chains, or invest as LPs in private equity funds, or direct their investments towards energy providers and data centers that support AI operations.

However, to seize the opportunity of agent AI, these combinations may need to extend their exposure to native tokens of public chains, as well as project tokens issued by on-chain applications and projects. The driving forces behind this shift are roughly as follows.

Demand for Cryptocurrencies Will Rise. To record a transaction on a particular chain, agents must use the native token of that chain to pay transaction fees. For example, to record on Solana, one must pay with SOL. As agent payments increase, the demand for the native tokens of public chains supporting these businesses may surge, creating value for every token holder. Initially, this demand will likely come from machine-to-machine small payments between enterprise software systems.

Blockchain Ecosystems Will Expand. The more transactions on a chain and the higher the demand for its native tokens, the more funds will flow into that chain's treasury. Blockchain foundations will use these treasury funds to grant developers, encourage them to develop applications on-chain, offer "bug bounties" for discovering security vulnerabilities, and incentivize those who validate transactions on the network. The more funds available for distribution, the more likely the ecosystem will grow and become secure, attracting more developers to build applications, issue their own tokens for project financing, and share ownership of the applications.

Web3 Applications Will Capture Market Share from Web2. As more applications go on-chain and development talent continues to flow in, the advantages of Web3 applications over Web2 will become increasingly evident. This has already happened in Web3 gaming: the gaming industry is shifting from the Web2 "single-player" model to the Web3 "player-owned economy."

Tap-to-earn applications have already attracted hundreds of millions of users globally. Players can now trade and sell in-game items (NFTs) on secondary markets and across platforms, truly owning and monetizing the assets they accumulate in games. Similar experiences and ownership transformations may unfold in a large number of consumer applications, driving market interest in the tokens issued by these projects.

The Flywheel Effect Will Start Turning. Protocols embedding agent payments into blockchain applications already exist, and they may bring a flywheel effect to these newly issued tokens. Users only need to instruct their agents to handle transactions and payments, without having to create wallets, buy tokens, or manage various tokens and cryptocurrencies themselves.

For users, the experience of using a Web3 application will seem similar to using a Web2 product. However, because the tokens here have both utility value and represent ownership, they can gain more benefits within the Web3 ecosystem.

To some extent, this transition will resemble the shift from Web1 to Web2: moving from Web1's web servers and static websites to Web2's cloud-based businesses and interactive applications. In both transitions, whether it is the underlying technology providers or the businesses built on these frameworks, the main players have shifted from established incumbents to a new set of players driving growth in the new era. This time, the baton will be passed to blockchain and various decentralized applications and projects.

Currently, investors have not yet fully grasped "how to capture the value created by blockchain and its ecosystem." They are accustomed to a centralized, company-led business world: to share in the value created by a company, they buy its stock.

However, I believe that one point will become increasingly clear in the coming years: to capture the value of decentralized networks and businesses, investors will need to buy related crypto assets. These assets are likely to become significant holdings in investment portfolios, especially for those looking to seize new opportunities in agent AI.