Fidelity Warns: The Prosperity of AI Agents May Not Equate to a Boom for Public Blockchains

Author: QQLink

Original Title: "Will AI Agents Really Bring Prosperity to Public Blockchains? Fidelity Reminds: Don’t Rush to Equate the Two Tracks"

If we turn back the clock a few years, the blockchain industry was more focused on "bringing users onto the chain." Users registered wallets, purchased tokens, conducted transactions, and engaged in asset activities through DeFi, NFTs, or other on-chain applications.

The emergence of AI agents may change this usage pattern.

An AI agent capable of autonomously executing tasks could theoretically complete searches, procurements, payments, data calls, and even software collaborations on behalf of users. If agents in the future need to possess independent wallets, call on-chain services, or conduct automatic settlements between machines, then blockchain could indeed become an optional infrastructure.

The problem lies in the significant difference between "can use" and "must use."

This is also where Fidelity Digital Assets' analysis deserves attention. The market has often easily derived from "AI agents need payment and identity" to "AI agents need public blockchains," and further to "the increase in public blockchain usage will drive up token value."

However, this actually contains at least three assumptions.

First, AI agents must conduct on-chain settlements; second, on-chain settlements must occur on permissionless public networks; third, the economic activities generated by public networks can effectively transmit to native token holders.

If any one of these links fails, the entire investment logic needs to be reassessed.

First Risk: AI Agents May Not Necessarily Need Public Blockchains

This is the first key question raised by Fidelity.

Imagine an AI agent within a company. It needs to access the company database, call cloud services, execute procurement tasks, and make payments according to employee permissions. For enterprises, a closed system built by large tech companies or financial institutions may better meet practical needs than an open public blockchain.

The reasons are not complicated.

Enterprises typically care more about system speed, cost, stability, identity verification, permission management, and regulatory responsibilities, rather than whether the network is entirely open.

If a closed infrastructure can provide lower transaction costs, more stable performance, and clearer data and compliance boundaries, then enterprises have no compelling reason to choose a public blockchain solely because of "decentralization."

This means that the growth of AI agents does not automatically translate into growth for public blockchains.

In the future, there may even be a somewhat contradictory situation: a significant increase in AI agents, but a considerable portion of activities occurring within corporate databases, private networks, alliance systems, or closed infrastructures controlled by large platforms.

For public blockchains, the real question is not "Will AI use blockchains?" but rather "Why must public blockchains be used?"

Second Risk: Increased On-Chain Transactions May Not Benefit Tokens

This is a layer that the market is most likely to overlook.

In the past, the crypto market often used a simple logic: increased network usage → increased transactions → increased fees → increased demand for native tokens → rising token value.

However, the payment activities brought by AI agents may not be so straightforward.

Assuming that a large number of AI agents complete micropayments through the blockchain in the future, the number of network transactions may indeed increase rapidly. But if the amount of each transaction is very small, or if the network's fees remain low for an extended period, then the massive number of transactions may not generate corresponding economic value.

More importantly, it may not be the holders of public blockchain tokens who truly earn revenue.

Stablecoin issuers, payment service providers, wallets, and companies responsible for agent infrastructure may all occupy different positions in the value chain.

This actually raises a long-standing question in public blockchain investments: How far apart are network prosperity and token value?

If AI brings more payments, but these payments are primarily made in stablecoins, then the growth of the AI economy may first strengthen the use of stablecoins, rather than directly enhancing the native assets of a particular public blockchain.

Therefore, in the future, when assessing AI + blockchain opportunities, merely counting on-chain transaction numbers may be far from sufficient; it is also necessary to observe fee revenues, asset settlement methods, and which layer captures the value.

Third Risk: More Code Written by AI Does Not Equate to More Value Created

AI is rapidly lowering software development costs, which is a relatively clear trend.

Features that previously required several engineers weeks to complete can now potentially be achieved in a shorter time using AI programming tools. For the blockchain industry, this means lower development barriers, allowing more teams to attempt to create wallets, smart contracts, DeFi applications, and various agent tools.

However, an important reminder from Fidelity is: The quantity of code is not the same concept as economic value.

If AI makes developing a blockchain application cheaper, the market may see a surge of new projects, but an increase in project numbers does not necessarily mean a corresponding increase in user demand.

Conversely, the lower the development barriers, the more likely there is to be an oversupply.

In the past, a project could establish a certain barrier based on technical development capabilities, but when AI commodifies part of the R&D work, the same functionality may quickly appear in multiple versions. Thus, the focus of competition will shift from "who can develop it" to "who has users, liquidity, brand, security records, and distribution channels."

This is a significant change for entrepreneurs.

AI lowers startup costs, but it may also lower competitive barriers.

Fourth Risk: Technological Advantages Are Becoming Less Scarce

If any team can quickly generate code using AI, then the scarcity of "technological leadership" itself may decline.

In the past, a project's moat might have come from complex smart contracts, underlying infrastructure, or the capabilities of the development team. However, in the future, when similar functionalities can be quickly replicated, relying solely on code to establish a long-term competitive advantage will become increasingly difficult.

This does not mean that technology is unimportant.

On the contrary, security, stability, and architectural capabilities may become even more critical.

The logic of competition is simply changing.

For AI + blockchain projects, what may become truly difficult to replicate are user relationships, liquidity, brand trust, ecosystem cooperation, and compliance capabilities.

This also explains why competition between blockchain projects in the future may increasingly resemble competition among internet platforms, rather than just traditional technical contests.

Fifth Risk: AI Lowers Development Costs, but Also Lowers Attack Costs

This is the most concerning risk for security practitioners among the six risks.

AI can help developers write code, but it can also assist attackers in finding issues in the code.

In the past, discovering vulnerabilities in smart contracts might require a professional security team to invest considerable time in code audits. However, as AI tools improve, the barriers to vulnerability analysis, code understanding, and automated testing may also decrease.

This means the industry may face two simultaneous trends.

On one hand, AI enables more teams to develop blockchain products; on the other hand, it may also empower more attackers with the ability to analyze and find vulnerabilities.

If the speed of development far exceeds the speed of security audits, the risks to the entire ecosystem may increase.

This is particularly important for institutional investors. When institutions enter a new asset market, they focus not only on returns but also on custody, permission management, smart contract security, and liability boundaries in the event of system failures.

Therefore, if AI truly drives rapid expansion of on-chain applications, whether the security infrastructure can mature in tandem may become a crucial factor determining the speed of institutional adoption.

Sixth Risk: What Institutions Really Need May Be "Controllable Blockchains"

The final question comes from regulation.

One of the biggest advantages of public blockchains is openness and permissionlessness, but this may conflict with some of the needs of large financial institutions.

Banks, payment institutions, and large enterprises need to know "who is operating," "what the agent can do," "where the data goes," and "who is responsible if something goes wrong" when using AI agents to handle assets.

This means that identity verification, permission management, audit trails, and compliance controls may be more important than openness itself.

Thus, a direction worth discussing emerges: the blockchain infrastructure used by institutions in the future may not be a completely open public network, but rather a system with stronger permission control capabilities.

This does not mean that public blockchains will necessarily be eliminated, but rather that the two architectures may coexist in the long term.

Public networks will be responsible for open settlements and asset circulation, while enterprises or financial institutions will control risks through permission layers, identity layers, and compliance infrastructure.

The real competition may not be as simple as "public chain or private chain," but rather who can find a better balance between openness and controllability for AI agents.

What Fidelity Truly Reminds the Market Is Not to Simply Add Two Narratives Together

Ultimately, there is indeed a possibility of combining AI and blockchain.

AI agents require payment capabilities, while blockchains possess features such as global settlement, stablecoins, and programmable assets; AI can improve software development efficiency and help users interact with complex on-chain applications. These are all real potential demands.

However, there remains a long distance between potential demand and real economic value.

In the past, the market liked to tell a very smooth story: AI agents increase, on-chain transactions increase, public blockchain usage rises, and token values soar.

Fidelity's six risks are essentially reminding investors that each arrow in this chain needs to be validated individually.

AI may drive blockchain, but it may also reinforce closed systems; on-chain payments may grow rapidly, but stablecoins and payment service providers may capture more value; AI may lead to more development, but it may also lead to rapid product homogenization; code may become cheaper, but vulnerabilities may also be easier to discover.

Therefore, what truly deserves attention is not whether "AI will save public blockchains," but rather which infrastructures can genuinely accommodate real demand and convert that demand into sustainable business value after the formation of the AI economy.

This may also be a crucial step for the next stage of the AI and blockchain narrative to move from "storytelling" to "accounting."

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