Author: QQLink
Original Title: 'Will AI Agents Really Bring a Public Blockchain Boom? Fidelity Reminds: Don't Be Quick to Equate the Two Tracks'
If we turn the clock back a few years, the blockchain industry talked more about 'users going on-chain.' Users would register wallets, buy tokens, trade, and then engage in asset activities through DeFi, NFTs, or other on-chain applications.
The emergence of AI agents may change this way of usage.
An AI agent capable of autonomously executing tasks could theoretically handle searches, procurement, payments, data calls, and even collaboration between software for users. If agents in the future need independent wallets, need to call on-chain services, or perform automated settlements between machines, then blockchain could indeed become an optional infrastructure.
The problem is, there is a big difference between 'can use' and 'must use.'
This is also why this analysis from Fidelity Digital Assets is worth attention. The market has historically been quick to deduce 'AI agents need public blockchains' from 'AI agents need payment and identity,' and then further deduce that 'increased public chain usage will drive token value growth.'
But this actually contains at least three assumptions.
First, AI agents must settle on-chain; second, on-chain settlement must occur on permissionless public networks; third, the economic activity generated on public networks can effectively benefit native token holders.
If any single link in this chain doesn't hold, the entire investment logic needs to be re-evaluated.
The First Risk: AI Agents May Not Need Public Blockchains
This is the first key question raised by Fidelity.
Imagine an AI agent within a company. It needs to access company databases, call cloud services, execute procurement tasks, and complete payments according to employee permissions. For enterprises, a closed system built by a large tech company or financial institution might better meet practical needs than an open public blockchain.
The reasons are not complicated.
Enterprises typically care more about system speed, cost, stability, identity authentication, permission management, and regulatory responsibility, rather than whether the network is completely open.
If a closed infrastructure can provide lower transaction costs, more stable performance, and clearer data and compliance boundaries, then enterprises have no strong reason to choose public blockchains solely for 'decentralization.'
This means the growth of AI agents itself cannot automatically translate into public chain growth.
In the future, we might even see a somewhat paradoxical situation: a massive increase in AI agents, but a significant portion of their activity occurring within corporate databases, private networks, consortium systems, or closed infrastructures controlled by large platforms.
For public chains, the real question is not 'will AI use blockchain?' but 'why must it use *public* blockchains?'
The Second Risk: Increased On-Chain Transactions Don't Necessarily Benefit Tokens
This is the layer the market most easily overlooks.
The crypto market has often used a simple logic in the past: increased network usage → increased transactions → increased fees → increased demand for native tokens → rising token value.
But the payment activities brought by AI agents might not be that direct.
Suppose in the future, a large number of AI agents complete micropayments via blockchain. The number of network transactions could indeed increase rapidly. However, if the amount per transaction is very small, or the network's fees remain persistently low, then the huge volume of transactions may not create matching economic value.
More importantly, the real beneficiaries of revenue may not be public chain token holders.
Stablecoin issuers, payment service providers, wallets, and companies responsible for agent infrastructure could all be positioned at different points in the value chain.
This actually raises a long-standing issue in public chain investing: How far apart are network prosperity and token value?
If AI brings more payments, but these payments are primarily completed with stablecoins, then the growth of the AI economy might first strengthen the use of stablecoins, not necessarily directly strengthen the native asset of any particular public chain.
Therefore, when judging AI + blockchain opportunities in the future, merely counting on-chain transaction numbers may be far from enough. One also needs to observe fee revenue, asset settlement methods, and which layer ultimately captures the value.
The Third Risk: AI Writing More Code Doesn't Equal Creating More Value
AI is rapidly lowering the cost of software development, a relatively clear trend.
Tasks that previously required several engineers weeks to complete can now be potentially implemented in less time using AI programming tools. For the blockchain industry, this means lower development barriers, allowing more teams to attempt creating wallets, smart contracts, DeFi applications, and various agent tools.
But an important reminder from Fidelity is: The quantity of code and economic value are not the same concept.
If AI makes developing a blockchain application cheaper, the market may welcome a surge of new projects, but an increase in project numbers does not mean user demand increases simultaneously.
Conversely, the lower the barrier to entry, the easier it is for supply to become oversaturated.
In the past, a project could build some barrier based on technical development capability. When AI commoditizes part of R&D work, multiple versions of the same functionality could quickly emerge. Consequently, the competitive focus shifts from 'who can develop it' to 'who has users, liquidity, brand, security track record, and distribution channels.'
This is a significant change for entrepreneurs.
AI lowers startup costs, but it may also lower barriers to competition.
The Fourth Risk: Technical Advantage Is Becoming Less Scarce
If any team can quickly generate code with AI assistance, the scarcity of 'technical leadership' itself may decline.
A project's moat in the past might have come from complex smart contracts, underlying infrastructure, or the development team's capability. But 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 doesn't mean technology is unimportant.
On the contrary, security, stability, and architectural capability may become more important.
It's just that the logic of competition is changing.
For AI + blockchain projects, the truly difficult things to replicate might become user relationships, liquidity, brand trust, ecosystem partnerships, and compliance capabilities.
This also explains why competition between blockchain projects in the future might increasingly resemble internet platform competition, not just traditional technology races.
The Fifth Risk: AI Lowers Development Costs, But Also Lowers Attack Costs
This is the most security-practitioner-wary of the six risks.
AI can help developers write code, but it can also help attackers find problems in code.
In the past, finding smart contract vulnerabilities might require dedicated security teams to invest significant time in code audits. But as AI tool capabilities improve, the barriers to vulnerability analysis, code comprehension, and automated testing may also drop.
This means the industry might simultaneously face two trends.
On one hand, AI enables more teams to develop blockchain products. On the other hand, it may also enable more attackers to analyze and find vulnerabilities.
If development speed far outpaces security audit speed, the risk for the entire ecosystem could rise.
This is particularly important for institutional investors. When institutions enter a nascent asset market, they focus not just on yield, but also on custody, permission management, smart contract security, and the boundaries of responsibility in case of system failure.
Therefore, if AI does drive rapid expansion of on-chain applications, whether security infrastructure can mature in sync may become a key factor determining the pace of institutional adoption.
The Sixth Risk: What Institutions May Really Need Is 'Controllable Blockchain'
The final issue stems from regulation.
One of the greatest advantages of public blockchains is their openness and permissionless nature, but this can precisely conflict with some needs of large financial institutions.
Banks, payment institutions, and large enterprises, when using AI agents to handle assets, need to know 'who is operating,' 'what the agent can do,' 'where the data goes,' and 'who is responsible for errors.'
This means identity authentication, 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 might not be fully open public networks, but systems with stronger permission control capabilities.
This doesn't mean public blockchains will necessarily be obsolete, but rather that the two architectures may coexist long-term.
Public networks handle open settlement and asset circulation, while enterprises or financial institutions control risks through permission layers, identity layers, and compliance infrastructure.
The real competition might not be as simple as 'public chain vs. private chain,' but who can find the balance between openness and controllability that is more suitable for AI agents.
What Fidelity Is Truly Reminding the Market: Don't Simply Add Two Narratives Together
Ultimately, there is indeed potential for AI and blockchain to combine.
AI agents need payment capabilities; blockchain has features like global settlement, stablecoins, and programmable assets. AI can improve software development efficiency and also help users interact with complex on-chain applications. These are real potential demands.
But the journey from potential demand to real economic value is still long.
The market in the past loved telling a very smooth story: AI agents increase → on-chain transactions increase → public chain usage rises → token values rise.
Fidelity's six risks are essentially reminding investors that every arrow in this chain needs to be validated separately.
AI may drive blockchain, but it may also strengthen closed systems; on-chain payments may grow rapidly, but stablecoins and payment service providers may capture more value; AI may make development more prosperous, but it may also quickly homogenize products; code becomes cheaper, but vulnerabilities also become easier to find.
Therefore, what's truly worth watching is not 'will AI save public chains?' but after the AI economy forms, which infrastructure can truly meet the real demand and convert that demand into sustainable business value.
This might also be the key step for the next phase of the AI and blockchain narrative to move from 'storytelling' to 'doing the math.'
