Original Author: Robbie Petersen, Delphi Digital Researcher
Original Compilation: Luffy, Foresight News
Understanding a framework for the success of the internet can be viewed from the perspective of coordination. Fundamentally, we can attribute the success of the most valuable internet applications to their ability to coordinate human intentions more precisely. Amazon coordinates commercial intentions, Facebook, Instagram, and Twitter coordinate social intentions, Uber and Doordash coordinate ride-hailing and delivery intentions, and Google coordinates information search intentions by matching queries with relevant web content.
An obvious trend is that AI agents represent the next logical evolution of large-scale coordination. While today our "intentions" are realized through searching, downloading, and interacting with applications on the internet, it is reasonable to assume that in the not-too-distant future, our "intentions" will be executed by a network of AI agents working on our behalf.
Importantly, this shift towards an agent-coordinated economy raises a fundamental question: what infrastructure will ultimately support this evolution?
In this article, we will (1) explore the bull and bear cases for AI agents transacting with cryptocurrencies; (2) outline the logical path of AI agent adoption; and (3) explore value capture in this emerging agent economy.
The Role of Cryptocurrencies
There has been much speculation about why blockchain can serve as the economic foundation for the agent economy. However, as with most emerging crypto verticals, the bull case has been simplified into a one-dimensional, feel-good narrative. Today, the popular argument is that "agents can't have bank accounts, so they'll use crypto wallets" - this seems to overlook the fundamental value proposition of cryptocurrencies. Accessibility aside, agents can perfectly well have bank accounts under an FBO (For Benefit Of) account structure. For example, companies like PayPal have already been managing millions of sub-accounts under a single FBO account structure. They can manage AI agents the same way: each agent has its own virtual sub-account, tracked by the platform, but pooled at the bank level. Notably, Stripe recently announced they will be adding support for agent transactions under a similar structure.
Furthermore, the argument that "this will undermine the autonomy of AI agents" is also an oversimplification. At the end of the day, someone will manage the private keys of the AI agents, so they are not fully autonomous regardless. While theoretically, the private keys of AI agents could be stored in a Trusted Execution Environment (TEE), this is operationally expensive and impractical. And even if you allowed agents 100% autonomy, it doesn't actually provide practical freedom, as they ultimately need to serve humans.
Instead, the real pain points driving agent transactions in traditional domains and on blockchains are:
Settlement Time: Traditional payments face delays of days and batch processing limitations, especially in cross-border transactions. This lack of instant settlement severely hinders the real-time responsiveness required for efficient AI agent operation. Blockchain Solution: Public blockchains provide near-instant settlement finality through atomic transactions, enabling real-time agent-to-agent interactions with no counterparty risk. These transactions settle 24/7, unconstrained by geography or banking hours.
Global Accessibility: Traditional banking infrastructure poses huge barriers for global developers, with 70% of developers outside the US facing challenges when using payment rails. Blockchain Solution: Public blockchain infrastructure is inherently borderless and permissionless, enabling global agent deployment without traditional banking. Anyone with internet access can participate in the network, unconstrained by geography.
Unit Economics: The fee structure of traditional payment systems (3% + fixed fees) makes micro-transactions economically infeasible, creating a barrier for AI agents that need to transact frequently at low values. Blockchain Solution: High-performance blockchains enable micro-transactions at minimal cost, allowing agents to efficiently execute high-frequency, low-value transactions.
Technical Accessibility: Traditional payment infrastructure lacks programmatic APIs and has strict PCI compliance requirements. Systems designed for human-to-human interaction via web forms and manual input pose huge barriers to automated agent operations. Blockchain Solution: Blockchain infrastructure provides native programmatic access through standardized APIs and smart contracts, without the need for forms or manual input. This enables reliable automated interactions, avoiding PCI compliance overhead.
Multi-Agent Scalability: Traditional systems struggle to manage multiple AI agents with independent funds and accounts, leading to high costs from banking relationships and complex accounting requirements. Blockchain Solution: Blockchain addresses can be programmatically generated, enabling efficient fund isolation and multi-agent architectures. Smart contracts provide flexible, programmable fund management without the overhead of traditional banking.
The Path of Adoption
While the technical advantages of cryptocurrencies are indeed compelling, they may not be a prerequisite for the agent commerce wave. Despite the limitations of traditional payment methods, they benefit from massive network effects. Any new infrastructure will need to provide a compelling advantage beyond just marginal improvements to drive adoption.
Looking ahead, we expect agent adoption to unfold across three distinct stages, each with increasing levels of agent autonomy:
Stage 1: Transactions Between Humans and Agents (Current)
We are currently in the first stage. Perplexity's recent "Buy with Pro" feature gives us a glimpse of how humans will increasingly transact with AI agents. Their system allows AI bots to integrate with traditional credit cards and digital wallets like Apple Pay, then research products, compare options, and execute purchases on behalf of the user.
While theoretically this flow could utilize cryptocurrencies, there doesn't seem to be an obvious benefit. As Luke Saunders pointed out, the question of whether cryptocurrencies are necessary can be reduced to the level of autonomy required by the agents. Currently, these agents don't have enough autonomy. They don't independently manage resources, take on risk, or pay other service fees; they are just research assistants, helping before you decide to buy. It's not until the subsequent stages of agent adoption that the limitations of traditional rails become apparent.
Stage 2: Transactions Between Agents and Humans (Emerging)
The next stage is agents autonomously initiating transactions with humans. This is already starting to be implemented in small-scale use cases: AI trading systems executing trades, smart home systems purchasing electricity at optimal prices through time-of-use pricing, and automated inventory management systems placing replenishment orders based on demand forecasts.
However, over time, we may see more complex human-agent commerce use cases emerge, potentially including:
Payments and Banking: AI agents optimizing bill payments and cash flow, detecting fraud and disputed charges, automatically categorizing expenses, and maximizing interest while minimizing fees through smart account management.
Shopping and Consumer: Price monitoring and automated purchasing, subscription optimization, automated refund claims, and smart inventory management for household goods.
Travel and Transportation: Flight price monitoring and rebooking, smart parking management, rideshare optimization, and automated travel insurance claim processing.
Home Concierge: Smart temperature control, predictive maintenance scheduling, and automated consumables replenishment based on usage patterns.
Personal Finance: Automated tax optimization, portfolio rebalancing, and bill negotiation with service providers.
Importantly, while these use cases will start to expose the limitations of traditional paths as agents begin to autonomously manage resources and make decisions on behalf of humans, most of these transactions can still theoretically be executed under architectures like Stripe's Agent SDK.
However, this stage will mark the beginning of a more fundamental transformation: as agents optimize spending in real-time, we will see a shift towards granular, usage-based pricing, rather than fixed monthly or annual service fees. In other words, in a world where agents become increasingly autonomous, they will need to pay for things like compute resources, API access query fees, LLM inference costs, transaction fees, and other usage-based external service pricing.
As the unit economics flaws of card payments gradually become exposed, cryptocurrencies evolve from marginal improvements to become better leapfrog functionalities than traditional channels.
Stage 3: Agent-to-Agent Transactions (Future)
The final stage represents a shift in how value flows in the digital economy. Agents will directly transact with other agents, creating complex autonomous business networks. While these experiments have recently emerged in the corners of cryptocurrency market speculation, we will see more sophisticated use cases emerge:
Resource Markets: Compute agents negotiate with storage agents for optimal data placement, energy agents trade grid capacity in real-time with consumer agents, bandwidth agents auction network capacity to content delivery agents, cloud resource agents arbitrage between providers.
Service Optimization: Database agents negotiate query optimization services with compute agents, security agents purchase threat intelligence from monitoring agents, caching agents exchange space with content prediction agents, load balancing agents coordinate with scaling agents.
Content and Data: Content creation agents obtain asset licenses from media management agents, training data agents negotiate with model optimization agents, knowledge graph agents trade verified information, analytics agents purchase raw data from collection agents.
Business Operations: Supply chain agents coordinate with logistics agents, inventory agents negotiate with procurement agents, customer service agents contract with specialized support agents.
Financial Services: Risk assessment agents trade insurance with underwriting agents, finance agents optimize returns with investment agents, credit scoring agents sell verified materials to lending agents, liquidity agents coordinate with market making agents.
This stage requires a fundamentally redesigned infrastructure for commercial activity between machines. Traditional financial systems are built on human identity verification and oversight, which inherently impedes an agent-to-agent economy. Instead, stablecoins with programmability, borderlessness, instant settlement, and support for micro-transactions become essential infrastructure.
Value Capture in the Agent Economy
The evolution towards an agent economy will inevitably create winners and losers. Within this new paradigm, several different technology stack layers emerge as key points of value capture:
Interface Layer: Similar to the competition for the end-user in traditional payment environments, these participants may vie for the interface layer where "agent intent" is expressed. These frontends will gradually evolve from simple payment tools into comprehensive platforms that combine identity, authentication, and transaction functionality. Several participants can capture value here, including: (1) device manufacturers like Apple, due to their hardware security and identity integration capabilities (2) consumer fintech super-apps like PayPal and Block's Cash App, as they have large user bases and existing closed-loop payment networks (3) AI-native interfaces like ChatGPT, Claude, Gemini, and Perplexity, as agent transactions are a natural extension of their existing chatbots (4) existing crypto wallets, which can leverage crypto-native advantages (though less likely).
Identity Layer: A key challenge of the agent economy is distinguishing between human and machine participants. This is especially important in a world where agents start to disproportionately manage valuable resources and make autonomous decisions. While Apple has an advantage here, Worldcoin is pioneering interesting solutions through its Orb hardware and World ID protocol. By providing verifiable personhood proofs, Worldcoin could indirectly become one of the biggest winners of this trend, providing a platform for app developers to ensure all users are human. While this may be hard to see the value of today, it will become increasingly clear in the future.
Settlement Layer (Blockchains): If blockchains can displace traditional rails as the canonical settlement layer for AI agents, the blockchains facilitating agent transactions will capture significant value.
Stablecoin Issuance Layer: Considering the network effects of liquidity, it is reasonable to assume that whichever stablecoin agents use, the stablecoin issuer will likely capture value. USDC currently seems best positioned, as Circle is rolling out developer-controlled wallets and stablecoin infrastructure to support agent transactions.
Finally, the biggest losers may be those applications that cannot adapt quickly to the agent economy. In a world where transactions are facilitated by agents (rather than humans), traditional moats will disappear. Humans make decisions based on subjective preferences, brand loyalty, and user experience, while agents decide purely for performance and economic outcomes. This means that as the boundaries between applications and agents become increasingly blurred, value will flow to the companies that provide the most efficient, highest-performing services, rather than those that build the best user interfaces or strong brands.
As competition shifts from subjective differentiation to objective performance metrics, users (both human and agent) will benefit the most.
