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AI agents made more than 138 million payments through the x402 protocol on the Base network by the end of Chainalysis’s 2026 reporting period. The number signals rapid growth in machine-to-machine commerce, but it is a count of identifiable x402 transactions—not a census of every AI payment and not proof that each transaction came from an independent autonomous agent.
The strongest geographic finding is narrower: among geolocated payments for AI-model inference, U.S.-linked wallets accounted for 88.1%, while Chinese-linked wallets accounted for 0.1%.
What the 138 million transactions measure
Chainalysis reported the figure on October 14, 2026, in its North America Crypto Adoption analysis. It counts x402 activity observed on Base during the company’s reporting period. Because the measurement depends on activity that can be identified as x402, it does not include every way an AI system might pay for a service, such as an ordinary card charge, an invoice settled through a bank, or an unclassified blockchain transfer.
The total also counts transactions, not agents, customers or completed business orders. One wallet or software system can generate many payments, and some flows may be testing, automated replenishment or transfers between wallets controlled by the same operator.
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Why the United States dominates AI-model spending in this dataset
The 88.1% U.S. figure applies only to the geographically identified subset of AI-model inference transactions. It should not be read as the U.S. share of all 138 million x402 payments, all global AI spending or all crypto activity.
| Wallet geography | Share of geolocated AI-model inference transactions | How to interpret it |
|---|---|---|
| United States | 88.1% | Wallets attributed to the U.S. in Chainalysis’s geolocated inference subset |
| China | 0.1% | Wallets attributed to China in the same subset |
Chainalysis research head Eric Jardine offered one possible explanation for the gap: Chinese developers may be more inclined to use open-weight models such as DeepSeek and Qwen, reducing their need to purchase metered inference services. That is a hypothesis, not a demonstrated cause. Wallet geolocation can also miss users, misattribute entities or leave activity unidentified.
What AI agents are buying with crypto
The reported payments cover more than text generation. Identified use cases include:
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- Metered AI-model inference
- Data feeds and search APIs
- Code review and security analysis
- Agent frameworks, software tools and other developer services
- Payment infrastructure and additional payment rails
- Gaming services
- Token issuance
- Some gambling-related services
This mix is consistent with agents calling software endpoints repeatedly, paying for a discrete result, then moving to another service. It does not establish that human users never participate; an agent can spend on behalf of a person or organization.
Why stablecoin micropayments fit agent workflows
More than 87% of the analyzed payments were worth between one-hundredth of a cent and 10 cents. At that scale, a conventional payment method can be uneconomical when fixed processing fees, account setup and settlement delays are considered. Stablecoin settlement gives software a way to transfer dollar-referenced value in very small increments, provided the service accepts the token and the operator manages wallet funding and compliance.
The data does not establish a universal minimum cost for cards, bank transfers or other networks. It does show the transaction range that x402 activity is currently serving; whether that range remains economical depends on network fees, exchange costs, service pricing and the stablecoin used.
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How x402 compares with other payment approaches
Teams evaluating machine payments should compare the full operating model rather than transaction counts alone.
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| Decision criterion | x402-style crypto payments | Cards or bank payments |
|---|---|---|
| Smallest economical payment | The observed activity is concentrated between 0.01 cent and 10 cents; actual cost varies by network and fees. | Comparable minimums were not stated in the cited reports. |
| Settlement asset | Stablecoin settlement is the model highlighted by the reports; the exact asset mix was not stated. | Usually fiat settlement through a card network or bank; provider terms vary. |
| Network and latency | Depends on the blockchain, wallet and service implementation; no universal latency was reported. | Depends on the card processor or banking rail; no comparable measurement was reported. |
| Funding and spending controls | Requires wallet funding, key management, spend limits and recovery procedures. | Uses account, card or bank controls established by the payment provider. |
| Counterparty and compliance visibility | Transfers are recorded on-chain, while identity and regulatory treatment depend on the provider and jurisdiction. | Provider-held records and established payment compliance processes apply. |
| Demand signal | Counts can include automated, testing or self-dealing flows, so volume alone is not proof of end-user demand. | The cited Chainalysis and CoinDesk analyses did not compare end-user demand across these rails. |
Adoption is becoming faster and more repeatable
Wallet behavior improved across the cohorts measured by Chainalysis. Of wallets first funded in the third quarter of 2025, 70.6% received additional funding within 30 days. For wallets first funded in the second quarter of 2026, the comparable repeat-funding rate was 89.3%.
The time from initial funding to the first x402 payment also fell sharply: the median declined from 73 days in late 2025 to 18 minutes in the third quarter of 2026. Together, these measures suggest that more newly funded wallets are being put to work quickly and funded again, although they still do not reveal whether the spending represents profitable commercial activity.
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Are these autonomous agents or automated scripts?
The available figures cannot answer that question transaction by transaction. Coinbase Ventures head Hoolie Tejwani described the broader direction by saying, “AI agents are becoming economic actors where every agent is effectively a new ‘user’ of the blockchain.” BlackRock digital-assets research head Will Su similarly pointed to potential demand for frequent, low-value payments around the clock.
Those statements describe a possible market structure, not a verification method. A genuinely autonomous agent would need to select a service, authorize a payment and pursue a task with limited human intervention. A script could produce the same on-chain pattern using fixed rules, copied prompts or operator-controlled wallets. Chainalysis measures identifiable x402 activity, and its own description calls the market experimental. The 138 million figure therefore demonstrates substantial protocol usage and improving wallet engagement, while the scale of independent agent-driven commerce remains unproven.
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