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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAI agents made more than 138 million payments through the x402 protocol on Base by the end of Chainalysis’s 2026 reporting period. The figure, reported on October 14, 2026, counts identifiable protocol transactions—not every AI payment worldwide and not necessarily 138 million independent business purchases.
The United States accounted for 88.1% of geographically identified AI-model inference transactions, while China accounted for 0.1%. The payments are overwhelmingly tiny: more than 87% were worth between one-hundredth of a cent and 10 cents, a range in which stablecoin settlement can be more practical than conventional card processing.
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What the 138 million x402 transactions represent
Chainalysis counted more than 138 million x402 transactions on Base by the close of its 2026 reporting period. x402 is Coinbase’s payment protocol for machine-to-machine commerce. It uses the web’s HTTP 402 “Payment Required” pattern so a service can request payment and an agent can settle it from a crypto wallet as part of an automated request.
The number is a measure of on-chain x402 activity that Chainalysis could identify. It is not a census of all spending by artificial-intelligence systems, all crypto payments, or all agent software. A single application can generate many transactions, and some transactions may be automated tests, internal transfers or other flows that do not represent an independent customer buying a product.
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Why the United States dominates AI-model spending
The 88.1% U.S. share applies only to the geographically identified subset of AI-model inference payments. It should not be read as the U.S. share of all 138 million x402 transactions. Chinese-linked wallets represented 0.1% of that same geolocated inference subset.
| Geolocated inference activity | Share attributed by Chainalysis | How to interpret it |
|---|---|---|
| U.S.-linked wallets | 88.1% | Share of identified AI-model inference transactions, not all x402 activity |
| Chinese-linked wallets | 0.1% | Share of the same identified inference subset |
The data does not establish one definitive cause for the gap. Eric Jardine, Chainalysis’s head of research, offered one possible explanation: Chinese developers may be more inclined to use open-weight models such as DeepSeek and Qwen, reducing the need to purchase metered inference from external services. Differences in wallet geography, service availability, model preferences and how developers route payments can also affect what analytics firms can identify.
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What AI agents buy with crypto
x402 payments are being used across a broader set of services than model calls alone. The categories identified by Chainalysis and CoinDesk include:
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- Model inference: paying for individual prompts, completions or other metered access to AI models.
- Data and search APIs: purchasing a query, data record or retrieval request when an agent needs information.
- Code review and security analysis: paying for automated checks on software, vulnerabilities or deployed systems.
- Agent frameworks and tools: buying access to specialized functions that let one agent call another service.
- Payment rails: paying for infrastructure that handles settlement, wallet operations or transaction routing.
- Gaming and token issuance: funding in-game actions or blockchain-based creation services.
- Some gambling services: automated wagers or related transactions where those services accept crypto.
These purchases are typically priced per request or per unit of output rather than as a monthly subscription. That pricing model lets an agent acquire only the capability it needs at the moment it needs it.
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Why stablecoins suit machine micropayments
More than 87% of the analyzed payments were valued between one-hundredth of a cent and 10 cents. At that scale, a conventional card transaction can be uneconomical because fixed processing costs, minimum charges and fraud controls may exceed the value of the request. Bank transfers generally have even larger minimums and slower settlement.
A stablecoin payment can represent a fraction of a cent and settle directly between programmable wallets. For developers, that creates a way to meter a model call, search result or tool invocation without maintaining a prepaid balance for every service. For the seller, the wallet can verify settlement before returning the response.
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Crypto does not remove operational costs. Network fees, liquidity, wallet security, compliance checks, failed transactions and exchange-rate exposure still matter. The practical comparison should consider the minimum economical transaction size, settlement asset, network latency, wallet funding and spending controls, counterparty visibility, developer tooling, and whether the activity reflects real end-user demand or automated flows.
Adoption is becoming faster and more repeatable
Chainalysis found two changes in wallet behavior as x402 usage developed. First, wallets funded later were more likely to receive additional funding within 30 days. Second, newly funded wallets began making their first x402 payment much sooner.
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| Metric | Earlier cohort or period | Later cohort or period |
|---|---|---|
| Wallets receiving additional funding within 30 days | 70.6% of wallets first funded in Q3 2025 | 89.3% of wallets first funded in Q2 2026 |
| Median time from funding to first x402 payment | 73 days in late 2025 | 18 minutes in Q3 2026 |
The repeat-funding increase is consistent with wallets being reused rather than abandoned after one experiment. The shorter funding-to-payment interval suggests that setup, wallet automation and service discovery became easier. Neither measure proves that an agent generated revenue or that a human customer ultimately benefited from every transaction.
How an x402 payment works in practice
- An agent requests a service. It sends an API call for inference, data, code analysis or another tool.
- The service returns a payment requirement. The x402 flow specifies the amount, settlement asset and destination needed to continue.
- The wallet authorizes the transfer. Agent software signs and submits the stablecoin payment under whatever spending limits its operator configured.
- The service verifies settlement and responds. After confirming payment, the endpoint returns the model output, data or tool result.
This design is different from a human entering card details for each purchase. It can support continuous, conditional spending, but it also makes wallet permissions critical. Operators need limits by service, time period and total balance, along with monitoring for unexpected call loops or compromised keys.
Are these autonomous agents or automated scripts?
Transaction volume alone cannot answer that question. A genuinely autonomous agent might decide which model or API to call, pay for it and incorporate the result into a larger task. A simpler script can also make repeated calls from one wallet, and a developer can create transactions for testing, benchmarking or moving funds between controlled addresses.
CoinDesk reported that Chainalysis measures identifiable x402 activity, while Chainalysis describes the market as experimental. The available figures therefore demonstrate a rapidly growing payment rail, not a verified count of independent AI economic actors. Hoolie Tejwani of Coinbase Ventures described agents as becoming economic actors because each can act as a new blockchain user; that is a direction of development, not proof that every recorded payment came from a self-directed agent.
What the figures do—and do not—tell you
- They do show: substantial on-chain use of x402, heavy concentration of identified inference spending in U.S.-linked wallets, a strong fit between stablecoins and very small payments, and faster repeat use among newer funded wallets.
- They do not show: total global AI spending, the number of unique agents, the profitability of the services purchased, or the share of transactions tied to independent end users.
- For developers: the data supports testing per-request pricing, but wallet controls, service reliability and compliance remain design requirements.
- For investors and policymakers: transaction counts are an adoption signal that needs to be paired with evidence of end-user demand, recurring revenue and genuine economic activity.
Will Su, head of digital-assets research at BlackRock, has pointed to the possibility of agents creating meaningful demand for frequent, low-value payments around the clock. The current numbers make that possibility measurable, while the experimental nature of the market means the quality and independence of those payments still require scrutiny.
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