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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAI agents made more than 138 million x402 transactions on Base by the end of Chainalysis’s 2026 reporting period. The total measures activity identified by that protocol, not every AI payment worldwide, and the strongest geographic finding applies specifically to geolocated AI-model inference payments: U.S.-linked wallets accounted for 88.1% of them.
What the 138 million transactions measure
Chainalysis counted more than 138 million x402 transactions on Base through its 2026 reporting period, according to its report published October 14, 2026. x402 is Coinbase’s payment protocol built around the HTTP 402 “Payment Required” standard, allowing software services to request payment before delivering a response. In this dataset, the count is protocol activity on Base rather than a census of all crypto payments made by AI systems.
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A transaction also does not necessarily represent a unique agent, a unique customer or a completed business sale. The same wallet can make many automated calls, and some activity may be testing, internal funding or other machine-generated flows.
Why U.S. wallets dominate AI-model spending
Among the subset of AI-model inference transactions that Chainalysis could geolocate, U.S.-linked wallets represented 88.1%. Chinese-linked wallets represented 0.1%. Those percentages describe only the geographically identified inference subset; they should not be read as a country breakdown of all x402 activity or all AI-agent payments.
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One possible explanation for China’s small share is model choice. Chainalysis research chief Eric Jardine said Chinese developers may be more inclined to use open-weight models such as DeepSeek and Qwen, reducing their need to buy metered inference from external providers. The data does not establish that this is the only cause. Differences in wallet attribution, access to payment services, developer location and the availability of local providers could also affect the measured shares.
What AI agents buy with crypto
The recorded payments cover more than text generation. Identified use cases include:
- Model inference, where an application pays for an API response or a unit of compute.
- Data and search APIs used to gather information for another task.
- Code review, vulnerability scanning and security analysis.
- Agent frameworks, plugins and other developer tools.
- Payment infrastructure that lets one software service pay another.
- Game-related services, token issuance and some gambling services.
This mix suggests an emerging machine-to-machine services market, but category labels do not prove that each payment produced valuable output for a human user.
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More than 87% of the analyzed payments were worth between one-hundredth of a cent and 10 cents. At that scale, card-network fees, minimum charges, fraud controls and settlement delays can make conventional payments impractical. Stablecoin settlement can move a very small amount without requiring a separate human checkout for every request.
How an x402 payment works conceptually
- An agent requests a resource such as an inference result or API response.
- The service indicates that payment is required under the HTTP 402 pattern.
- The agent’s wallet sends the requested crypto amount through the supported network.
- The service verifies settlement and returns the resource.
Actual implementations still need safeguards: spending limits, approved counterparties, key protection, refund handling, rate limits and monitoring for malicious or unexpectedly expensive requests. Low transaction values reduce payment friction, but they do not remove operational or compliance risk.
Evidence that repeat use is increasing
Chainalysis found stronger follow-on funding among newer wallet cohorts. Of wallets first funded in the second quarter of 2026, 89.3% received additional funding within 30 days, compared with 70.6% for wallets first funded in the third quarter of 2025. The median interval between funding and a first x402 payment also fell from 73 days in late 2025 to 18 minutes in the third quarter of 2026.
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| Measure | Finding | Scope and date |
|---|---|---|
| Repeat funding | 89.3% versus 70.6% | Additional funding within 30 days; Q2 2026 first-funded wallets compared with Q3 2025 cohorts |
| Funding-to-payment speed | 73 days to 18 minutes | Median interval; late 2025 compared with Q3 2026 |
| Inference geography | U.S. 88.1%; China 0.1% | Geolocated AI-model inference transactions only |
| Payment size | More than 87% between $0.0001 and $0.10 | Analyzed x402 payments |
These changes are consistent with faster experimentation and more regular use. They are not, by themselves, proof that agents are generating sustainable commercial revenue.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAre these autonomous agents or automated scripts?
The safest answer is that the dataset shows automated payment activity, not a verified census of independent, goal-directed agents. A developer may configure a script to make repeated API calls, preload a wallet, or route payments between accounts it controls. Chainalysis measures identifiable x402 activity, and describes the market as experimental.
Coinbase Ventures head Hoolie Tejwani called AI agents “economic actors where every agent is effectively a new ‘user’ of the blockchain.” BlackRock digital-assets research head Will Su likewise pointed to potential demand for frequent, low-value payments around the clock. Those are adoption theses, not measurements proving that every counted transaction came from an autonomous system acting for an end customer.
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How to judge an AI payment network
Teams evaluating x402 or another machine-payment method should compare:
- Minimum economical transaction: whether fees make fractions of a cent viable.
- Settlement asset: stablecoin or fiat exposure, volatility and redemption arrangements.
- Network and latency: confirmation time, uptime and congestion behavior.
- Wallet controls: per-agent budgets, allowlists, key rotation and emergency stops.
- Counterparty visibility: identity, sanctions screening, audit trails and refund processes.
- Developer tooling: SDKs, documentation, test environments and error handling.
- Demand quality: evidence of independent end-user purchases rather than automated or self-dealing flows.
The 138 million-transaction headline demonstrates that the rail is being exercised at scale. Determining whether it represents a durable economy requires separating genuine customer demand from testing, internal transfers and repeated calls by the same operators.
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