# How Agentic Is Agentic Commerce? A Population-Scale Measurement of x402 Adoption and Authenticity

arXiv: https://arxiv.org/abs/2607.12575 · HTML: https://mlsanity.com/paper/2607.12575/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 17 of 17)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)](https://mlsanity.com/paper/2206.08202/) (2022)
- [BlockSci: Design and applications of a blockchain analysis platform](https://mlsanity.com/paper/1709.02489/) (2017)
- [Detecting Financial Bots on the Ethereum Blockchain](https://mlsanity.com/paper/2403.19530/) (2024)
- [Understanding Security Issues in the NFT Ecosystem](https://mlsanity.com/paper/2111.08893/) (2021)
- [How to Peel a Million: Validating and Expanding Bitcoin Clusters](https://mlsanity.com/paper/2205.13882/) (2022)
- [Free-Riding the Agentic Web: A Systematic Security Analysis of x402 Payments](https://mlsanity.com/paper/2605.30998/) (2026)
- [Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges](https://mlsanity.com/paper/2102.07001/) (2021)
- [Detecting Sybil Addresses in Blockchain Airdrops: A Subgraph-based Feature Propagation and Fusion Approach](https://mlsanity.com/paper/2505.09313/) (2025)
- [CanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks](https://mlsanity.com/paper/2006.15794/) (2020)
- [Tracking Counterfeit Cryptocurrency End-to-end](https://mlsanity.com/paper/2011.02673/) (2020)
- [A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain](https://mlsanity.com/paper/2212.01225/) (2022)
- [Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage](https://mlsanity.com/paper/2309.03592/) (2023)
- [Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance](https://mlsanity.com/paper/2501.17335/) (2025)
- [From Agent Identity to Agent Economy: Measuring the Operational Readiness of ERC-8004 AI Agents](https://mlsanity.com/paper/2606.12128/) (2026)
- [Shedding Light on the Adoption of Let's Encrypt](https://mlsanity.com/paper/1611.00469/) (2016)
- [Layer-2 Adoption and Ethereum Mainnet Congestion: Regime-Aware Causal Evidence Across London, the Merge, and Dencun (2021-2024)](https://mlsanity.com/paper/2512.14724/) (2025)

## Built on by (most cited first, 2 of 2)

- [Authority-Inference Separation in Agentic Finance: First-Line Control, Blockchain Enforcement, and Replayable Assurance](https://mlsanity.com/paper/2608.30519/) (2026)
- [A Formal Analysis of Agent Payment Protocols](https://mlsanity.com/paper/2609.00060/) (2026)

## Abstract (arXiv)

AI agents are said to be forming an economy in which they pay, on their own, for the data, APIs, and compute they consume. x402, which settles a stablecoin payment on-chain for each purchase, is the most widely deployed protocol for this, and its hundreds of millions of settlements are read as proof that the economy has arrived. We show the count cannot be read as adoption: it is the one metric an interested party can manufacture almost for free, since the facilitator sponsors the gas and nothing on-chain marks who controls a payment. We give the first population-scale measurement of x402 on Base, supplemented with a coarser Solana census. Identifying settlements from their on-chain event and resolving the true payer through the meta-transaction layer, we sort each by what its trace can prove via a payment graph. Over a 280-day window Base carries 136,708,672 settlements worth 44,121,383.81, concentrated on every axis we measure (payer, recipient, and value Gini all above 0.98), yet 21.20% are fictitious and 63.78% internal settlement within a linked cluster. What is genuinely independent is bounded: it lies between the 187,861.35 that demonstrably reaches a nameable service and the 20,258,746.09 (45.92% of value) not provably manufactured. Finally, we resolve the count's manufacturable component, a coherent operator-driven economy, star-shaped, machine-timed, and gas-subsidized. Settlement count measures manufacturability, not adoption.