When AI Entities Start Borrowing: How Machine Credit Prices Trust?

The on-chain lending market is writing the early rules of AI credit.

Written by: Thejaswini M A

Compiled by: Saoirse, Foresight News

Humans have been practicing lending for thousands of years, yet even so, there is no guarantee that repayments will always be made on time.

By mid-2026, the total mortgage, credit card, auto loan, student loan, and various other debts of Americans will reach $18.8 trillion. Among these, 4.7%, approximately $880 billion, are in varying degrees of delinquency.

In the second quarter, about 7% of credit card debt was severely delinquent, meaning payments were overdue by at least 90 days. Additionally, over 23 million Americans still carry old credit card debts classified as bad debts by lenders.

Banks review users' long-term repayment records. In fact, banks hold an astonishing amount of personal information about us.

Banks know whether you have paid your last credit card bill on time, can access your mortgage records, are aware of the credit limits granted to you by other financial institutions, and know if you have ever been late on payments. When applying for large loans, banks also verify your income, employment status, and asset conditions. This is why institutions collect such historical data, generate credit scores, and sell these scores to lenders.

With so much data in hand, there were still 581,570 Americans who filed for non-business bankruptcy in the 12 months ending June 2026, a 12% increase year-on-year.

The credit system does not require borrowers to be flawless. Banks can stratify borrowers by risk, setting lending limits and implementing differentiated loan interest rates.

Now AI entities can hold funds and autonomously complete payments. So, can we replicate this credit logic for AI entities?

What conditions must a program entity meet to gain trust and obtain loans? What issues might arise from lending to machines? This article will explore Priors and other projects dedicated to bridging this gap.

I transferred $5 into the AgentCash wallet of the AI entity Hermes for it to spend autonomously; once the balance is exhausted, I recharge it again. This mechanism is designed this way. We can deposit stablecoins into the wallet, and the entity can settle API call fees through the x402 protocol.

However, if we want the AI entity to independently undertake payment tasks, contradictions arise: it needs to purchase data and call model computing power immediately, but the task reward may not arrive for a week.

In such business scenarios, repeated recharges are cumbersome, especially since funds often need to be transferred in bulk.

This is precisely why credit for AI entities has emerged. If my AI entity needs $10 to start work now, but the earnings will only be realized a week later, a short-term loan can fill the funding gap.

Additionally, there is another practical motivation. AI entities are beginning to interact with completely unfamiliar other entities. The Ethereum entity identity standard ERC-8004 grants each AI entity an on-chain identity, along with evaluation feedback, but the feedback content can be manipulated, and we do not have a reliable credibility certificate.

Lending, in essence, is pricing trust. Long before credit scores came into existence, lending games were built on reputation. In medieval Venice, merchants with good repayment records found it easier to raise funds for maritime trade— they were more capable of making profits to support the next voyage.

Even in medieval England, trade credit was deeply tied to the merchants'

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