Author: Han Qin, Jarsy CEO

Some friends asked what the real competition in the AI Agent Economy is?

Actually, many friends have shared their thoughts, and there are good insights regarding whether AI Agents really need crypto.

But the real discussion should not be whether AI Agents need Visa or Crypto, but whether AI Agents need a “traditional credit system” or an “algorithmic trust system”.

This question touches on the fundamental fork in future financial structures: should human society rely on credit backed by humans, or on trust ensured by mathematics?

Since BTC provided a proof of mathematical trust, this topic has gained significance.

Let’s first define the two systems. The essence of the credit system is to trust that a certain entity will not default; the source of trust comes from legal reputation, regulatory identity, and intermediary institutions, with the core structure being human to institution, to rules, to trust. Typical examples include the banking system, Visa & Mastercard, securities markets, and loan contracts.

However, the algorithmic trust system is different; its essence is that it does not require trusting anyone. The source of trust comes from mathematical proof, cryptography, signatures, consensus, and tamper-proof algorithms, with the core structure being code to mathematics, to automatic execution, to trust. Typical examples include blockchain, smart contracts, zk proofs, and MPC.

The most fundamental difference between the two lies at the philosophical level. The trust in the credit system comes from human institutions, failure occurs through human defaults, error correction is through courts, and boundaries are defined by nations. The trust in algorithmic systems comes from mathematical theorems, failure occurs through code vulnerabilities, error correction is through forks, and boundaries are defined by networks. So essentially speaking, the credit system equals trust in human entities, while the algorithmic system equals trust in code rules.

So why could human society initially only use a credit system? Because historically there were no technological conditions for algorithmic trust. To achieve algorithmic trust, one needs public-key cryptography, distributed networks, consensus algorithms, and verifiable computation, all of which have only emerged in the past few decades. Therefore, for thousands of years, the only feasible solution was to find someone everyone trusted, whether it was an elder, a king, or now, a central bank.

Why will the AI era push towards algorithmic trust? Because AI has changed the structure of transaction subjects. In the past, the transaction subject was human beings; now, the transaction subject is AI Agents plus humans. So the question arises: machines cannot understand legal reputations and social relationships; machines only understand verifiable rules.

Therefore, the AI native economy will inevitably lean towards the algorithmic trust system; otherwise, machines will not be able to participate smoothly.

Of course, the advantages of the credit system will not disappear. Many people mistakenly believe that crypto will replace the credit system, which is impossible. This is because the credit system is inherently suited for a world of high uncertainty, such as venture capital, healthcare, warfare, and entrepreneurship; these scenarios cannot be pre-defined by code and require judgment and flexibility in consensus, which algorithms cannot handle.

Secondly, real societies need human error correction; fraud, mistakes, and gray areas will certainly exist in the real world, and only humans can adjudicate these. Moreover, long-term trust relationships will also require traditional credit systems, such as family trusts, political alliances, and strategic cooperation, which rely on relational capital, not algorithms.

However, the advantages of algorithmic trust systems are in explosive growth. Today, people do not yet see this because the tipping point has not arrived. In high-frequency trading environments, machine speeds far surpass human trust speeds, giving crypto a crushing advantage. Additionally, cross-border transactions will also be a natural stronghold for crypto because algorithms don’t care about borders. Of course, the permissionless scenarios we have discussed are the main battlefield for crypto.

In the future, the real world will not be a binary choice; the true end structure will definitely be a layered trust architecture. The upper layer is the credit governance layer, responsible for rule-making, dispute resolution, and risk-bearing entities that remain national court institutions. The middle layer is the protocol execution layer, responsible for automatic execution, asset circulation, and the clearing entities are blockchain and smart contracts. The bottom layer is the computational verification layer, responsible for cryptographic proofs, data integrity, and consensus algorithms.

In the future, the biggest competition will not be crypto vs banks, but who will define the trust interface standards. Whoever defines the standards will control the ecosystem. History has proven that TCP/IP defined the internet, SWIFT defined financial communication, Visa defined consumer payments, and the next standard could potentially be a programmable trust protocol that defines the AI Agent Economy.

If the credit system is like a lawful state, then algorithmic trust is like an automatic machine society. The relationship between the two is not one of substitution but rather that the law establishes the rules and the machine executes the rules.

The credit system solves who is worthy of trust; algorithmic trust solves the need to trust no one.

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