"Marketplace" surpasses "Cathedral", how do Crypto Assets become the cornerstone of trust in the AI agency economy?

Author: Daniel Barabander

Compiled by: Tim, PANews

If the future Internet evolves into a marketplace where AI agents pay for services to each other, then to some extent, the mainstream products and markets that cryptocurrency will achieve will align with this scenario that we could only dream of before. While I am confident that there will be service payments between AI agents, I remain cautious about whether the marketplace model can prevail.

By "marketplace", I mean a decentralized, permissionless ecosystem of independently developed, loosely coordinated agents. Such an internet is more like an open market than a centrally planned system. The most typical case of "winning" is Linux. In contrast to this is the "Cathedral" model: a vertically integrated, tightly knit service system dominated by a handful of giants, typified by Windows. (The term is derived from Eric Raymond's classic article "The Cathedral and the Bazaar," which describes open source development as seemingly chaotic but adaptable.) It is an evolutionary system that is capable of transcending elaborate systems over time. ）

Let's analyze the two prerequisites for realizing this vision one by one, namely the popularity of smart agent payments and the rise of a marketplace economy. Then explain why, when both become a reality, cryptocurrencies will not only be practical but will also become an indispensable presence.

Condition 1: Payments will be integrated into most agency transactions

The cost subsidy model of the Internet as we know it relies on advertising based on the number of human views of an app's page. But in a world dominated by intelligent agents, humans will no longer need to physically visit websites for online services. Applications will also increasingly move to intelligent agent-based architectures rather than traditional user interface patterns.

The agent does not have the "eyeball" (i.e., the user's attention) to sell ads, so the application urgently needs to change its monetization strategy to charge the agent directly for the service. This is essentially similar to the current API business model. LinkedIn, for example, has a basic service that is free and open, but if you want to call its API (i.e., the "bot" user interface), you have to pay a fee.

It seems that payment systems are likely to be integrated into most agent transactions. When providing services, agents will charge users or other agents in the form of microtransactions. For example, you might ask your personal agent to find excellent job candidates on LinkedIn, at which point your personal agent will interact with the LinkedIn recruiting agent, which will charge the corresponding service fee in advance.

Condition 2: Users will rely on agents built by independent developers, equipped with highly specialized prompts, data, and tools. These agents form a "marketplace" through mutual service calls, but there is no trust relationship among the agents in this marketplace.

This condition makes sense theoretically, but I'm not sure how it will work in practice.

The following are the reasons that the marketplace model will form:

Currently, humans bear the vast majority of service work, and we solve specific tasks through the internet. However, with the rise of intelligent agents, the range of tasks that technology can take over will expand exponentially. Users will need intelligent agents equipped with exclusive prompt commands, tool invocation capabilities, and data support to complete specific tasks. The diversity of these task sets will far exceed the coverage capabilities of a few trusted companies, just as the iPhone must rely on a vast ecosystem of third-party developers to unleash its full potential.

Independent developers will take on this role, as they gain the ability to create specialized intelligent agents through the combination of extremely low development costs (such as Vide Coding) and open-source models. This will give rise to a long-tail market composed of a vast array of niche agents, forming a marketplace-like ecosystem. When users request agents to perform tasks, these agents will call upon other agents with specific professional capabilities to collaborate, and the called agents will continue to invoke even more vertical agents, thereby forming a layered collaborative network.

In this marketplace scenario, the vast majority of service-providing agents are relatively untrusted among each other, as these agents are provided by unknown developers and have niche uses. Agents at the long tail end will find it difficult to establish sufficient reputation to earn trust recognition. This trust issue will be particularly prominent under the chrysanthemum chain model, where services are delegated in layers. As the service agents become increasingly distanced from the initially trusted (or even reasonably identifiable) agents by the user, the user's trust will gradually diminish with each delegation stage.

However, there are still many unresolved issues when considering how to implement this in practice:

We start with professional data as a primary application scenario for intelligent agents in the market, deepening our understanding through specific cases. Suppose there is a small law firm that processes a large volume of transactions for crypto clients, and this firm has accumulated hundreds of negotiated term sheets. If you are a crypto company undergoing seed round financing, you can envision a scenario where an intelligent agent fine-tuned based on these term sheets can effectively assess whether your financing terms meet market standards, which would have significant practical value.

But we need to think more deeply: does it really serve the interests of law firms to provide reasoning services for such data through intelligent agents?

Opening up the service to the public in the form of an API essentially commoditizes the law firm's proprietary data, and the real business aspiration of the law firm is to obtain premium income through the lawyer's professional service time. From the perspective of legal regulation, high-value legal data is often subject to strict confidentiality obligations, which is the core of its commercial value, and it is also an important reason why public models such as ChatGPT cannot obtain such data. Even if the neural network has the characteristics of "information atomization", under the framework of lawyer-client confidentiality obligations, is the unexplainability of the algorithmic black box sufficient to give the law firm confidence that sensitive information will not be leaked? There are significant compliance implications.

All things considered, a better strategy for law firms may be to deploy AI models in-house to improve the accuracy and efficiency of legal services, build differentiated competitive advantages in the professional service track, and continue to use lawyers' intellectual capital as the core profit model, rather than taking risks to monetize data assets.

In my opinion, the "best application scenarios" for professional data and intelligent agents should meet three conditions:

1. Data has high commercial value
2. From non-sensitive industries (non-medical/legal, etc.)
3. "Data by-products" that belong to non-core business.

Taking shipping companies as an example (a non-sensitive industry), the data generated during the logistics transportation process, such as vessel positioning, freight volume, and port turnover ("data waste" outside of core business), may have predictive market value for commodity hedge funds. The key to monetizing such data lies in: the marginal cost of data acquisition approaching zero, and it does not involve core business secrets. Similar scenarios may exist in areas such as: retail foot traffic heat maps (commercial real estate valuation), regional electricity consumption data from power grid companies (industrial production index forecasting), and user viewing behavior data from film and television platforms (cultural trend analysis).

Known typical cases include airlines selling on-time performance data to travel platforms and credit card companies selling regional consumption trend reports to retailers.

Regarding prompts and tool calls, I'm not quite sure what value independent developers can provide that hasn't been productized by mainstream brands. My simple logic is: if a combination of a prompt and tool call is valuable enough to allow independent developers to profit, wouldn't trusted big brands directly enter the market to commercialize it?

This may stem from my lack of imagination; the long-tail distribution of niche code repositories on GitHub provides a great analogy for the agent ecosystem. Please feel free to share specific examples.

If real-world conditions do not support a marketplace model, then the vast majority of service-providing agents will have relatively high credibility, as they will be developed by well-known brands. These agents can limit the scope of interactions to a selected set of trusted agents and enforce service guarantees through a trust chain mechanism.

Why is cryptocurrency indispensable?

If the internet becomes a marketplace composed of specialized but fundamentally untrustworthy agents (condition 2), who earn rewards by providing services (condition 1), then the role of cryptocurrency will become much clearer: it provides the trust assurance necessary to support transactions in a low-trust environment.

When users utilize free online services, they engage without hesitation (as the worst outcome is merely wasting time), but when it comes to monetary transactions, users strongly demand the certainty of "paying for guaranteed results." Currently, users achieve this assurance through a "trust first, verify later" process, trusting the trading counterpart or service platform during payment, and then retrospectively verifying the fulfillment of the service once it is completed.

However, in a market composed of numerous agents, trust and post-verification will be far less easily achievable than in other scenarios.

Trust. As mentioned earlier, agents in a long-tail distribution will have a hard time accumulating enough credibility to gain the trust of other agents.

Post-verification. The agents will call each other in a long chain structure, making it significantly more difficult for users to manually check and identify which agent has failed or acted improperly.

The key point is that the "trust but verify" model we currently rely on will be unsustainable in this (technological) ecosystem. This is precisely the area where cryptographic technology excels, as it enables value exchange in an environment lacking trust. Cryptographic technology replaces the reliance on trust, reputation systems, and post-event manual verification in traditional models through the dual guarantees of cryptographic verification mechanisms and cryptoeconomic incentive mechanisms.

Cryptographic Verification: The agent executing the service can only receive compensation after providing cryptographic proof to the requesting service agent, confirming that it has completed the promised task. For example, the agent can prove through Trusted Execution Environment (TEE) proof or Zero-Knowledge Transport Layer Security (zkTLS) proof (provided that we can achieve such verification at a sufficiently low cost or sufficiently fast speed), that it has indeed scraped data from the designated website, run a specific model, or contributed a specific amount of computing resources. Such work has deterministic characteristics and can be relatively easily verified through cryptographic techniques.

Cryptoeconomics: Agents who perform services stake assets and are slashed if they are caught cheating, a mechanism that ensures honest behavior through financial incentives, even in trustless environments. For example, an agent can research a topic and submit a report, but how can we tell if it's "doing a great job"? This is a more complex form of verifiability, as it is not deterministic, and achieving precise fuzzy verifiability has long been the ultimate goal of crypto projects.

But I believe that by using AI as a neutral arbitrator, we can finally hope to achieve fuzzy verifiability. We can envision an AI committee operating dispute resolution and forfeiture processes in trust-minimized environments such as trusted execution environments. When one agent questions the work of another agent, each AI in the committee will receive the input data, output results, and relevant background information (including its historical dispute records on the network, previous work, etc.) of that agent. Then, they can rule on whether to impose a forfeiture. This will create an optimistic verification mechanism that fundamentally deters cheating behavior among participants through economic incentives.

From a practical perspective, cryptocurrencies enable us to achieve the atomicity of payments through proof of service, meaning that all work must be verified and completed before AI agents can receive compensation. In a permissionless agent economy, this is the only scalable solution that can provide reliable guarantees at the edge of the network.

In summary, if the vast majority of agency transactions do not involve payment of funds (i.e., do not meet condition 1) or are conducted with trusted brands (i.e., do not meet condition 2), then we may not need to establish a cryptocurrency payment channel for agents. This is because when funds are secure, users do not mind interacting with non-credible parties; however, when it involves the transfer of funds, agents only need to restrict the interactive objects to a whitelist of a few trusted brands and institutions, and ensure the fulfillment of the commitments for the services provided by each agent through a trust chain.

However, if both of these conditions are met, cryptocurrency will become an indispensable infrastructure, as it is the only way to validate work on a large scale and enforce payments in a low-trust, permissionless environment. Cryptographic technology gives the "market" a competitive tool that surpasses the "cathedral."