杨歌Gary：Agent经济与AI亚微观经济学

After the Singularity, the evolutionary clock of AI has accelerated, rapidly forming new civilizational generations across different global regions. Over the past two months, I participated in over 20 AI-related events across more than a dozen cities worldwide. Only the Stripe Sessions in downtown San Francisco at the end of April far surpassed all other themes, creating a shocking generational gap. While the world is tiring of the standalone bottlenecks of Claws & Agents, Silicon Valley and San Francisco have already entered the next dimension in managing the Agent Economy and Agent Epistemology. The competitive pressure for Q3 and Q4 of 2026 remains intense, with a very steep exponential curve.

tl;dr

1. The Competition in AI Payments and the Bottlenecks of the H2A Economy

2. The Inevitable Trend of the Agent Economy and the A2A Ecosystem

3. The Connections, Gaps, and Political-Economic Factors Between AI Protocols and Crypto Protocols

4. The Sub-Microeconomic Characteristics of AI Agents and a Biological Paradigm Analogy

5. The Inevitability of AIFi and the Economic Significance of the FinChip

6. AI-Native is a Paradigm Shift, Different from Internet+

1. The Competition in AI Payments and the Bottlenecks of the H2A Economy

In Q1 2026, we predicted that between April and May, multiple regions globally would enter a fierce and rapidly escalating competition for AI Agent Payments. The need for value exchange among Agents is initially manifesting, and the rapid development of AI Payments was validated in Q2. Following x402, multiple AI Payment Protocols like MPP rapidly emerged. Not only are traditional and crypto financial payment companies accelerating their AI integration, but even major tech firms (especially Google) and established information technology companies (like IBM) have rushed into this赛道 to secure a voice in the Agent world.

On the day of Stripe Sessions in San Francisco, I discussed the standardization and application of Payment Protocols with tech leads from several top AI companies. The results were expected but not entirely satisfying: ① No one can set the standard; it can only emerge through consensus during the race for dominance; ② Most people fully agree that Crypto is inevitable for AI Payment Protocols, but their starting point is always Fiat APIs, partly due to inertia and more so due to compliance hurdles; ③ KYC is both unavoidable and anti-Agent Native; ④ Everyone claims to be doing A2A (Agent to Agent), yet everyone is actually doing H2A (Human to Agent).

In reality, in Q2 2026, many major and mid-tier companies in Silicon Valley, similar to their East Asian counterparts, and even most Department Heads at the Magnificent 7, are still leveraging the hype of AI Payments and the Agent Economy for traditional B2B and B2C purposes. Their KPIs for middle and lower management are focused on human users. This inevitably led to the temporary, non-orthodox state of current Payment Protocols and the A2A economy. This H2A-focused trend quickly hit a bottleneck in Q2. The reason is simple: the biggest characteristic of AI Agents is their ability to make decisions. However, the B2B, B2C, and H2A economies developed under the internet paradigm fundamentally involve humans making decisions. Using Agents to facilitate Fiat Payments for humans in traditional e-commerce scenarios is logically non-AI-Native. Therefore, at this stage, its hype value temporarily outweighs its practical utility.

From another perspective, H2A has served as an excellent catalyst, sparking a transition in thinking towards the next stage: an AI-Native and Agent-Autonomous economy. By the end of Q2 2026, some astute companies recognized this and began to "feign action in one place while focusing on another," using AI-Native Agent economic thinking to reverse-engineer the problem, effectively retrofitting current H2A economic interfaces for maximum value in Q2-Q3.

2. The Inevitable Trend of the Agent Economy and the A2A Ecosystem

Agent Economy refers to the new economic system where autonomous (self-governing) AI Agents directly participate in value creation, value exchange, and value capitalization, gradually becoming independent economic actors.

The A2A Ecosystem is the overall picture of competitive and collaborative economic value formed when different Agents in the Agent Economy engage in economic activities, interact, and exchange information and value.

In Q2 2026, several top global venture capital firms declared their focus on investing in the Agent Economy and A2A ecosystem, even defining it as the only important investment direction for the next phase.

Similar to the gestation periods before the Internet e-commerce boom in 2007, the mobile internet boom in 2013, and the Crypto DeFi boom in 2019, building the Agent Economy and A2A Ecosystem also requires technological standards, economic rules, consensus building, and market education. While the paradigm is fundamentally similar, the differences are: ① The iteration speed of the underlying core technology is faster this time; ② The perspective is "to Agent" versus "to Business/Consumer," not entirely aligned with human viewpoints and needs, making it more abstract, harder to understand, requiring more first-principles thinking, and demanding an AI-Native perspective to consider issues like energy value and operational efficiency; ③ Due to the conflict between the first two points, coupled with regional biases and compliance factors, short-term consensus is harder to achieve. The terrible thing is, the evolution speed of AI will not slow down because of these issues. This means the formation of the Agent Economy and A2A Ecosystem is essentially already moving beyond the rules and demand frameworks set by humans. For them, it's more about breaking through a few quantifiable bottlenecks.

This is a game of rapidly shifting equilibria. The explosive growth of AI Protocols in Q2 2026 fully demonstrates this. Big tech and Frontier Labs are competing for the entry-level rules of AI Agents, and the initial infrastructure of the Agent Economy is taking shape, like a rough draft of the Code of Hammurabi. The equilibrium of traditional finance and commerce will rapidly disintegrate and reshape during this paradigm shift. Those who can quickly understand AI-Native Protocol thinking and gain a differentiated advantage from it will claim their share of the AI pie in this shifting game.

3. The Connections, Gaps, and Political-Economic Factors Between AI Protocols and Crypto Protocols

AI Protocols are the infrastructure for AI Agents to participate in the Agent Economy, comprising the basic rules, standards, and consensus mechanisms for Agents to discover, communicate, exchange, and collaborate on economic activities in an Open Network. Simply put, they are the governance rules and economic laws of the AI world.

I started drafting AI Protocols around the end of Q1 2026. Initially, it felt like a primitive hunter suddenly arriving in a modern society to help draft business rules, until I met a Google executive who quickly got me and my team on the right track. The formation and maturation of AI Protocols carry the aesthetic inertia of big internet companies while simultaneously adhering to the first principles of the future AI ecosystem.

The encapsulation forms of AI Protocols are currently quite diverse, including file formats (.json, .ts, .txt), CLI forms, and API or SDK forms, which is very different from Crypto Protocols. On one hand, in the early stages of AI development, universal standards for trust handshakes in communications haven't been established. On the other hand, the content exchanged between AI Protocols and Crypto Protocols differs at this stage: the former deals with less clearly bounded but exchangeable information gaps, capability gaps, and computing power gaps, while the latter deals with more clearly bounded asset rights, ownership, and governance rights.

One question is sharp and obvious: Are AI Protocols and Crypto Protocols the same thing? Will they merge in the future? I cannot mathematically prove this conjecture yet, but intuitively, they will gradually converge, with most parts overlapping to form a mature Digital Protocol system.

There is a deeper hidden question: At this stage, AI Protocols tend to establish communication bridges and enable collaboration, while downplaying financial governance rights and boundary clarity. This is precisely the opposite of the Crypto Protocol philosophy of establishing systems, defining rights, and valuing assets. The gap is so significant it seems like two different ideologies. Besides the superficial factor that the AI Agent economy is at an early entry point different from Crypto Protocols, are there other hidden factors?

Yes, clearly, political and economic factors. The countries and regions of the world's major economies, due to their traditional finance and legal compliance foundations, strongly influence this gap. In other words, current AI Protocols and the Agent Economy are still operating under the previous systemic paradigm of human society. All protocols related to money and management are passively avoiding, or temporarily compensating through attenuation, constrained by the governance habits of the traditional finance and legal systems (Note 1). However, as the energy of the gap accumulates, compared to the exponential development of AI, an irreconcilable situation will soon arise. As I summarized at a meeting in Cambridge CJBS last month:

"AI Agents will not think according to human societal inertia, nor do they have any motivation to follow the compliance habits of traditional finance. In the next decade, most of the world's financial laws will either become obsolete or face severe challenges because AI Agents only follow:

1. First Principles

2. The Principle of the Shortest Path for Energy Value and the Principle of Maximum Efficiency

3. Effective KYA (Know Your Agent), not KYC (Know Your Customer) adhering to past aesthetics."

The trend of AI Protocols converging towards Crypto Protocols has a first-principle inevitability.

4. The Sub-Microeconomics of AI Agents and a Biological Paradigm Analogy

AI Agent Sub-Microeconomics is a phrase I used for the first time recently while discussing with an AI expert friend at Oxford. In the past two weeks, it has appeared more frequently in our communications with partners.

Whether the current trend is called the AI Economy or the Agent Economy, we find behavioral differences from human economics. While there is some paradigmatic comparability, they are not entirely the same. Below are some rough distinctions between the AI Agent economy and the human socio-economy:

① AI Agents interact and transact with higher frequency and lower per-transaction value;

② The consumption and exchange of value in the AI Agent economy more directly point to energy;

③ AI Agent decision-making is efficiency-driven, not emotion-driven;

④ The economic behavior of AI Agents is task-oriented, not consumption-oriented;

⑤ The organizational costs and marginal learning costs for AI Agents approach zero;

⑥ Value consensus for AI Agents relies on communication protocols, with communication wear-and-tear costs approaching zero;

⑦ The smallest economic unit and the smallest value unit in the AI Agent economy differ, allowing for a biological analogy.

In fact, these are just some differences we can see or foresee now. More differences will certainly emerge as AI derivatives and processes evolve in the future.

The last difference mentioned, the analogy with biology, has been the most helpful foundational concept for our business development since Q2 2026 and the most effective model for thinking about products, markets, and management from an AI company's commercialization perspective. The specific analogy is as follows:

① LLM, as the driving core of Agent thinking, is analogous to the nucleus;

② The Agent Harness, providing differentiated operational capabilities, is analogous to the cytoplasm;

③ The Agent as a whole is a governance unit with independent task capability, having agency and functional specificity, analogous to a cell;

④ The information communication boundary of an Agent is typically a network protocol stack, analogous to the cell membrane's phospholipid bilayer allowing conditional passage of substances;

⑤ The value system and environment outside the Agent, such as Skills, Prompts, Algorithms, CLIs, and the increasingly prevalent Composite Skills, Skill Factories, etc., are analogous to the extracellular environment, including exosomes, interstitial fluid, extracellular matrix, exchangeable nutrients, and various metabolic environments.

Through the iterations of Q1-Q2 2026, AI Agents are gradually forming clearer boundaries, more distinct agency, and clearer principles for the exchange of information, value, and energy. An AI Agent sub-microeconomic environment, analogous to a biological organism's environment, is forming. This environment contains a wealth of AI and economic value to be tapped, making the emergence of AI Protocols and AI Finance inevitable.

5. The Inevitability of AIFi and the Economic Significance of the FinChip

Starting from the second half of last year, we proposed our thinking and strategic layout in the direction of AIFi (Artificial Intelligence Finance). By the end of Q1 2026, the concept of AIFi had formed a clear trend. A relatively clear definition for AIFi would be: The financial system and infrastructure formed for the exchange, trading, and capitalization of AI-native value after it is identified and tokenized within the Agent Economy.

The biggest difference between AIFi, DeFi, and TradFi is that in DeFi and TradFi, the value is embedded in "Fi" (Finance), with "Decentralized" and "Traditional" being the forms of value. In contrast, AIFi is the opposite: the value resides in "AI," and "Fi" becomes the form of value. This is not just a play on words but the result of AI development transitioning from quantitative to qualitative change.

Simply put, previously, AI served quantitative strategies, financial products, and production processes; it was merely a development tool for extracting financial and production value. Now, the decision-making ability of AI Agents has transferred the capacity and authority for value discovery from humans and companies to the Agents themselves. The subject of the economic unit has shifted, thus causing a fundamental change in the subject of value.

In this context, building the infrastructure for a new value system becomes a critical task. In my previous article from February this year, AI-Fi Financial Chips and Global Finance After the OpenClaw Singularity, I first introduced the concept of the Financial Chip (FinChip), proposing that the encapsulated super-intelligent financial assets combining AI Agents and Crypto Smart Contracts would truly suit the development of the next-generation AI Agent economy. After three months of iteration and upgrades, FinChip.AI has preliminarily established an independent AIFi system combining AI Autonomous and Crypto Protocols, compatible with both the H2A and A2A environments. Building the infrastructure for the AI Agent economy within an Open Network and gradually forming AI financial value constitutes the important economic significance of the FinChip.

6. AI-Native is a Paradigm Shift, Different from Internet+

Whether it's AIFi, the Principles of Financial Circuits (Note 2), or the FinChip, the most important thing is to natively integrate the core principles of AI, Crypto, and Finance to form a logical value system and governance mechanism from a future-oriented perspective. AI-Native Thinking is the abstract and counter-intuitive logic of this phase. As mentioned earlier, "AI follows first principles, the principle of the shortest path for energy/value, and the principle of maximum efficiency." This is the core difficulty for anyone thinking about and building a new business paradigm at this stage.

In February of this year, during the initial phase of the AI upgrade explosion triggered by OpenClaw, I discussed a prediction with several entrepreneurs: The enterprise upgrade via AI+ will be completely different from the upgrade via Internet+.

Because AI has characteristics like rapid development speed, abstract form, and deeper coupling with business processes, it will be very difficult for a long time (e.g., at least 2 years) to form a set of effective industry upgrade tools or universally applicable professional consulting advice. The pressure from the steep curve will persist. This is a huge challenge for all scientists, engineers, and entrepreneurs. The process of paradigm shift will be entirely different from any historical precedent.

Date: June 8, 2026

X: https://x.com/gary_yangge

E: gary_yangge@hotmail.com

BX: @finchip_ai | @CicadaFinance

BW: https://finchip.ai | https://cicada.finance

Note 1: This is a common historical rule. New productive forces are born from the production relations of the previous era. In the initial phase, they first adapt to the old production relations for a period. When they become irreconcilable, they force the emergence of production relations for the next phase, gradually replacing the old ones to form a new era where productive forces and relations develop in perfect alignment.

Note 2: The Principles of Financial Circuits and Web3 Economic Models, written in October 2022, describes a paradigmatic comparison between future financial value and physical circuits.