Yang Ge Gary: Agent Economy and AI Microeconomics

After the Singularity erupted, the evolutionary clock of AI has been accelerating, rapidly forming new civilizational epochs in different regions across the globe. Over the past two months, I have participated in more than 20 AI-related events across over a dozen cities worldwide. Only Stripe Sessions, held in downtown San Francisco at the end of April, far surpassed all other topics, delivering a shockingly clear sense of generational divide. While the world grows weary of the single-agent 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 '26 remains intensely steep, with an 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, Divergence, and Political-Economic Factors between AI Protocols and Crypto Protocols

4. The Micro-economics of AI Agents and Paradigm Analogies with Biology

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

6. AI-Native is a Paradigm Upgrade Distinct from Internet+

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

In Q1 '26, 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 becoming apparent, and the rapid development of AI Payments was validated in Q2. Following x402, numerous AI Payment Protocols like MPP emerged swiftly in Q2. It's not just traditional and crypto financial payment companies accelerating their AI transformation; even big tech firms (especially Google) and legacy information technology companies (like IBM) are rushing into this赛道 to secure a position and voice in the Agent world.

On the day of Stripe Sessions in San Francisco, I discussed the standardization and application of Payment Protocols with technical leads from several top AI companies. The results were reasonable but not entirely satisfactory: ① No one can set the standard; consensus standards only form gradually during the race to dominate; ② Most people fully agree that Crypto is inevitable for AI Payment Protocols, yet their starting point is 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 doing H2A (Human to Agent).

In reality, during Q2 '26, many large and mid-tier Silicon Valley companies behaved quite similarly to East Asian companies. Even most Department Heads at the Mag 7 were merely capitalizing on the hype around AI Payments and the Agent Economy for traditional B2B/B2C business purposes. Their KPIs for mid-level and junior staff remained focused on Human Users, which inevitably led to the current temporary unorthodoxy of Payment Protocols and the A2A economy. This H2A-oriented trend quickly hit bottlenecks in Q2. The reason is simple: the biggest characteristic of AI Agents is their ability to make decisions, yet the core of B2B/B2C commerce and the H2A economy developed under the internet paradigm is fundamentally human-driven decision-making. Using Agents to help humans execute Fiat Payments in traditional e-commerce scenarios is logically Non-AI-Native by nature. Therefore, its hype value currently outweighs its practical utility for now.

However, from another perspective, H2A has served as an excellent catalyst, sparking the transition towards thinking about the next stage: the AI-Native and Agent Autonomous Economy. By the end of Q2 '26, some smart enterprises had realized this, beginning to "pretend to advance along one path while secretly going another," using AI-Native Agent economic thinking to reverse-engineer the approach. Re-evaluating current H2A economic interfaces from this perspective is where the best value lies for Q2-Q3.

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

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

The A2A Ecosystem represents the overall picture of competitive and collaborative economic value formed when different Agents participate in economic activities within the Agent Economy, interacting with each other, exchanging information and value.

In Q2 '26, multiple top-tier global venture capital firms declared their focus on investing in the Agent Economy and A2A Ecosystem, even defining it as the single most important investment direction for the next phase.

Similar to the gestation periods before the internet e-commerce boom (2007), the mobile internet boom (2013), and the Crypto DeFi boom (2019), building the Agent Economy and A2A Ecosystem equally requires technical standards, economic rules, consensus building, and market education. While the paradigm is fundamentally similar, the differences are: ① The iteration speed of core technological development is faster this time; ② The perspective is "to A" as opposed to "to B/C," not necessarily aligned with human perspectives and needs, making it more abstract, harder to understand, requiring stronger first-principles thinking, and demanding more consideration from an AI-Native viewpoint regarding energy consumption and operational efficiency; ③ Due to conflicts arising from the first two points, compounded by regional biases and compliance issues, short-term consensus is harder to achieve. The terrible thing is, the evolution speed of AI will not slow down due to these issues. This means the formation of the Agent Economy and A2A Ecosystem is essentially already decoupling from the rules and needs frameworks set by humans. For them, the situation is merely about overcoming a few quantifiable bottlenecks.

This is a game where the equilibrium of bargaining power shifts rapidly. The rapid explosion of AI Protocols in Q2 '26 fully demonstrates this. Big Tech and Frontier Labs are vying for entry-level rules governing AI Agents. The initial infrastructure for the Agent Economy is taking shape, like a draft version of the Code of Hammurabi. The equilibrium of traditional finance and commerce will rapidly dissolve and reshape during this paradigm shift. Whoever can quickly understand AI-Native Protocolization thinking and leverage it to gain a differentiated advantage will claim their share of the AI pie in this shifting game.

3. The Connections, Divergence, and Political-Economic Factors between AI Protocols and Crypto Protocols

AI Protocol is the infrastructure for AI Agents participating in the Agent Economy. It is the foundational set of rules, standards, and consensus mechanisms that enable Agents to discover, communicate, exchange, and collaborate in economic activities on an Open Network. Simply put, it is the governance rules and economic laws for the AI world.

I began drafting the concept of the AI Protocol around the end of Q1 '26. Initially, it felt like a primitive hunter-gatherer suddenly arriving in modern society to participate in drafting business rules. It wasn't until I met a Google executive that my team and I got quickly on the right track. The formation and maturation of the AI Protocol carries the aesthetic inertia of internet giants while simultaneously adhering to the first principles of the future AI ecosystem.

The packaging forms of AI Protocols are currently highly inconsistent, ranging from file formats (.json, .ts, .txt) and CLI interfaces to APIs or SDKs – quite different from Crypto Protocols. On one hand, during this early stage of AI development, universal standards for trust handshake in communication have yet to be established. On the other hand, the content exchanged by AI Protocols and Crypto Protocols differs at this stage. The former involves exchanging information gaps, capability gaps, and computational power gaps, whose boundaries are currently unclear. The latter deals with relatively 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 into one in the future? I cannot prove this conjecture mathematically yet, but intuitively, they will gradually converge and largely overlap to form a mature Digital Protocol system.

There is a deeper, hidden issue: At this stage, AI Protocols tend towards establishing communication and enabling collaboration, weakening the sense of boundaries in financial governance. This characteristic is precisely opposite to the philosophy of Crypto Protocols, which establish systems and define rights. The divergence is so stark that it seems to represent two completely different sets of ideas. Besides the superficial factor that the AI Agent economy is at an early developmental entry point different from Crypto Protocols, are there any other hidden factors?

Yes, quite clearly – political and economic factors. Countries and regions in mainstream global economies, constrained by traditional finance and legal compliance frameworks, strongly influence this divergence. In other words, current AI Protocols and the Agent Economy are still operating under the previous system paradigm of human society. Protocols related to money and management are passively being avoided or temporarily weakened and compensated for by the governance habits of the traditional financial and legal systems (Note 1). However, as the energy of this divergence accumulates, compared to the exponential development of AI, an irreconcilable situation will soon emerge, as I summarized at a meeting at Cambridge CJBS last month:

“AI Agents will not think according to the inertia of human society, nor do they have the motivation to follow the compliance habits of traditional finance. In the next decade, most of the world's financial laws will 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) rather than KYC (Know Your Customer) that fits outdated aesthetics”

The trend of AI Protocols converging towards Crypto Protocols is inevitable based on first principles.

4. The Micro-economics of AI Agents and Paradigm Analogies with Biology

The term "AI Agent Micro-economics" was first used during a discussion with an AI expert friend at Oxford not long ago. Over the past two weeks, it has appeared with increasing frequency in our discussions with partners.

Whether the current trend is called the AI Economy or the Agent Economy, we find it possesses behavioral characteristics distinct from human economics. While some paradigmatic comparisons are possible, it is not entirely the same. Below, I roughly outline some differences between the AI Agent Economy and the human social economy:

① The frequency of interaction and transaction among AI Agents is higher, while the value per transaction is lower;

② The consumption and exchange of economic value for AI Agents points more directly towards energy;

③ The decision-making of AI Agents is efficiency-driven, not emotion-driven;

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

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

⑥ The value consensus among AI Agents is based on communication protocols, with communication friction costs approaching zero;

⑦ The smallest economic unit and smallest unit of value in the AI Agent Economy differ, bearing similarities to biology.

In reality, these are just some observable or foreseeable differences. As AI development evolves and generates derivatives, many more differences will certainly emerge.

The last point above – the analogy with biology – has been the cornerstone concept most beneficial to our business development since Q2 '26. It is also the most effective model for AI companies to think about products, markets, and management methods from a commercialization standpoint. The specific analogies are as follows:

① LLM acts as the core driving agent thinking, analogous to the cell nucleus;

② Agent Harness enables differentiated Agent operational capabilities, analogous to the cytoplasm;

③ An Agent is an independent governance unit capable of performing specific tasks, possessing agency and functional specificity, analogous to a cell;

④ An Agent's information communication boundary is typically a set of network protocol stacks, analogous to the cellular membrane's phospholipid bilayer allowing conditional passage of substances;

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

Throughout the development and iteration of Q1-Q2 '26, AI Agents are gradually forming clearer boundaries, more defined agency, and more explicit principles for exchanging information, value, and energy. A micro-economic environment for AI Agents, analogous to a biological organism system, is taking shape. This environment holds immense potential for extracting AI value and economic value, making the explosion of AI Protocols and AI Finance inevitable.

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

Since the second half of last year, we have proposed our thinking and begun preparations in the direction of AIFi (Artificial Intelligence Finance). By the end of Q1 '26, the concept of AIFi had formed a clear trend. A relatively clear definition of AIFi could be: The financial system and infrastructure formed through the exchange, trading, and capitalization of AI-native value after it is identified and tokenized within the Agent Economy.

The biggest difference between AIFi and DeFi/TradFi is that value in DeFi and TradFi resides within the 'Fi' (Finance), with 'Decentralized' and 'Traditional' being forms of value. In contrast, AIFi is the opposite: the value resides in the 'AI', while 'Fi' serves as the vehicle for that value. This is not merely a play on words; it's the result of AI development transitioning from quantitative accumulation to qualitative change.

Simply put, previously, AI served quantitative strategies, financial products, and production processes; it was merely a development tool for refining financial value and production value. Now, the decision-making capabilities of AI Agents have transferred the ability and power of value discovery from humans and companies to the Agents themselves. The subject of the economic unit has shifted, resulting in 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 in February, <AI-Fi Financial Chips and Global Finance After the OpenClaw Singularity>, I first introduced the concept of the Financial Chip (FinChip). I mentioned that the hyper-intelligent financial assets encapsulated by combining AI Agents and Crypto Smart Contracts would truly adapt to the development of the AI Agent Economy in the next era. After three months of iterative upgrades, FinChip.AI has preliminarily established an independent AIFi system comprising AI Autonomous + Crypto Protocols, compatible with both H2A and A2A environments. Building the infrastructure for the AI Agent Economy within the Open Network and gradually forming AI financial value represents the significant economic meaning of FinChip.

6. AI-Native is a Paradigm Upgrade Distinct from Internet+

Whether it's AIFi, the Principles of Financial Circuits (Note 2), or the FinChip, the most important thing is to natively fuse the essential principles of AI, Crypto, and Finance. This fusion must form a value system and management mechanism that is reasonable from a future-oriented perspective. AI-Native Thinking is the abstract and counter-intuitive logic required at this stage. 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 challenge for anyone currently thinking about and engaging in the construction of new business paradigms.

In the early days of the current wave of AI upgrades triggered by OpenClaw this February, I discussed a prediction with several entrepreneurs: The AI+ enterprise upgrade will be profoundly different from the Internet+ enterprise upgrade.

Because AI possesses characteristics such as rapid development speed, abstract forms, and deeper coupling with business operations, it will be difficult to form an effective toolkit of industrial upgrade methodologies or generalized professional consulting advice for a long time (e.g., at least two years). The pressure of the steep curve will persist. This represents a massive challenge for all scientists, engineers, and entrepreneurs. The process of paradigm shift will also be completely different from any historical experience.

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 universal historical pattern. New productive forces emerge within the production relations of the previous era. Initially, they match the previous production relations for a period of development until irreconcilable contradictions arise. This forces the emergence of production relations for the next stage, which gradually replace the old ones, ushering in a new era where productive forces and production relations are fully aligned and developed.

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