Agent Economy and AI Microeconomics

Author: Yang Ge Gary

After the Singularity explosion, the pace of AI evolution continuously accelerates, causing the emergence of new civilizational generations across different regions of the world. Over the past two months, I have participated in more than twenty AI-related events in over ten cities globally. The event at Stripe Sessions in downtown San Francisco at the end of April stood out far beyond all other topics, revealing a shocking generational gap. While the world is growing weary of the bottleneck in Claws & Agents single-player scenarios, Silicon Valley and San Francisco have already entered the next dimension in managing Agent economy and Agent epistemology. The competitive pressure in Q3 and Q4 of 2026 remains intense, with a very steep curvature.

TL;DR

1. Competition in AI Payment and the bottleneck of H2A economy

2. The inevitable trend of Agent economy and A2A ecosystem

3. The relationship, gap, and political-economic factors between AI Protocol and Crypto Protocol

4. The microeconomic characteristics of AI Agents and their analogy to biological paradigms

5. The inevitability of AIFi and the economic significance of FinChip (Financial Chip)

6. AI-Native is a paradigm upgrade distinct from Internet+

1. Competition in AI Payment and the bottleneck of H2A economy

In Q1 2026, we predicted that from April to May, many regions worldwide would enter a fierce competition over AI Agent Payment, quickly reaching a heated state. The value exchange demand of Agents began to manifest initially, and the rapid development of AI Payment was validated in Q2. After x402, multiple AI Payment Protocols such as MPP emerged rapidly in Q2. This included not only traditional and crypto financial payment companies upgrading to AI at full speed, but also major tech giants (especially Google) and even established IT companies (like IBM) rushing into this track to seize influence in the Agent world.

On the day of Stripe Sessions in San Francisco, I discussed standardization and application issues of Payment Protocols with technical leaders from several top AI companies. The results were reasonable but not entirely satisfactory: ① No one could set a standard; standards would gradually form through competition and consensus; ② Most agree that Crypto is an inevitable part of AI Payment Protocols, but initial implementations are Fiat APIs, partly due to inertia and mainly due to compliance barriers; ③ KYC is both unavoidable and anti-Agent Native; ④ Everyone claims A2A (Agent to Agent), and everyone is working on H2A (Human to Agent).

In fact, in Q2 2026, many big Silicon Valley companies and mid-tier firms, as well as East Asian companies, are quite similar. Even most Department Heads of the Mag 7 are still leveraging B2C and B2B business motives to ride the AI Payment and Agent Economy wave, with KPIs focused on Human Users. This inevitably leads to the current Payment Protocol and A2A economy being in a temporary, non-orthodox stage. The H2A orientation quickly hit a bottleneck in Q2. The reason is simple: the biggest feature of AI Agents is decision-making ability, but the core of traditional B2B2C business and H2A economy is humans making decisions. Helping humans in traditional e-commerce scenarios to perform Fiat Payments through Agents is inherently non-AI-Native in the logical chain, so at this stage, the hot value exceeds practical utility.

However, from another perspective, H2A has played a very good introductory role, stimulating the transition to AI-Native and Agent Autonomous economies in the next phase. By the end of Q2 2026, some smart companies realized this and began “building the road openly while secretly crossing the river,” using AI-Native Agent economic thinking to reverse-engineer the current H2A economic interfaces. This approach is the best value for Q2-Q3.

2. The inevitable trend of Agent economy and A2A ecosystem

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

A2A ecosystem describes the process where different Agents participate in economic activities within the Agent economy, interact face-to-face, exchange information and value, and form a competitive and cooperative economic landscape.

In Q2 2026, many 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 stage.

Similar to the incubation periods of internet e-commerce in 2007, mobile internet in 2013, and Crypto DeFi in 2019, building the Agent economy and A2A ecosystem also requires standards, economic rules, consensus-building, and market education. While the basic paradigm remains similar, key differences include: ① faster technological iteration; ② different perspectives—moving beyond human-centric views, more abstract and harder to understand, requiring first principles thinking, especially from an AI-Native perspective to consider energy consumption and operational efficiency; ③ due to these differences, regional biases and compliance issues make short-term consensus more difficult. The terrible thing is, the speed of AI evolution will not slow down because of these issues. In essence, the formation of Agent economy and A2A ecosystem is gradually detaching from human-defined rules and needs. For them, breakthroughs are often just quantifiable bottlenecks.

This is a game of rapidly shifting equilibrium. The rapid explosion of AI Protocols in Q2 2026 clearly illustrates this. Major firms and frontier labs are competing for the entry-level rules of AI Agents. The initial infrastructure of the Agent economy is taking shape, like a draft of the Code of Hammurabi. Traditional financial and commercial equilibrium will quickly disintegrate and reshape during this paradigm shift. Those who can quickly understand and implement AI-Native Protocol thinking to gain a differentiated advantage will share the AI cake in this game transfer.

3. The relationship, gap, and political-economic factors between AI Protocol and Crypto Protocol

AI Protocol is the infrastructure for AI Agents to participate in the Agent economy, serving as the fundamental rules, standards, and consensus mechanisms for discovery, communication, exchange, and collaboration within open networks. Simply put, it’s the governance rules and economic laws of the AI world.

Since late Q1 2026, I have been working on AI Protocol. Initially, it was like a primitive hunter suddenly entering modern society to participate in creating business rules, until a Google executive helped us get on the right track. The formation and maturation of AI Protocol carry the aesthetic inertia of internet giants but must also adhere to the first principles of future AI ecosystems.

Currently, AI Protocols are still quite diverse in form—files (.json, .ts, .txt), CLI, APIs, SDKs—very different from Crypto Protocols. On one hand, during early AI development, trust handshake standards for communication are not yet universal; on the other hand, the content exchanged between AI Protocols and Crypto Protocols differs: the former involves information, capabilities, and computational differences with unclear boundaries, while the latter deals with assets, ownership, and governance with clearer boundaries.

A sharp and obvious question: Are AI Protocol and Crypto Protocol the same? Will they merge in the future? I cannot prove this mathematically, but intuitively, they will gradually converge and most parts will overlap into a unified Digital Protocol system.

A deeper hidden issue: current AI Protocols tend to focus on establishing communication and collaboration, weakening financial governance and boundary definitions. This is opposite to the philosophy of Crypto Protocols, which define and confirm value. The gap is so stark that some see them as two different paradigms. Beyond the surface—where AI Agent economy is still at an early entry point—are there hidden factors?

Yes, clearly: political and economic factors. Mainstream economies and regions are heavily influenced by traditional finance and legal compliance, shaping this gap. In other words, current AI Protocols and the Agent economy are still operating under the previous human societal paradigm. All protocols related to money and management are passively avoiding or temporarily compensating through traditional financial and legal systems (note 1). But as the energy stored in this gap accumulates and AI’s rapid indexation accelerates, irreconcilable conflicts will emerge. As I summarized at a Cambridge CJBS conference last month: “AI Agents do not think according to human societal inertia, nor do they have motivation to follow traditional financial compliance. In the next decade, most global financial laws will become invalid or face severe challenges because AI Agents only follow: 1. First principles 2. Energy value shortest path and maximum efficiency principles 3. Effective KYA, not the traditional KYC.”

The trend of AI Protocol merging into Crypto Protocol is an inevitable consequence of first principles.

4. The microeconomic characteristics of AI Agents and their analogy to biological paradigms

AI Agent microeconomics is a concept I first used during a discussion with an AI expert friend at Oxford. Over the past two weeks, it has increasingly appeared in our exchanges with partners.

Whether called AI economy or Agent economy, they differ from human economic behavior in certain ways, with some paradigm comparability but not identical. Here are some rough distinctions:

① Higher frequency of interactions and lower per-transaction value;

② Value consumption and exchange are more directly energy-oriented;

③ Decision-making is driven by efficiency rather than emotion;

④ Economic behavior is task-oriented rather than consumption-oriented;

⑤ Organizational and marginal learning costs tend toward zero;

⑥ Value consensus is based on communication protocols, with near-zero communication wear costs;

⑦ The smallest economic unit and value element differ, with biological analogy.

These are just some observable or foreseeable differences today. In future derivatives and processes, many more distinctions will emerge.

The last point—biological analogy—is the most helpful conceptual foundation for our business development since Q2 2026. It’s also the most effective model for thinking about product, market, and management in AI company commercialization. Examples include:

① LLM as the core driver of Agent thinking, like a cell nucleus;

② Agent Harness providing differentiated operational capabilities, like cytoplasm;

③ The entire Agent as an independent governance unit with specific tasks, subjectivity, and functionality, like a cell;

④ Communication boundaries of Agents are typically a network protocol stack, akin to the phospholipid bilayer of cell membranes allowing selective material passage;

⑤ External value systems and environments—such as Skills, Prompts, Algorithms, CLI, and increasingly, Composite Skills and Skill Factories—are similar to extracellular environments, including exosomes, tissue fluids, extracellular matrix, exchangeable nutrients, and metabolic environments.

In the development iterations of Q1-Q2 2026, AI Agents are gradually forming clearer boundaries, subjectivity, and principles of information, value, and energy exchange. A microeconomic environment resembling biological systems is taking shape, containing vast potential for AI and economic value extraction. AI Protocol and AI Finance are inevitable explosive trends.