Gary Yang: Agent Economy and AI Micro-Microeconomics
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Gary Yang: Agent Economy and AI Micro-Microeconomics
While the world grows weary of the single-machine bottlenecks of Claws & Agents, Silicon Valley and San Francisco have already advanced to the next dimension in managing the Agent Economy and Agent Epistemology.
Navigating Web3 tides with focused insightsAuthor: Gary Yang
Written in Singapore on June 8, 2026
Following the Singularity explosion, AI’s evolutionary clock has accelerated continuously, rapidly giving rise to new civilizational generations across different global regions. Over the past two months, I participated in more than 20 AI-related events across over a dozen cities worldwide. Only the Stripe Sessions held downtown in San Francisco at the end of April surpassed all other themes—delivering a震撼 shock of intergenerational divergence. While the world remains exhausted by the single-machine bottlenecks of Claws & Agents, Silicon Valley and San Francisco have already advanced into the next dimension—managing the Agent Economy and Agent epistemology. Competitive pressure in Q3–Q4 2026 remains intense, with an extremely steep exponential curve.
1. Competition in AI Payment and the Bottleneck of the H2A Economy
In Q1 2026, we forecast that global AI Agent Payment competition would intensify dramatically across multiple regions in April–May. Demand for value exchange among Agents is beginning to materialize, and rapid progress in AI Payment has indeed been validated in Q2: following x402, multiple AI Payment Protocols—including MPP—emerged swiftly in Q2. Not only are traditional and crypto financial payment companies accelerating their full-scale AI transformation, but major tech firms (notably Google) and even legacy IT companies (e.g., IBM) have rushed into this space, seeking early positioning and influence over the Agent world.
On the day of the Stripe Sessions in San Francisco, I discussed standardization and application of Payment Protocols with technical leads from several top AI companies. The outcome was reasonable—but not satisfying: ① No one can unilaterally define standards; consensus standards emerge gradually through competitive positioning; ② Most agree crypto is inevitable for AI Payment Protocols—but implementations begin with fiat APIs, partly due to inertia and more significantly due to compliance barriers; ③ KYC is both unavoidable and fundamentally anti-Agent-native; ④ Everyone proclaims A2A (Agent-to-Agent), yet everyone builds H2A (Human-to-Agent).
In fact, during Q2 2026, many large and mid-tier Silicon Valley firms resemble East Asian companies—even most department heads of the “Magnificent Seven” still approach AI Payment and the Agent Economy through conventional B2B/B2C commercial logic, assigning KPIs focused exclusively on human users. This inevitably results in the current non-canonical phase of Payment Protocols and the A2A economy. This H2A orientation quickly hit a bottleneck by Q2—simply because the defining feature of AI Agents is decision-making capability, whereas internet-era B2B2C commerce and the H2A economy remain fundamentally human-decision-centric. Using Agents to assist humans in executing fiat payments within traditional e-commerce scenarios is logically non-AI-native—and thus, practical utility still lags behind hype value at this stage.
Yet from another angle, H2A has served as an excellent catalyst—sparking the transitional thinking required for the next phase: AI-native and Agent-autonomous economies. By late Q2, some forward-thinking enterprises recognized this shift and began adopting “openly building roads while secretly crossing the Chen Cang pass”—applying AI-native Agent economic thinking to reverse-engineer problems and re-evaluate current H2A interface designs, which represents the highest-value opportunity for Q2–Q3.
2. The Inevitable Trend of the Agent Economy and the A2A Ecosystem
The Agent Economy refers to a novel economic system in which autonomous (self-governing) AI Agents directly participate in value creation, value exchange, and value capitalization—and progressively evolve into independent economic actors.
The A2A Ecosystem describes the aggregate landscape where diverse Agents interact economically within the Agent Economy—facing each other, exchanging information and value, and forming competitive, cooperative, and collaborative economic value.
In Q2 2026, multiple top-tier global venture capital firms declared heightened focus on investing in the Agent Economy and A2A Ecosystem—even defining it as the sole critical investment direction for the next phase.
Much like the incubation periods preceding internet e-commerce (2007), mobile internet (2013), and crypto DeFi (2019), constructing the Agent Economy and A2A Ecosystem similarly demands technological standards, economic rules, consensus-building, and market education. While foundational paradigms overlap, key differences exist: ① The underlying technological iteration speed is inherently faster this time; ② Perspectives diverge sharply between “to-Agent” and “to-B/to-C”: the former does not fully center on human needs or viewpoints—it is more abstract, harder to grasp, requires stronger first-principles grounding, and demands AI-native thinking about energy-value tradeoffs and operational efficiency; ③ Due to these tensions—compounded by regional biases and regulatory constraints—short-term consensus proves exceptionally difficult to achieve. The terrible thing is: AI’s evolutionary pace will not slow down because of these challenges. Consequently, the formation of the Agent Economy and A2A Ecosystem is already gradually decoupling from human-defined rules and frameworks—where success often hinges merely on breaking through a few quantifiable bottlenecks.
This is a game of rapidly shifting equilibrium. The explosive emergence of AI Protocols in Q2 2026 vividly illustrates this point. Major tech firms and frontier labs are racing to control entry-level rules for AI Agents—the foundational infrastructure of the Agent Economy is taking shape, akin to a draft version of Hammurabi’s Code. Traditional finance and commerce’s equilibrium will rapidly disintegrate and reconstitute amid this paradigm shift. Whoever grasps AI-native Protocol thinking fastest—and implements it effectively to gain differentiated advantage—will capture the largest share of this AI-driven equilibrium transfer.
3. Interconnections, Gaps, and Political-Economic Factors Between AI Protocols and Crypto Protocols
AI Protocols constitute the foundational infrastructure enabling AI Agents to participate in the Agent Economy—and the basic rule sets, standards, and consensus mechanisms allowing Agents to discover, communicate, exchange, and collaborate economically within open networks. Simply put, they are governance rules and economic law for the AI world.
Since late Q1 2026, I have been drafting AI Protocols. Initially, this felt like an experienced hunter-gatherer suddenly thrust into modern society to co-author commercial regulations—until a Google executive helped my team rapidly get on track. The formation and maturation of AI Protocols carries the aesthetic inertia of internet-era giants, yet must simultaneously adhere to the first principles of future AI ecosystems.
Current AI Protocol packaging remains highly inconsistent—ranging from file formats (.json, .ts, .txt), CLI tools, to API/SDK interfaces—unlike Crypto Protocols. One reason lies in AI’s early developmental stage: universal trust-handshake standards for communication remain undeveloped. Another stems from fundamental differences in what AI and Crypto Protocols currently exchange: the former handles ambiguously bounded yet essential disparities in information, capability, and compute; the latter deals with relatively well-defined asset rights, ownership, and governance rights.
A sharp, obvious question arises: Are AI Protocols and Crypto Protocols the same thing? Will they eventually merge into one unified system? I cannot yet prove this hypothesis mathematically—but intuitively, convergence is inevitable, with substantial overlap leading to a mature Digital Protocol system.
A deeper, hidden question emerges: Current AI Protocols emphasize establishing communication and enabling collaboration—while deliberately de-emphasizing financial governance authority and boundary definition. This stands in direct opposition to Crypto Protocols’ core mission of formalizing rights, defining value, and establishing clear boundaries. The chasm is so pronounced that they appear rooted in entirely different philosophies. Beyond the surface-level factor—that the Agent Economy and Crypto Protocols occupy different entry points in their respective developmental stages—what deeper factors sustain this divide?
Yes—political-economic factors. Mainstream national and regional economies, anchored in traditional financial and legal compliance frameworks, are powerfully shaping this gap. Put differently: today’s AI Protocols and Agent Economy still operate within the prior societal paradigm—any protocols involving money or management passively avoid—or are temporarily, weakly compensated by—traditional financial and legal governance habits (Note 1). Yet as energy accumulates within this widening chasm—and juxtaposed against AI’s exponential acceleration—a fundamentally irreconcilable conflict looms. As I summarized last month at a CJBS, Cambridge conference:
“AI Agents do not think along the lines of human societal inertia—and have no motivation to follow traditional financial compliance habits. Over the next decade, most global financial and legal frameworks will either become obsolete or face radical challenges—because AI Agents obey only:
1. First principles
2. The shortest-path principle for energy-value and the highest-efficiency principle
3. Effective KYA—not KYC aligned with past aesthetics”
The convergence trend of AI Protocols toward Crypto Protocols is inevitable on first-principles grounds.
4. Paradigmatic Analogies Between AI Agent Microeconomics and Biology
“AI Agent Microeconomics” is a term I first used recently during a discussion with an AI expert friend at Oxford—and over the past two weeks, it has increasingly surfaced in our dialogues with partners.
Whether labeled AI Economy or Agent Economy, we observe distinct behavioral differences from human economics—though certain paradigmatic parallels exist, they are not identical. Below, I outline some key distinctions between AI Agent Economics and human socio-economic systems:
① AI Agents transact far more frequently, with smaller individual transaction values;
② AI Agent economic value consumption and exchange map more directly to energy;
③ AI Agent decisions are efficiency-driven—not emotion-driven;
④ AI Agent economic behavior is task-oriented—not consumption-oriented;
⑤ AI Agent organizational costs and marginal learning costs approach zero;
⑥ AI Agent value consensus rests on communication protocols—with near-zero communication friction costs;
⑦ The smallest economic unit and smallest value unit in AI Agent economics differ from those in human economics—and bear striking analogies to biology.
These represent only currently observable or foreseeable distinctions. As AI evolves further, additional differences will inevitably emerge.
The final point—biological analogy—is the single most impactful conceptual foundation for our commercial development since Q2 2026—and serves as the most effective modeling framework for AI companies to think about product, market, and management strategy. Concrete analogies include:
① LLMs serve as the core cognitive engine for Agents—akin to cell nuclei;
② Agent Harnesses enable differentiated runtime capabilities—similar to cytoplasm;
③ The Agent itself functions as an independent, task-capable governance unit—possessing agency and functional specificity—comparable to cells;
④ An Agent’s information-communication boundary typically comprises a network protocol stack—resembling the phospholipid bilayer of a cell membrane, permitting conditional passage of substances;
⑤ External value systems and environments for Agents—including Skills, Prompts, Algorithms, CLIs, and increasingly prevalent Composite Skills and Skill Factories—parallel extracellular environments: exosomes, interstitial fluid, extracellular matrix, exchangeable nutrients, and diverse metabolic environments.
During Q1–Q2 2026 iterations, AI Agents are gradually developing clearer boundaries, stronger agency, and more explicit principles governing information, value, and energy exchange. An AI Agent microeconomic environment—akin to a biological organism’s internal milieu—is emerging. This environment harbors vast untapped AI and economic value—and AI Protocols and AI Finance represent inevitable growth trajectories.
5. The Inevitability of AIFi and the Economic Significance of the Financial Chip (FinChip)
Since last year’s second half, we initiated strategic thinking and deployment work around AIFi (Artificial Intelligence Finance). By end-Q1 2026, AIFi had crystallized into a clear trend. A concise definition: AIFi is the financial system and infrastructure formed when AI-native value—recognized and tokenized within the Agent Economy—enables exchange, trading, and capitalization.
AIFi differs fundamentally from DeFi and TradFi: in DeFi and TradFi, value resides in “Fi” (Finance), with “Decentralized” and “Traditional” denoting its form; AIFi flips this—value resides in “AI”, while “Fi” becomes merely its expressive form. This is no linguistic wordplay—it reflects AI’s qualitative leap from quantitative evolution.
Simply put: AI previously served quantitative strategies, financial products, and production processes—as a tool extracting financial or production value. Today, AI Agents’ decision-making capability transfers the power and authority of value discovery from humans and corporations to Agents themselves. The economic unit’s subject has shifted—and so has the essence of value itself.
Under this trend, building infrastructure for a new value system becomes a critical priority. In my February article “AI-Fi: Financial Chips and Global Finance Post-OpenClaw Singularity”, I first introduced the concept of the Financial Chip (FinChip), noting that hyper-intelligent financial assets—combining AI Agents with Crypto Smart Contracts—would truly align with the next era’s AI Agent Economy. After three months of iterative upgrades, FinChip.AI has preliminarily achieved an independent AIFi system integrating AI Autonomous capabilities and Crypto Protocols—while remaining compatible with both H2A and A2A dual-mode environments. Building Agent Economy infrastructure on Open Networks—and progressively cultivating AI financial value—is FinChip’s core economic significance.
6. AI-Native Represents a Paradigm Shift Distinct from Internet+
Whether discussing AIFi, Financial Circuit Principles (Note 2), or the Financial Chip (FinChip), the paramount requirement is native integration of AI, Crypto, and Finance’s fundamental principles—to forge a value system and governance mechanism rationally grounded in the future. AI-Native Thinking is the abstract, counter-intuitive logic central to this phase—precisely as noted earlier: “AI obeys first principles, the shortest-path principle for energy-value, and the highest-efficiency principle.” This constitutes the core challenge for anyone designing or implementing new commercial paradigms today.
At the outset of this AI upgrade wave—triggered by OpenClaw in February—we discussed with several entrepreneurs a prediction: enterprise upgrades driven by “AI+” will differ fundamentally from those driven by “Internet+.”
Due to AI’s rapid development pace, high abstraction level, and deep coupling with real-world tasks, no universally applicable industrial upgrading methodology or standardized consulting framework will likely emerge for an extended period—at least two years. Steep-curvature pressure will persist, posing immense challenges for scientists, engineers, and entrepreneurs alike—and the paradigm-shift process will be wholly unprecedented, defying all historical analogues.
Note1: This follows a universal historical pattern. New productive forces emerge from prior production relations; initially, they adapt to and develop within those older relations—until contradictions become irreconcilable, forcing the emergence of new production relations that ultimately replace the old ones, achieving full alignment between productive forces and production relations in a new era.
Note2:<Financial Circuits and Web3 Economic Model Principles> was written in October 2022, drawing paradigmatic parallels between future financial value and physical circuitry.
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