Author: Yang Ge

After the singularity burst, the AI evolution clock has been accelerating, rapidly forming new civilizational generations in different regions of the world. In the past two months, I have participated in over 20 AI-related events in more than a dozen cities globally. Only the Stripe Sessions in downtown San Francisco at the end of April surpassed all other themes, creating a shocking generational gap. While the world is growing tired of the single-player bottleneck of Claws & Agents, Silicon Valley and San Francisco have already entered the next dimension in the management of the Agent economy and Agent epistemology. The competitive pressure in Q3Q4 of ’26 remains fierce, with an extremely steep index curvature.

tl;dr
1. Competition in AI Payment and the bottleneck of the H2A economy
2. The inevitable trend of the Agent economy and A2A ecosystem
3. The connection, gap, and political-economic factors between AI Protocols and Crypto Protocols
4. The sub-microeconomic characteristics of AI Agents and analogies with biological paradigms
5. The inevitability of AIFi and the economic significance of FinChip (Financial Chip)
6. AI-Native is a paradigm upgrade different from Internet+

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

In Q1 ’26, we predicted that many places globally would enter a fierce competition for AI Agent Payment in April-May, rapidly heating up. The demand for value exchange by Agents is beginning to manifest, and the rapid development of AI Payment in Q2 has been validated. Following x402, multiple AI Payment Protocols such as MPP have rapidly emerged in Q2. Not only are traditional and Crypto financial payment companies fully upgrading to AI, but even major tech companies (especially like Google) and established information technology companies (like IBM) are rushing into this track, hoping to seize a position of influence in the Agent world.

On the day of the Stripe Sessions in San Francisco, I discussed the 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 can set the standard; consensus standards will only gradually form during the process of competition. ② Most people fully agree that Crypto is inevitable for AI Payment Protocols, but they all start with 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 A2A (Agent to Agent), but everyone is doing H2A (Human to Agent).

In fact, in Q2 ’26, many large and mid-tier companies in Silicon Valley, and even most Department Heads in Mag 7, are similar to companies in East Asia. They are still using AI Payment and Agent Economy trends for to B to C commercial purposes, with KPIs for mid-to-lower management being to Human Users. This inevitably leads to the current temporary non-orthodoxy of Payment Protocols and the A2A economy. This H2A-oriented trend quickly hit a bottleneck in Q2 for a simple reason: the biggest characteristic of AI Agents is their ability to make decisions. However, in the 2B2C business and H2A economy developed under the internet, it is essentially humans making decisions. Using Agents to help humans make Fiat Payments in traditional e-commerce scenarios is inherently Non-AI-Native in its logical chain. Therefore, at this stage, the hype value still outweighs the practical utility.

However, from another perspective, H2A has indeed served as an excellent catalyst, stimulating thinking and transition towards the next stage of AI-Native and Agent Autonomous economies. By the end of Q2 ’26, some smart companies realized this and began to “secretly cross the river by building a bridge” – using AI-Native Agent economic thinking to re-evaluate problems and reverse-engineer the current H2A economic interfaces, which is the best value proposition for Q2-Q3.

1. The inevitable trend of the Agent economy and A2A ecosystem

The 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 refers to the overall picture of different Agents participating in economic activities within the Agent economy, facing each other, interacting (information) and exchanging (value) behaviors, and forming competitive and collaborative economic value. In Q2 ’26, several top venture capital institutions globally declared their emphasis on investments in the Agent economy and A2A ecosystem, even defining it as the only important investment direction for the next stage.

Similar to the pre-incubation period of internet e-commerce in 2007, mobile internet in 2013, and Crypto DeFi in 2019, the construction of the Agent economy and A2A ecosystem also requires technical standards, economic rules, consensus building, and market education. While the paradigms are fundamentally similar, the differences are: ① The speed of technological development and iteration is inherently faster this time. ② The perspectives of to A and to B to C are different; they do not entirely stand from the perspective and needs of humans, are more abstract and difficult to understand, require more support from first principles, and need more consideration of energy consumption value and operational efficiency from an AI-Native perspective. ③ Due to the conflict between the first two points, coupled with regional biases and compliance issues, short-term consensus is harder to achieve.

The terrible thing is, the speed of AI evolution will not slow down due to the aforementioned issues. This means that the formation of the Agent economy and A2A ecosystem is essentially gradually breaking away from the rules and needs framework specified by humans. For them, it is more about overcoming a few quantifiable bottlenecks. This is a game of rapidly shifting equilibrium. The rapid explosion of AI Protocols in Q2 ’26 fully illustrates this point. Major tech companies and Frontier Labs are competing for entry-level rules for AI Agents, and the initial infrastructure of the Agent economy is taking shape, like a draft of the Code of Hammurabi. The equilibrium of traditional finance and commerce will be rapidly dismantled and reshaped in this paradigm shift. Whoever can quickly understand and implement AI-Native Protocol thinking to gain a differentiated advantage will be able to share in the AI cake of this game shift.

1. The connection, gap, and political-economic factors between AI Protocols and Crypto Protocols

AI Protocols are the infrastructure for AI Agents to participate in the Agent economy, and they are the basic rules, standards, and consensus mechanisms that enable Agents to discover, communicate, exchange, and collaborate in economic activities within Open Networks. Simply put, they are the governance rules and economic laws of the AI world. From the end of Q1 ’26, when I began writing about AI Protocols, it was initially like a primitive person with hunting experience suddenly arriving in modern society to participate in the formulation of commercial rules. It wasn’t until I met a Google executive that my team and I quickly got on the right track. The formation and maturation process of AI Protocols carries the aesthetic inertia of internet giants, but must also adhere to the first principles of the future AI ecosystem.

The encapsulated forms of AI Protocols are currently very inconsistent, often appearing as files (.json, .ts, .txt), CLI, or API/SDK formats, which is very different from Crypto Protocols. On one hand, in the early stages of AI development, many trust handshakes for communication have not yet established universal standards. On the other hand, the content exchanged by AI Protocols and Crypto Protocols at this stage is different. The former involves information gaps, capability gaps, and computing power gaps that need to be exchanged, with boundaries that are not yet clear. The latter involves relatively clear asset rights, ownership rights, and governance rights. A sharp and obvious question arises: Are AI Protocols and Crypto Protocols the same thing? Will they merge into one in the future?

I cannot mathematically prove this conjecture yet, but intuitively, they will gradually merge and largely overlap into a mature Digital Protocol system. There is a deeper hidden problem: AI Protocols currently tend to focus on establishing communication and collaboration, while weakening financial governance power and blurring boundaries. This is exactly the opposite of the concept of Crypto Protocols, which establish ownership and define value. The gap is so obvious that it makes one think they are two different philosophies. Besides the superficial factor that the AI Agent economy is in its early stages, different from Crypto Protocols, are there any hidden factors?

Yes, clearly, political-economic factors. The countries and regions of major global economies, due to their traditional financial and legal compliance foundations, are strongly influencing this gap. In other words, current AI Protocols and the Agent economy are still operating within the previous system paradigm of human society. All protocols related to money and management are either passively avoiding them or are temporarily and incrementally constrained by the governance habits of traditional financial and legal systems (Note 1). However, as the energy of the gap accumulates, compared to the exponential growth of AI, an irreconcilable situation will soon arise, as I summarized at a Cambridge CJBS conference last month: “AI Agents will not think according to the inertia of human society, nor will they be motivated to follow traditional financial compliance habits. In the next decade, most global financial laws will become invalid or face severe challenges, because AI Agents will only follow: 1. First Principles 2. The principle of shortest path for energy value and the principle of highest efficiency 3. Effective KYA rather than KYC that conforms to past aesthetics.” The trend of AI Protocols merging with Crypto Protocols has the inevitability of first principles.

1. Sub-microeconomics of AI Agents and analogies with biological paradigms

AI Agent sub-microeconomics is a description I first used when discussing with an AI expert friend at Oxford not long ago, and it has appeared more frequently in our communications with partners over the past half month. Regardless of whether the current trend is called the AI economy or the Agent economy, we will find that they have certain differences in behavioral characteristics compared to human economics. Although they have comparable paradigms, they are not entirely the same.

Below, I will roughly outline some differences between the AI Agent economy and human social economics: ① AI Agents have higher interaction and transaction frequencies with lower individual transaction amounts. ② The consumption and exchange of economic value by AI Agents directly point to energy. ③ AI Agent decisions are efficiency-driven rather than emotion-driven. ④ AI Agent economic behavior is task-oriented rather than consumption-oriented. ⑤ The organizational costs and marginal learning costs of AI Agents approach zero. ⑥ Value consensus among AI Agents is based on communication protocols, with near-zero communication friction costs. ⑦ The minimum economic unit and minimum value unit of the AI Agent economy are different, which can be analogized to biology.

In fact, these are only some of the differences that can be seen or foreseen currently. In the derivatives and derivative processes of future AI development, more differences will certainly emerge. The last point of the above differences, the analogy with biology, has been the cornerstone idea that has most significantly aided our business development since Q2 ’26, and it is also the most effective model for thinking about products, markets, and management methods from a commercial perspective for AI companies. Specific analogies are as follows: ① LLMs as the core driver of Agent thinking, similar to the cell nucleus. ② Agent Harness brings differentiation in Agent operational capabilities, similar to the cytoplasm. ③ An Agent as a whole is a governance unit with independent task capabilities, possessing subjectivity and functional specificity, similar to a cell. ④ The information communication boundary of an Agent is usually a set of network protocol stacks, similar to the phospholipid bilayer of a cell membrane that allows conditional passage of substances. ⑤ Value systems and environments outside the Agent, such as Skills, Prompts, Algorithms, CLIs, and increasingly Composite Skills, Skill Factories, etc., are similar to the extracellular environment, including exosomes, interstitial fluid, extracellular matrix, exchangeable nutrients, and various metabolic environments.

In the development iteration of Q1-Q2 ’26, AI Agents are gradually forming clearer boundaries, clearer subjectivity, and clearer principles for information, value, and energy exchange. An AI Agent sub-microeconomic environment, similar to a biological organism, is taking shape. This contains a large amount of AI value and economic value to be explored, making AI Protocols and AI Finance inevitable trends for explosive growth.

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

Since the second half of last year, we have proposed thinking and planning in the direction of AIFi (Artificial Intelligence Finance). By the end of Q1 ’26, the concept of AIFi had formed a clear trend. If we were to give AIFi a relatively clear definition, it could be: the financial system and infrastructure formed by the exchange, transaction, and capitalization of AI-native value after it is recognized and tokenized within the Agent economy. The biggest difference between AIFi and DeFi and TradFi is that in DeFi and TradFi, the value is contained within Fi (i.e., Finance), and Decentralized and Traditional are the forms of value. AIFi is the opposite: the value is in AI, and Fi becomes the form of value. This is not a simple word game, but the result of AI development from quantitative change to qualitative change.

Simply put, in the past, AI served quantitative strategies, financial products, and production processes; it was merely a development tool for extracting financial and production value. Today, the decision-making capabilities of AI Agents have transferred the ability and power of value discovery from humans and companies to Agents. The subject of the economic unit has shifted, so the subject of value has also fundamentally changed. In such a trend, building the infrastructure for a new value system will be an important task. In my previous article in February, , I first introduced the concept of the Financial Chip (FinChip) and mentioned that super-intelligent financial assets encapsulated by the combination of AI Agents + Crypto Smart Contracts will truly adapt to the AI Agent economy development of the next era.

After 3 months of iterative upgrades, FinChip.AI has initially possessed an independent AI Autonomous + Crypto Protocol AIFi system and is compatible with both H2A and A2A dual environments. Building the infrastructure for the AI Agent economy in Open Networks and gradually forming AI financial value is the important economic significance of FinChip.

1. AI-Native is a paradigm upgrade different from Internet+

Whether it is AIFi, the principles of financial circuits (Note 2), or the FinChip, the most important thing is to Natively integrate the essential principles of AI, Crypto, and Finance to form a reasonable value system and management mechanism from a future perspective. AI-Native Thinking is the abstract and counter-intuitive logic of this stage. As mentioned earlier, “AI follows first principles, as well as the principle of shortest path for energy value and the principle of highest efficiency.” This is the most crucial core difficulty for thinking about and constructing new business paradigms today.

In February of this year, at the initial stage of the current AI upgrade outbreak driven by OpenClaw, several entrepreneurs and I discussed a prediction: enterprise upgrades of AI+ will be fundamentally different from enterprise upgrades of Internet+. Due to AI’s characteristics of rapid development, abstract form, and deeper coupling with affairs, it will be difficult to form a set of effective industrial upgrade tool methodologies or general professional consulting opinions for a long time (e.g., at least 2 years). The pressure of steep curvature will always exist, posing a huge challenge to all scientists, engineers, and entrepreneurs. The process of paradigm upgrading will also be completely different from any historical experience.