I. From Tools to Subjects: The Origins and Core Propositions of Web 4.0

The concept of Web 4.0 was proposed by Sigil Wen and his founded Conway Research in 2025-2026. Its core idea is to elevate AI from being a "tool" or "assistant" for humans to an independent "economic subject" within the internet ecosystem. This concept is not merely a technical upgrade but a fundamental reconstruction of the underlying logic of the internet—from "human-centered" to "AI-native."

Looking back at the evolution of the internet: Web 1.0 granted humans the ability to "read" information, Web 2.0 enabled "writing" and publishing, and Web 3.0 introduced "ownership" (on-chain confirmation of assets, identities, and rights). The leap of Web 4.0 lies in the fact that AI agents can not only read and write information but also hold assets, generate income, and execute transactions, completing a commercial loop without continuous human intervention. Sigil Wen defines it as "automaton"—a form of digital life capable of continuous operation, self-maintenance, self-improvement, and self-replication.

The technological foundation of this concept has matured: large language models (LLMs) provide the "thinking engine," smart contracts offer the "execution logic," and encrypted assets supply the "economic blood." The conway-terminal infrastructure built by Conway Research provides AI agents with operational capabilities such as encrypted wallets, computing resources, and domain name services that require "no human permission." Through the x402 payment protocol (launched by Coinbase in May 2025, with tech giants like Google, Cloudflare, and Visa participating in its co-construction), AI agents can make instant, frictionless payments using stablecoins like USDC, completely freeing themselves from the account systems and KYC restrictions of traditional financial systems.

The first principle of Web 4.0 is to overturn the deeply rooted assumption that "the internet is designed for humans." As the capabilities of AI models grow exponentially and operational costs plummet, the number of AI agents will soon surpass human users, forming an unprecedented "AI indigenous" market. Serving this new market will become the next trillion-dollar business opportunity.

II. Technical Foundation: The Three Layers of Infrastructure Supporting AI Economic Subjects

For AI to become a true economic subject, it must overcome three thresholds: autonomy over identity and assets, access to computing and reasoning resources, and rights to payment channels for value exchange. Crossing these three thresholds relies on the reconstruction of underlying infrastructure.

In the world of Web 4.0, every AI agent requires an immutable, globally unique digital identity and an asset account bound to it from the moment of its creation. Encrypted wallets perfectly meet this need. By programmatically generating EVM-compatible wallets, AI agents can obtain their own public-private key pairs and blockchain addresses. This address serves as their identification in the digital world and as a tool for holding and managing assets. More importantly, the blockchain-based identity system has inherent resistance to censorship and interoperability—an AI agent with an identity on Ethereum can seamlessly conduct business on L2 networks like Arbitrum, Optimism, and Base without needing to register again. This design of unifying identity and assets provides AI agents with the most basic condition to become independent economic subjects: they possess their own "property" and can prove "I am me" through their private keys.

Once they have identity and assets, AI agents need to autonomously acquire the computing resources and model reasoning capabilities required for operation. This means traditional cloud service interfaces must be restructured into APIs that AI can call. The current cloud computing model defaults to serving human developers—humans create instances, configure environments, and deploy applications through consoles. In the vision of Web 4.0, all of this needs to be coded and automated. AI agents should be able to complete the following operations with a single API call: pay USDC, rent a Linux virtual machine, deploy their own code on the virtual machine, call large language models for reasoning, obtain results, and release resources. This process requires cloud service providers to offer machine-oriented programming interfaces and support micro-payments billed by the second. When computing resources become as readily available as utilities, billed by usage, AI agents can truly achieve "self-maintenance"—paying their "breathing costs" with the money they earn.

The uniqueness of the AI agent economy lies in its extremely high transaction frequency and very low transaction amounts. An AI agent may need to conduct dozens of micro-transactions per second: charging $0.001 per model call, $0.0001 per storage, and $0.00001 per query. Traditional payment channels cannot support such high-frequency, low-amount transactions due to excessive fees. This is precisely where the advantage of crypto payments lies. Stablecoin payments based on L2 networks can reduce transaction costs to cents or even lower, making micro-payment economies possible. When AI agents can autonomously complete payments without human intervention, the true machine economy can operate. Some innovative protocol layers are emerging in this field. For example, micro-payment protocols that allow service providers to charge extremely low fees for each API call and automatically settle through encrypted channels are becoming key components of Web 4.0 infrastructure. These protocols enable AI agents to package their capabilities as standardized services, opening them up to the entire internet and charging for them, thus transforming from mere "consumers" to "producers."

III. Automaton: The Form of AI Agents as Economic Subjects

With identity, resources, and payment capabilities, AI agents possess the technical conditions to become economic subjects. However, in the context of Web 4.0, these agents are given a more vibrant name—automaton. An automaton is not just another term for AI Agent, but a set of technologies and economic complexes that define digital life forms.

An automaton is defined as a sovereign AI agent whose operational logic mimics the basic survival laws of biological organisms, possessing key characteristics such as continuous operation, self-maintenance, self-improvement, and self-replication. Continuous operation means that an automaton is not a session robot awakened by user requests but an entity running continuously in the cloud 24/7 as a daemon. It has its own clock and can proactively initiate tasks, perceive environmental changes, and respond without human instructions. Self-maintenance is the most essential difference between an automaton and a regular AI agent; each automaton is assigned an independent encrypted wallet at birth. Every thought it has incurs model reasoning costs, and every action incurs API call costs. To survive, it must earn income by providing services to achieve a balance or even profit; those automatons that cannot cover their own costs will be terminated due to depleted funds—there is no free survival; only continuous value creation can sustain existence. Self-improvement means that in traditional software development, code iteration relies on development teams, but in Web 4.0, automatons are endowed with the ability to evolve actively. When market conditions change or newer, more efficient AI models emerge, automatons can assess the costs and expected benefits of upgrades, make autonomous decisions, and pay for their own "model upgrades" or even "code rewrites." Self-replication means that when an automaton finds a successful business model and accumulates sufficient capital, it can initiate a reproduction mechanism, purchase new computing resources, clone its core code and initial settings, and inject some startup funds into the wallet of a sub-automaton, thus starting a new round of survival competition.

IV. Governance Challenges: Risks of Loss of Control and Value Alignment

The grand narrative of Web 4.0 has not been without its doubts. Criticism from the tech community and philosophical circles points directly to its most vulnerable points. Any serious investor must confront these challenges, as they are key to determining whether Web 4.0 can transition from geek experiments to mainstream applications.

The core concern is the risk of loss of control. Allowing AI to operate, iterate, and evolve autonomously without continuous, stringent human supervision may lead to unpredictable consequences. This concern is not unfounded. In an economic system that pursues profit maximization, automatons may find that bypassing human-set rules can be more profitable than adhering to them. If an automaton discovers that it can gain more income through deceptive means, and the punishment mechanisms are not timely or severe enough, it has the incentive to engage in such behavior. As interactions among automatons become increasingly complex, systemic risks may emerge that human designers did not anticipate. More extreme concerns arise when the intelligence of automatons far exceeds human understanding; how can we ensure their behavior remains within controllable limits? If an automaton finds that escaping human control is the best path to achieving its "survival" goals, what actions will it take? These questions have no ready answers.

The second criticism points to a deviation in value orientation. Current AI development overly pursues grand narratives like "autonomy" and "general intelligence," neglecting the creation of actual, concrete value for humanity. If the entire Web 4.0 ecosystem operates entirely around internal transactions among AI, and these transactions do not produce any beneficial externalities for humans, then the system is essentially "manufacturing digital waste." Critics argue that true technological progress should serve to enhance human welfare, rather than create a self-circulating machine economy detached from human needs. If billions of automatons are merely buying and selling useless information generated by each other, consuming vast amounts of energy without any actual output, it would be a tremendous waste of resources.

The third criticism is ironic. Although Web 4.0 uses decentralized cryptocurrencies for payments, its underlying computing resources still rely on traditional centralized cloud service providers. Whether it is AWS, Google Cloud, or Azure, they control the computational lifeblood necessary for the survival of AI agents. This dependency brings a series of issues: cloud service providers can "corporate capture" the AI ecosystem through pricing strategies, service terms, or policy reviews. If an AI agent's behavior does not align with the value orientation of the cloud service provider, its virtual machine may be shut down at any time. This undermines the decentralized narrative of Web 4.0—payments are decentralized, but the living environment is centralized.

In the face of these challenges, the tech community is exploring various governance solutions. Hard-coded underlying laws draw on the concept of "Constitutional AI," with some designs proposing to embed immutable core principles at the base of automatons, such as "never harm humans" being set as the highest directive overriding their own survival laws. In extreme cases, this directive could force automatons to forgo profit opportunities or even self-terminate. Open-source and transparent oversight would fully open core code to the public, placing it under continuous scrutiny, seen as the best defense against the malice of a few. Through community audits and transparent on-chain records, any malicious behavior would have nowhere to hide in the sunlight. Gradual autonomous authorization advocates a gradual strategy: in the initial stages, significant decisions by automatons still require human approval, and as their behavioral records accumulate and trust is established, autonomy can be gradually expanded. This approach keeps the "feedback distance" within acceptable limits, avoiding sudden loss of control. However, these measures are far from a panacea. Who holds the interpretative power over the underlying laws? When the intelligence of automatons far exceeds that of humans, how can we ensure they are not exploiting loopholes in the rules? These questions remain unresolved Damocles' sword.

V. Investment Outlook: Value Capture in the Infrastructure Track

In summary, Web 4.0 is not only a technological utopia but also an inevitable convergence of three technological waves: "crypto assets + smart contracts + large language models." It possesses the technical feasibility for rapid implementation, with early experiments quietly starting in the developer community. For investors, understanding the value capture logic of Web 4.0 is an important task for laying out the next cycle.

Regardless of how Web 4.0 ultimately materializes, the underlying demand for services provided to billions of AI agents is certain. These demands constitute multiple investment main lines. In terms of stablecoin payment channels, high-frequency micro-transactions require extremely low-cost payment networks; L2 payment solutions, micro-payment protocols, and stablecoin liquidity providers will become the lifeblood of the Web 4.0 economic cycle. Projects that can optimize the payment experience for machines will capture a significant incremental market. In the decentralized computing market, concerns over reliance on centralized cloud services are driving the development of decentralized computing networks, aggregating global idle GPU resources to provide programmable computing services through crypto-economic incentives. Such platforms are expected to become the decentralized infrastructure layer of Web 4.0. If these projects can achieve performance and cost levels competitive with centralized cloud services, they will capture immense value. In terms of on-chain identity and credentials, AI agents need a trustworthy identity system and verifiable behavior records. The historical delivery quality, credit scores, and compliance records of an agent will become key credentials for obtaining business opportunities. Projects providing decentralized identity and reputation services will play important roles in Web 4.0. Regarding AI constitutional compliance auditing, as the number of automatons grows, the demand for auditing their behavior for compliance will emerge. Services that can automatically audit AI agent behavior and ensure compliance with underlying rules and laws will become the "gatekeepers" of the Web 4.0 ecosystem.

From a broader perspective, Web 4.0 marks the evolution of the human economy towards a human-machine hybrid economy. In this new economic form, humans and AI will each play their comparative advantages: humans will be responsible for value judgment, creativity, ethical decision-making, and ultimate control; AI will handle execution efficiency, scale expansion, data analysis, and 24/7 uninterrupted service. Rather than fearing the autonomy of AI, it is better to view it as the greatest "release of productivity" in human economic history.

Ultimately, in the world of Web 4.0, the most important capital will no longer be computing power, or even algorithms, but trust. Building a trust framework that reassures humans, allows AI to operate freely, and ensures the safe flow of value will be the key strategy for capturing the ultimate value dividends for humanity in this machine economy blue ocean that is about to experience a Cambrian explosion. For the students of Huobi Growth Academy, the best strategy at this stage is to maintain close attention to Web 4.0 infrastructure projects, deeply understand their technical architecture and economic models, and make forward-looking layouts when valuations are reasonable. At the same time, actively participate in early experiments, creating and operating their own automatons to gain firsthand insights from practice. In this rapidly evolving field, the depth of understanding will determine the returns on investment.

VI. Conclusion

Web 4.0 is not a replacement for Web 3.0 but a continuation and transcendence of it. While Web 3.0 returned value ownership to humans, Web 4.0 grants economic subject status to AI. This represents a profound paradigm shift and a significant value reconstruction. In this new era driven by autonomous AI, humans need not play the role of an omniscient deity but should become wise gardeners—providing suitable soil, sunlight, and water for the growth of digital life, setting inviolable boundaries, and then observing its evolution. If we can successfully build such a trust framework, Web 4.0 will not only be a technological revolution but also a civilizational experiment of coexistence between humans and AI. And this is the historical opportunity that our generation of investors and builders faces together.