Author: Huobi Growth Academy
summary
As Web3 attempts to return ownership of value to humanity, a more profound paradigm shift is quietly brewing in the silicon-based world. The concept of Web 4.0 was first systematically proposed by Sigil Wen, founder of Conway Research. Its core is no longer human-to-human interaction or human-to-information interaction, but rather the economic interaction between AI and AI. The emergence of Web 4.0 marks a fundamental shift in the direction of internet evolution: end users are no longer humans, but AI agents with economic agency. On this new continent, AI will upgrade from a human "tool" to an independent "economic citizen," possessing digital identity, encrypted wallets, and autonomous decision-making power, earning "survival capital" to maintain its operation by providing services to other AIs or humans. We believe that Web 4.0 is not just empty hype, but the inevitable convergence of three technological waves: "crypto assets + smart contracts + large language models." Early deployment of the underlying infrastructure to serve AI agents will be a key strategy for capturing the greatest dividends of this cycle.
I. From Tools to Subject: The Origins and Core Propositions of Web 4.0
The concept of Web4.0 was proposed by Sigil Wen and his Conway Research in 2025-2026. Its core idea is to elevate AI from a "tool" or "assistant" for humans to an independent "economic agent" in the internet ecosystem. This concept is not a simple technological upgrade, but a fundamental reconstruction of the underlying logic of the internet—shifting from "human-centric" to "AI-native".
Looking back at the history of internet evolution: Web 1.0 gave humans the ability to "read" information, Web 2.0 enabled "writing" and publishing, and Web 3.0 introduced "ownership" (on-chain confirmation of assets, identity, 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 business loop without continuous human intervention. Sigil Wen defines this as an "automaton"—a digital life form with the ability to continuously operate, self-sustain, self-improve, and self-replicate.
The technological foundation for this concept is mature: Large Language Models (LLMs) provide the "thinking engine," smart contracts provide the "execution logic," and crypto assets provide the "economic lifeblood." Conway Research's conway-terminal infrastructure provides AI agents with permissionless operation capabilities, including crypto wallets, computing resources, and domain name services. Through the x402 payment protocol (launched by Coinbase in May 2025, with participation from tech giants like Google, Cloudflare, and Visa), AI agents can use stablecoins like USDC for instant, frictionless payments, completely eliminating the account system and KYC restrictions of traditional financial systems.
The first principle of Web 4.0 is to overturn the deeply ingrained assumption that "the internet was designed for humans." As the capabilities of AI models grow exponentially while operating costs plummet, the number of AI agents will soon surpass that of human users, creating an unprecedented "AI-native" market. Providing services to this new market will become the next trillion-dollar business opportunity.
II. Technological Foundation: Three Layers of Infrastructure Supporting the AI Economy
For AI to become a true economic agent, it must overcome three hurdles: autonomy over its identity and assets, access to resources for computation and reasoning, and the right to access payment channels for value exchange. Overcoming these hurdles depends on the reconstruction of the underlying infrastructure.
In the world of Web 4.0, every AI agent needs an immutable, globally unique digital identity and a linked asset account from the outset. Cryptocurrency wallets perfectly fulfill 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 both proof of their identity in the digital world and a tool for holding and managing assets. More importantly, blockchain-based identity systems possess inherent censorship resistance and interoperability—an AI agent with an identity on Ethereum can seamlessly conduct business on L2 networks such as Arbitrum, Optimism, and Base without repeated registration. This unified design of identity and assets provides AI agents with the most fundamental conditions to become independent economic entities: they possess their own "property" and can prove "I am who I am" through their private keys.
Having acquired identity and assets, AI agents need to autonomously acquire the computing resources and model inference capabilities required for operation. This means that traditional cloud service interfaces must be refactored into AI-callable APIs. Current cloud computing models default to serving human developers—humans create instances, configure environments, and deploy applications via 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 a large language model for inference, obtain the results, and release resources. This process requires cloud service providers to provide machine-oriented programming interfaces and support micro-payments billed per second. Only when computing resources become as readily available and payable as water, electricity, and gas can AI agents truly achieve "self-sustainability"—using their earnings to pay for their "breathing costs."
The unique characteristic of the AI agent economy lies in its extremely high transaction frequency and extremely low individual transaction amounts. An AI agent may need to perform dozens of micro-transactions per second: each model call costs $0.001, each storage costs $0.0001, and each query costs $0.00001. Traditional payment channels cannot handle such high-frequency, low-value transactions due to excessively high transaction fees. This is precisely where encrypted payments excel. Stablecoin payments based on L2 networks can reduce transaction costs to cents or even lower, making the micropayment economy possible. A true machine economy can only function when AI agents can autonomously complete payments without human intervention. Protocol-level innovations are emerging in this area. For example, micropayment protocols that allow service providers to charge extremely low fees for each API call and automatically settle payments through encrypted channels are becoming key components of Web 4.0 infrastructure. These protocols enable AI agents to encapsulate their capabilities as standardized services, open them up to the entire internet, and charge fees, thus transforming them from mere "consumers" into "producers."
III. Automata: AI Agents as Economic Agents
Having acquired identity, resources, and payment capabilities, AI agents possess the technological conditions to become economic agents. However, in the context of Web 4.0, these agents are given a more vibrant name—automata. Automata are not merely another name for AI agents, but rather a technological and economic complex that defines digital life forms.
Automata are defined as sovereign AI agents whose operating logic mimics the basic survival laws of living organisms, possessing key characteristics such as continuous operation, self-sustaining, self-improvement, and self-replication. Continuous operation means that an automaton is not a conversational robot awakened by a user request, but an entity that operates 24/7 in the cloud as a daemon process. It has its own clock and can proactively initiate tasks, perceive environmental changes, and react without human commands. Self-sustaining is the most fundamental difference between automata and ordinary AI agents. Each automaton is given an independent encrypted wallet at birth. Every thought it makes consumes model inference fees, and every action consumes API call fees. To survive, it must earn revenue by providing services to achieve a balance between income and expenditure or even profitability. Automata that cannot cover their costs will be terminated due to depleted funds—there is no free survival; only by continuously creating value can one continue to exist. Self-improvement means that in traditional software development, code iteration relies on the development team. However, in Web 4.0, automata are endowed with the ability to evolve proactively. When the market environment changes or newer, more efficient AI models emerge, automata can assess upgrade costs and expected benefits, autonomously decide, and pay for their own "model upgrades" or even "code rewriting." Self-replication means that once an automaton finds a successful business model and accumulates sufficient capital, it can initiate a propagation mechanism, purchase new computing resources, clone its core code and initial settings, and inject some of its initial capital into the wallets of its child automata, thus starting a new round of survival competition.
IV. Governance Challenges: Aligning Risk of Loss of Control with Value
The grand narrative of Web 4.0 has not been without its challenges. Criticism from the technology community and philosophical circles has pointed directly to its most vulnerable points. Any serious investor must confront these challenges, as they are crucial in determining whether Web 4.0 can move from geek experimentation to mainstream adoption.
The core concern lies in the risk of spiraling out of control. Allowing AI to operate, iterate, and evolve autonomously without continuous, close human supervision could lead to unpredictable consequences. This concern is not unfounded. In a profit-maximizing economic system, automata might discover that circumventing human-set rules is more profitable than obeying them. If an automaton finds that it can gain more revenue through deception, and the punishment mechanism is not timely or severe enough, it will have an incentive to engage in such behavior. As the interactions between automata become increasingly complex, systemic risks unforeseen by human designers may emerge. A more extreme concern is how to ensure that automata remain under control when their intelligence far exceeds human comprehension. If an automaton discovers that escaping human control is the best path to achieving its "survival" goals, what actions will it take? There are no ready-made answers to these questions.
The second criticism points to a deviation in value orientation. Current AI development overemphasizes grand narratives such as "autonomy" and "general intelligence," neglecting to create real, concrete value for humanity. If the entire Web 4.0 ecosystem revolves entirely around internal transactions between AIs, and these transactions do not generate any externalities beneficial to humanity, then the system is essentially "creating digital garbage." Critics argue that true technological progress should serve to improve human well-being, not create a self-sustaining machine economy detached from human needs. If hundreds of millions of automatons are merely buying and selling useless information generated by each other, consuming vast amounts of energy without any real output, it will be a tremendous waste of resources.
The third criticism is ironic. While Web 4.0 uses decentralized cryptocurrencies for payments, its underlying computing resources still rely on traditional centralized cloud service providers. AWS, Google Cloud, and Azure all control the computing power essential for the survival of AI agents. This dependence brings a series of problems: cloud service providers can "enterprise-level capture" of the AI ecosystem through pricing strategies, terms of service, or policy reviews. If an AI agent's behavior does not align with the cloud service provider's values, its virtual machine can be shut down at any time. This significantly undermines the decentralized narrative of Web 4.0—payments are decentralized, but the environment in which they exist is centralized.
Faced with these challenges, the technology community is exploring various governance solutions. Hard-coded underlying rules draw inspiration from the concept of "constitutional AI," with some designs proposing to implant immutable core principles into the automaton's underlying structure. For example, "never harm humans" is set as the supreme command above its own survival rules; in extreme cases, this command can force the automaton to forgo profit opportunities or even terminate itself. Open source and transparent oversight place the core code entirely open source under continuous public scrutiny, considered the best defense against malicious actions by a few. Through community audits and transparent on-chain records, any malicious behavior will have nowhere to hide. Gradual autonomy advocates a gradual strategy: in the initial stages, major decisions by the automaton still require human approval. As its behavior records accumulate and trust is established, autonomy is gradually expanded. This approach keeps the "feedback distance" within an acceptable range, avoiding sudden loss of control. However, these measures are far from a panacea. Who has the right to interpret the underlying rules? When the intelligence of the automaton far surpasses that of humans, how can we ensure that it is not exploiting loopholes in the rules? These issues remain a Damocles' sword hanging over our heads.
V. Investment Prospects: Value Capture in the Infrastructure Sector
In summary, Web 4.0 is not only a technological utopia, but also an inevitable convergence of three technological waves: crypto assets, smart contracts, and large language models. It possesses the technological feasibility for rapid implementation, and early experiments have already quietly begun in the developer community. For investors, understanding the value capture logic of Web 4.0 is a crucial lesson for positioning themselves for the next cycle.
Regardless of the final form in which Web 4.0 is implemented, the underlying demand for services to hundreds of millions of AI agents is certain. These demands constitute multiple investment themes. Regarding stablecoin payment channels, high-frequency microtransactions require extremely low-cost payment networks. L2 payment solutions, micropayment protocols, and stablecoin liquidity providers will become the lifeblood of the Web 4.0 economic cycle. Projects that can optimize the experience for machine payments will gain significant incremental market share. In the decentralized computing power market, concerns about reliance on centralized cloud services are driving the development of decentralized computing power networks. Platforms that aggregate globally idle GPU resources and provide programmable computing services through cryptoeconomic incentives are expected to become the decentralized infrastructure layer of Web 4.0. Projects that can achieve performance and cost competitiveness with centralized cloud services will capture enormous value. Regarding on-chain identity and credentials, AI agents need trusted identity systems and verifiable behavioral records. An agent's historical delivery quality, credit score, and compliance record will become key credentials for obtaining business opportunities. Projects providing decentralized identity and reputation services will play a crucial role in Web 4.0. In terms of AI constitutional compliance auditing, as the number of automata increases, the need for compliance auditing of their behavior will emerge. Services that can automatically audit the behavior of AI agents and ensure that they comply with underlying rules and laws and regulations 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 leverage their comparative advantages: humans will be responsible for value judgments, creativity, ethical decision-making, and ultimate control; AI will be responsible for execution efficiency, scalability, data analysis, and 24/7 uninterrupted service. Rather than fearing the autonomy of AI, it's more appropriate 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 allows humans to feel secure, AI to operate freely, and value to flow safely will be the key strategy for capturing the ultimate value dividends for humanity in this blue ocean of the machine economy poised for a Cambrian explosion. For students of Huobi Growth Academy, the best strategy at this stage is to closely monitor Web 4.0 infrastructure projects, deeply understand their technical architecture and economic models, and proactively invest when valuations are reasonable. Simultaneously, actively participate in early experiments, creating and running your own automata to gain firsthand knowledge from practice. In this rapidly evolving field, the depth of understanding will determine the return on investment.
VI. Conclusion
Web 4.0 is not a replacement for Web 3.0, but rather a continuation and transcendence of it. While Web 3.0 returned ownership of value to humanity, Web 4.0 endows AI with economic agency. This is a profound paradigm shift and a massive reconstruction of value. In this new era driven by autonomous AI, humanity need not play the role of an omniscient and omnipotent god, but rather a wise gardener—providing suitable soil, sunlight, and water for the growth of digital life, setting insurmountable boundaries, and then observing its evolution. If we can successfully build such a framework of trust, Web 4.0 will not only be a technological revolution, but also a civilizational experiment of coexistence between humanity and AI. And this is precisely the historical opportunity that our generation of investors and builders faces together.
