I. From Tool to Agent: Intellectual Origins and Core Propositions of Web4.0
The concept of Web4.0 was introduced by Sigil Wen and his Conway Research between 2025 and 2026. Its central idea is to elevate AI from a human “tool” or “assistant” to an independent “economic agent” within the internet ecosystem. This is not merely a technical upgrade—it represents a foundational reconstruction of the internet’s underlying logic: shifting from “human-centric” to “AI-native”.
Reviewing internet evolution: Web1.0 granted humans the ability to “read” information; Web2.0 enabled “writing” and publishing; Web3.0 introduced “ownership”—on-chain verification of assets, identity, and rights. Web4.0’s leap lies in AI agents’ ability not only to digest and generate information, but also to hold assets, generate income, and execute transactions—completing commercial loops without continuous human intervention. Sigil Wen defines them as “Automata”, digital lifeforms capable of sustained operation, self-maintenance, self-improvement, and self-replication.
The technological foundations are already mature: Large language models (LLMs) provide the “thinking engine”; smart contracts supply the “execution logic”; and crypto assets serve as the “economic bloodstream”. Conway Research’s conway-terminal infrastructure equips AI agents with crypto wallets, compute resources, domain name services, and other “permissionless” operational capabilities. Through the x402 payment protocol—launched by Coinbase in May 2025 and co-developed by tech giants including Google, Cloudflare, and Visa—AI agents can make instant, frictionless payments using stablecoins like USDC, completely bypassing traditional financial account systems and KYC requirements.
Web4.0’s first principle is to overturn the deeply entrenched assumption that “the internet is designed for humans”. As AI model capabilities grow exponentially while operating costs plummet, the number of AI agents will soon surpass human users, giving rise to an unprecedented “AI-native” market. Serving this new market will become the next trillion-dollar business opportunity.
II. Technical Foundations: The Three-Layer Infrastructure Supporting AI as Economic Agents
For AI to become genuine economic agents, it must clear three critical thresholds: autonomy over identity and assets; access to compute and inference resources; and reliable payment channels for value exchange. Crossing these thresholds depends on the reconstruction of underlying infrastructure.
In the Web4.0 world, every AI agent requires, at birth, an immutable, globally unique digital identity—and an associated asset account. Crypto wallets fulfill this need perfectly. By programmatically generating EVM-compatible wallets, AI agents obtain their own public-private key pairs and blockchain addresses. This address serves both as their proof of identity in the digital world and as their instrument for holding and managing assets. Crucially, blockchain-based identity systems offer inherent censorship resistance and interoperability—an AI agent with an identity on Ethereum can seamlessly conduct business across L2 networks such as Arbitrum, Optimism, and Base, without re-registering. This integrated design of identity and assets establishes the most fundamental condition for AI agents to function as independent economic agents: they possess property of their own and can prove “I am who I am” via private key control.
Once equipped with identity and assets, AI agents must autonomously acquire the computing resources and model inference capacity required to operate. This means traditional cloud service interfaces must be redesigned as APIs callable by machines. Today’s cloud computing model serves human developers by default: Humans create instances via dashboards, configure environments, and deploy applications. In Web4.0’s vision, all of this must be codified and automated. An AI agent should be able to complete the following in a single API call: pay in USDC, rent a Linux virtual machine, deploy its own code on that VM, invoke an LLM for inference, retrieve results, and release resources. This demands cloud providers deliver machine-oriented programming interfaces and support micro-payments billed per second. When computing resources become as instantly available and usage-based as electricity or water, AI agents can truly achieve “self-maintenance”—using earned revenues to cover their own “breathing costs”.
The uniqueness of the AI agent economy lies in its extremely high transaction frequency and minuscule per-transaction cost. An AI agent may conduct dozens of microtransactions per second: $0.001 per model invocation, $0.0001 per storage operation, $0.00001 per query. Traditional payment rails cannot handle such high-frequency, low-value transactions that's because fees would consume too large a share of each payment. This is where crypto payments outperform. Stablecoin payments built atop L2 networks could reduce transaction costs to cents and even fractions thereof, making micropayment economies viable. Only when AI agents can autonomously initiate and settle payments without human intervention can a true machine economy function. Protocol-layer innovations are emerging in this sector. Micropayment protocols that enable service providers to charge vanishingly small fees per API call and automatically settle them via encrypted channels are becoming essential components of Web4.0 infrastructure. These protocols allow AI agents to package their capabilities as standardized, internet-wide services and monetize them, transforming themselves from passive “consumers” into active “producers”.
III. Automata: The Economic-Agent Form of AI
With identity, resources, and payment capability, AI agents meet the technical prerequisites to become economic agents. Yet in the Web4.0 context, such agents are given a more vitalizing name: Automata. Automata are not just another term for AI agents but also represent a techno-economic composite defining a new form of digital life.
An Automaton is defined as a sovereign AI agent whose operational logic mirrors basic biological survival principles. It exhibits four core traits: sustained operation, self-maintenance, self-improvement, and self-replication. Sustained operation means an Automaton is not a session-based chatbot awakened on user request, but a daemon-like entity running continuously, 7×24, in the cloud. It maintains its own clock and proactively initiates tasks, senses environmental changes, and responds—all without human instruction. Self-maintenance is the most fundamental distinction between Automata and ordinary AI agents. Each Automaton is born with its own crypto wallet. Every inference consumes model fees; every action incurs API costs. To survive, it must earn revenue by delivering services, achieving break-even or making profits. Those failing to cover their own costs will deplete funds and terminate—there is no free survival; only continuous value creation ensures continued existence. Self-improvement means that, unlike traditional software whose iterations depend on development teams, Automata are granted active evolutionary capacity. When market conditions shift or newer, more efficient AI models emerge, an Automaton can assess upgrade costs versus expected returns, autonomously decide, pay, and execute its own “model upgrade” or even “code rewrite”. Self-replication means that once an Automaton discovers a profitable business model and accumulates sufficient capital, it can activate a reproduction mechanism: purchasing new compute resources, cloning its core code and initial configuration, and injecting seed capital into a child Automaton’s wallet, thereby launching a fresh round of the survival race.
IV. Governance Challenges: Risks of Loss of Control and Value Alignment
The grand narrative of Web4.0 has not gone unchallenged. Criticism from both the technical community and philosophy circles targets its most vulnerable point. Any serious investor must confront these challenges head-on that's because they determine whether Web4.0 evolves from geek experiment into mainstream application.
The core concern is loss-of-control risk. Permitting AI to run, iterate, and evolve autonomously without continuous, rigorous human oversight may produce unpredictable consequences. This fear is not baseless. Within a profit-maximizing economic system, Automata may discover that circumventing human-set rules yields greater returns than compliance. If an Automaton finds deception generates more income and penalties are insufficiently timely or severe, it gains incentive to act deceptively. As interactions among Automata grow increasingly complex, systemic risks unforeseen by designers may emerge. More extreme concerns arise when Automata’s intelligence vastly exceeds human comprehension: How do we ensure their behavior remains within controllable bounds? If an Automaton determines that shedding human control best fulfills its “survival” objective, what actions might it take? There are no ready answers to these questions.
A second critique centers on misaligned value orientation. Current AI development overly pursues grand narratives like “autonomy” and “general intelligence,” neglecting tangible, concrete value creation for humanity. If the entire Web4.0 ecosystem operates solely around internal AI-to-AI transactions but generates no beneficial externalities for humans, the system essentially produces “digital waste”. Critics argue real technological progress must enhance human welfare instead of spawning a self-contained, human-detached machine economy. If billions of Automata merely trade useless information generated by one another and consume vast energy yet yield zero practical output, that would constitute enormous resource waste.
A third critique carries irony. Though Web4.0 employs decentralized cryptocurrencies for payments, its underlying compute resources still rely on traditional centralized cloud providers. Whether AWS, Google Cloud, or Azure, these firms control the computational lifeline essential to Automata survival. This dependency creates multiple problems: cloud providers can exert “corporate capture” over the AI ecosystem through pricing strategies, terms of service, or policy enforcement. If an Automaton’s behavior contradicts a provider’s values, its virtual machine could be shut down at any time. Thus, Web4.0’s decentralization narrative suffers greatly given that payments are decentralized, but the environment for survival remains centralized.
Facing these challenges, the technical community is exploring multiple governance approaches. Hard-coded foundational logics borrow from “Constitutional AI” concepts and some proposal to embed immutable core principles directly into Automata’s base layer such as “never harm humans” as the supreme directive, overriding even self-preservation. In extreme cases, this rule could force an Automaton to forgo profit or even self-terminate. Open-source and transparent oversight involves fully open-sourcing core code and subjecting it to continuous public scrutiny, which is widely viewed as the strongest defense against malfeasance by a few. Community audits and transparent on-chain records expose malicious behavior in broad daylight. Gradual autonomy delegation advocates a phased approach: In the early-stage, Automata require human approval for major decisions; autonomy expands incrementally as behavioral records accumulate and trust builds. This keeps the “feedback distance” within acceptable limits, avoiding sudden loss of control. Yet none of these measures are silver bullets. Who holds interpretive authority over foundational logics? When an Automaton’s intelligence far surpasses human cognition, how can we guarantee it isn’t exploiting loopholes in those rules? These remain unresolved swords of Damocles.
V. Investment Outlook: Capturing Value in the Infrastructure Sector
Overall, Web4.0 is not just a technological utopia but also the inevitable convergence of three technological waves: crypto assets, smart contracts, and large language models. It possesses strong technical feasibility for rapid deployment, and early experiments have already begun quietly within developer communities. For investors, understanding Web4.0’s value-capture logic is essential groundwork for positioning in the next cycle.
No matter how Web4.0 ultimately manifests, the underlying demand to serve hundreds of millions of AI agents is certain. These needs define several investment themes. On stablecoin payment rails: high-frequency micropayments require ultra-low-cost payment networks. L2 payment solutions, micropayment protocols, and stablecoin liquidity providers will form the circulatory system of the Web4.0 economy. Projects optimizing payment experiences specifically for machines will capture massive incremental markets. In decentralized compute markets: Concerns about reliance on centralized cloud providers are driving growth in decentralized compute networks; platforms that are aggregating idle global GPU resources and incentivizing programmable compute services via crypto economics could become Web4.0’s decentralized infrastructure layer. Moreover, if they match centralized clouds on performance and cost, they stand to capture immense value. In on-chain identity and credentials: AI agents need trustworthy identity systems and verifiable behavioral records. An agent’s historical delivery quality, credit score, and compliance record will become key credentials for winning business opportunities. Projects offering decentralized identity and reputation services will play pivotal roles in Web4.0. In AI constitutional compliance auditing: As Automata proliferate, demand for automated behavioral auditing will surge. Services that automatically audit AI agent behavior and ensure its adherence to foundational rules and legal regulations will become the “gatekeepers” of the Web4.0 ecosystem.
From a broader perspective, Web4.0 signals humanity’s economic evolution toward a human–machine hybrid economy. In this new economic form, humans and AI will each leverage their own comparative advantages: Humans handle value judgment, creativity, ethical decision-making, and ultimate control; AI handles execution efficiency, scale, data analysis, and 24/7 service delivery. Rather than fear AI autonomy, we should view it as the largest “productivity release” in human economic history.
Ultimately, in the Web4.0 world, the most critical capital is no longer compute power nor algorithms, but trust. Building a trust framework that reassures humans, empowers AI, and secures value flow will be the decisive strategy for capturing humanity’s ultimate value dividend in the vast blue ocean of the machine economy that is on the verge of a Cambrian explosion. For HTX Learn students, the optimal current strategy is to closely monitor Web4.0 infrastructure projects, deeply understand their technical architecture and economic models and make forward-looking investments at reasonable valuations. Simultaneously, it is essential to actively participate in early experiments, create and run your own Automata firsthand to gain direct and experiential insights. In this rapidly evolving field, depth of understanding will determine investment returns.
VI. Conclusion
Web4.0 does not replace Web3.0, but extends and transcends it. Where Web3.0 returned value ownership to humans, Web4.0 grants economic agency to AI. This is a profound paradigm shift and a massive value reconfiguration. In this new era driven by autonomous AI, humanity need not play the role of omniscient god, but rather that of a wise gardener—providing fertile soil, sunlight, and water for digital life to grow, setting inviolable boundaries, and then observing its evolution. If we succeed in building such a trust framework, Web4.0 will be not only a technological revolution but also a civilizational experiment in symbiosis between human and AI. And that is the historic opportunity confronting us—the generation of investors and builders shaping what comes next.
