De la infraestructura de potencia computacional a la captura de valor a nivel de aplicación: El cambio de paradigma de Venice Token y la economía de IA Web3

In the second quarter of 2026, the AI narrative in the crypto market is undergoing a quiet but profound structural adjustment. The market's focus is shifting from "who has the most GPUs" to "who can truly enable AI to be used by users at scale."

As of July 1, 2026 (Beijing time), according to Gate market data, the price of Venice Token (VVV) is $12.6332, with a market cap of approximately $595 million, ranking 108th. Its price change in the past 24 hours is -2.39%, -5.39% in the past 7 days, and -32.10% in the past 30 days. Despite short-term correction pressure, its cumulative increase over the past year is still 359.13%, with an all-time high of $21.4559 and an all-time low of $0.9150. This price trajectory reflects not mere speculative volatility, but a deeper industry trend: AI application layer crypto is becoming the new main stage for value capture in the Web3 world.

From AI Agents to AI Application Layer: A Value Migration Underway

2026 is becoming a key turning point for the deep intersection of Crypto and AI. Over the past two years, AI has transformed from a "tool" to an "autonomous economic participant"—AI Agents are no longer just chatbots answering questions; they have begun to autonomously initiate transactions, call APIs, manage asset portfolios, and even hire other Agents to complete tasks.

The essence of this evolution is AI's leap from "technical capability" to "economic entity." In early 2026, the number of daily active AI Agents on-chain reached 250,000, an increase of over 400% compared to 2025. Automated trading bots currently account for an estimated 65% of global crypto trading volume. When AI Agents become independent market participants, they need identities, payment channels, reputation records, and verifiable execution environments—exactly the problems that blockchain is best at solving.

In this context, market attention naturally shifts from the "infrastructure layer" to the "application layer." In the first quarter of 2026, the narrative heat of the AI track in the crypto market rebounded significantly, but unlike before, the market focus is turning from pure "computing infrastructure" to value capture at the "AI application layer." Infrastructure tokens often have high valuations but slower "burn" rates, while application tokens are where user adoption truly happens. As applications gain millions of users, the utility of tokens in fees, access, or governance forms a tighter supply-demand loop.

In September 2025, the Ethereum Foundation established the decentralized AI (dAI) team, and Vitalik Buterin published a systematic AI strategy framework in early 2026. In the framework, he clearly stated that Ethereum should become the "trust layer" of the AI world, allowing AI expansion to be built on verifiable, auditable, and constrained infrastructure. This top-level design signal further confirms that the Web3 AI narrative has entered a substantive construction phase from the proof-of-concept stage.

The Formation Logic of the Web3 AI Economic Model: From Data Rent to Value Co-creation

The traditional AI economy follows a one-way value flow model of "centralized platform - user - data." For example, OpenAI's API pricing is based on model type and token processing volume, with enterprise plans ranging from $5,000 to $150,000 per month. Users exchange data for services, and interaction history is recorded, stored, and used for model training—this is essentially a form of "data rent."

The Web3 AI economic model attempts to overturn this logic. Its core features can be summarized at three levels:

First, decentralized value distribution. In the traditional centralized AI paradigm, user data becomes fuel for the platform to continuously optimize models, but users themselves cannot receive any return for their data contributions. In the decentralized AI paradigm, users are no longer passive data providers; instead, they become part of the platform's economic system by holding and staking tokens. This shift from "data being collected" to "data under my control" constitutes the core advantage of Web3 AI in the dimension of data ownership.

Second, on-chain economic incentives. Take ChainOpera AI's COAI token as an example. Its design goal is to drive the continuous growth of the AI Agent economy through on-chain incentives. COAI is not just a payment token, but a key infrastructure for the entire intelligent network—integrating AI capabilities, data resources, and computing power into a unified value network through Proof of Intelligence, Agent marketplace, distributed computing network, and on-chain governance system. In this model, the token plays a triple role as payment layer, incentive layer, and governance layer.

Third, market-based resource pricing. Top Silicon Valley venture capital firm a16z predicted in its 2026 "Big Ideas" report that AI Agents will become "first-class citizens" in Web3 networks. When AI computing units merge with blockchain value credentials, a new economic operating system—the decentralized intelligent agent economy—is being born. Token economics is comprehensively reshaping cloud computing and AI infrastructure, driving competition from computing power to "token efficiency per watt," and business models from subscription to pay-per-use.

Trend of AI Content Assetization: From Digital Content to Programmable Value

AI content assetization is another important dimension of the Web3 AI economic model that is accelerating.

Driven by generative technology, AI creation and smart asset scale have surged, but both traditional internet and Web3 ecosystems face gaps in rights confirmation, circulation, and revenue distribution. In 2026, this dilemma is being broken by a series of innovative projects. Taking the film and television copyright field as an example, the company "Fu Bo" issued the world's first film and television copyright RWA project, packaging decentralized copyright revenue from platforms like YouTube into transferable digital assets. Its V-ALPHA content asset evaluation engine, combining 20 years of operational data with AI models, performs dynamic profiling and transparent data tracking of IP, making film and television content for the first time have the scientific valuation support required for financial assets. 60% of the project is Hollywood film and TV content revenue with stable long-term income, and 40% is growth-oriented AI content monetization revenue.

The essence of this trend is the leap from "digital economy" to "digital asset economy." In the traditional internet paradigm, content creators' revenue depends on the platform's distribution mechanism and settlement cycle; in the Web3 paradigm, content itself can gain liquidity, divisibility, and programmability through tokenization. AI-generated content is no longer just an information product but can be valued, traded, and combined like a financial asset.

In the context of AI Agents, the meaning of content assetization is more profound. When executing tasks, AI Agents generate a large number of intermediate products—analysis reports, trading strategies, code snippets, creative plans, etc. Under the traditional model, these products are difficult to price and trade, but under a tokenized framework, they can be transformed into verifiable, tradable digital assets. This shift from "content production" to "asset creation" is redefining the value capture method of the AI application layer.

Tokenized AI Usage Rights: The Dual-Layer Architecture of Venice Token

Among all projects exploring the Web3 AI economic model, Venice Token provides a case worth in-depth analysis.

Venice was launched in May 2024 by ShapeShift founder Erik Voorhees, with a core positioning of privacy protection and uncensored access. Unlike traditional AI services that rely on centralized servers, Venice adopts a local-first privacy architecture—user conversation data is encrypted and stored on local devices, the platform does not record or use it for model training, and all AI models are open-source and transparent. This architectural difference is not just a technical choice but represents two completely different trust models: centralized AI requires users to trust the provider not to misuse data or tamper with output; decentralized AI seeks to eliminate trust dependence on a single intermediary through the technical architecture itself.

Venice's token economic design is particularly noteworthy. It adopts a dual-token structure of VVV and DIEM: VVV is the native token of the Venice network, responsible for value capture and network incentives; DIEM is used to manage and consume AI inference resources. Each DIEM represents $1 of API credit per day. Users can obtain DIEM by staking VVV, thereby accessing the platform's AI inference capabilities at a predictable cost.

The subtlety of this design is that it separates "usage rights" from "ownership." VVV represents ownership of network value—holders can participate in the network's economic growth and governance decisions; DIEM represents usage rights to computing power resources—its value is pegged to actual AI inference costs. This separation allows AI computing power resources to be priced and traded like commodities while maintaining the value capture ability of the token economy.

From a market performance perspective, this design has received initial verification. In 2025, Venice API users reached 15,000, and by March 2026, it increased to 2 million. In March 2026, OpenClaw listed Venice as its top recommended model provider, driving VVV's price from about $1.5 to a high of $8.4 within a month, a maximum increase of over 500%. In April 2026, Vitalik Buterin publicly shared his local LLM usage solution—running the open-source model Qwen3.5 with 35 billion parameters locally on a computer equipped with an NVIDIA 5090 GPU. Although this signal is symbolic, it further strengthens the trend that the "privacy-first, local-first" AI usage paradigm is gaining mainstream recognition.

Conclusion

The crypto market in 2026 is witnessing a clear structural trend: the AI narrative is moving from "concept hype" to "application implementation," from "infrastructure competition" to "application layer value capture." AI application layer crypto is no longer a vague narrative label, but an industry direction being verified by the token economic models, user growth data, and market performance of projects like Venice Token.

The formation of the Web3 AI economic model is essentially a systematic restructuring of the traditional centralized AI value distribution model—from data rent to value co-creation, from platform monopoly to protocol sharing, from closed ecosystems to open networks. The exploration of AI content assetization and tokenized usage rights models further expands the boundaries of this restructuring—making AI not only a technical capability but also an economic resource that can be programmed, traded, and combined.

In this process, Venice Token, with its privacy-first architectural design and dual-layer token economic model, provides a typical sample of entering the Web3 AI economy from the application layer. Its price performance over the past year, explosive growth in API users, and public recognition by industry leaders all point to a judgment being verified by the market: the era of AI application layer tokens may have just begun.

FAQ

Q1: What is the essential difference between Venice Token (VVV) and traditional AI concept coins?

The core difference of VVV is that it is not purely concept hype; it has a practical AI product (Venice.ai) and a clear token economic model. Its dual-token structure (VVV + DIEM) separates network ownership from computing power usage rights, forming a sustainable value capture mechanism. As of July 2026, its API users have grown from 15,000 in 2025 to 2 million.

Q2: How is Venice's privacy architecture technically implemented?

Venice adopts a local-first privacy architecture. User conversation data is encrypted and stored on local devices; the platform does not record or use it for model training. The platform provides four privacy levels, with the "Private" mode achieving zero data retention, fully using self-hosted open-source models. All AI models are open-source and transparent, allowing users to verify data security through the technical architecture itself.

Q3: What role does DIEM play in the Venice ecosystem?

DIEM is a companion token issued by Venice; each DIEM represents $1 of API credit per day. Users can obtain DIEM by staking VVV, for consuming the platform's AI inference resources. This mechanism enables AI computing power resources to be standardized priced and traded, while ensuring the scarcity of VVV as a value capture vehicle.

Q4: What is the investment logic for Web3 AI application layer tokens?

The investment logic for Web3 AI application layer tokens is based on the judgment that "application tokens are where user adoption happens." Unlike infrastructure tokens, application tokens form a tighter supply-demand loop through utility such as fees, access, or governance. As AI Agents become independent economic participants, the value capture ability of application layer tokens is expected to continue strengthening.

Q5: What is the practical significance of the AI content assetization trend for creators?

AI content assetization enables creators to transform AI-generated content or copyright revenue into tradable digital assets. Taking the film and TV copyright RWA project as an example, creators can package decentralized copyright revenue into tokenized assets, discounting future revenue in advance without waiting for platform settlements. This trend is turning content from "information products" into "programmable financial assets."