From computing power infrastructure to application-layer value capture: Paradigm shift of Venice Token and Web3 AI economy

In the second quarter of 2026, the AI narrative in the crypto market is undergoing a quiet yet profound structural shift. The market’s focus is moving from “who has the most GPUs” to “who can truly scale AI usage among users.”

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 has been -2.39% over the past 24 hours, -5.39% over the past 7 days, and -32.10% over the past 30 days. Despite short-term pullback pressure, its cumulative increase over the past year still reaches 359.13%. Its all-time high is $21.4559, and its all-time low is $0.9150. What this price trajectory reflects is not merely speculative volatility, but a deeper industry trend: AI application layer crypto is becoming the new home for value capture in the Web3 world.

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

2026 is becoming a key turning point where Crypto and AI intersect deeply. Over the past two years, AI has evolved from “a supporting tool” into “an autonomous economic participant.” AI Agents are no longer just chatbots that answer 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.” At the beginning of 2026, the number of daily active AI Agents on-chain reached 250,000—up more than 400% from 2025. Automated trading bots are currently estimated to account for 65% of global crypto trading volume. When AI Agents become independent market participants, they need identity, payment channels, reputation records, and verifiable execution environments—and these needs happen to be exactly the kinds of problems blockchains are best at solving.

Against this backdrop, market attention naturally shifts from the “infrastructure layer” to the “application layer.” In Q1 2026, the narrative heat of the AI track in the crypto market rebounded significantly, but unlike the past, the market focus is moving from simply capturing value in “compute infrastructure” to capturing value in the “AI application layer.” Infrastructure tokens often have higher valuations but slower “burn” rates, while application tokens are where real user adoption happens. As applications gain millions of users, the utility of tokens in fees, access, or governance will form a tighter supply-and-demand feedback loop.

In September 2025, the Ethereum Foundation established a decentralized AI (dAI) team, and Vitalik Buterin published a systematic AI strategy framework in early 2026. In that framework, he explicitly proposed that Ethereum should become the “trust layer” of the AI world, enabling AI expansion to be built on infrastructure that is verifiable, auditable, and constrainable. This top-level design signal further confirms that the Web3 AI narrative has moved from the proof-of-concept stage into substantive construction.

The Logic Behind the Formation of Web3 AI Economic Models: From Data Renting to Value Co-Creation

Traditional AI economics follows a one-way value flow model of “centralized platform - user - data.” Taking OpenAI as an example, its API pricing is based on model type and Token processing volume, and enterprise-tier plans can cost as much as $5,000 to $150,000 per month. Users exchange data for services, and interaction history is recorded, stored, and used to train models—this is essentially a form of “data renting.”

Web3 AI economic models aim to overturn this logic. Their core features can be summarized into three aspects:

First, the decentralization of value distribution. In traditional centralized AI paradigms, users’ data becomes the fuel for the platform to continuously optimize models, but users themselves receive no return from their data contributions. In decentralized AI paradigms, 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 being under my control” constitutes Web3 AI’s core advantage in terms of data ownership.

Second, the on-chainization of economic incentives. Taking 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 only a payment token, but also a key infrastructure of the entire intelligent network—by using Proof of Intelligence, an Agent marketplace, a distributed computing network, and an on-chain governance system, it integrates AI capabilities, data resources, and compute power into a unified value network. In this model, the token plays three roles at once: a payment layer, an incentive layer, and a governance layer.

Third, the marketization of resource pricing. In its 2026 “Big Ideas” report, Silicon Valley venture capital firm a16z predicted that AI Agents will become “first-class citizens” in Web3 networks. When the computing units of artificial intelligence merge with blockchain’s value credentials, a brand-new economic operating system—decentralized agent economics—is being born. Token economics is comprehensively reshaping cloud computing and AI infrastructure, pushing competition for compute power toward “Token efficiency per watt,” and shifting business models from subscription-based to usage-based billing.

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

AI content assetization is another important dimension in the Web3 AI economic model that is accelerating into shape.

Driven by generative technology, the scale of AI creation and intelligent assets is surging, but both traditional internet and Web3 ecosystems suffer from gaps in rights confirmation, circulation, and revenue distribution. In 2026, this dilemma is being broken by a series of innovative projects. For example, in the film and television copyright sector, Fubon Group issued the world’s first film and television copyright RWA project, bundling fragmented copyright revenues from platforms such as YouTube into transferable digital assets. Its launched V-ALPHA content asset valuation engine combines 20 years of operational data with AI models to provide dynamic profiling and transparent data tracking of IP, so that film and television content can, for the first time, receive the scientific valuation support required for financial assets. The project is composed of 60% long-term stable Hollywood film and television content revenue, and 40% growth-oriented AI content monetization revenue.

The essence of this trend is the shift from a “digital economy” to a “digital asset economy.” In the traditional internet paradigm, content creators’ earnings depend on the platform’s distribution mechanisms and settlement cycles. In the Web3 paradigm, the content itself can gain liquidity, divisibility, and programmability through tokenization. AI-generated content is no longer just an information product—it can be valued, traded, and assembled like financial assets.

In the context of AI Agents, the meaning of content assetization is even more far-reaching. When AI Agents execute tasks, they generate large numbers of intermediate outputs—such as analysis reports, trading strategies, code snippets, creative proposals, and more. Under traditional models, these outputs are difficult to price and trade, but under tokenized frameworks, they can be converted into verifiable, tradable digital assets. This shift from “content production” to “asset creation” is redefining how value is captured in the AI application layer.

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

Among all projects discussing Web3 AI economic models, Venice Token provides a case worth an in-depth analysis.

Venice was launched in May 2024 by ShapeShift founder Erik Voorhees. Its core positioning is 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 users’ local devices; the platform does not record it, nor use it for model training; and all AI models are open-source and transparent. This architectural difference is not merely a technical choice—it represents two fundamentally different trust models. Centralized AI requires users to trust the service provider not to misuse data or tamper with outputs, while decentralized AI attempts to eliminate reliance on trust in a single intermediary through the technical architecture itself.

Venice’s token economics design is especially noteworthy. It uses a two-layer 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 an API credit quota of $1 per day. Users can obtain DIEM by staking VVV, allowing them to access the platform’s AI inference capability at a predictable cost.

The ingenuity of this design lies in separating “usage rights” from “ownership.” VVV represents ownership of network value—holders can participate in the network’s economic growth and governance decisions. DIEM represents the right to use computing resources—its value is anchored to the actual cost of AI inference. This separation allows AI compute resources to be priced and traded like commodities while still maintaining the value-capture capability of the token economy.

From the standpoint of market performance, this design has received initial validation. In 2025, the number of Venice API users reached 15,000; by March 2026, it increased to 2 million. In March 2026, OpenClaw listed Venice as its recommended model provider, driving the VVV price from about $1.5 to a peak of $8.4 within one month—an increase of more than 500% at most. In April 2026, Vitalik Buterin publicly shared his local LLM usage plan—running the 35-billion-parameter open-source model Qwen3.5 locally on a computer equipped with an NVIDIA 5090 GPU. Although this signal is somewhat symbolic, it further reinforces 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 deployment,” from “infrastructure competition” to “value capture at the application layer.” AI application layer crypto is no longer a vague narrative label—it is an industry direction being validated by token economic models, user growth data, and market performance of projects such as Venice Token.

The formation of Web3 AI economic models is, in essence, a systemic restructuring of traditional centralized AI value distribution—moving from data renting to value co-creation, from platform monopoly to protocol sharing, and from closed ecosystems to open networks. The exploration of AI content assetization and tokenized usage-right 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—through its privacy-first architectural design and two-layer token economics model—provides a typical sample of entering the Web3 AI economy from the application layer. Its price performance over the past year, the explosive growth in API users, and public recognition from industry leaders all point to a market-validated judgment: the era of AI application layer tokens may have just begun.

FAQ

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

The core difference of VVV is that it is not merely concept hype, but has real usable AI products (Venice.ai) and a clear token economics model. Its two-layer token structure (VVV + DIEM) separates network ownership from compute usage rights, forming a sustainable value capture mechanism. As of July 2026, its API user base has grown from 15000 in 2025 to 2 million.

Q2: How does Venice’s privacy architecture work at the technical level?

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

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

DIEM is a companion token launched by Venice. Each DIEM represents an API credit quota of $1 per day. Users can obtain DIEM by staking VVV to consume the platform’s AI inference resources. This mechanism allows AI compute resources to be standardized for pricing and trading, while also ensuring the scarcity of VVV as a value-capture vehicle.

Q4: What is the investment logic behind 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-and-demand feedback loop through utilities such as fees, access, or governance. As AI Agents become independent economic participants, the value-capture capability of application layer tokens is expected to keep strengthening.

Q5: What practical significance does the AI content assetization trend have for creators?

AI content assetization enables creators to convert AI-generated content or copyright revenue into transferable digital assets. For example, in the film and television copyright RWA project, creators can bundle fragmented copyright revenues into tokenized assets, discounting future revenue in advance without having to wait for platform settlements. This trend is transforming content from an “information product” into a “programmable financial asset.”