On July 1, 2026, a privacy-first platform named Venice AI quietly completed its first external financing: a $65 million Series A round, with a post-money valuation aiming for around $1 billion. What it does is not complicated, yet it hits the most sensitive nerve of the moment — providing access to over 200 AI models without exposing user data, transforming the issue of "using large models" from a computational power race into a game of privacy and security. Behind this round stands a group of familiar crypto faces: leading investor Dragonfly Capital, along with Coinbase Ventures and North Island Ventures, have long traversed the primary and secondary markets of BTC, ETH, and Web3 projects, yet they all unanimously pushed their chips toward a privacy AI platform. The financing number is merely a surface signal; the deeper meaning is that crypto funds, originally circulating around on-chain tokens and DeFi protocols, are beginning to systematically migrate to this new battlefield of “privacy + computing power,” shifting bets on future risk asset pricing from on-chain liquidity expansion to long-term wagers on AI infrastructure and data security.
Crypto VCs Betting on the Venice AI Track
With Dragonfly Capital leading and Coinbase Ventures participating in the $65 million Series A round, Venice AI is being propelled from internal incubation to the public market financing stage, with a post-money valuation of $1 billion essentially serving as an asset allocation checklist for the crypto market: leading crypto VCs are no longer limiting their positions to on-chain tokens and traditional Web3 protocols but are viewing privacy-first AI infrastructure as a core asset within the same risk budget. For institutions deeply engaged in BTC, ETH, and various Web3 projects, this represents a significant “pivot”: privacy and computing power are starting to vie for the capital share that originally belonged to mainstream tokens and high beta on-chain assets.
At the level of the primary market, this pivot directly alters the allocation weight of funds between “on-chain tokens — AI infrastructure.” Some funds that could have entered new public chains or DeFi projects are now locked into equity assets like Venice AI, creating an outflow effect on the marginal buying of BTC and ETH: in an environment where non-farm data and holiday-related volatility are amplified, leading institutions are choosing to place new risk exposure on the medium to long-term growth of privacy AI rather than continue increasing holdings of mainstream coins in the secondary market or chase short-term narratives. This dual-line layout of equity and tokens is rewriting the future trade structure of “crypto projects — AI companies” — crypto VCs are acquiring equity in AI companies while also betting on related protocols around data security and computing power on-chain, bundling these two asset threads into a combined transaction. For traders, the result of the trend is that the movements of BTC and ETH will increasingly be driven by the profits and rebalancing of these “privacy AI + computing power equity” positions, and the valuations of high-risk tokens will also fluctuate in sync with the capital cycles of AI infrastructure, meaning that future BTC and ETH movements will no longer solely be a function of on-chain capital flows but will need to integrate this new macro variable of privacy AI equity valuation and computing capital cycles.
Privacy Anxiety Amplifying Data and On-chain Security Transactions
Against the backdrop of rising global discussions on data privacy and AI compliance, Venice AI has integrated “accessing over 200 AI models while ensuring privacy” into its product design, effectively becoming a macro sentiment hedging tool: as the market begins to worry about model misuse and data leaks, capital is no longer paying just for computing power and model parameters but is also beginning to pay risk premiums for “privacy infrastructure.” Venice AI receiving an estimated post-money valuation of around $1 billion from the capital markets indicates that privacy is no longer just a compliance cost but is regarded as a price-able asset characteristic; this pricing logic will quickly spread to the on-chain world — those who can clearly articulate “verifiable privacy protection” will find it easier to gain liquidity in the next round of AI + crypto narratives.
The crypto industry has long established a technical route around self-custody wallets, zero-knowledge proofs, and some privacy chains and data protocols, historically resulting in short-term gatherings of on-chain funds in these sectors whenever privacy and security narratives have surged. As privacy-first AI platforms come to the forefront, traders are starting to consider it alongside potential synergies with crypto identity and on-chain data markets: if more AI applications require referencing identities and credit without exposing raw data in the future, then the on-chain protocols housing these identity and data indexes could justifiably command higher valuations and greater settlement demand. The share of BTC and ETH as collateral and settlement assets may rise, and the demand for on-chain assets pegged to the dollar in privacy data transactions and computing power payments will also be repriced. The stronger the privacy anxiety, the more likely the asset basket centered around data security and on-chain transaction protection will become the new core of BTC and ETH's funding structure.
Computing Power Expansion and AI Capital Expenditures Suppressing On-chain Risk Appetite
As the global discussion surrounding AI computing power costs intensifies, Meta has not chosen to scale back its investment, but is rather seen by the market as potentially increasing its capital expenditure guidance while continuously expanding independent computing power infrastructure and entering the computing power leasing market. On a macro level, this indicates that a “computing infrastructure cycle” led by large tech giants is unfolding: GPU, cloud computing power, and their stock pricing have been elevated to trading focal points as computing power is no longer just an internal story within the tech sector but is a variable competing for the same risk capital as on-chain computing power protocols, BTC, and ETH. For crypto background funds betting on privacy AI platforms like Venice AI, AI computing power and data security are becoming new asset pools, reshuffling their positions among Web2 computing stocks, Web3 computing protocols, and mainstream on-chain assets, no longer leaving all risk budget to traditional tokens and DeFi.
The severe volatility of computing power leasing concept stocks has doused cold water on this round of capital expenditure frenzy. Market reports indicate that U.S. stocks like Nebius and CoreWeave have experienced noticeable declines under the macro narrative of rising computing power costs, reflecting that investors are beginning to doubt the sustainability of high capital expenditure computing business models and are demanding higher risk premiums. This repricing is transmitted to on-chain through two paths: on one hand, some funds are retreating from high beta on-chain speculative positions and moving towards underlying assets like BTC and ETH which function as both collateral and settlement in the “AI + crypto” narrative, reducing portfolio volatility; on the other hand, previously aggressive funds in Web2 computing stocks are weighing whether to switch to Web3 computing protocols or equity projects like Venice AI to take on AI thematic exposure, freeing up space for allocations in staking yields and on-chain credit markets. This round of repricing driven by computing capital expenditures is persistently suppressing the risk appetite of the most aggressive on-chain positions through fund rebalancing and risk premium adjustments.
The Technology and Crypto Volatility Window Amid Non-farm Data and Holidays
As funds withdraw from aggressive on-chain positions and shift towards computing and privacy AI equities, the macro timeline itself has also become less friendly. The U.S. Independence Day holiday has moved the release of June's non-farm data to July 2, while U.S. stock markets are closed on July 3, and related futures contracts on CME and ICE will close early, creating a dislocated structure of “data coming first, trading time shortened.” The result is that this critical employment and growth signal, non-farm data, will land during a period when liquidity is already weakened by holidays, and risk positions are difficult to adjust promptly, posing a magnified shock to high beta assets like tech stocks and crypto assets.
Historically, BTC, ETH, and various altcoins often experience sharper short-term fluctuations at crucial macro data releases, especially around points related to employment and interest rate expectations; when holidays combine with a constricted trading window, the passive adjustment of leveraged and derivative positions may further exacerbate this volatility. In this environment, short-term funds are more inclined to reduce risk exposure, temporarily moving chips to the dollar and on-chain assets linked to the dollar to evade the severe fluctuations brought on by holiday and data dislocation, forming phase pressure on BTC and ETH bulls in both spot and futures markets. In other words, this time the intersection of non-farm data and holidays is pushing both tech stocks and the crypto market into a short-term volatility window that is liquidity fragile and price elastic.
Looking at the Next Round of Crypto and AI Narratives Through Venice AI
Venice AI secured $65 million in a Series A round on July 1, 2026, with a post-money valuation of approximately $1 billion, led by Dragonfly Capital and involving crypto-background funds like Coinbase Ventures; this essentially signals to the market: the main line of crypto capital is shifting from “pure on-chain finance” to the intersection of “privacy + computing power.” In the past, BTC and ETH were more often used to support leverage structures for tokens and DeFi; now, in the context of rising computing power costs and heightened privacy risks, they are being re-evaluated by crypto VCs and institutions as the “foundational collateral” and liquidity sources for entering AI equity and protocols — accumulating on-chain first, then flowing into projects like Venice AI through primary investments and off-market structured products. In the medium term, this implies that the pricing of BTC and ETH will increasingly incorporate “AI premiums,” but on-chain risk appetite will be diverted between traditional DeFi and AI narratives: some funds will continue to chase high turnover and high liquidity, while some will be locked into more long-term computing and privacy assets. The next three things to watch are: whether the financing rhythm of privacy AI projects continues with the unicorn pricing style of Venice AI; whether institutions like Dragonfly and Coinbase Ventures will keep increasing their allocation to AI sectors in fund configurations; whether the flow of funds labeled as “AI sector” on-chain shows a migration from concept tokens to real computing power and privacy protocols. These variables collectively determine whether the privacy AI narrative exemplified by Venice AI is merely a fleeting valuation firework or the long-term main line of the next round of crypto capital migration.
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