VVV Surges Over 60% in a Day: Is Privacy + AI the Next Big Narrative?

Venice AI's token VVV hit a new high again today, briefly breaking above $25. The rally was triggered by a mathematical research controversy that made many people realize the importance of "private inference."

New York University mathematician Tristan Buckmaster wrote in a public statement that he and his collaborators had input the entire project draft into Codex. After learning that an internal OpenAI team had also made related progress, he asked whether the other party's model had access to those conversations or had been trained on them. The answer he received was that the model did not review user data, but his follow-up question about training was not answered.

OpenAI's response denied that researchers or agents accessed specific user data to solve the problem, while acknowledging that although unlikely, it could not rule out that de-identified data from the research group's private conversations with Codex had helped improve the model.

The community began to question: when the stakes are high enough, can these labs see all your work and get results before you do?

Trader based16z has already expressed his trade. He disclosed going long VVV through spot, perpetual contracts, and over-the-counter call options with a strike price of $25. In his view, this incident has made the importance of private inference widely recognized, and Venice is the most suitable liquid asset to carry this narrative. He also compared VVV's circulating market cap to ZEC when it was at $50. This is his judgment on the revaluation of privacy.

As AI moves from answering common-sense questions to participating in papers, code, product development, and trading strategies, what users type into the input box becomes more valuable.

Who can let people use AI while retaining control over their work content and execution process? Three Web3 projects have provided their own answers.

VVV: Turning Privacy into a Business

Venice AI, founded by Erik Voorhees, offers a chat application for consumers and an API for developers to call models. It aggregates different models, using privacy and fewer restrictions as product differentiators.

Venice AI's privacy has three levels. Venice's "anonymous mode" hides user identity, but upstream models can still see the request content. "Zero-retention mode" relies on service providers honoring their commitments. "TEE mode" places inference in a protected hardware environment; "end-to-end encryption mode" encrypts from the user's device until it enters the protected environment before decryption.

This business has already taken shape. Investor Banyan disclosed that Venice's annualized revenue increased from $14 million in January this year to over $100 million by August.

VVV is the token issued by Venice on Base. For every $100 of Venice API credits purchased by users, $5 is used to buy back and burn VVV. On the supply side, new emissions of VVV tokens are also declining. On September 1, annual emissions were reduced from 3 million to 2.5 million tokens, with a further planned reduction to 2 million on October 1.

Another source of demand for VVV is DIEM. Holders can lock staked VVV to mint DIEM. By staking 1 DIEM, they can receive a refreshed $1 API credit daily. Thus, developers and agents can hold an asset that continuously generates usage credits, preparing for future model calls.

On September 14, DIEM's target supply will complete a phased expansion from 38,000 to 40,000 tokens, providing room for new minting. This move also hints at the Venice team's optimism about user growth.

The bullish case for VVV is clear: under the privacy threat of AI, Venice has found its market position. More people paying to use Venice brings more buybacks; more people needing continuous inference credits brings lock-up demand.

NEAR: Verifiable Private Inference

Following the clues from Venice AI, we find a familiar figure: NEAR. This public chain is expanding the use cases of the NEAR token through confidential computing and agent services.

In March this year, Venice and NEAR AI announced an integration, allowing users to choose verifiable private inference services provided by NEAR AI. When consumers make requests on Venice, the underlying privacy computing capabilities are provided by service providers such as NEAR AI.

The core capability of NEAR AI Cloud is to run models within a TEE, a trusted execution environment isolated by hardware. By design, plaintext during computation is confined to the protected area, and infrastructure operators cannot directly read it; users can verify hardware attestations to confirm that requests entered the appropriate environment. This provides teams wishing to protect research and business data with a cloud computing option.

Open-weight models can be deployed in this environment. When calling closed-source models such as Claude, GPT, and Gemini through the gateway, requests are still handed over to upstream service providers, and NEAR's confidential computing guarantees cannot cover the other party's servers. NEAR's value on this path comes from its ability to protect the computation process and organize model services.

This capability is already linked to the NEAR token. Staking payments launched on July 30 allow token holders to convert NEAR staking rewards into inference credits, with credits varying based on staking amount, token price, and yield. Users retain ownership of the underlying tokens and can unstake to exit. For teams that call models over the long term, this adds a use case for holding NEAR.

Another opportunity for NEAR lies in payments. For AI agents to complete tasks, besides calling models, they also need to purchase data, pay for services, and move assets across different chains. NEAR Intents provides an intent execution method that allows users to submit desired outcomes, with solvers competing to complete the exchange and execution. This infrastructure can connect complex cross-chain operations into the task flows of AI agents.

According to DeFillama statistics, NEAR Intents generated approximately $9.32 million in total fees in the second quarter of this year, of which the protocol actually retained about $1.5 million; retained revenue is used for market buybacks of NEAR.

Therefore, NEAR's bullish narrative is supported by two businesses: providing computation for sensitive tasks, and providing execution and payments for cross-chain tasks. The former can reach users through applications like Venice, while the latter has the opportunity to grow alongside AI agents.

TAO: Open Supply of AI Models

Bittensor has always been the most high-profile AI project in crypto. TAO is the native token of the Bittensor network.

Bittensor organizes different tasks into independent subnets. Miners provide services such as inference, storage, and prediction, validators evaluate service quality, and the network distributes rewards according to rules. A team can organize competition around a specific need, while the entire network hosts multiple such markets.

As AI demand expands, applications need more replaceable suppliers, and small teams need to find customers, computing resources, and funding. Bittensor attempts to organize this supply through an open incentive market, allowing different teams to compete on specific tasks.

Some subnets have already begun generating external revenue. Taking Chutes, which provides model inference services, as an example, its revenue in the second quarter of this year was approximately $1.37 million, from subscriptions, usage-based billing, and instance services.

How does TAO capture this growth? Each subnet has its own alpha token, paired with TAO to form trading pools. Staking TAO to a subnet actually exchanges it for the corresponding alpha token; TAO thus serves as the base asset for capital allocation across subnets. If more competitive services emerge in the network, demand for participating in these markets has the opportunity to expand.

On the supply side, TAO retains a scarcity design familiar to crypto markets. TAO has a maximum supply of 21 million tokens, completed its first halving in December 2025, and currently issues 0.5 tokens per block, approximately 3,600 tokens per day.

VVV's surge provides us with an observation window. As models become more powerful, people entrust them with more sensitive information, and "crypto/privacy AI" has naturally found its product-market fit.