VVV surges over 20% to $6.78, becoming the top-performing altcoin among top 300. A whale who bought $330K two months ago now holds $310K in profit without selling. Supply cuts of 25% and 17% locked supply underpin the rally.

Privacy-focused AI startup Venice AI completed a $65M Series A led by Dragonfly, at a $1B valuation. Its mixed equity-token financing model draws criticism over potential conflicts of interest.

Crypto market cap edges up 0.3% to $2.65T on April 22. OPG leads gainers with 115.77% daily rise, RAVE up 43%. KelpDAO bridge attack launders funds to BTC; RHEA Finance loses $400K. Coinbase lists Diem; NY AG sues Coinbase and Gemini.

Galaxy Digital research vice president Lucas Tcheyan argues that an “on-chain inference capital market” is beginning to emerge as AI inference, GPU supply, payment rails, tokenization tools and financing infrastructure converge into a more integrated system. In the piece, republished by WuBlockchain and translated by TechFlow, he frames inference as a fast-growing economic layer that is moving beyond centralized APIs controlled by companies such as OpenAI and Anthropic. The report breaks the market into several connected parts. On the off-chain side, GPU index providers including Ornn and Silicon Data are trying to standardize compute pricing, while ICE and CME have announced plans for GPU futures. On-chain, the stack includes decentralized inference providers, model developers, router layers, agent payment standards, tokenized access markets and credit protocols that finance GPU hardware. Tcheyan focuses on four examples. Venice turns future inference access into transferable claims through its VVV and DIEM token structure. Pearl and Ambient try to tie network security to real inference work through “useful proof of work,” though both still face open questions around real demand and token value capture. USD.AI takes a different route by using stablecoin deposits to fund GPU-backed loans for smaller compute operators. Galaxy’s conclusion is that the sector remains early: financing has found the clearest product-market fit so far, while the broader tokenized inference economy still needs to prove durable demand, execution and pricing power.

Venice, an AI infrastructure project, has revised the tokenomics of VVV and DIEM by adding a new programmatic buyback-and-burn path and raising DIEM’s supply target. Under the update, API credit purchases will now contribute to VVV burns alongside existing subscription-based burns tied to Pro, Pro+, and Max plans. Venice said that for every $100 spent on Venice API credits, $5 will be automatically used to buy and burn VVV, with the burn activity tracked separately on its official burn page. The company also said it will gradually increase DIEM’s target supply from 38,000 to 40,000 starting Aug. 3, creating room for up to 2,000 additional DIEM to be minted. DIEM is the second token in the Venice ecosystem and is used to provide access to AI model credits, with each DIEM corresponding to $1 in daily Venice AI credit. Because minting DIEM requires users to stake and lock VVV, Venice said the higher supply target would also increase the amount of daily AI API credits available while keeping its supply-and-demand mechanism in place through a rising mint rate as supply approaches the cap.

Venice AI said on July 18 that it has updated its tokenomics with two changes centered on token supply management. First, the project introduced a programmed burn mechanism tied to API credit purchases. Under the new arrangement, for every $100 spent on API credits, $5 will be used to buy VVV on the open market and permanently burn those tokens. Second, Venice AI raised its DIEM supply target for the first time. The target will increase in stages from 38,000 to 40,000, adding 2,000 units in total. According to the announcement, the phased rollout is expected to reach the maximum target on Sept. 14. The update was disclosed by Venice AI in an official announcement cited by BlockBeats.

Venice AI has announced two changes to its tokenomics, according to ChainCatcher. The first is a new programmatic burn mechanism tied to API credit purchases. Under the update, every $100 spent on API credits will direct $5 to buy VVV on the open market and permanently burn those tokens. The second change is the first increase in the DIEM supply target. Venice AI said the target will be raised in stages from 38,000 to 40,000, adding 2,000 units in total. The company expects the maximum target to be reached on Sept. 14. The announcement focused on these two adjustments and did not include additional details in the source text.

Galaxy Digital has laid out a broad framework for what it calls the “inference capital markets,” arguing that AI inference is moving from a purely technical service into an asset class that can be priced, hedged, financed and traded. In a research report written by Galaxy Digital Vice President of Research Lucas Tcheyan and circulated in Chinese by TechFlow, the firm links several parallel developments into one market structure: the rise of GPU price indexes, planned GPU futures from Intercontinental Exchange and CME Group, tokenized claims on future AI inference, useful proof-of-work networks that subsidize inference production, and stablecoin-funded lending against GPU hardware. The report’s central claim is that inference has now overtaken training as the main driver of global GPU demand, while autonomous agents are emerging as a new class of machine-native buyers that can pay for model output programmatically. Galaxy argues that the market is still early and fragmented. It sees progress on the off-chain side, where Ornn, Silicon Data and Compute Desk are building reference pricing for compute, and where Kalshi, ICE and CME are already moving toward tradable GPU-linked products. On-chain, the report highlights Venice’s VVV and DIEM system for tokenized inference access, Pearl and Ambient’s different attempts to turn inference production into useful proof-of-work, and USD.AI’s stablecoin-based credit model for financing AI hardware. Even so, the report says the sector has not yet solved its hardest questions: whether real demand for verifiable, censorship-resistant inference will grow beyond a niche, how token value can be tied to actual product usage instead of emissions and speculation, and whether legal enforcement and collateral recovery in GPU-backed lending can hold up in a true stress cycle.