Venice: The Private Compute Economy @AskVenice now serves 3M users and processes 100B tokens daily. The team just raised $65M at a $1B valuation, led by @dragonfly_xyz with participation of @cbventures and other investors. The capital will support global expansion and Venice-owned datacenter capacity. Every prompt stays private through browser-side encryption, zero server storage, anonymous access, and TEE/E2EE model execution. The compute economy runs through $VVV and $DIEM: > $VVV is the capital asset for staking, yield, Pro access, and $DIEM issuance > 1 staked DIEM provides $1 in renewing inference credits every day > Venice retains 30M+ VVV from the current 80M supply > Annual VVV emissions have fallen from 6M to 3M > Platform revenue funds monthly VVV buybacks and burns Agents will consume inference continuously, turning compute access into an onchain capital market. Venice is building the capital market for private intelligence.

Every AI model running today needs compute. These companies are powering the AI buildout: Neocloud: CoreWeave ($CRWV), Nebius Group ($NBIS), Digital Ocean ($DOCN) AI Compute Infrastructure: Hut 8 ($HUT), IREN ($IREN), Core Scientific ($CORZ), TeraWulf ($WULF), Applied Digital ($APLD), Hewlett Packard Enterprise ($HPE), SBI Holdings ($SBI) The Grayscale AI Compute ETF $GCPU offers exposure to a basket of companies powering the compute economy. Learn more:

Crucible Capital founder @Melt_Dem says compute will be this century’s oil, and America should win the same way it built the petrodollar: financial engineering. "I am a compute maximalist. The core view we have is a view I call capital markets maximalism. America is incredible at financial engineering" "This is why the US dollar is the petrodollar. If the oil industry was the most consequential of the last century, I think the compute economy will be the most consequential of this century. How does America win? Shit ton of financial engineering. Let's do what we're really good at." "If banks are stepping back, if credit funds get more rigorous with their underwriting, we see the whole debt complex repricing from 7 to 9% into the low teens. That means we need new sources of capital to step in." @CrucibleCap

Graphics Processing Unit (GPU) rental prices are hitting new highs, yet compute stocks are sitting near one-year lows. As AI agent demand continues to accelerate, compute scarcity is set to deepen. The Grayscale AI Compute ETF $GCPU offers exposure to the businesses powering the compute economy. Learn more:

We’re hiring a Product Manager to lead SiliconMark—what I often describe as the “CARFAX for compute.” We started with GPUs because the same chip can deliver very different real-world performance depending on the system, networking, cooling and configuration. But our ambition is much larger: to build trusted benchmarks for every layer of the compute stack—from chips and clusters to infrastructure, models, tokens and the economic value ultimately delivered. SiliconMark is already one of our fastest-growing products. We’re looking for someone who can own this vision end to end: benchmark design, methodology, scoring, certification, customer experience and go-to-market. This is an opportunity to help define how the entire compute economy measures performance, quality and value—not just what people claim to offer, but what every layer of the stack actually delivers. Apply here: #Hiring# #ProductManagement# #GPU# #AIInfrastructure# #Compute#

Confidential APT. $2B+ perp volume on @DecibelTrade. @shelbyserves connecting AI to the compute economy. Quantum-ready infrastructure. @AptosLabs' @AveryChing with @TheBlockCo's @gazza_jenks at Consensus Miami 2026 on all things Aptos, the full stack for markets and machines.

. @Silicon_Data and @computeexchange were both built after the ChatGPT moment. But I still wouldn’t call either company truly AI-native—yet. Being founded in the AI era doesn’t automatically make an organization AI-native. Giving every employee access to ChatGPT certainly doesn’t. I’ve been thinking about the organizational structures of both companies, and the exercise has made me realize that AI-native organizations will not all look the same. @Silicon_Data is organized around building the independent reference layer for the compute economy: data infrastructure, indices, benchmarking, research, product commercialization and market adoption. @computeexchange is organized around creating liquidity: sourcing, verification, pricing, matching, contracting and settlement. Agents can transform both companies—but differently. At @Silicon_Data, agents can accelerate data analysis, research, product development, content production and customer intelligence. At @computeexchange, they can automate inventory normalization, provider onboarding, RFQs, matching and transaction workflows. This has also changed how I think about organizational design. Traditional companies are built around people, roles and reporting lines. Knowledge is distributed across individual brains, inboxes, documents, Slack channels and meetings. In that sense, a human organization is web-based: every person is a node, and work moves through the relationships connecting those nodes. An agent organization may be fundamentally different. It is Brain-based. Instead of every agent holding a fragmented version of the company, agents can operate from a centralized institutional Brain containing shared knowledge, history, decisions, priorities, permissions and real-time operating context. Each Brain sits a task-ownership system. Instead of asking, “Whose job is this?” the organization asks: What needs to be accomplished? What context and authority does it require? Should a human, an agent or a human-agent team own it? What constitutes completion? Who remains accountable? Humans continue to operate through networks of relationships, judgment, negotiation and trust. Agents operate through centralized knowledge, shared context and structured task ownership. The task layer tells you what needs to happen, who—or what—owns it, and whether it has actually been completed. To me, becoming AI-native means continuously redesigning this boundary between people, agents, knowledge and work. We are still experimenting. I’ll share what works, what fails, and how the two organizations evolve.

Projects Fundraising on RootData (Jun 22 - Jun 28) Last week, 23 blockchain companies/projects received funding. 💎 @bitbank_inc — Cryptocurrency exchange, acquired by SBI Holdings in $289M. 💎 @fomo — Cross-chain trading app, raised $75M in Series B by @IndexVentures, @usv, @benchmark & others. 💎 @alliumlabs — Blockchain data infrastructure, raised $40M in Series B by @AmplifyPartners, @kleinerperkins & @TheoryVC. 💎 @OnyxOdds — Financial products for the compute economy, raised $33M in seed round by @a16zcrypto, @CrucibleCap, Galaxy Ventures & others. 💎 @OnyxOdds — Sports prediction and betting platform, raised $20M led by @Payward. 💎 @CNPYNetwork — Layer 1 development framework, raised $8.5M by @Arrington_Cap, @fenbushi, @snzholding & others. 💎 @CambrianNetwork — AI infrastructure agreements, raised $6M by @polychain, @FranklnTempletn, @NomadCapital_io & others. 💎 @TurboFlow_xyz — Onchain trading platform, raised $6M in seed round by @PanteraCapital, @DCGco and Susquehanna Crypto.