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RAX Finance
@RaxFinance
The Capital Markets Layer for AI Compute. Making compute fungible and tradable. Backed by @HashKey_Capital & @FBGCapital
259 Following    74.6K Followers
Compute has an expiration problem An unused GPU-hour can't be stored for tomorrow. Once that hour passes, the capacity is gone But access to compute can be standardized, priced, and traded before it's consumed. That's what we're building at RAX Finance Making compute fungible and tradable.
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🚀 RAX Ambassador Round 2 is OPEN! 🎯 10 slots available 📅 Apply within 5 days ♾️ Ongoing program (3-strike inactivity rule) Weekly Missions → Content Creation: +10–30 pts → Community Support: +5 pts → Up to 35 pts/week 🎁 Weekly Unboxing Opportunity rewards for ambassadors. Your points contribute to your reward weight. Who we’re looking for: ✓ Consistent contributors ✓ Community-minded participants ✓ Long-term thinkers Not just here for a one-off post — we value people who show up and grow with us. 📝 Apply now: Be the Architect. Own the Engine.
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You can buy a barrel of oil without knowing which well it came from. But renting GPU compute still means choosing a provider, region, hardware configuration, and contract. That's the difference between buying a commodity and negotiating a service. AI compute is ready for a better market structure. And that's exactly what RAX Finance is building.
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643 AI data center campuses. Across 55 countries. That's the infrastructure footprint captured in one recent industry census. And every new location brings its own energy costs, capacity constraints, and compute economics. AI may be global, but the cost of powering it isn't universal.
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A GPU doesn't generate revenue just because it's powerful It generates revenue when someone needs its compute. That distinction matters a lot more than people think.
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There's a difference between having a price and having a market. GPU providers already publish prices. But those quotes vary by region, hardware, contract length, and availability. A functioning commodity market needs something more: standardized units, reliable delivery, and prices participants can actually trade against. That's where compute markets get interesting.
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What is an hour of AI compute actually worth? Not all compute is priced the same. The value of AI compute changes based on: • GPU model • Location • Availability • Demand As AI infrastructure scales, transparent price discovery becomes essential. That’s the market infrastructure RAX is building.
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Hot take time: AI compute should be priced like: A) Oil — global commodity, single benchmark (WTI/Brent model) B) Electricity — regional grid, locational pricing (LMP model) Reply with A or B + your reasoning. We're clearly Team B (hence building regional indices), but curious what you think. Especially want to hear from folks who disagree 👀
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Friday check-in 👋 What’s caught your attention in AI compute this week? A new GPU, a data centre trend, or the future of ComputeFi? We’re curious what the Rax community is following. Drop your thoughts below 👀
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Compute is being planned on a multi-year horizon. Chipmakers are already securing EUV tools for 2029 and 2030. ASML says customers are asking for more machines every day, even as 2027–2028 capacity expands. The industry is locking in the manufacturing base for the next wave of AI capacity years in advance. Via @wallstengine
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Exactly. The bottleneck is connecting power, construction, equipment, and cloud capacity on one timeline. The suppliers and operators determine whether a site gets built quickly or stays stuck in the queue. Compute capacity creates most of its value only after it’s online and serving demand.
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Yotta brought the AI infrastructure ecosystem under one roof, giving IREN leaders three days of direct conversations with the suppliers, builders and operators shaping what comes next. - IREN is strengthening ties with leading construction and equipment partners - Suppliers want to partner with developers that can execute - Vertical integration from grid to AI Cloud is how IREN delivers with speed and reliability Watch the highlights.
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Why compute pricing lacks regional benchmarks: Observation 1: Massive regional spreads Same H100 card, wildly different economics across markets Not 10-20% differences — we're seeing 2-3x spreads Driven by: power costs, export controls, demand concentration Observation 2: No one publishes regional clears Cloud marketplaces show listing prices (not actual trades) Real contracts are confidential bilateral deals Lenders can't price risk without a settlement benchmark Observation 3: This is exactly where electricity was in the 1990s Before ISOs started publishing Locational Marginal Prices Before you could hedge regional basis risk Before financing could scale RAX is building the missing layer: regional price discovery for compute. Not because it's novel (electricity solved this 30 years ago) But because compute is $180B annual spend with zero pricing infrastructure in Asia/MENA. That's the gap we're closing.
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The compute market is at the stage where: • Electricity was in the 1990s (before deregulation) • Oil was in the 1950s (before posted prices → spot markets) • Carbon credits are today (trading exists, but fragmented) We're watching commodity infrastructure form in real-time. What we're building:
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$40B, just to buy the chips. SpaceX is lining up Apollo-led financing for Nvidia capacity: about $10B in bank loans and $30B in investment-grade debt, aimed at Vera Rubin. It is a compute purchase large enough to be financed like infrastructure. Power, land, and chips are now the constraint. The market is underwriting the factory, not the demo. Via @wallstengine
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$SPCX SEEKS $40B TO FUND MASSIVE $NVDA CHIP PURCHASE: FT SpaceX is looking to raise about $40B in financing led by Apollo to fund purchases of Nvidia chips. The plan includes roughly $10B in bank loans and $30B in investment-grade debt, with the transaction expected to close in 2027. Elon Musk said in August that SpaceX had “decided to build exclusively on Nvidia,” specifically pointing to its Vera Rubin architecture, as the company ramps spending on AI and data-center infrastructure.
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Three things every commodity market needs: 1. Fungible units Gold: 1 oz = 1 oz anywhere Compute: every contract is unique ❌ 2. Price transparency Oil: WTI, Brent published daily Compute: real deals are confidential ❌ 3. Tradeable infrastructure Electricity: day-ahead markets exist Compute: can't resell a signed contract ❌ That's the gap we're closing. More on commodity formation:
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To everyone who’s been waiting with us — we hear you. 🤍 We know many of you have been on the Waitlist for a while, and we truly appreciate your patience. We’re now moving deeper into ComputeFi, with discussions already underway for our first batch of idle compute partnerships. And yes — your community points still matter. Holding an NFT will not only come with its own benefits, but also boost your community points. We’re getting closer, and there’s more to share soon! Thank you for staying with us! We’re building this with you!
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Compute is being contracted like capacity, not bought like hardware. Oracle is selling Tencent access to ~100,000 advanced chips over 5 years, about $7B, with ~30% paid upfront. Broadcom is lining up as much as $42B of financing against Anthropic’s multi-year compute commitment. Nebius is buying inference tech so GPUs spend less time idle. That is the real market: power, chips, utilization, and who is willing to prepay for the output. The scarce layer is still compute.
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A TON OF THINGS HAPPENED IN THE STOCK MARKET TODAY. Here's a full recap: 1. Palantir $PLTR and Armada announced a partnership to deliver sovereign AI on modular data center infrastructure manufactured in the U.S. and allied nations. Under the agreement, Palantir named Armada its inaugural Certified Modular Data Center Partner, combining Palantir’s Sovereign AI Operating System with Armada’s Galleon modular data centers, Armada Platform, and Sovereign AI Grid. The joint offering allows enterprises and governments to deploy open-weight AI models on infrastructure they own and control, with the ability to fine-tune models on proprietary data while keeping compute, data, and hardware entirely within their own security boundary. The companies said the modular approach can bring new AI capacity online in months instead of years, addressing growing demand for sovereign AI infrastructure. 2. Nebius $NBIS has acquired Inferize, an AI inference optimization startup, for an undisclosed amount. Inferize’s technology is designed to reduce model “cold starts,” cutting the time GPUs remain idle while models load and new inference capacity comes online. Nebius plans to integrate the technology and team into its Token Factory inference platform to improve GPU utilization, lower inference costs, and serve more AI workloads using the same infrastructure. 3. NVIDIA $NVDA and SoftBank have each completed the final $10B installment of their respective $30B commitments to OpenAI’s latest funding round. The financing totaled $122B in commitments at an $852B valuation and included Amazon’s $50B investment. SoftBank’s total investment in OpenAI has now reached approximately $64.6B, giving it an estimated 13% ownership stake. According to The Information, OpenAI is already targeting another $30B funding round at a valuation of around $1.4T. 4. Netflix $NFLX co-CEO Ted Sarandos said the company “isn’t growing as fast as I want us to,” while noting that some of Netflix’s strategic decisions are intentionally creating headwinds to subscriber growth. He also revealed that live programming accounts for roughly 5% of Netflix’s $20B annual content budget but generates only about 1% of total viewing, underscoring the company’s continued focus on balancing long-term strategic investments with overall engagement. 5. Nike $NKE reported Q1 FY2027 results with EPS of $0.48, beating expectations of $0.43, while revenue missed at $11.21B versus $11.32B expected, down 4% YoY. Gross margin came in stronger than expected at 42.8%, helped by lower warehousing and logistics costs, but weakness persisted across NIKE Direct (-8% YoY), Digital (-13% YoY), and Greater China (-22% YoY). The biggest disappointment was guidance, with Nike forecasting high-single-digit revenue declines for FY2027 and adjusted EPS of $1.15–$1.35, well below the $1.65–$1.67 consensus. Management said it continues to see challenges in Sportswear, Jordan Brand, and Greater China, while rolling out its new Pace operating model to accelerate its long-term turnaround. 6. Broadcom $AVGO has agreed to provide Anthropic with up to $42B in financing tied to its AI infrastructure expansion, according to Anthropic’s IPO prospectus reviewed by Reuters. The facility could fund roughly one-third of Anthropic’s $125.2B five-year TPU compute commitment, and the debt may be convertible into Anthropic equity. Anthropic is also expected to become Broadcom’s largest compute customer in 2027. The filing notes that Broadcom’s dual role as both a hardware supplier and financing partner could create potential conflicts related to pricing and access to compute. 7. Amazon $AMZN plans to expand its in-house delivery network from roughly 23,700 U.S. ZIP codes today to about 39,400 by 2029, according to an internal planning document reviewed by Business Insider. That expansion would allow Amazon to reach approximately 95% of U.S. ZIP codes and 99.9% of customer demand. The plan includes building 165 new delivery stations between 2027 and 2029 and adding roughly 4,500 ZIP codes through its Hub Delivery rural network. The initiative reportedly began under Project Aurelian, a contingency plan created when negotiations with the U.S. Postal Service became uncertain, though Amazon said the projections remain preliminary and could change. 8. The top 10 most active options today by contracts traded were $NVDA with 2.6M contracts, $MU with 1.5M contracts, $TSLA with 1.3M contracts, $AAPL with 1.1M contracts, $SPCX with 786K contracts, $AMZN with 640K contracts, $GOOGL with 637K contracts, $INTC with 582K contracts, $NKE with 561K contracts, and $MSFT with 447K contracts. 9. Oracle $ORCL has reportedly signed a 5-year, $7B AI compute agreement with Tencent, according to the Financial Times. Under the deal, Oracle will provide access to roughly 100,000 advanced AI chips across its Southeast Asia data centers, with about 30% of the contract value paid upfront. Tencent plans to use the capacity to train AI models and expand its portfolio of agentic AI products, as the company accelerates AI infrastructure spending. The investment has already impacted Tencent’s financials, with Q2 infrastructure capex surging 176% YoY to $7.9B and free cash flow turning negative at -$2B for the first time in more than a decade. 10. AI-related companies continue to dominate capital markets, now accounting for roughly 40% of the S&P 500's total market capitalization. In emerging markets, just 3 semiconductor companies make up 28% of the MSCI Emerging Markets Index, while AI-related issuers have represented 49% of all investment-grade bond issuance so far in 2026. The concentration is even more pronounced in private markets, where 87% of year-to-date venture capital funding has gone to AI companies. For comparison, during the 1999 Dot-Com bubble, internet companies accounted for less than 40% of venture capital funding, highlighting the unprecedented concentration of investment flowing into AI today. 11. Since 1928, the S&P 500 has gained an average of 2.1% during the first half of October in U.S. midterm election years, making it the strongest-performing half-month of the fourth quarter. Positive returns occurred 71% of the time over that period, the third-highest hit rate of any half-month in Q4. The seasonal strength follows a historically weak second half of September, which has averaged a 1.1% decline. Momentum has also tended to remain positive later in the quarter, with the second half of October averaging a 0.3% gain and the first half of November delivering an average 1.7% return with a 79% positive hit rate. 12. Bond ETFs have attracted $23B in inflows over the last 5 trading days, accounting for 62% of all ETF net inflows despite representing only about 15% of total ETF assets. Fixed-income funds also made up 6 of the 8 most-purchased ETFs over the period, with investors allocating capital across cash, municipal bonds, high yield, Treasuries, and $TLT. The broad-based demand suggests investors are increasingly positioning around the bond market as yields remain near multi-year highs and volatility persists across asset classes. WALL STREET IS THE GREATEST SHOW ON EARTH.
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Compute capacity is shifting east. East Asia: Semiconductor and data centre expansion Southeast Asia: Power + connectivity advantages India: Scale + cost efficiency MENA: Capital meets ambition Infrastructure is growing fast. But pricing transparency? Still catching up. Building a more transparent and accessible compute market is exactly what @RaxFinance is working toward.
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The listed price is not the traded price. Public marketplaces show H100 at $3.25/hr. Real year-long contracts clear at $2.10–2.70. Why the gap? Bilateral negotiations are confidential. Nobody publishes what actually traded. Price discovery needs transparency. 🔗 Join the Waitlist:
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Commodity formation playbook: Gold → London Fix (1919) → futures market Oil → posted prices → spot market → Brent/WTI Electricity → bilateral → pools → day-ahead markets Compute → ? We're watching the pattern repeat in real-time. 🔗 Join the Waitlist:
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