Cassidy: AI compute demand is surging—and suddenly the people building the server racks have receipts.
Cassidy: New to this story? Here’s where it stands.
Cassidy: Zankore started as a Southeast Asia neocloud plan from Ooredoo
Cassidy: NVIDIA and Nokia: 200 megawatts of initial AI capacity from Indonesian data centers
Cassidy: built to expand across the region.
Cassidy: The shareholders called it a multibillion-dollar commitment and forecast about $13 billion in revenue over five years.
Cassidy: The question is how it gets all of that into live capacity.
Bill: This is AI Daily Briefing.
Bill: Today giant cloud commitments and cheaper open-model serving
Bill: all happening in a global capacity rush—so who’s taking the risk if the economics shift before the hardware comes online?
Bill: We'll keep tracking this story — Zankore Southeast Asia AI compute platform.
Bill: Follow the show so the next update finds you.
Cassidy: This one's from 94.7 The Beast:
Quote: Aug 11 (Reuters) – CoreWeave lifted its annual capital spending forecast on Tuesday after beating second-quarter estimates
Quote: encouraged by a surge in demand for its AI cloud computing services
Quote: sending the company’s shares more than 14% higher in extended trading.
Quote: The AI cloud company also lifted its targets for 2026 revenue and adjusted profit
Quote: banking on a ballooning order book
Quote: after its revenue backlog topped $100 billion in the June quarter.
Cassidy: CoreWeave just raised 2026 capex to $35 billion to $39 billion, with $104.2 billion in backlog.
Cassidy: That capacity build has customers behind it.
Bill: Customers are attached, sure.
Bill: But $104.2 billion is future demand priced on today’s serving economics.
Bill: I’d like the average contract vintage and every customer’s renegotiation language before I throw confetti.
Cassidy: They added more than $25 billion in net commitments this quarter and say near-term capacity is sold out.
Cassidy: That gives CoreWeave bargaining power with buyers that hyperscalers usually keep for themselves.
Bill: And revenue more than doubled to $2.58 billion.
Bill: There’s an actual business here.
Bill: Still, spending up to $39 billion in one year means the demand curve has to stay very friendly for a very long time.
Cassidy: Here's The Economic Times:
Quote: The Amsterdam-based AI infrastructure firm reported total revenue of $582.3 million for the quarter ended June
Quote: compared with analysts' average estimate of $572.75 million
Quote: according to data compiled by LSEG.
Quote: Revenue at its core AI cloud business, which accounted for about 98% of group revenue, rose more than 500%.
Cassidy: Nebius put up $582.3 million against a $572.75 million estimate, and 98% of that revenue came from AI cloud.
Cassidy: That is a very concentrated way to beat expectations.
Bill: The four contracts averaging more than $1 billion each are the part I’m staring at.
Bill: Great. But before I celebrate, I want to know when those contracts were signed and how the pricing escalates.
Bill: And what happens if 2028 inference is radically cheaper than the spreadsheet assumed?
Cassidy: Put that beside the CoreWeave numbers, and the signal is clear: neoclouds are landing enormous commitments and raising prices for capacity.
Cassidy: The hyperscalers no longer have compute pricing to themselves.
Bill: Nebius says AI-cloud revenue grew more than 500%, so yes, demand is real.
Bill: But four billion-dollar-plus deals mean a handful of customers could define the whole forecast.
Bill: One procurement rethink can hit those numbers fast.
Cassidy: This one's from Analytics India Magazine:
Quote: IBM and Together AI have signed a multi-year $240 million agreement to deploy a large cluster of NVIDIA HGX B300 systems on IBM Cloud
Quote: with the infrastructure expected to be available in the first quarter of 2027.
Quote: Together AI will use the cluster to provide inference for open-source AI models.
Quote: The deployment will be the first dedicated large-scale inference cluster on IBM Cloud using NVIDIA HGX B300 systems and NVIDIA Spectrum-X
Quote: Ethernet networking.
Bill: Together AI says it’s already serving 400 trillion tokens a month.
Bill: This $240 million B300 cluster is a throughput bet with an actual workload behind it.
Cassidy: And IBM gets to be the infrastructure answer for open weights at enterprise scale.
Cassidy: Together can distribute the models all day
Cassidy: while IBM Cloud owns the B300 cluster and the Spectrum-X network carrying the billable inference.
Bill: I like the stated goal here: cheaper serving, not another vague promise of bigger models.
Bill: But Q1 2027 delivery matters—by then
Bill: the cost per token customers expect could look very different from the economics in a deal signed this week.
Cassidy: CoreWeave and Nebius just put serious demand numbers on the board.
Cassidy: Here, we’ve got a named $240 million contract dedicated to inference for 400 trillion monthly tokens.
Cassidy: The open-source stack has acquired a landlord.
Cassidy: Here's Zhang Weilan at Global Times:
Quote: The instance is designed to handle inference for mixture-of-experts models with up to 10 trillion parameters
Quote: the company said in a statement sent to the Global Times on Wednesday.
Quote: This marks the first supernode-form computing architecture in China to successfully run large language models exceeding 2 trillion
Quote: parameters according to the company.
Bill: Alibaba’s M890 is already on sale in Ulanqab, built for mixture-of-experts inference up to 10 trillion parameters.
Bill: And it scales the interconnect from 16 cards to 64—because at this size, the networking is the computer.
Cassidy: Right—and China’s first supernode architecture to run a model above two trillion parameters marks a real milestone.
Cassidy: The leaderboard angle matters less.
Cassidy: Alibaba says KimiK3 and Qwen3.8Max are already commercial workloads on it; that’s the detail I care about.
Bill: IBM and Together are building B300 inference capacity for 400 trillion tokens a month.
Bill: Alibaba is making the same serving-layer bet on a separate stack in Inner Mongolia.
Bill: Enterprises may be pricing one AI demand curve against two ecosystems that don’t neatly substitute for each other.
Cassidy: Ulanqab’s cold climate helps cool these machines, sure.
Cassidy: Commercial availability is the important shift.
Cassidy: The hyperscalers don’t get to be the only ones deciding what compute costs anymore.
Cassidy: This one's from FutureCIO:
Quote: To meet the rising demand for secure
Quote: scalable AI computing
Quote: the initiative aims to develop one of the region’s largest AI infrastructure platforms
Quote: targeting a deployment of 1 gigawatt (GW) of NVIDIA DSX AI Factory capacity over the long term.
Cassidy: Following up on the Zankore compute platform we tracked last week: Indosat now puts the long-term target at one gigawatt.
Cassidy: The first 200 megawatts are due in the first half of 2027, with NVIDIA GB300 NVL72 systems.
Cassidy: This is more than a regional-tech press release now.
Bill: I can model 200 megawatts.
Bill: The one-gigawatt target is ambition.
Bill: Getting from one to the other means power delivery, cooling, networking, permits, and a demand forecast that has to stay hot for years.
Cassidy: The partner split is clear.
Cassidy: Ooredoo brings long-term capital, Nokia handles the network layer, NVIDIA brings the stack, and Indosat executes locally.
Cassidy: Indonesia is becoming a serious piece of the compute map while a lot of coverage is still staring only at Virginia and Texas.
Bill: NVIDIA says its DSX MaxLPS can lift computing capacity by up to 40% inside existing power limits.
Bill: Great, if it holds under real workloads.
Bill: But efficiency gains don’t erase the grid problem when the destination is a gigawatt.
Cassidy: If you want more on AI’s impact in government and defense
Cassidy: check out Anthropic Pentagon Watch
Cassidy: a daily briefing on Anthropic’s fight with the DoD over Claude
Cassidy: military AI use autonomous weapons
Cassidy: and AI procurement blacklisting—wherever you listen to podcasts.
Cassidy: We’re watching two 2027 milestones.
Cassidy: Together AI’s dedicated NVIDIA HGX B300 inference cluster on IBM Cloud is expected in the first quarter.
Cassidy: Zankore’s first phase is scheduled for the first half, with about 200 megawatts of GB300 NVL72-powered AI capacity.
Cassidy: Links to every story are in the show notes, so take a look at the ones you’d like to explore further.
Cassidy: That’s AI Daily Briefing for today.
Cassidy: This is a Lantern Podcast.