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  • etisalat’s 20-to-500 Tbps Plan Shows UAE AI Campuses Need Routes Out, Not Just Racks

    etisalat’s 20-to-500 Tbps Plan Shows UAE AI Campuses Need Routes Out, Not Just Racks

    TL;DR · 30-second read

    The Short Version

    The United Arab Emirates is building huge computer centers for artificial intelligence. Those buildings are only useful if information can move quickly between them and the rest of the world.

    etisalat, the country’s 50-year-old phone and internet company, says it will make its connections to other countries about 25 times bigger by 2030. Think of a two-lane road becoming a fifty-lane highway.

    The company has not yet said what this will cost or exactly which new routes it will build.

    etisalat announced on October 6, 2026, in a release distributed via PR Newswire, a project to expand its international connectivity capacity from 20 terabits per second (Tbps, or 20 trillion bits per second) to more than 500 Tbps by 2030, a roughly 25-fold increase. The launch in Abu Dhabi was witnessed by Sheikh Mansour bin Zayed Al Nahyan, the UAE’s Vice President and Deputy Prime Minister, in his capacity as Chairman of the Emirates Investment Authority, the group’s principal shareholder.

    The company says the project will add diverse international routes with a low-latency design, and that it is aimed at the needs of UAE AI campuses, global cloud providers and the group’s own businesses across the 38 countries where it operates.

    Executive Summary

    etisalat’s announcement attaches a hard number to a question that usually gets less attention than chips and power: how much data can actually move in and out of a country that wants to be an AI hub. The group’s chairman, Jassem Mohamed Bu Ataba Alzaabi, framed the jump from 20 Tbps to more than 500 Tbps as a response to growing global data processing and data flows, explicitly naming the UAE’s AI campuses and global cloud providers as the users it is built for.

    The project is described as the first major undertaking under a new group strategy built around four business lines: telecommunications, AI and enterprise solutions, infrastructure, and fintech. It was launched alongside the start of etisalat’s 50th anniversary celebrations.

    What matters for the industry is the framing. By treating international bandwidth as a prerequisite for AI ambitions rather than a by-product of them, etisalat is signaling that a data center campus in the Gulf is only as useful as the routes connecting it to customers, data and partner clouds elsewhere. The target, baseline and deadline are stated; the cost, specific routes and partners are not.

    A Compute Hub Is Only as Useful as Its Exits

    The core logic of the announcement sits in one line from the group chairman: the increase is meant to meet the requirements of the UAE’s AI campuses, global cloud providers, and the group’s businesses and customers across its markets. That is an admission that compute built inside the UAE is not primarily a domestic product. AI campuses that serve international customers, and global cloud providers that run regions in the country, depend on moving large volumes of data across borders: datasets coming in, model outputs and services going out, and copies of data replicated between regions for backup and redundancy.

    Against that demand, the stated baseline is 20 Tbps of international capacity. etisalat’s own plan implies that figure is too small for the AI build-out the country is pursuing, since it is targeting a 25-fold increase within roughly four years. The operational consequence is straightforward: if data center floor space and processors arrive faster than international routes, campuses risk being limited by how much they can send and receive rather than by how much they can compute. Racks without routes out serve a smaller market.

    Who is affected follows directly. Developers and tenants of UAE AI campuses need confidence that connectivity will scale with their deployments; global cloud providers weighing capacity in the region need low-latency paths to their other regions; and etisalat’s customers in its 38 markets, which the release puts at more than 250 million, sit on the other end of many of those links.

    Route Diversity Is the Resilience Argument

    The release stresses not just more capacity but more varied routes, with an architecture designed for low latency, meaning less delay between sending data and its arrival. Diversity matters because international traffic from the Gulf travels over a limited set of submarine cables and cross-border land paths. When traffic is concentrated on a few corridors, a single fault or disruption can affect a large share of a country’s external capacity.

    For AI and cloud customers, that turns route diversity into a commercial requirement as much as a technical one. Service-level commitments, disaster recovery plans and sovereignty rules all depend on knowing that traffic can be rerouted without severe slowdowns. The chairman linked the project directly to the UAE’s digital sovereignty, suggesting that control over how and where data leaves the country is part of the design goal, not only raw throughput.

    A Clear Target, an Unpriced Plan

    Taken on its own terms, the announcement is specific where many infrastructure pledges are not: it gives a baseline (20 Tbps), a target (more than 500 Tbps) and a deadline (2030). It also comes with visible backing, launched in the presence of the chairman of the group’s principal shareholder and positioned as the opening project of a new corporate strategy.

    What the release does not substantiate is how the target will be met. It names no investment figure, no specific cable systems or land routes, no construction partners and no interim milestones. Because capacity on international routes can be added both by building new paths and by upgrading equipment on existing ones, a large headline multiple can mean very different levels of capital spending and physical construction. Until etisalat details that mix, the 500 Tbps figure is best read as a firm statement of intent and a useful signal of how the UAE’s AI planners view connectivity, rather than a costed program.

    Background

    etisalat is the UAE-based telecommunications group that is marking its 50th anniversary in 2026. Its principal shareholder is the Emirates Investment Authority, and the group says it operates in 38 countries with more than 250 million customers. Readers may know the group by the e& brand it adopted in 2022; its anniversary announcement describes the group continuing under the etisalat name as it repositions itself as an AI-driven telecom company.

    The UAE has made artificial intelligence a national priority and is positioning itself as a global hub for advanced technology, including large AI data center campuses and regions operated by global cloud providers. Those facilities rely on international connectivity, the submarine cables and cross-border land routes that carry data between the country and the rest of the world, which is the capacity etisalat now aims to expand roughly 25-fold.

    Sources

    Source: In Anwesenheit von Mansour bin Zayed startet Etisalat ein Projekt zum Ausbau der digitalen Infrastruktur in den VAE und im Ausland, um die internationale Konnektivität bis 2030 auf über 500 Tbit/s zu steigern (English version: In the presence of Mansour bin Zayed etisalat launches project to expand digital infrastructure within the UAE and abroad to increase international connectivity to more than 500 Tbps by 2030), etisalat’s October 6, 2026 announcement of its plan to expand international connectivity from 20 Tbps to more than 500 Tbps by 2030.

  • Nvidia Reportedly Pauses Some Cloud Revenue-Sharing Deals

    Nvidia Reportedly Pauses Some Cloud Revenue-Sharing Deals

    Data Center Dynamics reports that Nvidia has paused certain cloud revenue-sharing arrangements with partner providers that host its GPUs. The report frames the change as a narrowing, not a wholesale cancellation, of a program that had aligned Nvidia’s commercial interests with a set of cloud operators buying its accelerators.

    Specific counterparties, dollar figures, and the effective date of the pause were not disclosed in the summary available to us at publication.

    Executive Summary

    Revenue-sharing programs between chipmakers and their downstream cloud partners are unusual, and Nvidia’s version had become one of the more talked-about commercial mechanics in the AI infrastructure market. Pausing parts of it — even temporarily — matters because these deals influence which cloud providers get preferential access to scarce GPUs, how quickly capacity comes online, and how partners price AI compute to end customers.

    The report does not, in the material available to us, describe the program being ended. Read narrowly, a pause suggests review and possible restructuring rather than retreat. Read against the current regulatory backdrop — with U.S. and European authorities scrutinizing AI supply-chain concentration — the timing is at least noteworthy.

    For buyers of AI compute, the immediate question is whether pricing or availability at affected partners will move. For investors, the question is whether Nvidia is tidying up commercial terms ahead of closer regulatory attention, or reallocating incentives toward hyperscalers and sovereign buyers with different economics.

    Why Revenue-Sharing Deals Existed In The First Place

    When a component supplier shares in the revenue its customers earn reselling that component’s output, it signals two things: the supplier believes downstream demand is real, and it wants to steer scarce inventory toward partners who can activate it quickly. During the acute GPU shortage of the past few years, Nvidia had both motives. Sharing cloud revenue with select hosting partners created an incentive for those partners to buy more accelerators, build faster, and pass Nvidia stack choices — networking, software, reference designs — through to end customers.

    That alignment is efficient when supply is constrained and demand is uncertain. It becomes harder to justify as the market matures, competitors ship credible alternatives, and hyperscalers negotiate directly at a scale that dwarfs the partner tier.

    What A Pause Signals Versus What It Doesn’t

    A pause is a smaller signal than a cancellation, and the reporting available to us stops short of the latter. The most benign reading is administrative: contracts get repapered when programs scale, and terms that made sense in 2023 may not survive contact with 2026 volumes. A more consequential reading is that Nvidia is preparing to restructure partner economics in a form less likely to draw antitrust attention — for instance, moving from revenue share to volume rebates, marketing development funds, or technical co-investment.

    What the pause does not, by itself, tell us: whether affected partners will see any change in allocation, whether pricing to end customers will shift, or whether the pause is uniform across geographies. Absent that detail, sharp conclusions are premature.

    The Antitrust Backdrop

    Regulators on both sides of the Atlantic have taken an interest in how dominant AI infrastructure providers structure commercial relationships. Revenue-sharing tied to preferential supply is exactly the kind of arrangement that invites questions about tying, foreclosure, and market power. Nvidia’s structural advantages in AI compute — its installed base, CUDA software moat, and networking assets — are real and durable, and they make the company careful about arrangements that could be characterized as leveraging one market to entrench another.

    Our editorial view is that Nvidia’s underlying position is strong enough that it does not need aggressive contractual mechanics to defend it, and that restructuring partner terms into forms more familiar to regulators is likely to grow, not shrink, the addressable market by making more cloud operators comfortable participating.

    Winners, Losers, And Second-Order Effects

    If revenue sharing is being narrowed at the partner tier, the relative winners are hyperscalers and large sovereign buyers whose deals were never structured this way. The relative losers, at least on paper, are smaller GPU-cloud specialists whose unit economics benefited from the arrangement. In practice, much depends on what replaces the paused terms: a well-designed rebate or co-marketing structure can preserve most of the economics without the regulatory optics of revenue share.

    For enterprise buyers of AI compute, the practical takeaway is to ask providers directly how their Nvidia commercial relationship is structured today and whether recent changes affect quoted pricing or capacity commitments. Contracts signed in the next few quarters may look different from those signed last year.

    Background

    Nvidia is the dominant supplier of accelerators used to train and serve modern AI models, with a business built on GPUs, high-speed networking (via its Mellanox acquisition), and the CUDA software stack that most AI frameworks target. Its data-center segment has grown rapidly as hyperscalers, enterprises, and a new tier of GPU-focused cloud specialists have built out AI capacity.

    Alongside direct hardware sales, Nvidia has developed commercial relationships with cloud partners that go beyond a standard supplier arrangement — including reference architectures, co-marketing, and reportedly revenue-sharing structures with select hosting providers. These programs have become a subject of interest as regulators examine the commercial mechanics of the AI supply chain.

    Source: Nvidia pauses some cloud revenue-sharing deals, report — Data Center Dynamics summary of reporting that Nvidia has narrowed certain revenue-sharing arrangements with cloud partners.

  • Goldman Sachs: AI Capex Pivots Toward Inference and Enterprise Use

    Goldman Sachs: AI Capex Pivots Toward Inference and Enterprise Use

    Goldman Sachs published a note dated July 10, 2026 arguing that AI investment is rotating from headline-grabbing training clusters toward inference workloads and broader enterprise adoption. The bank frames the shift as a maturing phase of the AI capital cycle rather than a slowdown.

    Executive Summary

    The Goldman Sachs view, as summarized in the release, is that the marginal AI dollar is increasingly directed at inference — the runtime serving of trained models to end users and applications — and at enterprise deployments that put those models to work inside businesses. Training remains significant, but the growth vector is moving.

    For infrastructure operators, that framing matters because inference and enterprise AI have a different physical and economic profile than training. They favor latency-sensitive placement, steadier utilization curves, and integration with existing corporate data — all of which reshape where capacity is built, how it is cooled and powered, and which vendors capture the spend.

    What ‘Shift to Inference’ Actually Means for Infrastructure

    Training a large model is a bursty, capital-intensive event: tens of thousands of accelerators wired together, run flat-out for weeks, tolerant of remote siting as long as power and interconnect are cheap. Inference — the act of answering a user’s query with a trained model — is the opposite. It runs continuously, scales with usage, and rewards proximity to users and to enterprise data. If Goldman’s read is right, the next tranche of AI capex will look less like one giant campus in a remote grid pocket and more like distributed capacity closer to demand.

    That has second-order consequences the note itself does not spell out. Metro data centers, edge sites, and existing enterprise colocation footprints become more strategically valuable. Networking — low-latency fiber between inference points, users, and data gravity centers — becomes a first-class concern rather than a training-cluster afterthought.

    Enterprise Adoption Changes the Buyer

    A capex signal tied to enterprise adoption implies a different customer mix than the hyperscaler-and-frontier-lab spending that has dominated headlines. Enterprises buy differently: they care about data residency, regulatory posture, integration with existing systems, and predictable unit economics. They are also more sensitive to total cost of ownership than to raw peak FLOPS.

    If that customer base grows as the note suggests, the winners are likely to include vendors and operators that can package AI capacity as a consumable service — with governance, observability, and support — rather than raw GPU hours. It also expands the addressable market for private cloud, sovereign cloud, and hybrid deployments where the model runs near the data.

    Reading the Capex Signal With Appropriate Caution

    Analyst notes are directional, not deterministic. Goldman is describing a rotation in how AI dollars are spent, not a retreat from AI spending overall, and the release as summarized does not quantify the magnitude, timing, or geographic distribution of that rotation. It is fair to ask what data underpins the call — enterprise deal flow, hyperscaler capex disclosures, chip shipment mix — and how much of the shift is already priced into infrastructure equities.

    The same scrutiny applies to the counter-narrative. Claims that training demand is peaking have been made before and repeatedly revised as new model generations arrived. A durable inference-led phase would still coexist with periodic training surges tied to frontier releases. Buyers planning multi-year builds should treat the shift as a change in mix, not a substitution.

    Background

    AI infrastructure spending accelerated sharply from 2023 onward, dominated by large training clusters built by hyperscalers and frontier model developers. That phase concentrated capital in a small number of very large sites optimized for dense accelerator deployments, cheap power, and high-bandwidth interconnect.

    As foundation models have matured and enterprise pilots have moved toward production, industry attention has increasingly turned to inference — the runtime side of AI — and to the operational, data, and governance challenges of deploying models inside businesses. Goldman’s July 2026 note sits within that broader transition, articulating a capex signal that many operators and vendors have been positioning for.

    Source: AI Investment Is Shifting as Inference, Enterprise Adoption Accelerate – Goldman Sachs — Goldman Sachs note dated July 10, 2026 describing a rotation in AI capital spending toward inference workloads and enterprise adoption.