In 2026, the "Neocloud" market—specialized, high-performance cloud providers that challenge the traditional "Big Three" hyperscalers—has matured into a $42 billion industry. Unlike AWS or Azure, these providers focus almost exclusively on GPU-accelerated computing, AI factory infrastructure, and sovereign data requirements.

Here are the top Neoclouds leading the market in 2026:

1. CoreWeave

Widely considered the "gold standard" for AI infrastructure, CoreWeave has solidified its lead by securing massive allocations of NVIDIA’s latest hardware.

• Best For: Massive-scale LLM training and high-concurrency inference.

• Key Advantage: Direct access to NVIDIA Blackwell (B200) and Hopper (H200) clusters with native Kubernetes orchestration.

• 2026 Vibe: The enterprise-grade alternative for teams that find hyperscalers too slow or restrictive.

2. Nebius

A rising power in the European and U.S. markets, Nebius has positioned itself as the "AI-native" cloud.

• Best For: European data sovereignty and cost-effective GPU clusters.

• Key Advantage: Operates strategic hubs in Europe and North America, offering highly commoditized GPU access that undercuts hyperscaler pricing while maintaining Tier-3 data center standards.

3. Crusoe (formerly Crusoe Energy)

Crusoe has pivoted from crypto-mining to building "AI Factories" powered by wasted energy sources.

• Best For: Sustainable, high-performance computing.

• Key Advantage: Their Leviathan modular data centers can be deployed in weeks and are powered by stranded energy (like flared natural gas or renewables), making them the choice for ESG-conscious AI labs.

4. Lambda (formerly Lambda Labs)

Lambda remains the favorite for research-intensive organizations and academic institutions.

• Best For: 1-click GPU clusters and deep learning development.

• Key Advantage: Extreme simplicity. They offer a "on-demand" experience that feels like a local machine but scales to thousands of nodes, with pre-installed ML frameworks that just work.

5. Civo

Civo has successfully carved out a niche as the leading "developer-first" low-cost GPU cloud.

• Best For: Startups and independent developers.

• Key Advantage: Zero egress fees and transparent pricing. They are the only low-cost provider in 2026 that is fully ISO 27001 and SOC 2 certified, making them "production-ready" for regulated industries.

Comparison of Specialty Tiers

If you are looking for specific performance metrics, the market has split into these categories:

The global AI cloud infrastructure sector has entered an aggressive, multi-year expansion phase in mid-2026, driven by a projected $500 billion to $650 billion hyperscaler spending wave. Discover the top AI cloud infrastructure and neocloud stocks leading this multi-year supercycle, analyze the critical bottlenecks in the hosting stack, and learn how to trade stock futures with USDT on BingX TradFi to capture instant global market momentum.

Artificial intelligence (AI) has successfully outgrown its initial software training phase to trigger the largest physical computing buildout in modern history. By mid-2026, the global AI compute market is no longer driven by speculative model prototyping, but by the massive deployment of operational cloud hosting infrastructure, data center facilities, and agentic AI architectures.

Tech giants and cloud hyperscalers are projected to deploy between $500 billion and $650 billion in capital expenditures (capex) this year alone for AI-related infrastructure. Total spending by Big Tech on AI data centers is expected to top $700 billion in 2026, moving rapidly toward $240 billion+ by 2034 at a 14% CAGR.

As the global semiconductor and infrastructure markets approach trillion-dollar milestones in 2026, traditional capital boundaries are dissolving. The rise of tokenized stocks, digital assets that track real-world equities 1:1 on public blockchains, allows crypto-native capital to integrate directly into global equity markets.

In addition to tokenized stocks, platforms like BingX TradFi let global investors trade leading U.S. stock futures using USDT collateral. This framework provides 24/7 fractional exposure to premier AI cloud infrastructure leaders without requiring traditional, cross-border brokerage accounts, channeling liquidity straight into the core infrastructure of the modern digital economy.

The Global AI Cloud Infrastructure Market in 2026: Key Structural Trends

Here is the revised, data-heavy structural trends section, updated to feature precise industry metrics and explicit institutional citations from Gartner, IDC, the International Energy Agency (IEA), and Fortune Business Insights.

The AI cloud landscape has evolved from simple chip stockpiling into a highly complex, interconnected utility grid. According to Gartner, worldwide spending on AI foundations, platforms, and services is forecast to hit an unprecedented $2.52 trillion in 2026, representing a massive 44% increase year-over-year. Within this macro wave, the IDC projects that dedicated global AI infrastructure spending alone will capture $487 billion this year, marking a 53% structural expansion from last year’s levels, with a clear trajectory toward eclipsing $1 trillion by 2029.

This 2026 cloud infrastructure supercycle is defined by four foundational structural trends:

1. The Proliferation of Agentic AI and Structural Backlogs

While training foundational large language models (LLMs) remains a fixed capital cost, 2026 marks the official inflection point where multi-step, autonomous Agentic AI systems dominate enterprise workloads. The International Energy Agency (IEA) highlights that the rapid ascent of complex AI agents, which execute continuous, real-time reasoning and multi-step tasks, is causing a permanent structural shift. Some independent research nodes calculate that live inference pipelines now command up to 80% to 90% of all active AI data center computing power.

This operational shift requires massive, predictable computing power via the cloud. Hyperscalers are managing unprecedented demand, visible in skyrocketing Remaining Performance Obligations (RPO), contracted future revenue customers have already agreed to pay. Microsoft holds a $625 billion commercial cloud backlog, while Oracle's RPO has surged 325% to $553 billion, proving that enterprise capacity demand stretches multi-years into the future before new physical infrastructure even goes online.

2. The Rise of the Neocloud vs. Hyperscaler Resiliency

The market is witnessing a distinct separation between classic hyperscalers and specialized AI-native cloud providers, known as the Neocloud. Elite neoclouds like Amazon AWS and Google Cloud focus strictly on high-density GPU cluster deployment, logging triple-digit top-line growth.

However, because neoclouds rely heavily on aggressive debt financing to fund their capacity, raising tens of billions of dollars in debt capital and stretching interest expenses, established hyperscalers maintain a major structural advantage. Mega-cap titans leverage stable, massively cash-generative core businesses like enterprise software, search advertising, and e-commerce, to self-fund their massive capital deployments. This internal liquidity buffer acts as a defensive shield against potential capex digestion pauses or valuation multiple compression in the public markets.

3. Power, Clean Energy, and Geographic Land Grabs

Raw electricity has become the single greatest physical constraint for next-generation data center execution. Data from the IEA indicates that global data center electricity consumption is set to double by 2030, with specialized AI data center power usage poised to triple. According to independent analysis compiled by the Brookings Institution, global data center energy demand could approach 1,050 TWh in 2026. If the data center sector were a nation, it would rank as the fifth-largest energy consumer on earth, trailing only China, the U.S., India, and Russia.

With individual high-density AI server racks exceeding 120 kW, modern gigawatt-scale AI factories are entirely unfeasible under traditional air-cooling mechanics. This energy crunch has forced a multi-billion dollar pivot toward dedicated, long-duration energy systems. The IEA notes that the pipeline of conditional off-take agreements between data center operators and small modular nuclear reactor (SMR) projects has ballooned from 25 gigawatts at the end of 2024 to 45 gigawatts by mid-2026, highlighting a massive, historic scramble to guarantee baseline operational grid uptime.

4. Sovereignty and Data Residency Mandates

Geopolitical fragmentation and strict regulatory frameworks, such as Europe's GDPR and AI Act, have accelerated the sovereign cloud trend into a core growth catalyst. According to Fortune Business Insights, the global sovereign cloud market size has exploded to valued benchmarks of $195.35 billion in 2026, exhibiting a compounding annual growth rate (CAGR) of 24.6% through 2034.

Regulated enterprises, financial bodies, and national defense agencies now legally require that highly sensitive AI models and proprietary data lakes be trained, processed, and hosted strictly within regional physical borders under local legal jurisdictions. Large enterprises are projected to dominate roughly 68% of this market space in 2026. This mandate has forced infrastructure providers to aggressively deploy localized, jurisdiction-bound data hub facilities. Major examples include Amazon Web Services launching its dedicated AWS European Sovereign Cloud and Microsoft rapidly rolling out localized, compliant sovereign cloud zones across Austria, Denmark, Greece, Italy, and Poland.

What Are the Best AI Cloud Infrastructure Stocks to Watch in 2026?

The following list identifies the leading cloud hyperscalers, neocloud pioneers, and mission-critical hardware ecosystem builders driving the global AI infrastructure cycle in the second half of 2026.

1. Microsoft (MSFT)

• 2026 Valuation Benchmark: ~$3.1 Trillion Market Cap

• Core Role: Azure Cloud Powerhouse and AI Agent Leader

Microsoft remains the absolute titan of the AI infrastructure expansion. The company is deploying a staggering $190 billion in capital expenditures for 2026 to scale its global Azure footprint. Approximately two-thirds of this capex is feeding short-lived computing assets (GPUs and CPUs), while one-third funds long-duration physical infrastructure built to last 15 years or more.

Microsoft's true competitive defense is its structural revenue visibility. Its commercial cloud backlog (RPO) has surged 110% year-over-year to $625 billion, giving it roughly 2.5 years of clear future revenue locked in before servers even go online.

To break the ultimate constraint, power, Microsoft secured a historic 20-year, 835 MW power purchase agreement with Constellation Energy to restart the Three Mile Island nuclear facility (now the Crane Clean Energy Center). Combined with a multi-billion dollar data center footprint expansion across Austria, Germany, Italy, Greece, and the UK, Azure is systematically boxing out infrastructure competitors.

2. Amazon (AMZN)

• Core Role: AWS Market Leader & Southeast Asian Infrastructure Vanguard

Amazon is the single largest absolute capital spender in the AI space, budgeting approximately $200 billion for capital expenditures in 2026 alone. Amazon Web Services (AWS) is experiencing a powerful re-acceleration, hitting a 28% year-over-year revenue growth rate in Q1 2026, driven by an insatiable demand for generative AI hosting.

AWS is aggressively diversifying its infrastructure layers. In terms of compute hardware, Meta recently committed to deploying tens of millions of hosted AWS Graviton Arm cores to power its multi-step agentic architectures.

On the geographic front, Amazon has committed to investing over $33 billion across Southeast Asia (Indonesia, Malaysia, Singapore, and Thailand) through 2039 to build localized AWS regions. To insulate these expansions from environmental backlash, Amazon is deploying advanced sustainability initiatives, running all Singapore data centers on recycled NEWater and forging large-scale reclaimed water partnerships in Malaysia.

3. Alphabet (GOOGL)

• Core Role: Google Cloud High-Growth Stack and Multi-Gigawatt TPU Infrastructure

Alphabet’s Google Cloud has established itself as the fastest-growing service among the big three hyperscalers. Driven by the rapid enterprise adoption of Gemini and agentic enterprise tools, Google Cloud surged 63% year-over-year in a recent quarter, hitting a $20 billion quarterly revenue run rate.

Alphabet has achieved structural insulation against hardware shortages by scaling its internal custom silicon pipeline. On May 5, 2026, AI research lab Anthropic committed to a massive $200 billion, five-year infrastructure agreement with Google Cloud, utilizing multi-gigawatt clusters of Google's custom Tensor Processing Units (TPUs) engineered in partnership with Broadcom.

Furthermore, to fulfill capacity needs outside traditional data center models, Alphabet partnered with Blackstone in a massive $25 billion joint venture to bring 500 megawatts of specialized compute-as-a-service capacity online by 2027.

4. Oracle (ORCL)

• Core Role: Enterprise Cloud Migration and Hyper-Scale Backlog Champion

Oracle has successfully shed its reputation as a legacy database provider to emerge as one of the hottest hyper-scale AI infrastructure plays of 2026. In its fiscal third-quarter 2026 earnings, Oracle reported a 44% jump in cloud revenue to $8.9 billion, while its specialized Oracle Cloud Infrastructure (OCI) skyrocketed 84% to $4.9 billion.

Oracle’s ultimate bullish catalyst is its jaw-dropping remaining performance obligations (RPO) backlog, which escalated 325% to $553 billion. Oracle has turned its enterprise software relationships into an infrastructure goldmine, hosting critical AI workloads for OpenAI, Meta, Nvidia, and Microsoft simultaneously. Wall Street consensus price targets have rolled up to the $230–$275+ range as OCI expands data center footprints globally to meet capacity demands that are vastly outstriping current supply.

5. Broadcom (AVGO)

• Core Role: Co-Designed Custom ASICs (XPUs) & Scale-Out Networking Moat

Broadcom functions as the foundational engineering backbone for custom hyperscaler infrastructure. In Q1 fiscal 2026, Broadcom reported record quarterly revenue of $19.3 billion (up 29% YoY), driven by an explosive 106% year-over-year jump in AI semiconductor revenue to $8.4 billion. Following blowout results, management guided Q2 AI semiconductor revenue to $10.7 billion, a 140% YoY increase that translates into an annualized run-rate of $42.8 billion by mid-year.

CEO Hock Tan announced a clear line of sight to surpassing $100 billion in cumulative AI chip revenue by 2027. This trajectory is secured by custom application-specific integrated circuit (ASIC) co-design pipelines with elite customers. Google relies on Broadcom for its 7th-generation Ironwood TPU, and Anthropic is deploying a 1-gigawatt TPU layout via Broadcom in 2026 (projected to surge past 3 gigawatts in 2027). Furthermore, Broadcom dominates data center cluster fabrics: its Tomahawk 6 switch (100 Tbps) is capturing massive scale-out market share as superlinear cluster expansions hit physical bandwidth limits.

6. Meta Platforms (META)

• Core Role: Wholesale Agentic Infrastructure & Open-Source AI Model Engine

Meta represents a unique infrastructure hybrid: it is both a massive consumer of cloud capacity and an open-source architecture engine via its Llama foundation models. For 2026, Meta aggressively scaled up its capital expenditure guidance to a massive $125 billion to $145 billion band to lock down physical data center footprints, custom silicon designs, and GPU arrays amid rising core hardware costs.

While expanding internally, Meta is structurally shifting toward a wholesale infrastructure integration model. In late April 2026, Meta executed a massive agreement with Amazon Web Services to deploy tens of millions of hosted AWS Graviton Arm cores into its compute architecture. Because the multi-step reasoning, real-time code generation, and task orchestration behind Meta's agentic AI applications are hyper-CPU intensive, Meta is leaning on AWS's infrastructure layer rather than attempting to self-build equivalent low-latency cluster processing nodes.

7. Micron Technology (MU)

• Core Role: Ultra-Dense High-Bandwidth Memory (HBM) & Flash Storage Monopoly

Micron Technology has completed its structural transformation from a highly cyclical, commodity memory fabricator into an indispensable bottleneck asset of the AI revolution. In fiscal Q2 2026, Micron reported a staggering 196% year-over-year revenue increase to $23.9 billion, subsequently issuing historic Q3 sales guidance of $33.5 billion at record-high gross margins near 81%. Growth is driven exclusively by severe scarcity: DRAM average selling prices (ASP) surged by 65% to 67% sequentially.

While the market remains hyper-focused on Micron missing an initial allocation of HBM4 chips for Nvidia’s Vera Rubin platform, Micron’s broader AI pipeline is fully sold out through 2026 and beyond. Crucially, the hidden engine of Micron's 2026 outperformance is the inference-driven demand for ultra-dense flash memory storage. On May 5, 2026, Micron began shipping its 245TB Micron 6600 ION SSD, the world’s highest-capacity commercially available solid-state drive. Built with advanced G9 QLC NAND, this drive delivers up to 84x better energy efficiency for AI workloads and requires 82% fewer physical server racks than legacy hard disk drive (HDD) deployments, effectively resolving the data center footprint and cooling limits holding back hyperscalers.

Comparison of Leading AI Cloud Infrastructure Companies

Based on actual 2026 operational numbers, capital allocations, and Wall Street consensus outlooks, here is an updated analytical breakdown of the top infrastructure plays:

How to Trade AI Cloud Infrastructure Stocks on BingX

BingX provides global market participants with highly optimized, crypto-native tools to capture price exposure across the premier AI cloud infrastructure ecosystem. Traders can execute macro theses through two distinct, secure pathways depending on capital allocation styles and structural preferences.

Trade Tokenized AI Cloud Stocks on BingX Spot

For investors targeting direct, non-leveraged asset exposure tracking real-world equities on a 1:1 economic basis, the BingX Spot market provides secure access to tokenized tech shares issued via regulated asset frameworks.

1. Log into your verified BingX account and activate comprehensive security protocols, such as Google 2FA.

2. Fund your Spot Wallet by depositing stablecoins like USDT through your preferred network layer, e.g., TRC-20, ERC-20, or Arbitrum.

3. Navigate to the Spot Trading terminal and search for fully backed tokenized stock symbols, such as MSFTON/USDT (Microsoft tokenized stock) or Google tokenized stocks like GOOGLON/USDT or GOOGLX/USDT.

4. Deploy the built-in BingX AI Analyst panel within the chart window to instantly visualize automated support/resistance zones, volume anomalies, and real-time technical indicators.

5. Define your parameters via a Market or Limit order, specify your USDT transaction volume, and confirm execution. Your tokenized equity balance will instantly reflect inside your spot account.

Trade AI Cloud Stock Futures with USDT on BingX TradFi

Following the expansion of its Infinite Vision strategy on its 8th anniversary, BingX TradFi offers robust USDT-settled perpetual contracts mirroring leading U.S. technology equities and cloud infrastructure providers.(This setup allows active market participants to trade traditional finance products using unified, crypto-native wallets.

1. Head to the BingX TradFi portal or the Advanced Futures interface.

2. Allocate working capital by moving your desired quantity of USDT from your main Spot account into your Futures account.

3. Select your targeted asset contract from a liquid directory of equity perpetual pairs, such as MSFT-USDT, AMZN-USDT, or ORCL-USDT.

4. Determine your macro direction. Select Open Long if you anticipate near-term upside from data center capital deployments, or Open Short to capitalize on tech sector capex pullbacks. Configure your leverage parameters defensively based on your personal risk threshold.

5. Integrate the BingX AI trading assistant to scan immediate order-book liquidity. Input your position sizing, establish precise Take-Profit (TP) and Stop-Loss (SL) orders to insulate against sudden volatility spikes, and execute the trade. Real-time PnL will settle dynamically inside your wallet in USDT.

Risks and Key Considerations When Trading AI Cloud Stocks

Despite the undeniable multi-year structural tailwinds backing the AI cloud cycle, market participants must manage capital allocation against significant systemic risks:

• Valuation Compression and Capex Digestion Pauses: Infrastructure stocks are trading at elevated multiples that discount aggressive growth years into the future. If mega-cap hyperscalers indicate a capex digestion pause, slowing down internal infrastructure spend to wait for software software monetization to catch up multiples across the sector will compress violently.

• Neocloud Financial Leverage and Liquidity Realities: Neocloud plays feature eye-popping revenue pipelines but suffers widening net losses due to intense capital expenditure and high interest expenses. If debt markets tighten or financing costs rise further, highly leveraged neocloud operations face strict liquidity bottlenecks.

• Shareholder Activism and ESG Compliance Risks: As cloud computing becomes increasingly militarized, civil and investor backlash is rising. Institutional shareholder coalitions, managing over $1.15 trillion in assets, are aggressively pushing for strict disclosure safeguards over government surveillance contracts and infrastructure data privacy. Platforms failing these compliance frameworks risk facing multi-billion dollar regulatory penalties or lost state contracts.

Final Thoughts: Should You Add AI Cloud Infrastructure Stocks to Your Portfolio?

The architecture of global cloud computing is undergoing a permanent rewrite. As enterprises transition toward autonomous, agentic ecosystems, the demand for high-density, power-secured data center hosting is outstripping available supply. Allocating capital across the infrastructure spectrum, balancing stable cash-generative hyperscalers like Microsoft and Alphabet against high-beta growth vehicles like Meta, offers a highly diversified method for navigating this multi-year technology supercycle. Using tokenized spot vehicles or flexible stock futures via BingX TradFi enables global capital to execute these macro-driven equity theses efficiently using unified, crypto-native rails.

Per title, would greatly appreciate if you could answer my question above and share any interesting insights. Thanks very much!

https://semianalysis.com/2025/03/26/the-gpu-cloud-clustermax-rating-system-how-to-rent-gpus/

This is an awesome comparison of different neocloud providers that SemiAnalysis released today. I haven't gotten through the whole thing yet, but there's a lot about Nebius in here, including:

"Nebius currently stands out by offering the best absolute pricing while still maintaining robust technical capabilities. Their approach involves aggressive cost optimization strategies, including the adoption of Original Design Manufacturer (ODM) hardware for GPU servers, significantly lowering their total cost of ownership. For example, by bypassing traditional OEM providers like Dell or Supermicro, which can price servers with as high as 10-15% gross margins, Nebius achieves cost reductions through a custom-designed ODM chassis, minimizing gross margins from a typical 10-15% down to around 2%.

This strategy not only reduces initial hardware expenditures but also lowers ongoing power consumption, allowing Nebius to pass substantial savings onto customers. All Hyperscalers use this ODM strategy, too, but Nebius stands out as the only non-Hyperscaler that deploys an ODM-built chassis."

Trying to understad the economics of CoreWeave, Nebius and IREN beyond the obvious "AI demand is exploding" since Reuters recently compared the neocloud buildout to the alternative telecom companies of the late 1990s.

The internet bulls were ultimately right. Internet usage exploded, and the world needed massive amounts of fiber. But that didn't necessarily make the companies financing all that infrastructure good investments. Something similar could potentially happen with AI compute.

Neoclouds are spending enormous amounts of money securing GPUs, power and data center capacity while compute remains scarce. CoreWeave, for example, had $72B in total liabilities at the end of June, according to Reuters, up 56% since the beginning of the year.

Right now the demand is clearly there. The question I'm struggling with is what these businesses look like once supply catches up.

If GPU availability improves and compute pricing becomes more competitive, what creates the moat?

2 Oct 2025: Microsoft Bets $33 Billion on Neoclouds like Nebius to Ease AI Crunch https://www.bloomberg.com/news/articles/2025-10-02/microsoft-neocloud-deals-a-strategy-for-ai-supply-crunch

"Microsoft Corp.’s deal with neocloud company Nebius Group NV will provide computing power to internal teams creating large language models and a consumer AI assistant. The strategy is aimed at coping with a shortage of AI data center capacity and letting Microsoft free up its own server farms to provide lucrative AI services to customers. As part of the deal, Microsoft will get access to more than 100,000 of Nvidia Corp.’s latest GB300 chips."

"Microsoft has inked more than $33 billion in commitments to such providers, including Nebius, CoreWeave Inc., Nscale and Lambda."

30 Sep 2025: CoreWeave signs $14 billion AI infrastructure deal with Meta https://www.reuters.com/technology/coreweave-signs-14-billion-ai-deal-with-meta-bloomberg-news-reports-2025-09-30/

"CoreWeave (CRWV.O) said it has signed a $14 billion agreement with Meta (META.O) to supply computing power, the latest multi-billion dollar deal as businesses ramp up infrastructure to meet the demand for artificial intelligence applications. As a part of the agreement, CoreWeave will provide the Facebook-parent access to Nvidia's (NVDA.O) latest GB300 systems, according to a Bloomberg report."

12 Jun 2025: CoreWeave to offer compute capacity in Google's new cloud deal with OpenAI https://www.reuters.com/business/coreweave-offer-compute-capacity-googles-new-cloud-deal-with-openai-sources-say-2025-06-11/

"The so-called neocloud company, which sells cloud computing services built on Nvidia's (NVDA.O) graphics processing units, is slated to provide computing capacity to Google's cloud unit, and Alphabet's (GOOGL.O) Google will then sell that computing capacity to OpenAI to meet the growing demand for services like ChatGPT, the sources said."

The first time I heard about neoclouds was from a friend suggesting I look at miners that were going to convert to AI/HPC about a year ago. Since by then I had held RIOT for nearly a year with zero performance I dismissed the idea swiftly.

However when Nebius, a reborn Yandex, appeared on my radar I dug deeper and bought a substantial position in Nebius at around $25 per share, largely due to the fact it was run by Volozh, a fiercely talented entrepreneur.

Although it seems crazy now, a year ago, the market was still shaking from the release of DeepSeek that would supposedly kill the need for AI compute through more efficient models that activated only part of processes needed instead of brute force methodology. Since then AI did get better, but the need for AI did not disappear.

This has led to neoclouds completely obliterating all price targets, with Nebius rising over 7x, although I did derisk my stake entirely at $65 per share, only keeping my profits in for the ride to $120. Miners shifting to AI data centers also received 10-15x runs.

The reason behind all that is it is assumed that hyperscalers like Meta and Anthropic will require an ever rising amount of compute and GPUs to function which will require more and more infrastructure. To that end, these neoclouds moved to secure land with access to electricity on GW levels and offer their services to hyperscalers to essentially build data centers to suit to accelerate their time to market.

Since data centers are essentially real estate development projects not unlike industrial real estate, many hyperscalers decided to buy up this capacity from the likes of IREN, Coreweave, Nebius, Wulf and others in deals valued in billions over periods of 5-10 years in order not to deal with the hassle. Some of these platforms like Nebius moved to build software to manage the data centers and virtually integrate.

Over the past year we’ve seen a numerous series of deals from AI hyperscalers as they rushed to secure AI/HPC compute, sending AI HPC stocks soaring with each deal in an echochamber effect.

The math behind these is quite simple. Neoclouds buy the land and secure access to power on the grid (which saves hyperscalers time to compute). Once a tenant is located that agrees to pay X billions for Y MW of compute, they do a deal where they pay an advance to the neoclouds and then a fee every year for the compute.

The hyperscaler then has to build the data center (including substations, cooling, etc) and purchase the GPUs that will be plugged in.

The GPUs are typically financed by a loan against the GPUs and the data center by the advance payment and equity from the neocloud or convert note / ATM raises.

The twist is, GPUs are amortised within 5 years, the average length of the contract, and these represent 2/3 of the cost of the data center, so at the end only the data center, substantions, cooling infrastructure remains which account for only 1/3 of the cost of the project. This means the next time a contract comes along you end up with 1/3 of the costs as net margin.

So over a 10-year period, the net operating income is around 1/6th of the revenue received on the contracts or around 17-18% of the project value. DCF methods over 15 years and applying a discount rate of 6% would give you about 20% of the project as NOI.

Of course from that you would substract additional development costs, SGA, financing costs, etc but you would still end up in the 15% APY range which for high quality deals that could be monetised if sold to data center reits to get cash at a higher multiple is definitely an interesting looking option.

Neoclouds have built a large part of their valuation around their secured power, meaning they encourage investors to look at their powered land portfolio, assume it will all be built out due to heavy demand and apply valuation models based on that potential future revenue.

This is becoming the reason as to why I have now fully exited my Neoclouds position.

First of all, the run up, ever since the Iran war has been almost 2x from the bottom and that is on top of a previous run close to 2x. In any case, this surge was due for a correction, which is now playing out.

However the real reason I am out of this sector at this moment is different - neoclouds are stuck in a land where their margins will always be subpar.

One one end, hyperscalers are going through neoclouds because it is cheaper for them than building their own data centers which requires more hassle, investment costs, etc which would dilute the performance of their metrics. It is therefore highly unlikely that neoclouds have any pricing power as the replacement cost will forever be the metric judged against.

This is in addition to the fact, many companies are trying to disrupt the powered land aspect of the offering by offering to build data sources for data centers independent of the grid. This would greatly weaken current neoclouds as well if any success is seen on that end.

Perhaps even more importantly, neoclouds need to secure GPUs from NVIDIA to run their data centers, without them a data center simply can’t operate.

Now the most valuable company in history, NVDA is actually deciding which companies to sell its GPUs to and which operator it will gives its latest chips to.

This is how NvDA got the $21 billion Meta contract to Nebius for example by giving it advance access to its Ruben chips, which it received as NVDA is an investor in Nebius. However Nvidia will always want to make sure its main profit centers are on the core holding, meaning margins from neoclouds are further challenged by the strongest supplier in the market.

Neoclouds therefore find themselves at an intersection where buyers have the bargaining power of replacement costs, its powered land assets are being challenged and its supplier is in de facto control of when it can actually build and what it can sell. All of these are happening at the same time.

None of this is priced into the price after the current run up. It eventually will be. I am watching for a pullback 20-30% in the neoclouds space and would say we are lower a year from now than we are today.

There is quite a bit of discourse assuming that the conventional hyperscalers are in direct competition with the Nebius business model.

However, I believe that to be a fairly reductive analysis of what is going on.

The AI market is only just starting to develop and will be vast in size and application. There are many twists, and turns, known, and unknown, ahead.

One of the reasons I believe the Nebius business model to be unique is that they understand that to be successful requires expert support along each stage of the end-to-end AI proposition.

The idea that a Business Sales executive, from a hyperscaler, is going to provide a turnkey AI solution for the vast array of AI applications belies the complexity of the journey ahead.

Hyperscalers are basically saying we will build the road, but you the customer are responsible for the car. The road is difficult to build, and capital-intensive, but at this early stage customers need more help than that. This approach passes the hard job to the customer.

Nebius, on the other hand, are saying "we're all in this together, so let's get to work helping the people needed to make this revolution a reality."

The age of deterministic product-centric IT software solutions is being challenged at the very core of its principles.

Pivoting to large-scale, real-time stochastic modelling in business is going to be a VERY steep learning curve for everyone.

The idea that anyone has this sussed right now is laughable. Even the short-term winners, like Nvidia, know this is merely the beginning.

Nebius aren't going it alone. They are forging relationships in business, Government and academia to bring all the expertise needed to push ahead. For example, they are inviting academic researchers to use Nebius AI Cloud (see link).

The same business models that created the behemoth IT software firms are unlikely to directly translate to the brave new world we are all hurtling towards. They will need to be evolve along the journey.

By having core businesses in each pillar of the AI arena, Nebius aren't just trying to sell a.n.other cloud product. They are eating their own dog food with the other businesses in the wider group. Learning collectively along the way.

The future is uncertain, as it always has been.

However, fortune has also always favoured the brave not the complacent.

what do people think about neoclouds? it seems like the market is still trying to figure out wha this vertical could turn into. from a business model standpoint, margins are thin, GPU rental prices per hour are rapidly falling, and building out new capacity takes time. software services tacked on top could provide a means to differentiation in order to improve margins.

i'm trying to look back at history and see if there have been similar companies in the past. for example all of the web hosting / CDN platforms that emerged during the Internet pretty much went to zero. are neoclouds destined for the same or are they an entirely different beast?

With Nebius and CoreWeave announcing good results and their stocks going up, I think it's worth pointing out that SpaceX's rental deals with Anthropic and Google mean it has to be considered as a NeoCloud provider itself.

At SpaceX's company meeting, Musk claimed that by as early as next month (Sept), the revenue from AI will be more than from the rest of the company combined.

https://www.benzinga.com/markets/prediction-markets/26/08/61145623/spacex-ai-100-billion-revenue-run-rate

Grok isn't what Musk wants it to be, but renting out GPUs is a way for SpaceX to bring in some revenue and profits while it tries to make Grok more than a second or third class LLM. Remember, Space-X is charging more per GPU-rental-hour than anyone else, yet Anthropic and Google have signed on. Why? Because the compute capacity is available now. This is a big advantage over the other 3 NeoClouds.

SemiAnalysis published a recent article on GPU rental and SpaceX's role:

https://newsletter.semianalysis.com/p/spacex-10gw-in-2027-why-its-real

"Elon needs to build datacenters faster than anyone else. We believe that he can. What gives us this confidence? We’ve written a few times about Elon’s speed, with 122 days to build Colossus 1’s 300MW, six months to build 200MW at Colossus 2, the decision to build an onsite generation plant 1km across the border to avoid permitting, and much more.

There’s been even more displays of speed since then. The power plant in Southaven has expanded from 27 turbines (~495MW) in February 2026, to 69 turbines (1.7GW) in July 2026.

As well as the arrival of “MiniHard,” which upon vertical construction in March 2026, will likely reach 450-500MW in just ~5 months! That doesn't mean Elon builds better than everyone else. He's just applying a different playbook.

Switchgear and large power transformers are sold out for 2 years? Just buy power modules from China, and skip LPTs by delivering medium voltage power from power gen to low voltage transfos, which are much more widely available. Elon’s companies have the world's most talented electrical engineers - no one knows better than them how to manage speed vs efficiency tradeoffs. Most datacenter operators in the world optimize for efficiency and quality - it's the only way to land a 15-20 year take-or-pay hyperscaler datacenter contract. SpaceX will focus on entirely different tradeoffs: it's speed above all. In times of compute constraints and extremely high AI token margins, a 500mw cluster available in three months, with 90-day cancellation policy, is one of the most scarce and valuable asset in the world. All typical “quality” metrics don't matter. Proof? Google, the most vertically integrated infra company in the world, somehow ended up signing with SpaceX.

Gas turbines are 5yr+ backlogged? GEV’s are, but there are plenty of other options - our Energy Model 30+ manufacturers of gas gen equipment that have secured large scale orders to serve datacenters. There is plenty of available capacity if you look hard enough and you're open to working with new suppliers. Secondary market volumes for turbines is surging - for example, all the turbines initially scheduled to go to Oracle’s New Mexico site are on the market. Secondary market prices are very high, but Elon can pay.

Labor is the ultimate constraint? Just parallelize as much as possible, reduce the commissioning process, and preassemble as much as possible. Reports out Colossus 2’s peak daily labor at ~3k construction workers, which as about ex lower than other gigawatt-scale datacenters under construction. Elon has a long history of accomplishments with less staff than industry standard - just look at Tesla and SpaceX’s history."

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What does this mean for the legacy NeoCloud providers? The good news for them is that demand for GPU hours is still through the roof, so everyone will sell whatever they can put together. But, SpaceX is going to get a lot of attention and business due to timing.

This also reinforces for me that long-term the NeoCloud business will suck. Despite what Nebius and CoreWeave are saying, the big deals don't include software infrastructure; the big model providers and hyperscalers want raw GPU access. Sure, there will be smaller startups, including some AI application companies, that may want to have access to some software infrastructure so they don't have to hire that expertise internally, but that's a tiny portion of the business today, and I think unlikely to grow to be significant.

I'll be taking advantage of this pop in the NeoClouds to unload my positions. There's probably still some money to be made, but I think there are greener fields, especially in the medium/long term, to plough.

Is there any ETF include the AI Datacenter Companies Like Iren, Nebius , Coreweave and so on ?

Hope ya’ll enjoy. Put a lot of work diving into this one.

https://open.substack.com/pub/matthewteufel/p/nebius-vs-noise-how-to-spot-a-real?r=27kmr7&utm_medium=ios

I saw somewhere someone asking about the actual experience of someone using Nebius' services, but the responses of the post seemed somewhat lackluster. I found an article (linked below) about how the author thinks that Nebius has one of the best developer experiences (i.e., most developer friendly, with developers being one group of Nebius' users) out of all the neoclouds. The second part of that article also discusses how the seemingly unconnected parts of Nebius might actually reinforce each other, for example with Avride being the example-customer for Nebius AI cloud, or somehow using Toloka to strengthen and differentiate Nebius' cloud offerings.

Nebius Part 1: the Smooth (Developer) Operator

https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves#/

To sum up the article:

1. The core business of these neoclouds stems from a shortage of GPUs. In effect, there will be buyer be demand for any company that has GPUs at the moment.

2. This is a low margin / commoditised business

3. Their future hinges on building a differentiated cloud offering

Coreweave seems to be working on this through acquisitions (ie. weights & biases), Nebius has the engineering power and experience (through Yandex) to build their cloud offering around AI native start-ups.

But have any of these companies demonstrated that they will be able to do this better than hyper-scalers?

I'm also not an AI bear - in fact, my portfolio is very heavily AI-weighted. Note that I am not short NBIS, nor any other NeoCloud, I just don't think they're worth my investment dollars given other opportunities in the market.

Essentially, I think the Big 3 HyperScalers (Amazon, Google, & Microsoft) will eventually own the business of Cloud AI Computing, just as they do today with Cloud Computing.

In fact, the only reason the NeoClouds are on anyone's radar today is that AI compute demand is so high and the build-outs so expensive and involved that the HyperScalers decided that hiring others to build some of their data centers is the best choice they have today. But, that's not going to be a lasting situation.

Today the HyperScalers can't directly hire enough people to build out the geographically wide spread out teams necessary to identify sites, acquire building permits, line up multiple suppliers and building contractors. And then run them all simultaneously. And even if they could, that would mean vast increases in headcount and Capex reporting (the latter is already too high for Mr. Market's view). And, it spreads the risk of any build-out complications among multiple approaches (internal and external) and vendors.

The NeoClouds jumped on this because it brings in vast amounts of revenue on a near and mid-term basis, which they believe will eventually lead to them running their own Cloud AI Compute business. But, we don't see that happening today (at least neither Nebius nor CoreWeave show that in their reporting today), and I don't see it happening to a significant extent in the future.

Think about that for a minute:

1. What dollar amount or percentage of Nebius' sales involve Nebius' software (infrastructure or otherwise)?

We know that percentage-wise, Nebius has $Billions lined up for bare metal hardware rental contracts. And while they don't disclose the "all important" software side of the business, it can'e be more than $HundredsofMillions. That's one of two orders of magnitude smaller. CoreWeave is pretty much the same.

2) How quickly are Nebius software sales growing?

OK, so it's small today, but is it growing at a pace that it will soon become a big revenue and profit generator for the company? Again, Nebius doesn't disclose this, yet this business is what most bulls cite as the raison d'être for investing in the company. Today, what keeps Nebius, CoreWeave, Iren, etc. afloat are the bare metal mega-deals. But, even Nebius' management admits those are less profitable. And should those be the future business for the NeoClouds, they'll get quite the market haircut.

So, why and how will Nebius outsmart AWS (and GCP and Azure) with its AI Software Infrastructure? Does anyone here really think Andy Jassey or his counterparts over at Google and Microsoft don't see the same potential for Cloud AI Computing that Arkady Volozh does? And even if not today, given how well Azure caught up to AWS, do you not think Amazon wouldn't put serious meat behind the arrow to catch up?

When we look at AI workloads today, two aspects reveal themselves:

1. They're complex

2. More and more, they involve more than just AI

These both work against the NeoCloud Software Infrastructure business. The complexity means that the AI customer has to be sophisticated. The non-sophisticated Ai customer simply uses a ChatGPT or Claude CoWork interface, but those Frontier Labs are already hosting that themselves and they certainly don't need a Nebius to run their AI Compute Hardware. While the typical AWS user is someone at a non-computing related company wanting to run database queries or serve up web pages, run an e-commerce site, etc. today the typical AI compute user is part of a sophisticated tech-first team deciding on LLMs, perhaps running open source models with custome parameters, constructing agents to integrate AI within their company, etc. These teams already have people who can use Slurm, Kubernetes, vLLM, Triton Server, Ollama, LangChain, or other open source projects to manage their AI Compute.

We see that Anthropic was happy to grab raw compute from Space-XAI, and OpenAI is built on Azure, and now Oracle ($300B deal!), and even Amazon ($38B) for a combination of Nvidia GPU as well as AWS access.

The latter is the future, I believe. Just as we saw the market relatively recently recognize that AI workflows need more than just GPUs but also CPUs (and Nvidia's servers internally bundle a Vera CPU with every two Rubin GPUs), the world will eventually recognize that workflows involving AI still need to do things like serve up web pages, deal with databases, even run or be run by Agents, etc. that are best handled by CPUs, which are best handled by the existing Cloud Computing infrastructure that's available today - which is AWS, GCP, Azure.

How do the NeoClouds compete with that? It's much easier for the HyperScalers to add AI software infrastructure to their existing cloud offerings than for the NeoClouds to add what took Amazon, Google, and Microsoft decades to create in their regular compute cloud offerings. Amazon has a history of offering turnkey open source project access for free (you pay for the compute and storage).

How many customers are savvy enough to build AI workflows but not savvy enough to build on top of the many open source packages to run the infrastructure? And then, how many want the lock-up of being tied to some Nebius' software instead of being able to move to CoreWeave or even AWS, GCP, or Azure? They might use it for convenience and quick start-up, but no company with expansion plans want to be permanently tied to Nebius' or CoreWeave's software stack.

Before I'll believe that Nebius or CoreWeave have some inherent advantage with their infrastructure software, I'll want to see either company release some numbers on that business. Neither does today, and that says alot especially when you think about who they're competing against.