• In less than a year’s time, memory stocks have gone from just another way to play the AI trade to arguably its biggest beneficiaries from a return perspective. 
• Micron, Samsung and SK Hynix now rank among the world’s top 20 most valuable stocks, each with market capitalizations well above $1 trillion. 
• Widespread shortages across HBM, conventional DRAM, LPDDR5 and NAND SSDs are affecting the industry, putting immense pricing power in the hands of memory companies. 
• The top AI processor companies have greatly increased memory content across their systems to meet the changing needs of frontier model developers. 
• While memory makers are pointing to a prolonged shortage, there are multiple key risks to stay aware of as it relates to the memory trade. 

Over the past 10 months, memory chip stocks have gone from being solid beneficiaries of the AI boom to capturing a massively outsized piece of the return pie. 

The inflection in Micron’s performance demonstrates this. From the beginning of 2025 to the end of August 2025, Micron added around $36 billion to its market capitalization, rising to $133 billion for a strong 37% gain. Since then, returns have exploded, with Micron (MU) soaring more than 900% from August 2025, and up over 1600% since the April 2025 low. 

Perhaps the most striking figure is that over these 10 months, Micron added more than $1 trillion to its market capitalization, which now sits near $1.35 trillion. Along with this, industry watchers expect the memory market to far exceed $1 trillion in revenue by 2027. 

This chart is a comparison of Micron’s stock performance since the April 2025 low, showing a gain of over 1,600%, far outpacing Nvidia, the semiconductor ETF, and the Nasdaq-100, highlighting the strength of the AI memory-driven rally.

The sheer velocity of the move reflects how quickly investors have repriced memory’s role in AI infrastructure. What was once viewed as a cyclical, commoditized segment of semiconductors is now becoming one of AI’s most drastic bottlenecks, as shortages spread across HBM, conventional DRAM, LPDDR5X and NAND SSDs. 

The I/O Fund has been covering this dynamic for nearly three years. We first explored it in our deep dives on AMD’s AI acceleration strategy and Lam Research’s leadership in HBM and DRAM equipment in the summer of 2023. In December 2023, we expanded on this theme in our analysis of the 2024 memory and PC rebound, highlighting the shift from cyclical demand to AI-driven secular growth “that is strong enough to transform commoditized hardware into a secular trend,” with HBM and high-performance DRAM “in the early stages of a multiyear growth cycle.” 

This thesis led us to make memory stocks some of our highest allocations of 10%+ in 2026, even as many market participants feared memory had topped for good. 

The question now is whether the memory trade has already run too far, or whether shortages and the upside in memory pricing support more upside. Below, you’ll see pricing power is still intact, with meaningful new supply not arriving until 2028, or later, with the only overhang being how pricing power flows to memory suppliers under long-term agreements.  

The discussion is data-driven, but also nuanced, because there may be no bigger debate in the market today than whether memory can continue its historic run. 

What Triggered the AI Memory Supply Crunch? 

First off, it is worth taking a step back to understand what brought about the shortages seen today. Traditionally, memory demand has been very cyclical, with much of the market driven by consumer spending on mobile phones and PCs. H2 2022 saw the memory market enter its worst cyclical downturn since the Great Financial Crisis. Sales and earnings plummeted from pandemic highs, with Samsung’s operating profit falling by 95% YoY in Q1 2023. 

This led to significant production cuts, with Micron and SK Hynix announcing reductions of 15%–25% and cutting capex by 40%–50%. Samsung also implemented meaningful cuts to conventional DRAM and NAND output in 2023. At the same time, both Samsung and SK Hynix began aggressively expanding HBM capacity for 2024, albeit from a relatively small base—with estimates placing HBM at just 8% of total DRAM sales in 2023. 

mid

Amid this, the AI market had its watershed moment: the release of ChatGPT in November 2022. Nvidia’s data center revenue would go on to soar 217% in its fiscal year 2024 (roughly calendar year 2023), its fastest growth rate during the AI era. 

This helped memory demand improve, but companies supported this demand by drawing down inventory from extremely high levels. Notably, at the beginning of 2023, Micron’s days of inventory outstanding, excluding write-downs, were 235. Without adjusting for impairments, it would have taken the company nearly eight months to sell its inventory. By the beginning of 2024, that figure had fallen drastically to 160. 

The decisions to cut capacity, reduce capex, and allow inventory levels to drop were logical, considering that these firms were fresh off their worst decline in a decade and a half. However, as data center demand continued to explode and HBM capacity remained relatively low, those decisions set the stage for the drastic supply shortages we are seeing today. 

This chart is a comparison of AI memory stock performance since the August 2025 low, showing U.S. memory up over 1,200% and South Korean memory over 700%, significantly outperforming the semiconductor ETF and Nasdaq-100.

HBM Shortage: AI Infrastructure’s Core Memory Bottleneck 

Memory shortages are surfacing across essentially all product types. However, perhaps the most easily identifiable shortage is in high-bandwidth memory (HBM). This comes as HBM is the type of memory packaged directly alongside AI accelerators, from NVIDIA and AMD GPUs to Google TPUs. HBM, a DRAM form factor, uses advanced packaging to stack up to 12 (and soon 16) DRAM chips, offering higher bandwidth, capacity, performance, and power efficiency compared to conventional DRAM. 

Micron, Samsung, and SK Hynix are the three companies that control the HBM market. Due to massive hyperscaler demand, all three are sold out of their HBM capacity for 2026. 

Rapid Market Growth Outpaces Supply Increases 

Looking back a few years, we can see how fast the HBM market has grown. Bloomberg Intelligence placed the size of the HBM market at $4 billion in 2023. Meanwhile, at the beginning of 2026, SK Hynix cited data from Bank of America placing the HBM market at $34.6 billion in 2025. Taking the $3.9 billion 2023 estimate, the HBM market grew by an astounding 198% CAGR from 2023-2025. Notably, BofA forecasts additional growth of 58% YOY to $54.6 billion in 2026. 

Considering this growth rate, making capacity investments at the scale required for supply and demand to balance was antithetical to the position of memory suppliers in 2023. Even if they wanted to, actually achieving this would not have been feasible, given that increasing production capacity is a lengthy and expensive process. Notably, these three companies combined for free cash flow of -$18.5 billion in 2023. Looking at conventional DRAM, a knock-on effect from HBM imbalances is exacerbating shortages there. 

HBM Reallocation Is Tightening DRAM Supply 

In conventional DRAM, which centers around double-data rate 5 DRAM (DDR5), the situation is somewhat similar but has different mechanics. DDR5, and increasingly low-power DDR5 (LPDDR5), are high-performance memory chips paired with AI CPUs. Nvidia uses 480 GB of LPDDR5X per Grace CPU, and will more than triple that figure to 1.5 TB in its Vera CPU. 

Meanwhile, AMD uses standard DDR5 in its current generation EPYC Turin CPUs. The company plans to first support LPDDR5X in its next generation EPYC server CPU "Verano," which is expected to become available in 2027. 

DRAM Pricing Surges 

Related to this, SK Hynix noted all the way back in October 2025 that its conventional DRAM, NAND, and HBM capacity was all sold out for 2026. Micron and Samsung have not said their conventional DRAM capacity is sold out, but we can see based on price increases that the shortage is very significant. As commodity products, conventional DRAM sales take place at monthly/quarterly contract prices or spot prices, while memory suppliers are allocating HBM capacity through long-term contracts. 

In Q3 2025, DRAM contract prices soared by 171.8% YoY. Meanwhile, in Q1 2026, TrendForce estimates that conventional DRAM contract prices increased by 93%-98% QoQ and projects another 58%-63% QoQ increase during Q2 2026. 

One of the key factors contributing to conventional DRAM tightness is memory makers relocating capacity away from these products and toward higher-margin HBM. Importantly, this move from conventional DRAM to HBM does not translate into a 1:1 shift in bit supply. 

Why HBM Production Reduces DRAM Supply 

Current generation HBM3E requires approximately 3X the wafer capacity per GB compared to DDR5. This makes the strain on conventional DRAM exponentially worse, as reallocating wafer capacity toward HBM disproportionately reduces the wafer supply available for conventional DRAM production.  

Furthermore, Micron noted in May 2026 that this ratio will continue to grow as suppliers transition from HBM3E to future generations in HBM4 and HBM4E. Notably, Nvidia’s upcoming Rubin generation and AMD’s upcoming Instinct MI450 accelerators will use HBM4, while Google’s TPU v8 will use HBM3E. 

SSD Shortages Add to the AI Memory Crunch 

NAND flash SSDs, which store large amounts of data beyond what DRAM can store at a given time, are also facing a supply crunch. Kioxia has a joint venture with SanDisk in operating SSD fabs. Near the beginning of 2026, Shunsuke Nakato, Managing Director of Kioxia's Memory Business Unit, said that the company’s capacity was sold out for the year. Notably, 60% of the capacity from the joint venture goes to Kioxia. Furthermore, while interviewing SanDisk’s CEO, Bernstein analyst Mark Newman estimated that the company’s ASP per GB increased by 140% QoQ in Q1. 

Perhaps even more striking are statements made by Everpure (formerly known as Pure Storage) CEO Charles Giancarlo in an open letter to customers. Everpure, which makes NAND-based storage systems, says its “input costs of many high-volume semiconductor components have surged between 300 percent and 900 percent (4x to 10x) since mid-2025.” Additionally, HDD makers Seagate and Western Digital have said their capacity is sold out for 2026. 

How Long Will the Memory Shortage Last? 

Looking ahead, memory suppliers are all saying that shortages will continue but are providing differing statements about how long. 

SK Hynix Signals Prolonged AI Memory Shortage Into the Next Decade 

SK Hynix has outlined a particularly long path to normalization. At Computex in June, Chairman Chey Tae-won reiterated his stance that shortages would persist into 2030. This comes even as the firm plans to nearly double its monthly DRAM wafer capacity from 550,000 today to 1 million by 2030. By 2034, the company expects to triple DRAM capacity, a timeline that is 10 years ahead of its previous plan. 

Pursuant to its investment plans, SK Hynix is said to be in the final stages of listing its American Depository Receipts (ADRs) on the NASDAQ. The current expectation is that the offering will represent around 2.5% of the firm’s outstanding shares. This would imply a gross proceeds value near $26 billion based on recent prices and exchange rates. That would be very significant, potentially increasing its cash by 72% from $36.1 billion last quarter to around $62 billion.  

Micron, Samsung, and SanDisk Expect Tight Supply Through 2027+ 

Micron is also adding fabs, with initial wafers expected at its Idaho 1 facility in mid-2027, and with several others to follow. Related to this, Micron's VP of Marketing, Christopher Moore, said in a January interview, “you're not really gonna see real output, meaningful output by the time we get all the qualification done and customers are accepting it and you get the tools, everything up and running until 2028.”  

In a May interview with Bloomberg, Micron CEO Sanjay Mehrotra echoed this, saying, “we see that meaningful new supply in the industry doesn't really start ramping until 2028 timeframe.” In its latest earnings report, Micron added “Even as we expect industry supply to improve gradually in 2028, we currently do not have line of sight as to when memory supply will be able to catch up with increasing demand." 

Image showing Micron’s planned fab expansions. The first leading edge DRAM and HBM site in Idaho is not expected to come online until mid-2027, with the second not coming online until late 2028. The first two leading-edge DRAM sites in New York do not come online until 2030, while shipments from the leading-edge DRAM and HBM site in Japan are not expected until 2028. Source: TrendForce TrendForce 

Meanwhile, in its latest earnings call, Jaejune Kim, EVP of Samsung’s Memory Business, said, “And unlike previous years, customers who are concerned about supply shortages are actually bringing forward their demand for 2027 already. So currently, just based on prebooked demand alone, the supply-demand gap is looking to widen further in 2027 versus this year.” 

Specific to NAND, SanDisk’s CEO said at the JPMorgan Technology Conference, “We see this market undersupplied for a long period of time.” More specifically, he noted that in 2025, the company had a “clear point of view” that the market would become undersupplied through 2026. He added, “And I think we can say through the end of '27, we have that same level of conviction now.” 

Across these statements, we can see that executives from top memory companies are all indicating that shortages will continue until at least some part of 2028. Importantly, Samsung indicated that shortages would intensify in 2027, while Micron doesn’t expect meaningful output increases until 2028. Given this, it may not be so far-fetched to think supply and demand will not balance until near the end of the decade. 

HBM and DRAM Demand Surge as AI Models and Infrastructure Scale 

While the memory shortage is clearly in place today, it is important to understand the underlying factors within AI models and infrastructure driving this shortage and contributing to its continuation. 

Model Complexity and KV Cache Requirements on HBM 

Model complexity and the KV cache are two primary drivers of increased HBM demand, especially as it pertains to inference deployments. Increasingly complex models are being trained and deployed for multi-step inference or agentic tasks, requiring larger context windows.  

Context windows represent the amount of information that the model can remember at a given time to execute tasks, with window length increasing dramatically over time, such as for OpenAI’s models. For example, according to Artificial Analysis, OpenAI’s GPT-3.5 Turbo, released in 2023, had a context window of just 4k tokens. This increased to 128k tokens in GPT-4.5 Preview in early 2025, while OpenAI’s latest model, GPT-5.5 (xhigh), has a context window of 922k tokens, a 230X increase in the span of three years.  

Chart showing the context windows of three OpenAI models. GPT-3.5 Turbo’s context window is 4k tokens, increased to 128k tokens in GPT-4.5 Preview, while OpenAI’s latest model, GPT-5.5 (xhigh), has a context window of 922k tokens. Source: Artificial Analysis 

The reason this sharp increase in context windows is important for the memory thesis is because context windows define the potential size of a model's KV cache, or the actual working memory that a model continually references during inference. HBM is particularly important here as the goal is to keep as much of the KV cache on HBM — the fastest memory available — as possible.  

However, if HBM capacity is not large enough to hold the entire KV cache, that remaining portion can be offloaded to slower conventional DRAM or SSDs, introducing latency during inference or leaving expensive GPUs (or other accelerators) underutilized.  

We can roughly put in perspective potential memory requirements for frontier models, using OpenAI’s GPT-4 with an estimated 1.8T parameters and a 128K context window as a benchmark. At FP8 precision, storing the model weights would require 1.8TB of HBM capacity (at 1 byte per parameter), while a 25% activation buffer would add 450GB.  

On a single Nvidia GB200 NVL72, this would leave roughly 11.1TB of HBM capacity free for the KV cache. Assuming 120 layers and a hidden size of 16,384, KV cache requirements per token would be ~3.9MB at FP8, meaning one NVL72 could in theory support maximum tokens of ~2.85 million, or around 22 concurrent requests at the max 128K context window. 

This problem becomes further multiplied as inference demand grows, resulting in more requests from many concurrent users. Consider that OpenAI has dozens of production models available and over 900 million weekly active users, implying that at peak usage it could be handling tens of millions of concurrent requests, each consuming KV cache memory.    

HBM Content Soars Over GPU Generations 

Notably, Nvidia’s 8-GPU DGX H100 server contained 640 GB of HBM, or 80 GB per chip. The B200 moved to 1.44 TB of HBM in an 8-GPU configuration, resulting in 180 GB per chip, or 125% higher than the H100. The B300 contained 288 GB of HBM per chip, 60% more than the B200, and 3.6X more than the H100. 

Overall, the latest system available, the 72-GPU GB300, supports up to 21.7 TB of HBM. This results in rack scale deployments that contain nearly 34X more HBM content than the DGX H100 server. This shift came over approximately three years, with the H100 entering full production in September 2022, while the GB300 entered full production in August 2025. This rapid increase in HBM content over a short period is another significant contributor to HBM shortages. However, it is important to note that Rubin will remain at 288 GB per chip, but use HBM4 rather than the HBM3E in Blackwell to provide higher bandwidth. 

Chart showing the increase in HBM content per Nvidia GPU from H100 to Blackwell Ultra. HBM content starts at 80 GB in H100 and moves progressively higher to 141 GB in the H200 and 192 GB in Blackwell to 288 GB in Blackwell Ultra, equating to 3.6X increase across these GPU generations. Source: Nvidia Nvidia 

This shift is similarly evident at AMD. The company’s Instinct MI250 GPUs, launched in November 2021, supported 128 GB of HBM per chip. Meanwhile, the company’s MI450 series will deliver nearly 3.4X HBM capacity than MI250 at 432 GB per chip. The “Helios” MI450 72-GPU rack scale system delivers 31 TB of total HBM4 content—or 1.5X higher than the GB300 NLV72. Shipments are expected in the second half of 2026. 

Conventional DRAM Pressure Points: Vera Content Triples as CPU Demand Increases 

As noted, Nvidia’s Vera CPUs will support more than triple the LPDDR5X per chip of the Grace CPU, which is likely to put additional pressure on conventional DRAM demand. However, this comes down to more than just per-chip DRAM content. 

Nvidia has announced that it will launch a standalone Vera rack containing 256 CPUs—or 7X more than the 36 CPUs in the Vera Rubin NVL72. This is due to the increasing importance of agentic AI, which is expected to increase CPU demand significantly. With this, LPDDR5 demand could be further pressured from two sides: higher content per CPU and higher overall CPU sales. 

Notably, TrendForce cites the rising importance of CPUs as a rationale for increasing its 2026 DRAM market forecast to $618.7 billion, representing 303% YoY growth. The firm projects DRAM growth of another 46% YoY in 2027, and for the overall DRAM and NAND market to hit $1.28 trillion. This would equate to a 5.7X increase in just two years versus the $225 billion market in 2025. 

To learn more about the emerging CPU bottleneck, read I/O Fund’s June 2026 article: AMD, Nvidia, Arm, Intel: Inside the $120 Billion CPU Gold RushAMD, Nvidia, Arm, Intel: Inside the $120 Billion CPU Gold RushAMD, Nvidia, Arm, Intel: Inside the $120 Billion CPU Gold Rush 

Risks to the Memory Thesis: TurboQuant and Long-Term Agreements 

There are two major risks investors should watch as the memory trade matures, which is whether shortage-driven pricing surges will continue to flow disproportionately to memory suppliers, and whether software-based efficiency gains could reduce the intensity of memory usage.  

In recent earnings calls, memory suppliers have discussed hyperscalers and AI infrastructure customers seeking longer-duration contracts of up to five years. There are some positives to these agreements, which is they smooth out the cyclicality that many investors fear given these agreements guarantee any inventory will be quickly absorbed, resulting in more stability.  

On the flip side, memory stocks are surging precisely because pricing is uncapped right now, therefore this introduces a new era for many memory stocks to where they transition to becoming secularly certain, yet the epic volatility in both directions is more muted.  

Notably, there are various deal structures for these agreements, yet in most instances, they lock-in demand for many years in exchange for some kind of cap in memory component pricing.  

The second risk is model and system efficiency through improvements such as Google’s TurboQuant. TurboQuant is a compression method that directly addresses the KV cache bottleneck. Google says that TurboQuant can reduce KV cache memory size by 6X while simultaneously preserving model accuracy and accelerating speed by up to 8X. 

However, the increased usage of HBM in Google’s own systems is a telling point that pushes back on TurboQuant fears. The company’s latest TPUs, the 8t and 8i, support 216 GB and 288 GB of HBM per chip, respectively. These figures are 13% higher and 50% higher than the 192 GB of HBM capacity offered by Ironwood v7. Thus, Google, which developed TurboQuant, is itself substantially increasing HBM capacity in spite of the efficiency gains. 

Conclusion:

Micron has been one of our largest positions in 2026, and for good reason. Over the past 10 months, the company added more than $1 trillion to its market capitalization, which now sits near $1.35 trillion. That move reflects not only Micron’s execution, but also the market’s growing recognition that memory demand is entering a much larger cycle. 

Investing in memory has been anything but easy. Many market participants feared the cycle was topping at the start of 2026, but the I/O Fund’s disciplined process kept us in the position at a high allocation, frequently above 10%, for year-to-date returns of 277%. 

This analysis is a small sample of what we do behind our paywall. We are not simply stating memory is an important bottleneck, but rather we show, with data, how the shortage is pressuring every layer of the memory stack, including HBM for accelerators, LPDDR5X and DDR5 for CPUs, and NAND SSDs for storage and agentic inference.  

As Q2 wraps up, the I/O Fund is preparing to identify the next wave of AI winners in our upcoming Top 15 AI Stocks for Q3 2026 report. Previous reports identified Micron as a major beneficiary, along with names such as Bloom Energy, up over 1800% since our April 2025 entry, and lesser-known AI networking stocks up over 600% since our November 2025 entry. 

We publish more than 100 paywalled articles each year on AI stocks, hold weekly 1-hour webinars, and offer an actively managed portfolio with real-time trade alerts. 

Don’t miss the next wave of the AI trade
Join I/O Fund

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in MU at the time of writing and may own stocks pictured in the charts.   

Leo Miller, AI and Semiconductor Investment Writer at I/O Fund, contributed to this analysis. 

👉🏻 Share with a Fellow Investor
Heare with a Fellow Investor
Help someone else benefit from this insight.

Recommended Reading:

• Nvidia, CoreWeave, and Nebius: Inside the Circular Financing of the GPU Boom
• AMD, Nvidia, Arm, Intel: Inside the $120 Billion CPU Gold Rush
• Google TPU v8 vs Nvidia: How Inference Is Rewriting the AI Market
• The AI Networking Stock That Beat Nvidia by 7X YTD for Returns of 135% YTD 

• Neoclouds are seeing massive hyperscaler demand as companies race to scale AI infrastructure, resulting in rapid revenue and backlog growth. 
• Leaders like CoreWeave and Nebius enable this through access to the latest Nvidia GPU’s while also optimizing compute utilization.  
• However, the bearish argument behind hyperscaler demand lies in their desire to offload their capex spending and shift costs to the operating expense line. 
• CoreWeave’s and Nebius’ growth is far from profitable, as they seek to capture AI demand with limited cash flow and soaring debt loads in an increasingly tough macro backdrop.  
• Circular financing, demonstrated by Nvidia’s investments and financial backstopping, is another key item to monitor closely 

Neoclouds are one of the more hotly debated AI business models, with CoreWeave and Nebius being the two most widely recognized names. These companies have seen their sales, backlog, and share prices soar, differentiating themselves through quick access to the latest GPU compute and GPU utilization advantages that allow hyperscalers to rapidly add efficient compute capacity. 

Notably, CoreWeave and Nebius have each secured 3.5 GWs of contracted power capacity; while these power footprints are key considering power is a hindrance to data center expansion, the vast majority of their contracted power capacity has yet to come online. CoreWeave is targeting 1.7 GW of active power by the end of 2026, while Nebius is targeting 800 MW to 1 GW of connected power. 

In turn, they are quickly working to convert their contracted power to active power, and thus convert large backlogs into revenue. Yet doing so is extremely expensive, and neoclouds do not have the same cash nor operating cash flow profiles of Big Tech. This is leading neoclouds to employ unique and circular financing structures, raising some red flags. 

In this analysis, I dive into the two public neoclouds that are riding Nvidia equity, hyperscaler contracts, and GPU-backed debt to fund the buildout, and what it means for the durability of the surge. 

Microsoft and Meta’s $120B+ Bet on Neoclouds 

The size of hyperscaler-neocloud partnerships compared to their current revenue is astounding. Microsoft has struck the most neocloud deals, with approximately $60 billion worth of commitments between CoreWeave, Nebius, and other private players such as Nscale. Meanwhile, Meta has committed $35.2 billion to CoreWeave in total after its recent $21 billion expansion, and an up to $27 billion deal with Nebius for a total commitment of up to $62.2 billion. Along with Meta, OpenAI is one of CoreWeave’s two largest customers, while CoreWeave also has a multi-year compute agreement with Anthropic.  

Alone, Microsoft and Meta’s total commitments extend up to $122.2 billion – for perspective, that is ~90% of the TTM revenue of AWS being allocated towards neoclouds over long-term capacity deals. When factoring in hyperscaler-backed deals from OpenAI and Anthropic (although exact deal value is unknown), total potential commitments surpass $145 billion.  

Keep in mind, CoreWeave’s FY2026 estimated revenue is $12.6B and Nebius FY26 revenue is expected to be $3.4B – therefore, these partnerships are leading to commitments that are an order of magnitude higher than current sales.  

The reason hyperscalers are willing to allocate this capital to a relatively new business model in the neoclouds is three-fold – quick access to leading GPU generations, optimized compute utilization, and the added benefit of not having to recognize capex on the balance sheet – we look at each of these drivers below. 

Neocloud Advantage is Offering Quick Access to GPUs 

At its root, neocloud demand is a product of hyperscalers' insatiable demand for compute capacity. However, neoclouds can often add compute capacity much faster than hyperscalers can through internal builds, offering a key value proposition for Big Tech. As hyperscalers spend hundreds of billions a year on AI compute, minimizing the lag between data center expenses and revenue generation is critical to maximizing their return on investment. 

Supporting the argument around neocloud’s advantage lying within time to deployment, commercial real estate giant JLL notes, “Neoclouds can deploy high-density GPU infrastructure within months compared to multi-year builds for hyperscale data centers, providing crucial time-to-market advantages for businesses needing rapid AI development.”

mid

In CoreWeave’s S-1 Registration filing, it lists “Faster access to the latest AI infrastructure advancements” as one of its key benefits to customers. Specifically, CoreWeave says “we were among the first to deliver NVIDIA H100, H200, and GH200 clusters into production at AI scale, and the first cloud provider to make NVIDIA GB200 NVL72-based instances generally available. We are able to deploy the newest chips in our infrastructure and provide the compute capacity to customers in as little as two weeks from receipt.”  

Nebius makes a similar statement in its Annual Report, noting its “consistent track record of being one of the first to deploy the latest generation of NVIDIA GPU chips.” 

CoreWeave and Nebius' relationship with Nvidia is key to acquiring the latest GPUs ahead of others. Nvidia recently invested $2 billion in both CoreWeave and Nebius. Under these partnerships, CoreWeave and Nebius will each look to deploy more than 5 GW of data center capacity by 2030. 

CoreWeave recently demonstrated its ability to offer quick access to the latest chips and newest architectures to hit the market once again, being the first to have a Vera Rubin system up and running at the start of June.  This provides evidence that partnering with CoreWeave and Nebius can help hyperscalers access as much of the latest GPU compute as possible in short order. 

Beyond Hardware: Neocloud Platforms Offering Higher GPU Utilization  

Aside from raw compute access, CoreWeave and other neoclouds layer on software and additional capabilities that improve GPU utilization – a key value add for hyperscalers.  

For example, CoreWeave Kubernetes Service (CKS) helps coordinate the allocation of workloads across thousands of GPUs, while its SUNK service helps optimize GPU utilization by allowing training and inference workloads to run on the same cluster. CoreWeave Tensorizer enables high-speed model loading, reducing GPU idle time. 

Combining these software and optimization capabilities with rapid fault detection and remediation services, CoreWeave believes it can offer higher GPU utilization rates than hyperscalers, based on the model FLOPs utilization (MFU) metric. The “MFU gap” is a metric that describes the gap between compute capacity and usage, which today often ranges between 30% to 40%. 

The MFU gap can become quite costly as it represents a more realistic way to measure the performance of GPUs — rather than only taking into account if a GPU is sitting idle or not. According to Trainy AI: “GPU Utilization is only measuring whether a kernel is executing at a given time. It has no indication of whether your kernel is using all cores available, or parallelizing the workload to the GPU’s maximum capability.”  

Chart comparing theoretical model FLOPS utilization (100%) with observed performance (35%–45%), illustrating a significant efficiency gap in AI workloads. Source: CoreWeave CoreWeave 

When going public, CoreWeave published its MFU rate at 35% to 45%, stating it is 20% higher than competitors, which means other AI data centers had MFU rates more in the 30% range. However, in a March 2025 blog post, CoreWeave noted that it was achieving an MFU of >50% on Hopper GPUs. This ability to stand up next-generation GPU hardware in short fashion combined with improved utilization rates is where the neoclouds’ advantage lies.  

Behind the Balance Sheet: Why Hyperscalers Are Leasing Neocloud Capacity 

By leasing compute capacity from neoclouds, hyperscalers shift their cost timeline from being a large upfront capex outflow to an operational expense outflow spread over long-term contracts. The need to spread costs is becoming increasingly evident due to the massive spending hyperscalers are engaged in.  

Although this is the “bear” case on why hyperscalers work with neoclouds—contrasting this with the rationale behind GPU access and utilization is key because one could argue that hyperscalers are quite capable of software optimizations and GPU utilization on their own (in fact, they are the longstanding incumbent here with deep expertise in cloud operations and workload optimizations). 

Take Meta for example. Analysts are currently expecting the company to generate $136 billion in cash from operations in 2026. With its stated capex guidance of $125 billion to $145 billion, the company could easily be free cash flow negative during the year. However, as noted, Meta also has up to $62.2 billion in neocloud agreements. If Meta built the equivalent value of capacity itself, the firm would recognize that spending as balance sheet capex, weighing further on its already pressured free cash flow.  

On the other hand, neocloud agreements add nothing to Meta’s capex, as the costs are recognized as operating expenses over the life of the contracts. Notably, Meta’s contracts with CoreWeave and Nebius extend through 2031-2032, meaning that opex payments could average less than $10 billion annually. 

Looking at Microsoft, we can see a similar situation. In calendar year 2026, the company is guiding for capex of $190 billion, while analyst forecast $200 billion in cash from operations over the same period. If these figures materialize, the company would consume 95% of its OCF on capex. The $60 billion in neocloud agreements, recognized as operating expenses over many years, expands its capacity while keeping that spend off its cash flow statement. 

As hyperscalers offload their capex, neoclouds are the ones taking that capex on—resulting in their massive funding needs.  

Circular Financing: Nvidia’s Role as an Investor, Supplier, and Demand Backstop 

Both Nebius and CoreWeave lend some of their advantage to Nvidia, as it is this partnership with the GPU leader that offers them that ability to be among the first providers to stand up and deploy next-gen platforms such as Blackwell Ultra and now Rubin.  

Having Nvidia as a partner also could play a role in helping CoreWeave and Nebius secure funding at much better terms, extending presence and support beyond the hyperscalers to another investment-grade firm with a strong balance sheet and cash flows. Nvidia’s LTM free cash flow was $119 billion, the second highest of any company in the world, only behind Apple. The downside, however, is that Nvidia’s relationship with the two is one of the most identifiable instances of circular financing.  

This stems from the multi-billion-dollar investments that Nvidia has made in CoreWeave and Nebius. Notably, Nvidia’s latest $2 billion investments in each company were not its first. Nvidia’s Q1 2025 13F filing revealed a CoreWeave stake worth $896.7 million at the time, while its Q4 2025 13F revealed a $33 million stake in Nebius. Thus, the investment relationship between Nvidia and these firms extends well beyond one year. 

Furthermore, in the case of CoreWeave, Nvidia has also provided a significant financial backstop against unsold GPU capacity. Under the agreement with an initial value of $6.3 billion, “in instances where [CoreWeave’s] datacenter capacity is not fully utilized by its own customers, NVIDIA is obligated to purchase the residual unsold capacity through April 13, 2032.” In other words, Nvidia is committed to purchasing unsold GPU capacity if CoreWeave is unable to find another buyer. With an initial value of $6.3 billion, there is the potential that the arrangement could become larger over time. 

As Nvidia makes these investments, CoreWeave and Nebius are going right back to Nvidia to purchase large volumes of GPUs – a clear representation of circular financing. By providing a relatively small amount of equity funding, Nvidia secures relationships with these neoclouds that intend to purchase tens of billions' worth of GPUs.  

Nvidia could see long-term benefits by supporting CoreWeave and Nebius through their ramp-up phases where cash flow is deeply negative. If the firms can eventually become self-sustainable, Nvidia would have two large-scale customers that it can continue selling its latest systems to for years to come. However, for the neoclouds, the concern is whether they have to continually raise cash into the foreseeable future to build new infrastructure and when that would level out, as revenue lags capex 2:1. 

How Neoclouds Are Funding AI Expansion: Debt, Equity, and Circular Financing 

Both CoreWeave and Nebius are eyeing rapid ramps in active power – CoreWeave currently has 1GW of its 3.5GW contracted power pipeline active, but it aims to convert the majority of that over to active capacity by the end of 2027, while Nebius similarly has 3.5GW of contracted power and a goal of reaching up to 1GW of connected (active or can be activated upon GPU installation) by the end of 2026.  

However, as with all AI buildouts right now, the keywords are “active power” as energy constraints are intensifying across the board.  

CoreWeave’s Balance Sheet Challenged, Debt Quickly Rising 

CoreWeave’s balance sheet is in a difficult position, as the company looks to rapidly expand its active power footprint at a rate that is not supported by its cash balance and its operating cash flow. 

Revenue of $2.08 billion rose by 112% YoY in its latest quarter. However, operating cash flows (OCF) came in at $2.98 billion, compared to capex of $7.7 billion, leading to free cash flow of -$4.71 billion. This mismatch led to the firm’s cash balance falling by $890 million, or 28.3% QoQ to $2.27 billion. Meanwhile, debt increased by nearly $3.5 billion, or 16.1% QoQ to $24.86 billion – this is set to rise further in Q2 as CoreWeave just announced a $3.5 billion senior note raise on June 11. 

Chart showing CoreWeave’s quarterly capex rising sharply to approximately $7.7 billion, while revenue reached around $2.07 billion over the same period. Source: YChartsYCharts

For the full-year, CoreWeave expects to spend $31 billion to $35 billion on capex, or $33 billion at the midpoint. This implies capex spending for the remainder of the year of $25.3 billion. Analysts currently estimate that the company will generate $8.68 billion in operating cash flow in 2026, or just $5.7 billion for the rest of the year. Given CoreWeave’s $2.27 billion cash balance, this creates a huge funding gap of $17.33 billion. In practice, CoreWeave is likely to raise more than this to avoid further decreasing its already somewhat thin cash cushion. 

CoreWeave has used equity issuance in the past as a funding source, but debt issuance far outweighs this. Looking at its first five earnings reports since going public, its total equity issuance is only $3.5 billion, while debt issuance was more than 5X higher at $18.81 billion. Thus, a further increase in debt is likely to be the primary way that CoreWeave continues to fund its capex plans while already having a net cash position of -$22.6 billion. Looking into its unique funding structures shows that debt will continue to be a key lever that the firm pulls. 

Nebius: Stronger Balance Sheet but Ongoing Funding Needs 

Nebius is comparatively in a much better position, with $9.37 billion in cash to $8.45 billion in debt, for a net cash balance of $920 million. Revenue rose 684% YoY to $339 million in its latest quarter, while operating cash flow was $2.26 billion, rising by 170.7% QoQ due to significant customer prepayments. Capex came in at $2.47 billion, resulting in FCF of -$214.9 million.  

However, Nebius is also looking to rapidly expand its active power footprint, with the firm’s midpoint capex guidance for the full year at $22.5 billion. This implies $20 billion in spending over the remainder of the year. Including the company’s cash and contractual commitments of approximately $6.9 billion, Nebius currently needs to draw $6.3 billion in additional funding to support the midpoint of its capex forecast. 

Like CoreWeave, Nebius has also leaned heavily on debt rather than equity issuance to fund itself, although to a lesser extent. Since Q4 2024, Nebius’ total equity issuance was approximately $3.92 billion when including the $2 billion in pre-funded warrants Nvidia recently purchased. Over the same period, its debt issuance was $8.32 billion. In its latest earnings call, Nebius noted asset backed financing, corporate debt, and equity issuance as options for raising capital.  

Notably, Nebius’ undeployed 25 million share at-the-market equity program could go a long way toward bridging its 2026 funding gap. At a $200 share price (around 10% below the stock’s current level), fully utilizing this program would generate gross proceeds of $5 billion while diluting shareholders by approximately 8%. However, given past trends, asset backed and corporate debt are likely to be the primary path forward. 

Overall, this breakdown of CoreWeave and Nebius’ funding requirements for 2026 is just one stage of a much larger push to convert its contracted power into active power. After all this spending, CoreWeave aims to have just under 50% (1.7 GW) of its contracted power active. Meanwhile, Nebius hitting the upper bound of its connected power target would account for less than 30% of its contracted power, which includes power that is either active or can be activated once GPUs are installed.  

In turn, the companies will continue to need to find more and more funding to scale until CFO converges with capex. With the spread between these figures still very wide, the likely result is further increases in debt loads and/or shareholder dilution over several years. 

GPU-Backed Debt: Inside CoreWeave’s Funding Engine for AI Infrastructure 

CoreWeave relies heavily on GPU-backed delayed draw term loans (DDTLs), having closed six separate facilities. Under DDTLs, CoreWeave draws down funds intermittently as it uses them to pay for different stages of data center buildouts.  

Notably, the company’s $8.5 billion DDTL 4.0, closed in March, was the first of its kind to receive an investment-grade credit rating. As of Q1 2026, CoreWeave had only drawn $1.26 billion worth of DDTL 4.0. This is the only portion of the $8.5 billion that currently shows up in CoreWeave’s total debt. Thus, as the firm draws down more of DDTL 4.0 over time, its debt will also increase.

Table showing CoreWeave’s debt structure with total debt of approximately $25.1 billion, including multiple delayed draw term loan (DDTL) facilities and senior notes. Notably, the DDTL 4.0 facility totals $8.5 billion, but only $1.26 billion has been drawn, indicating significant future debt expansion as capital is deployed. Source: CoreWeaveCoreWeave 

CoreWeave notes that the investment-grade rating is “supported by a long-term customer contract with an investment-grade AI enterprise," which is presumably tied to Meta’s latest contract. Essentially, the contract that CoreWeave has signed with the investment-grade customer, as well as the value of the GPUs it buys, are collateral for the debt. This is why the facility can achieve an investment-grade credit rating despite CoreWeave itself having a poor balance sheet, allowing for much more favorable interest rates that CoreWeave could not otherwise receive. 

Still, CoreWeave's ability to receive better interest rates than peers relies on backing from investment grade customer contracts. Notably, DDTL 5.0, closed in May (and is thus not included in the table above), was backed by two non-investment-grade customer contracts. This resulted in the facility not receiving an investment grade rating and thus having a higher interest rate. 

Interest Rate Pressure: A Growing Risk to Profitability 

Increases in general rates apply further upward pressure on the rates that CoreWeave and other neoclouds can receive in future funding rounds. The fixed rate tranche of DDTL 4.0 is tied to U.S. Treasuries with an average weighted maturity of 3.14 years, plus a 2% premium. This portion of the yield curve has seen rates rise significantly since the beginning of the year from less than 3.6% to nearly 4.2%.

Chart showing the 3-year U.S. Treasury rate rising from below 3.6% in early 2026 to approximately 4.16% by June, reflecting a sharp increase in short- to mid-term interest rates. Source: YCharts.YCharts.

Notably, CoreWeave’s interest payments are already elevated, coming in at $536 million in Q1. This equates to 25.8% of its $2.08 billion in revenue, and 46.3% of its $1.157 billion in adjusted EBITDA. The company is guiding for midpoint revenue of $2.525 billion next quarter, and midpoint interest expense of $690 million—which would push its interest to revenue ratio up to 27.3%. With this, interest expense is expected to become an even more relevant line item while already putting significant pressure on profitability. 

The Neocloud Race: Balancing Surging AI Demand With Rising Debt and Circular Risk 

Overall, neoclouds clearly have significant growth momentum, with revenues and backlogs spiking, while attracting interest from investment-grade hyperscalers such as Microsoft and Meta, and AI labs including OpenAI and Anthropic. Access to leading Nvidia systems, and GPU utilization advantages make neoclouds an option for hyperscalers looking to quickly scale AI compute capacity. 

At the same time, the mismatch between operating cash flow and capex is causing debt levels to rise rapidly, which is a dynamic that is unlikely to change in the near term. Elevated interest rates remain an external risk, while circular financing raises questions around the degree to which neocloud growth depends on Nvidia’s capital support, and the extent to which Nvidia’s GPU demand is increasingly tied to the neocloud model. 

As Q2 wraps up, I/O Fund is preparing to identify the next wave of AI winners in our upcoming Top 15 AI Stocks for Q3 2026 report, with coverage across AI networking, memory, energy, custom silicon, and the infrastructure bottlenecks driving the next leg of the trade. 

Premium Members will also receive upcoming thematic reports on the latest shifts in AI networking and a new catalyst we believe could become one of the more important opportunities in the second half of the year. 

We publish more than 100 paywalled articles each year on AI stocks, supported by an actively managed portfolio and real-time trade alerts.

Don’t miss out on the AI trade 👉 Join I/O Fund Premium today.oin I/O Fund Premium today.

Please note: The I/O Fund conducts research and draws conclusions for the company’s portfolio. We then share that information with our readers and offer real-time trade notifications. This is not a guarantee of a stock’s performance and it is not financial advice. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis. Beth Kindig and the I/O Fund own shares in NVDA at the time of writing and may own stocks pictured in the charts.  

Leo Miller, AI and Semiconductor Investment Writer at I/O Fund, contributed to this analysis. Leo Miller owns shares of NVDA and META.

👉🏻 Share with a Fellow Investor
Heare with a Fellow Investor
Help someone else benefit from this insight.

Recommended Reading:

• AMD, Nvidia, Arm, Intel: Inside the $120 Billion CPU Gold Rush
• Google TPU v8 vs Nvidia: How Inference Is Rewriting the AI Market
• The AI Networking Stock That Beat Nvidia by 7X YTD for Returns of 135% YTD
• Bloom Energy — Our 2026 Top Pick Was the Best Performing Stock in April