Intel is set to raise $19.7 billion by selling new common stock in a bid to finance the building out of new production capacity, the development of next-generation leading-edge process technologies like 14A and others, and day-to-day operations. While the company does not assign money to a particular project, Intel needs to build capacity to land orders from large external clients, so capacity expansion will likely be a priority. According to Bloomberg, the share sale attracted $100 billion in demand.

Intel will sell 210,526,315 shares for $95 apiece through an underwritten public offering. In addition, participating banks have 30 days to acquire as many as 31,578,947 more shares at the same $95 price, minus applicable underwriting discounts. Should they exercise all their options, Intel could sell approximately 242.1 million shares altogether and increase the proceeds to roughly $23 billion. Without the additional shares, Intel expects net proceeds of approximately $19.7 billion after underwriting discounts, commissions, and estimated expenses. The transaction is scheduled to close on August 12, 2026.

Intel's market capitalization increased from roughly $90 billion last August to $491 billion at press time, so the time is right to sell some shares and raise some much-needed cash, as the company must compete against giants like TSMC and Samsung, which spend tens of billions of dollars every year on new fabs and advanced process technologies. Meanwhile, Intel's capitalization reached its all-time high of $673 billion on June 20, 2026.

Intel has not assigned the money it is going to raise to particular projects and says the capital can be used across the business, including for capital expenditures and working capital. The company is currently ramping up its Fab 52 in Arizona and is on track to start using adjacent Fab 62 when it needs to. In addition, the company still has to build its fab complex in Ohio, which is expected to cost over $100 billion when fully built, so it badly needs money.

In its risk disclosures, the company specifically mentioned Intel 14A — which is due to enter mass production in 2028 — and other advanced process technologies, manufacturing expansion required to support them, and the need to secure design wins and volume commitments from major external foundry customers. While Intel does caution that these long-term investments amounting to tens of billions may not generate adequate returns, it is impossible to land sizeable contracts from external customers without having production capacity readily available.

Interestingly, Intel also mentioned alternative financing arrangements, government grants, and the U.S. government's significant equity position in the company among relevant factors.

House Select Committee on China Chairman John Moolenaar has written a letter that demands the U.S. government enforce an existing export control measure designed to prevent Chinese companies from getting advanced chips produced by contract chipmakers like TSMC or Samsung Foundry.

When Joe Biden was about to leave the office in early 2025, he signed a law that required chipmakers to determine their end customers in a bid to prevent contract manufacturers from unknowingly fabricating chips using American technologies for companies that served as intermediaries for restricted organizations tied to the Communist Party or the People Liberation Army. The regulation was introduced after it was discovered that chips made by TSMC for China-based Sophgo were actually Huawei's Ascend 910B AI accelerators. However, the Trump administration announced in May 2025 that it would not enforce this semiconductor regulation known as the 'Foundry Due Diligence Rule,' which naturally created uncertainty about whether the foundry-focused requirements would be actively implemented at all.

"The announcement created ambiguity as to whether front-end fabricators like TSMC can export unpackaged advanced dies to non-approved designers located outside of China, without performing the due diligence specified in the Foundry Due Diligence Interim Final Rule (IFR)," the letter reads. "These exports enabled Huawei to obtain millions of controlled Ascend logic dies from TSMC, using its front company, Sophgo in 2023 and 2024."

Before the adoption of the 'Foundry Due Diligence Rule,' Chinese companies and/or their intermediaries could misrepresent chip specifications and end users when placing orders with TSMC or other foundries, which allowed restricted devices to be made despite U.S. export controls. Under the rule, foundries and OSAT providers exporting chips produced on 14/16nm-class process technologies or more advanced nodes must presume those devices qualify as controlled AI processors subject to a global licensing requirement, unless they meet an exemption. According to the letter, foundries have generally complied with these requirements since the rule took effect. As a result, Moolenaar essentially asks the government to enforce the existing controls rather than introduce stricter export controls.

Moolenaar believes that the Commerce Department's Bureau of Industry and Security could clarify its current stance on the Foundry Due Diligence Rule in two ways:

• By issuing guidance, which confirms that the worldwide Regional Stability (RS) licensing requirement still applies to exports from front-end foundries;
• By formally annulling the AI Diffusion IFR and amending §744.23 to explicitly restore that requirement for both foundries and OSAT providers.

Either approach would eliminate conflicting interpretations and reinforce enforcement of existing export controls, Moolenaar believes.

Nvidia late on Monday announced that it had signed memorandums of understanding with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to establish independent financing platforms that could mobilize more than $500 billion in third-party capital to invest in AI infrastructure. Nvidia's goal is to ensure that its clients building AI data centers (which Nvidia calls AI factories) can get enough money from powerful financial companies. As a result, Nvidia will reinforce its position on the AI hardware market as the funds will exclusively finance Nvidia-based AI data centers.

The proposed funds (or platforms, as Nvidia calls them) are intended to provide dedicated pools of capital for customers — such as AI labs, cloud service providers, or enterprises — that deploy Nvidia-based infrastructure. Rather than financing projects itself, Nvidia intends to work with six investment firms to enable access to long-term funding at attractive rates. The company believes that AI infrastructure should not be viewed as conventional IT equipment, but as tools that make sustained economic returns, which is why it must be financed appropriately.

"We are in a pivotal moment of a historic AI investment cycle," said David Solomon, Chairman and CEO of Goldman Sachs. "Nvidia's full-stack platform is in high demand and uniquely positioned at the center of that global buildout. Our investment and distribution roles reflect our confidence in Nvidia's leadership, and we are excited for the new opportunity to create a market for credit backed by NVIDIA compute."

The financial companies believe that AI data centers can be treated as long-duration infrastructure assets rather than conventional IT equipment, in part because Nvidia compute can generate revenue over an extended period and retain value across different workloads and operators. As a result, they appear to believe that AI infrastructure can support long-term financing at attractive rates, although the companies do not explicitly claim that financing AI data centers carries lower credit risk than financing conventional IT deployments. Furthermore, it should be noted that Nvidia and financial companies will inevitably finance companies that would otherwise struggle to obtain capital to finance their AI data centers. This will ultimately help Nvidia sell more hardware and software while allowing its financial partners to capitalize on the rapid expansion of Nvidia's AI ecosystem.

Without any doubt, the arrangement will help to rapidly build AI infrastructure, which will increase adoption of AI technologies. However, this arrangement increases the risk of an AI infrastructure bubble as it potentially weakens one of the natural brakes on overbuilding: the availability and price of capital. Furthermore, Nvidia's help with arranging financing for its own customers introduces an element of circular financing into the AI boom, something that the industry faced during the dot-com bubble era in the late 1990s – early 2000s. However, this does not necessarily prove there is a bubble, as there is genuine, enormous demand for AI hardware and Nvidia sells plenty of such hardware.

Perhaps the biggest concern about the arrangement is that while Nvidia and its partners state that AI infrastructure can provide long-term value, AI accelerators, such as Nvidia's GPUs, have short and uncertain economic lives as the company and its industry peers introduce new and better-performing AI hardware every year, which devalues the previous generation.

"Nvidia has reached an important milestone: we began by building chips; today, we are helping create a new class of productive, investable infrastructure: AI factories," said Jensen Huang, founder and CEO of Nvidia. "In AI, compute is revenue. Nvidia compute is uniquely suited for this role. It is broadly adopted, flexible across models and workloads, fungible and transferable across customers and operators, and continuously improved through CUDA software — extending its useful life and improving its economics over time. It is supported by a deep global ecosystem of developers, customers, and offtakers. That is why we are bringing the world's leading long-term capital providers together to independently underwrite AI infrastructure. These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country in the age of AI."

Apple used to be among a few companies willing to buy memory and other components worth billions of dollars under long-term supply contracts at fixed prices. But the artificial intelligence era represents a new reality with new purchasing champions, marking a tectonic shift in the high-tech world. Alphabet, Microsoft, Meta, and Amazon have purchase commitments totaling about $2 trillion, and a significant portion of these commitments are for memory, according to estimates by analyst Claus Aasholm. While the commitments are approximate, span many years, and should be generally taken with a grain of salt, they still reflect the direction the industry is moving.

Combined purchasing commitments from the four major hyperscalers shown in the chart —Amazon, Alphabet, Meta, and Microsoft — reached nearly $2 trillion by Q2 2026, with Alphabet and Microsoft accounting for the overwhelming majority of the total.

The rapid expansion suggests several major findings. Firstly, the AI infrastructure race is accelerating, not stabilizing. Secondly, AI infrastructure investments are driven by a handful of hyperscale cloud service providers (CSPs) whose long-term procurement commitments now vastly exceed those of traditional consumer electronics companies such as Apple.

Thirdly, memory has become a strategic asset — perhaps a competition weapon — rather than a commodity. Fourthly, suppliers of memory — both 3D NAND and DRAM — are gaining pricing power. Finally, demand for memory will likely drive major capacity expansion at Micron, Samsung, and SK hynix, even though so far these companies have been exceptionally disciplined about their capacity investments.

Almost $2 trillion commitments

Google shows by far the most aggressive increase in purchasing commitments, rising from roughly $140 – $150 billion in Q3 2025 to around $811 billion by Q2 2026 (though these are total purchase commitments by Alphabet, not specifically memory purchase commitments), while Microsoft follows a similar trajectory and reaches approximately $678 billion in total obligations, which includes, but is not limited to memory.

Meta is also ramping commitments substantially to around $349.3 billion (again, these are total commitments), whereas Amazon increased its commitments more gradually to roughly $130 billion. By contrast, Apple — which makes the world's most popular smartphone, and which was the largest consumer of memory just a couple of years ago — remains almost flat throughout the period at approximately $57 billion (of which $56.2 billion is payable within 12 months). Apple's commitments fall well short of Nvidia's commitments of $119 billion.

Again, we are talking about total purchase commitments, which include foundry capacity, 3D NAND, and DRAM memory, but are not limited to them. Alphabet, Amazon, Meta, and Microsoft all build custom silicon and custom servers, so a significant portion of these commitments is to various EMS providers.

While $1.968 trillion of purchase commitments for memory and storage alone would be an absurdly large amount of money, a huge portion of these commitments consists of contract manufacturing obligations as well as memory chips. This suggests that the foundry, 3D NAND, and DRAM markets are entering a new phase in which hyperscalers are willing to make vastly larger forward purchasing commitments than traditional consumer-electronics companies, giving suppliers a strong incentive to prioritize customers prepared to secure future capacity on that scale.

Strategic assets

While Claus Aasholm's chart is explicitly dedicated to memory, it does describe total purchase commitments of tech giants, so the chart can reasonably be read as evidence that memory and capacity at TSMC, Samsung Foundry, and GlobalFoundries are becoming a strategic asset rather than merely another component to procure at the best available price.

AI infrastructure requires enormous quantities of AI accelerators, DRAM (including HBM), and 3D NAND. Meanwhile, the supply of high-end memory (HBM) is constrained by fab capacity at major DRAM makers, whereas the supply of AI accelerators is constrained by both wafer capacity and foundries and packaging capacity at foundries and their OSAT partners. As a result, hyperscaler CSPs have an incentive to lock in supply years ahead, even if doing so requires exceptionally large purchasing commitments.

That also changes the relationship between semiconductor suppliers and their customers. In theory, a company willing to guarantee hundreds of billions of dollars of future purchases can effectively help underwrite expansions of foundry, memory, and advanced packaging capacity and, in return, secure priority access to scarce products and future process technologies. In reality, TSMC can well afford capacity expansion using the money it gets from hyperscalers and give priority to its largest customers. In this environment, access to DDR5, HBM, and 3D NAND memory becomes part of the competitive advantage rather than merely a procurement exercise.

This is also what makes Apple's position in the graph interesting: its purchasing commitments barely move while those of Alphabet, Amazon, Meta, and Microsoft surge. If the trend continues, Apple may remain one of the world's largest semiconductor buyers in absolute terms, but the question is whether it will be among the key customers that foundries, memory makers, and OSATs plan their future capacity expansions.

An inflection point

Perhaps the most interesting takeaway of the findings revealed by long-term purchase commitments is that the industry's center of gravity appears to have shifted.

During the smartphone era, foundries (well, TSMC has won) and memory suppliers often competed aggressively for Apple's business because of its enormous purchasing power. Today, hyperscalers building AI infrastructure are making purchasing commitments that dwarf those of traditional CE companies like Apple, which may well represent a strategic inflection point akin to the one Andy Grove described in his 'Only the Paranoid Survive' book.

Will this tectonic shift result in prioritization of customers capable of enabling future capacity expansions through massive long-term purchase agreements, or will foundries and memory makers remain more or less disciplined with their capacity expansions so as not to lose a lot when demand declines? This is a question that has yet to be asked.

In any case, the AI megatrend has transformed semiconductors — from foundries to advanced packaging and from DDR5 to HBM4 — into strategic assets that can no longer be treated as ordinary components procured on demand. And this is something that will continue in the long run.