Techstrong IT

IBM Aims to Simplify Carbon Accounting with Emissions API

James Maguire — Fri, 01 May 2026 17:20:24 +0000

As corporate climate disclosures grow more complex, IBM is touting a software interface as a tool to streamline an often fragmented process. The company’s Envizi Emissions API is designed to integrate greenhouse gas calculations directly into existing enterprise systems, rather than relying on standalone sustainability platforms.

The API, now in general availability, features a large repository of emissions factors, including more than 140,000 datasets spanning regions and industries. These datasets underpin calculations across Scope 1, 2 and 3 emissions categories, which collectively capture direct emissions, purchased energy, and value chain impacts.

The company is also catering to software developers, who face challenges in building emissions tracking capabilities. Developing an in-house calculation engine requires access to reliable data and the ability to scale across various use cases. For many vendors, that process can take months or longer.

By offering a pre-built API, IBM is promoting Envizi as a foundational layer for emissions-focused applications. Developers can integrate calculation capabilities into supply chain tools or risk management systems without maintaining their own emissions datasets.

The Envizi platform itself has evolved steadily since IBM acquired the company in 2022. It now includes tools for supply chain analysis and regulatory reporting, in keeping with frameworks like the Greenhouse Gas Protocol. The emissions API extends its functionality into external applications and workflows.

New Excel Extension

The features of the Envizi Emissions API point to a trend in enterprise carbon accounting. Instead of treating emissions reporting as a periodic, compliance-driven exercise, companies are increasingly seeking continuous, embedded insight that can inform operational decisions.

The challenge is that carbon accounting remains a struggle for many enterprises, with teams mired in spreadsheets, pulling together disparate data sources and sometimes using manual calculations.

To address this, IBM has this week introduced an Excel-based extension to Envizi, aimed at companies that still rely on spreadsheets for emissions calculations. The new offering embeds standardized, protocol-aligned calculation logic and emissions factors directly into Excel, allowing teams to produce more consistent results without abandoning familiar workflows.

The move reflects a pragmatic acknowledgment that many companies, particularly those early to carbon accounting reporting or managing complex Scope 3 data, continue to depend on spreadsheets despite their limitations.

Additionally, IBM has included pre-configured templates and modular components intended to shorten onboarding time. Again, this allows organizations to begin standardizing emissions calculations without extensive system changes, an important factor for companies still early in their carbon accounting process.

Navigating Regulations

Regulators are tightening disclosure requirements, particularly in the EU, and in some cases investors are demanding greater transparency regarding corporate emissions. Also, enterprise managers from procurement teams to finance departments are beginning to factor carbon impacts into decision-making.

In that environment, emissions data is shifting from a backward-looking report to a forward-looking input. Embedding calculations into everyday systems could allow companies to evaluate the environmental impact of decisions in real time, rather than after the fact.

In any case, over time carbon accounting has gained a higher profile in the enterprise, and tools that reduce complexity while improving consistency are likely to play an ever-greater role.

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TSMC to Invest $56B in Fabs This Year, and It’s Still Not Enough

Andy Patrizio — Fri, 01 May 2026 15:54:44 +0000

Taiwan Semiconductor Manufacturing Company (TSMC) said it plans to spend between $52 and $56 billion in capital expenditures this year, but it expects the parts shortage to remain through 2027.

The comments were made by Jen-Chau Huang, senior VP & CFO, on a conference call with financial analysts to discuss TSMC’s first quarter earnings. It was a great quarter, too, with a 58% jump in net profit and 41% revenue growth. The company raised its 2026 revenue outlook to over 30% growth.

There has been considerable shifting in TSMC’s customer base. Back in 4Q 2019, HPC was only 29% of TSMC’s revenue, while smartphones accounted for 53% of revenue. Today, things have flipped, with HPC now accounting for 61% of TSMC’s revenue and smartphones accounting for only 26%.

In TSMC’s case, CapEx is an indicator of locked-in customer prepayments and supply agreements. In other words, TSMC’s customers are prepaying for capacity to build products that they will need in the future. By raising its CapEx guidance, TSMC is signaling that customers like Nvidia and AMD are already competing for capacity in 2027-28.

TSMC is making a massive investment in the United States. Its Arizona fab will run $165 billion total and take up more than 1,100 acres. TSMC Arizona will bring the company’s most advanced process nodes to the US, but putting an advanced manufacturing facility that requires vast amounts of water in the desert seems dubious.

And despite all of this spending, TSMC says that it can’t keep up with the enormous demand AI is placing on the whole sector. For some time, there have been considerable shortages of everything from main components such as GPUs, CPUs, and memory, to the electrical equipment used to build the facilities, such as voltage regulators, ICs, cabling, and even concrete.

TSMC Chairman and CEO C.C. Wei told the call it takes two to three years to build a fab, “no shortcuts,” and another year or two to ramp it up. So even though construction is moving forward at the Arizona plant, it’s still a long process of construction.

A new Taiwan plant is expected to be operational by the first half of 2027, while the TSMC Arizona plant will be ready by the second half of 2027. Also, a new plant in Japan is set to begin manufacturing by 2028.

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5 IT Funding Deals to Watch

Mike Vizard — Fri, 01 May 2026 14:51:44 +0000

Capital flow into enterprise IT infrastructure remained pointed and selective this week, with investors concentrating on AI-native platforms that promise to reshape data movement, web automation, observability, and core foundation models. The five rounds below stretched from a ten-figure seed for a UK superintelligence lab to a focused Series A in observability, and together they offer a useful read on where money is going as AI workloads continue to reshape the IT stack.

Ineffable Intelligence

Round: Seed | Sector: AI Infrastructure

The London-based superintelligence lab emerged from stealth with a $1.1 billion seed round at a $5.1 billion valuation, backed by Sequoia, Nvidia, and Google. The size and syndicate tell their own story: investors are willing to write near-Series-C checks at the seed stage when the team and thesis line up with the current foundation-model arms race. For enterprise IT leaders, the deal underscores how much capital is being pre-positioned to support the compute, networking, and platform layers that any future frontier model will require.

Hightouch

Round: Series D | Sector: Data Platform

Hightouch closed a $150 million Series D at a $2.75 billion valuation, led by Goldman Sachs Alternatives with participation from Bain Capital Ventures. The company has built its position on activating warehouse data directly into operational systems, and the new round funds a deeper push into AI-driven decisioning on top of that pipeline. The valuation reflects a broader pattern in the data layer, where vendors that sit between the warehouse and downstream applications are commanding premium multiples.

Parallel Web Systems

Round: Series B | Sector: AI Agent Infrastructure

Sequoia led a $100 million Series B in Parallel Web Systems at a $2 billion valuation, just five months after its last raise. The company is building infrastructure that lets AI agents read and act on the open web, an area that has shifted from research curiosity to a real enterprise integration problem. The pace of the markup signals that web-scale agent tooling is being treated as core IT plumbing, not as a niche developer experiment.

Orkes

Round: Series B | Sector: Workflow Orchestration

Orkes raised $60 million in a Series B led by Advanced Venture Partners to expand its agentic workflow orchestration platform, which builds on the open-source Conductor project. The funding lands as enterprises move beyond AI prototypes and start grappling with the operational reality of deploying agents in production. Workflow orchestration is emerging as a control plane for that work, and Orkes is one of the few vendors with the runtime maturity to support it at scale.

OpenObserve

Round: Series A | Sector: Observability / AIOps

OpenObserve closed a $10 million Series A from Nexus Venture Partners and Dell Technologies Capital to grow its open-source observability platform. The pitch is familiar but pointed: a unified store for logs, metrics, and traces designed to cut the cost and complexity that have come to define incumbent stacks. With Dell Technologies Capital on the cap table, the company also gains a credible channel into large IT environments where observability sprawl is a budget line of its own.

The Weekly Funding Pulse tracks the most significant IT infrastructure, cloud, semiconductor, networking, and ITSM funding rounds each week. Coverage is curated for enterprise IT professionals and decision-makers.

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HPE Expands Ruggedized Server Portfolio for Running Complex Apps at Network Edge

Mike Vizard — Thu, 30 Apr 2026 18:50:18 +0000

Hewlett-Packard Enterprise (HPE) today extended its server portfolio to address a broader range of use cases involving both edge computing applications and disconnected environments.

Scheduled to be made available later this year, a ruggedized HPE ProLiant Compute EL2000 chassis is now providing the foundation for two additional Gen12 servers that are based on Intel Xeon 6 processors. It is designed to deploy up to two HPE ProLiant Compute EL220 Gen12 servers or one EL240 Gen12 server that can scale from eight to 144 cores. Additionally, IT teams can configure the EL240 Gen12 server with NVIDIA RTX PRO 4500 or NVIDIA RTX PRO 6000 Blackwell Server Edition graphical processor units (GPUs).

An enhanced 2U HPE ProLiant DL145 Gen11 server, meanwhile, is based on AMD EPYC 8005 series processors, can be configured with up to 84 cores and will be available beginning in May. There is also a version of the HPE ProLiant DL145 Gen11 server that has been validated for AI inferencing applications at the network edge using the NVIDIA RTX PRO 4500 Blackwell Server Edition GPU and another version that has been certified to run Azure Local, an on-premises edition of the Microsoft cloud computing environment.

Finally, each platform is now available with an Environmental Ruggedization Option Kit ideal for harsh locations, including high- or low-altitudes, extreme temperatures, and hazardous transit.

Krista Satterthwaite, senior vice president and general manager for compute at HPE, said that as IT continues to evolve, especially in the age of artificial intelligence (AI), it’s clear more data than ever will be processed at the network edge. There is simply too much data being generated that needs to be analyzed in near real-time, which, because of latency concerns, will preclude sending that data to the cloud to be processed, she added.

In fact, the Futurum Group projects hybrid and edge architectures will account for 43% of the AI platform market by 2030.

There is, of course, a certain amount of irony in that shift. For the past decade, many IT teams have been focused on moving as much data as possible into the cloud. However, in the AI era many of them are finding there is once again a pressing need to bring the compute needed to wherever data is being generated and stored. Given the fact that more data than ever will be generated and analyzed at the network edge, the need to deploy AI inference engines as close as possible to the point where AI data is being consumed is driving more IT teams toward an IT architecture that is based on hybrid cloud computing.

Regardless of which type of application workload is deployed where, the one thing that is clear is that IT environments need to be more fluid. An application workload that has been deployed in one environment may need, for any number of reasons, to be moved to another platform. The challenge, as always, is to make sure that the right IT infrastructure is always available to optimally run a workload no matter where and how it may have been originally deployed.

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AI Superchip Maker Cerebras files for IPO

Andy Patrizio — Thu, 30 Apr 2026 15:03:54 +0000

There are literally dozens of startups all gunning for a piece of the AI semiconductor pie that Nvidia has had almost entirely to itself. The vast majority of these companies will never get a product out or will be acquired by a bigger player, perhaps even NVIDIA.

But if there is one company positioned to give Nvidia a run for its money, it is Cerebras Systems. The company’s Wafer Scale Engine chip is the size of a pizza box and has 850,000 cores. By contrast, an NVIDIA GPU has about 5,000 cores and is about two inches square.

The cores are called Sparse Linear Algebra Cores, or SLA. The cores are designed specifically for AI work and are optimized for the type of math that is essential to neural network calculation. These cores are designed to do nothing but SLA math. That makes them a one-trick pony but it’s a good trick.

Cerebras’s claim to fame is parallel processing, with 850,000 cores all on one chip about 10 inches square and all connected by a high-speed interconnect. AI training is highly dependent on parallelism, putting it in Cerebras’s wheelhouse.
Sure, you can build an NVIDIA cluster with 850,000 cores, but you’d need a data center the size of a sports arena and a nuclear power plant to run it. NVIDIA servers contain eight GPUs max. After that, if the training application needs more cores, it has to go out to another physical machine over networking and that slows things down. Networking connections are fast, but they can’t match the interconnect between CPU cores, thus making them bottlenecks.

The result is tremendous training speed. In 2024, Cerebras announced a benchmark in molecular dynamics simulations. Data from third-party benchmark firm Artificial Analysis showed a single Cerebras CS-2 system with one WSE-2 chip ran 748x faster than Frontier, the world’s fastest supercomputer at the time.

Mind you, that benchmark ran on a single CS-2 chip in a single rack using 27 kilowatts of power. Frontier has 37,000 GPUs and CPUs in rows of cabinets and consumes 21 megawatts of power. It’s not even close.

Now Cerebras is going public, claiming a $23 billion valuation backed by a $20 billion Master Relationship Agreement with OpenAI for 750 MW of inference compute capacity. And unlike a lot of AI startups, Cerebras has revenues to show for its work. For 2025, it saw 76% year-over-year revenue growth to $510 million in 2025, but it also had a non-GAAP net loss of $75.7 million.

However, the devil is in the details. The vast majority of Cerebras’s revenue comes from four sources, starting with two related entities in the United Arab Emirates: the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), which accounted for 62.0% of total revenue in 2025, and Group 42 (G42), which accounted for 24.0% of 2025 revenue.

This makes Cerebras extremely vulnerable to the instabilities of the Middle East. If the UAE gets drawn into the war with Iran, that might impact potential business, even though Cerebras’s involvement has nothing to do with energy production.

The other source of revenue are from OpenAI, which signed a multi-year, $20 billion agreement with Cerebras, and Amazon Web Services, which did not disclose its revenue. It goes without saying that no vendor wants to be beholden to just four customers, especially when two of them are in one of the most unstable parts of the world.

Its finances appear to be pretty stable, but that can be deceiving. It reported $237.8 million in GAAP net income for 2025, a dramatic improvement over the $481.6 million loss in 2024. But it achieved this through a one-time, non-cash accounting trick related to one of its customers. When this paper gain is excluded, the company actually posted a non-GAAP net loss of $75.7 million.

Its deal with OpenAI is complicated. In 2025 it signed a Master Relationship Agreement (MRA) with OpenAI valued at over $20 billion. Under the MRA, OpenAI is contractually obligated to purchase 750 MW of AI inference compute capacity, with an option to expand to 2 GW by 2030.

Cerebras has a potent technology. What it doesn’t have are customers and reliable income. An IPO in such a state is a very risky proposition. Being a public company is a distraction it does not need when it should be focused on getting its customer base out of the single digits.

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EU Expands Big Tech Crackdown to Cloud and AI Markets

James Maguire — Wed, 29 Apr 2026 17:15:07 +0000

The European Union is preparing to extend its regulatory reach beyond consumer-facing platforms to target cloud computing and AI, pushing its efforts to curb large US tech companies into a more assertive stance.

In a new report, the European Commission said its Digital Markets Act (DMA), in force since May 2023, has improved competition in areas such as app distribution and data portability. The DMA regulates US giants like Amazon, Microsoft and Alphabet. Officials now intend to apply similar scrutiny to infrastructure layers of the digital economy, particularly cloud services and AI systems.

The Commission is assessing whether certain AI offerings, including virtual assistants, should fall under the same gatekeeper designation applied to digital platforms. Ongoing investigations are examining whether leading cloud providers should be formally classified under DMA rules, which would impose obligations aimed at preventing anti-competitive behavior.

The EU’s new initiatives to curb cloud and AI companies are part of its long-term resistance to influence of American tech firms, an effort that promotes EU sovereignty as a top priority.

“Europe’s digital economy doesn’t just use American tech, it’s structurally dependent on it,” Dion Hinchcliffe, VP of CIO Practice at the Futurum Group, told Techstrong.it. “So every sovereignty push runs straight into decades of architectural lock-in. However, where Europe truly excels is reg-tech: turning privacy, compliance, and governance into enforceable apps and infrastructure at scale.” This focus on regulation has helped support a second tier of European cloud and SaaS players, including OVHcloud, Deutsche Telekom, Mistral AI, and Nextcloud, he said.

“But make no mistake: A clean break from U.S. hyperscalers isn’t going to happen for a while. Instead, it’s a long, uneven build toward selective autonomy and will have many twists and turns before their goals are achieved.”

Fair Competition vs Innovation

Over the past two years, the EU has imposed more than $7 billion in fines on US tech companies under competition and digital services laws. American officials denounce these actions as disproportionate, claiming they risk undermining innovation.

European policymakers maintain that enforcement is necessary to ensure fair competition and protect consumers. Officials point to behavioral changes by companies following regulatory intervention, including adjustments to data practices and interoperability features.

AI is coming to the forefront in this debate. US leaders have warned that excessive AI regulation could limit Europe’s ability to participate fully in this emerging sector, particularly given its reliance on external providers for cloud infrastructure. European leaders have made it clear they see regulation as a prerequisite for building a sustainable and competitive digital ecosystem.

Furthermore, European regulators say the DMA framework was designed to evolve alongside emerging technologies. Their emphasis on cloud and AI suggests concern that control over compute and data pipelines could entrench market dominance more effectively than traditional platform models.

Public Opinion Favors Regulation

European public sentiment appears to support regulation, at least in principle. Recent polling across major EU countries shows that a majority of respondents favor reducing dependence on American tech providers, including cloud storage and communications platforms. Yet respondents were divided on whether such a transition is practical, highlighting the gap between political ambition and technical reality.

While EU leaders are pursuing digital sovereignty, the region shows few signs of shedding its reliance on US-based cloud and AI services, in fact it may even be growing as American AI firms rapidly develop. Still, the EU’s expanding regulatory framework may help it compete by shaping market behavior without fully displacing essential providers.

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Intel Reports Better-Than-Expected Results Due to Strong AI Demand

Andy Patrizio — Tue, 28 Apr 2026 19:40:32 +0000

One year after people began preparing Intel’s obituary, they’re tearing it up, as the company has posted a remarkable turnaround in fortune.

In the first quarter of 2026, Intel’s revenue rose 7% to $13.6 billion, more than $1 billion higher than Wall Street expected. Intel’s non-GAAP earnings per share (which excludes one-time charges) came in at a shocking $0.29, blowing away the $0.01 EPS analysts had predicted.

The revenue beat marks Intel’s sixth consecutive quarter of beating its own guidance, which is no aberration, it’s a trend, a trend of a comeback.

The company said resilient demand, despite rising inflationary pressures, drove the newly launched Series 3 to become its strongest product launch in five years. Series 3 serves enthusiast and high-end desktop market.

“The next wave of AI will move intelligence closer to the end user, shifting from foundational models to inference and agentic systems. This transition is increasing demand for Intel’s CPUs, wafers, and advanced packaging,” said Lip-Bu Tan, Intel CEO, in a statement. “We are addressing this opportunity by listening to customers and enabling their success through our technical expertise and differentiated IP. This operational reset has delivered a sixth consecutive quarter of revenue above expectations and strengthened relationships with key partners.”

For the quarter, Intel’s Client Computing segment, which is primarily PC desktop and notebook chips, reported $7.7 billion in revenue, above Wall Street expectations of $7.1 billion. Intel’s Data Center and AI group generated $5.1 billion in revenue, well ahead of the projected $4.41 billion. Revenue for the foundry business rose 16 percent to $5.4 billion.

The company has forecast a second-quarter revenue range of $13.8 billion to $14.8 billion, with EPS expectations of $0.08 and non-GAAP EPS of $0.20. Wall Street was anticipating $13.03 billion.

But the real winner in all of this? The federal government. Last August, Tan negotiated a bailout deal in which the government acquired about 10% of Intel’s shares for $8.9 billion. Thanks to Intel’s stock ballooning from $23 per share to nearly $80 per share, that stake is now worth nearly $35 billion.

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Ten Great IT Job Opportunities

Mike Vizard — Tue, 28 Apr 2026 15:00:47 +0000

Techstrong.it is now providing a weekly IT jobs report through which opportunities for IT professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for IT professionals to advance their careers.

Of course, the pool of available IT talent is still relatively constrained, so when one IT professional takes on a new role, it tends to create opportunities for others.

The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the IT job market. In the meantime, for your consideration.

Dice

Booz Allen Hamilton
El Segundo, CA
Infrastructure Engineer
$86,800 to $198,000

Corelight, Inc.
Lead Cloud Infrastructure Engineer / Site Reliability Engineer (SRE)
$172,000 to $219,000

American Electric Power
Columbus, OH
Infrastructure Engineer
$74,551 to $93,188

Leidos
Alexandria, VA
Journeyman Infrastructure Engineer
$87,100 to $157,450

American Electric Power
Columbus, OH
End User Infrastructure Engineer
$87,633 to $109,543

Walmart Inc.
Bentonville, AR
(USA) Systems and Infrastructure Engineer III
$80,000 to $155,000

LinkedIn

EdgeCore Digital Infrastructure
Reno, NV
Information Technology Operations Engineer
$120,000 to $140,000

CARET
CA
Senior Infrastructure Engineer
$130,000 to $160,000

CFS
South Bend, IN
Infrastructure Engineer
$100,000 to $120,000

Paperless Post
New York, NY
Senior – Staff Infrastructure Engineer
$156,885 to $220,000

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Gartner: IT Spending to Hit $6.31 Trillion in 2026

James Maguire — Mon, 27 Apr 2026 19:01:28 +0000

Global IT spending is entering a new phase of expansion, propelled by surging investment in AI and the infrastructure required to support it. Gartner forecasts that worldwide IT spending will hit $6.31 trillion in 2026, a remarkable 13.5% increase from last year.

The biggest shift is occurring in data center systems, where spending is expected to grow by a jaw-dropping 55.8%, easily outpacing all other IT segments. This leap reflects the massive, industry-wide scaling of AI workloads, which call for high-performance computing platforms and pricey specialized hardware. As enterprises and hyperscalers expand their AI capabilities, vast levels of capital are being spent on servers, accelerators, and advanced memory.

“The push towards more variety in AI workloads in the cloud, and moving towards an agentic AI operation in many enterprises, is stretching the demand for compute resources,” Jack Gold, President of J. Gold Associates, told Techstrong.it.

“Most inference and agent workloads run on traditional CPUs rather than GPUs, and while we see continued investments in GPU capacity, CPUs are now approaching parity in investments driven by the change from training to agents, along with physical AI, where general purpose compute is required and CPUs outperform GPUs in this area.”

IT Services Remains Largest Category

While infrastructure dominates growth, IT services remain the largest spending category, projected to exceed $1.87 trillion. The investment goes toward sustained demand for implementation and managed services as enterprises move from experimentation to real-world deployment of AI systems. The sheer complexity of these environments is reinforcing reliance on service providers to manage hybrid and cloud-based architectures.

Software is also seeing strong gains, driven largely by generative AI. Investment in AI model development and related platforms is expanding at a faster rate than traditional enterprise software, prompting changes in how organizations allocate budgets.

Most significant: AI is no longer treated as a standalone initiative but is increasingly embedded across applications and workflows.

The overall IT sector, however, is not moving uniformly. Device spending is expected to rise to approximately $856 billion, but growth is slowing relative to other segments. Higher memory costs are pushing up prices, which in turn is delaying replacement cycles, particularly in lower-margin categories. This trend highlights the uneven impact of AI across the IT landscape.

Far Different Spending Rates

What the Gartner numbers reveal is a market moving at very different rates. Hyperscalers and AI-focused vendors are spending aggressively (some might say recklessly), while more traditional enterprise segments are growing at a more measured pace. The result is a widening investment gap between areas directly tied to AI and those that are indirectly affected by its cost pressures.

On the high end, hyperscalers are expanding capacity to support AI training and inference, driving significant increases in server and data center investment. Spending in this category is expected to approach $788 billion, highlighting the scale of infrastructure required to sustain AI growth.

At the same time, pricing pressures in memory components are both affecting device sales and shaping procurement strategies. In sum, organizations are balancing the need to invest in AI capabilities with the financial realities of constrained supply and rising input costs.

Higher Than Expected

As strong as AI spend has been of late, Gartner’s revised outlook includes upward adjustments that indicate earlier projections underestimated both the pace and scale of AI-driven investment. This is particularly true in infrastructure, software, and cloud services.

Overall, AI is not simply adding incremental demand on a cyclical basis, it is reorganizing the structure of how IT budgets are distributed. Infrastructure is becoming the center of enterprise strategy, while services and software are evolving to support increasingly complex, data-intensive environments.

For decision makers, investment in AI is essential for competitiveness, and Gartner’s numbers suggest that this trend will continue as AI continues to scale.

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China’s Minerals Leverage Raises Quantum Security Concerns

James Maguire — Mon, 27 Apr 2026 18:00:12 +0000

China’s growing influence over critical minerals could threaten the supply chains needed to build quantum computers and related communications systems, according to a new study from researchers at Stanford University, Los Alamos National Laboratory and the Centre for International Governance Innovation.

The research paper argues that current government frameworks for critical minerals are poorly suited to quantum technology. Many quantum systems depend on small volumes of highly specialized materials. These obscure inputs can determine whether a quantum processor or detector can be built and maintained.

The quantities of these minerals used in quantum development, though tiny, are absolutely essential. All of the hyperscalers use some element of specialized critical materials in their quantum efforts. Google’s Willow chip, a pioneering effort in quantum, uses indium. IBM, widely seen as the market leader in the emerging sector, uses niobium in its quantum chips.

China’s Aggressive Strategy

Niobium is used in Josephson junctions, the core circuit element in many leading quantum machines, and the US imports all of its niobium. While Brazil dominates production, Chinese companies have invested heavily in Brazilian niobium producers, giving Beijing influence over a mineral essential for quantum systems.

Nickel presents another risk. Superconducting quantum computers require shielding from stray magnetic fields, often using nickel-iron alloys. Although US nickel import dependence is lower than its niobium dependence, China has extensive influence over Indonesian nickel refining, giving the country a potential supply constraint.

The concern is not only a hard cutoff of supply. China has shown that licensing rules and export procedures can move markets unpredictably. Its export restrictions over materials such as bismuth, antimony, gallium, germanium and rare earth elements have raised alarms across semiconductor and quantum supply chains. Even a temporary interruption is a problem because qualified suppliers and components can take years to validate.

Proposed Solution

The research study proposes a Quantum Criticality and Critical Minerals dashboard. The tool would track inventory metrics, substitution options, qualification timelines, geopolitical signals and other key levels. The goal is to give governments and allied quantum firms earlier warning before a materials issue becomes a program-level failure.

The tool may well be needed, because China is accelerating its own quantum ecosystem. Beijing has invested for decades in quantum research, including regional innovation clusters and domestic suppliers. Chinese groups have advanced in quantum communications, superconducting systems, photonic platforms, neutral-atom machines, sensors and related components. China has also pushed to reduce reliance on Western equipment, including dilution refrigerators used to cool superconducting quantum computers.

In the face of this aggressive strategy, Western export-control strategy may be limited. Restrictions on quantum equipment may slow China in some areas, but they can also create incentives for domestic substitution. For instance, in China’s emerging cryogenics sector, controls and entity listings helped create a market for local suppliers. Still, major gaps remain for China, including helium-3, high-performance cryocoolers, certain amplifiers and independently verified system reliability.

For enterprise tech leaders, some of whom are planning migration to post-quantum cryptography, the report is a wake-up call. The report indicates that quantum’s evolution is threatened by the materials and manufacturing networks that make those systems possible.

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