President Trump just approved two long-stalled mining projects tied to strategic U.S. minerals — including silver, which recently broke $68 after soaring 129% last year.

So why should you care?

Because silver isn’t acting like a traditional metal anymore. It’s become essential for AI, energy, and defense… and demand is climbing faster than U.S. supply can keep up. When that happens, prices don’t rise slowly — they can reprice overnight.

>> See why this matters for your savings

And if your retirement accounts are tied to stocks or a dollar that’s been losing ground, shifts like this become even more important to understand.

>> Download Your Silver Info Guide

Moments like this don’t come often — and understanding what’s unfolding now could make all the difference in 2026.

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Featured Story from MarketBeat.com

The Supply Chain Quietly Powering the AI Boom—And 4 Ways to Play It

Authored by Bridget Bennett. Article Posted: 2/23/2026. 

Key Points

• Nanotechnology is the atomic-scale foundation that enables continued AI chip performance gains.
• The “picks-and-shovels” layer—lithography, fabrication tools, and contamination control—can offer durable exposure to AI-driven semiconductor demand.
• Several key suppliers sit behind the headlines, helping advanced chip production scale despite cyclical volatility.MKL
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Artificial intelligence has dominated market headlines for more than a year. Investors have chased chip designers, data center operators, and software platforms powering large language models. But beneath that boom sits a layer of engineering so small it’s measured in billionths of a meter.

In a recent conversation with Keith Kaplan of Tradesmith, the focus turned to nanotechnology—the atomic-scale science that makes modern AI hardware possible. As Kaplan put it plainly, “Without nanotech? There’s no AI boom, none at all.”

The Atomic-Scale Engine Powering AI

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Nanotechnology involves engineering at the scale of a nanometer—one billionth of a meter. Today’s advanced AI chips contain transistors that are only a few nanometers wide, thinner than a human hair and built with features measured in atoms.

That’s not theoretical science. NVIDIA’s (NASDAQ: NVDA) most advanced AI chips pack hundreds of billions of transistors onto a single piece of silicon. Each transistor exists because nanotechnology enables engineers to pattern and layer materials with extreme precision inside ultra-clean fabrication facilities.

The semiconductor industry is projected to approach the $1 trillion mark, fueled largely by AI demand. But that growth depends on continuing to shrink and refine chip architectures at the atomic level. Nanotechnology is the foundation of that progress.

Kaplan described it as a “multi-decade mega trend,” adding, “This is not a quick flip.” While volatility is part of the semiconductor cycle, the underlying demand drivers—AI, robotics, autonomous vehicles, and precision medicine—are unlikely to disappear anytime soon.

The Lithography Bottleneck Few Investors Appreciate

At the top of the nanotech supply chain sits ASML Holding (NASDAQ: ASML), a company that rarely grabs mainstream AI headlines but plays a critical role in making advanced chips possible.

ASML is the sole provider of extreme ultraviolet (EUV) lithography machines—the massive systems used to etch incredibly small transistor patterns onto silicon wafers. Each machine reportedly costs more than $300 million, weighs as much as two Boeing 737s, and can take months to assemble.

Without these systems, leading two- and three-nanometer chips simply cannot be produced.

That near-monopoly position gives ASML significant leverage within the semiconductor ecosystem. As AI chip demand accelerates, lithography capacity often becomes the bottleneck.

Building Chips One Atomic Layer at a Time

The next layer in the stack is fabrication equipment—the tools that deposit and shape ultra-thin films across a chip’s surface. Applied Materials (NASDAQ: AMAT) operates squarely in this space.

Modern chips can contain more than 100 distinct layers, each just fractions of a nanometer thick. Applied Materials designs and manufactures the equipment that allows chipmakers to build those layers with extreme accuracy and consistency, serving major producers across the globe.

As chips become more complex and transistor geometries shrink, fabrication precision becomes even more critical—reinforcing demand for advanced tooling.

For investors wary of high-flying AI valuations, companies embedded deep within the infrastructure layer can offer exposure to long-term growth drivers while benefiting from durable competitive advantages.

Quality Control at the Edge of Physics

Even the most advanced lithography and deposition tools would fail without pristine production environments. That’s where Entegris (NASDAQ: ENTG) enters the picture.

Entegris provides ultra-pure chemicals, gases, and filtration systems used in semiconductor manufacturing. The level of purity required in chip production far exceeds everyday standards—the water used in fabrication must be tens of thousands of times cleaner than typical drinking water.

As transistor sizes shrink and materials become more exotic, contamination risks rise, increasing the importance of high-end materials handling and filtration systems. While less visible than headline chipmakers, Entegris plays a vital role in maintaining yield and performance at advanced nodes.

The Company That Brings It All Together

At the top of the application layer sits Taiwan Semiconductor Manufacturing (NYSE: TSM), the world’s leading contract chip manufacturer.

TSMC produces the majority of the world’s most advanced chips used by companies such as Apple (NASDAQ: AAPL), NVIDIA, and Advanced Micro Devices (NASDAQ: AMD). It integrates lithography systems, deposition tools, and ultra-clean materials into finished, high-performance processors that power data centers, smartphones, and autonomous systems.

The company has also committed substantial investment to expanding manufacturing capacity in the United States, including a massive buildout in Arizona—an expansion that underscores the strategic importance of advanced semiconductor production in an AI-driven economy.

A Long-Term Powerhouse—With Volatility

Investors naturally question stocks that have already posted significant gains. Many semiconductor and AI-adjacent names have surged over the past year, prompting concerns about valuation and momentum.

Kaplan acknowledged that volatility is part of the equation, particularly in semiconductors. But he emphasized the structural backdrop: “This is a multi-decade mega trend, and this mega trend will not fade.”

From targeted drug delivery and biosensors in healthcare to longer-range electric vehicles and autonomous robotics, nanotechnology applications extend well beyond AI data centers. Yet AI remains the most scalable and capital-intensive driver today.

The broader takeaway is straightforward: AI headlines may spotlight software models and flashy chip launches, but the real backbone of the revolution is built at the atomic level. For investors willing to look deeper into the supply chain, nanotechnology may be one of the most underappreciated pillars of the AI era.

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Featured Story from MarketBeat.com

These 5 Stocks Are at the Center of the AI Supply Squeeze

Authored by Ryan Hasson. Article Posted: 2/23/2026. 

Key Points

• AI adoption is accelerating globally, but infrastructure buildout is lagging, creating bottlenecks across various industries.
• Companies like SanDisk and Micron are benefiting from supply constraints, which are strengthening pricing power and earnings momentum.
• ASML and GE Vernova sit at critical chokepoints in chip manufacturing and power generation, positioning them to gain as AI demand continues to scale.
• Special Report: [Sponsorship-Ad-6-Format3]

Artificial intelligence continues to dominate headlines, boardrooms, and capital markets. It is not just a buzzword but a structural technologicalshift reshaping industries in real time. Despite the attention, adoption remains relatively early: research published by Microsoft (NASDAQ: MSFT) estimates that only about one in six people currently use generative AI tools. Even so, global AI adoption surged 20% in 2025 to nearly 400 million users.

That growth highlights two realities. First, demand is accelerating quickly. Second, the runway for further adoption remains enormous—especially in emerging markets. As models become more capable and real-world use cases expand, AI will likely become more embedded in workflows, enterprise systems, and consumer applications.

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Alongside rising adoption is a second-order effect: infrastructure strain. AI models demand enormous compute, memory bandwidth, storage, specialized chips, and electricity. In several parts of the supply chain, demand is already outpacing supply—and when bottlenecks emerge, pricing power tends to follow.

Below are five stocks positioned at critical pressure points in the AI ecosystem that could benefit from sustained demand and potential supply constraints.

Sandisk: Storage at the Core of AI

Sandisk (NASDAQ: SNDK) has been one of the market’s strongest performers this year, after leading the S&P for 2025. The company develops NAND flash memory used across data centers, enterprise systems, mobile devices, and edge platforms.

AI workloads are storage-intensive: training and running large language models requires rapid access to massive datasets. As hyperscalers and enterprises scale AI deployments, demand for high-performance storage rises in step.

Over the past year, a global NAND flash shortage collided with accelerating AI demand, driving prices sharply higher—NAND prices nearly doubled in the second half of last year. That dynamic created a powerful earnings tailwind.

In its latest quarterly report (Jan. 29, 2026), Sandisk reported EPS of $6.20 versus analyst expectations of $3.31. Revenue rose 61% year over year to $3.03 billion, beating consensus, and guidance called for next-quarter revenue of $4.4 billion to $4.8 billion with gross margins of 65% to 67%.

With supply still tight and AI-related storage demand growing, Sandisk remains directly exposed to one of the most important infrastructure layers in the AI stack.

Micron Technology: High-Bandwidth Memory Powerhouse

Micron Technology (NASDAQ: MU) is another memory-focused beneficiary of AI-driven bottlenecks. The company is a leading supplier of high-bandwidth memory (HBM), a critical component in advanced AI accelerators and GPUs.

HBM allows processors to move vast amounts of data quickly—a necessity for training and inference at scale. Micron is one of only a handful of global suppliers in this segment, giving it leverage when supply tightens.

Shares have climbed sharply—up roughly 50% this year. In its fiscal first-quarter 2026 results, Micron reported EPS of $4.78, beating expectations of $3.77, and revenue surged nearly 57% year over year to $13.64 billion.

Analysts expect continued strength, with upcoming quarterly estimates projecting significant year-over-year growth. Institutional ownership remains high, and the stock has attracted substantial net inflows over the past 12 months.

As AI models grow larger and more complex, memory intensity per chip will likely continue rising—supporting Micron’s positioning in a market where supply constraints can translate into pricing power.

Nebius: Scaling AI-Native Infrastructure

Nebius Group (NASDAQ: NBIS) operates in a different segment of the AI ecosystem: full-stack, AI-native infrastructure. The company provides AI-tailored cloud services, developer tools, and large-scale data center capacity for training and running models. As enterprises seek purpose-built infrastructure rather than generic cloud compute, Nebius is expanding aggressively.

The company is targeting between 800 megawatts and 1 gigawatt of connected capacity by the end of 2026, with contracted power guidance recently raised to more than 3 gigawatts. That scale matters in an environment where AI data center capacity is increasingly scarce.

In its most recent quarterly report, Nebius posted year-over-year revenue growth of more than 500%, though quarterly revenue slightly missed expectations due to timing-related capacity. Management reiterated its ambitious 2026 ARR target of $7 billion to $9 billion and emphasized that demand continues to outpace supply.

Longer-duration contracts, improved pricing, and strong enterprise demand suggest capacity constraints could continue to support favorable economics. Shares have surged over the past year as investors recognize the company’s scale and market position.

ASML Holding: The Gatekeeper of Advanced Chips

ASML Holding (NASDAQ: ASML) sits at perhaps the most critical chokepoint in the semiconductor supply chain. The Dutch company manufactures advanced photolithography systems, including extreme ultraviolet lithography (EUV) machines required to produce leading-edge chips. Without ASML’s equipment, advanced AI processors cannot be manufactured at scale.

Chip designers may capture headlines, but foundries rely on ASML’s machines to fabricate cutting-edge semiconductors. The company’s near-monopoly in EUV lithography gives it unique pricing power and strategic importance.

As global demand for AI chips rises, foundries must invest in new fabrication capacity, which sustains demand for ASML’s systems. Shares have rallied strongly over the past year, and analysts remain broadly constructive on the long-term outlook.

GE Vernova: Powering the AI Revolution

GE Vernova (NYSE: GEV) may not design chips or build servers, but it addresses another emerging bottleneck: electricity. AI data centers consume massive amounts of power. As hyperscalers expand capacity, reliable and scalable power generation becomes essential, and in some regions grid infrastructure is already strained.

GE Vernova designs and services equipment across the power generation and grid value chain—gas turbines, renewables, and grid modernization technologies. Its positioning makes it a pick-and-shovel play on the physical infrastructure required to sustain AI growth.

The stock has surged more than 130% over the past year and continues to trade with growth-like characteristics. In its most recent quarterly report, GE Vernova significantly exceeded earnings expectations—in part due to a one-off tax benefit—and raised its multi-year outlook. Management projects 2026 revenue between $44 billion and $45 billion, with longer-term targets beyond $56 billion by 2028.

As AI-driven electricity demand accelerates, companies enabling generation and transmission capacity are likely to remain in focus.

Where Demand Meets Constraint

AI adoption is expanding rapidly, but infrastructure buildout is struggling to keep pace. Memory, advanced chips, data center capacity, and power generation are all under pressure.

When demand outstrips supply, pricing power often strengthens. Sandisk and Micron sit at the heart of memory bottlenecks. Nebius is scaling AI-specific infrastructure in a capacity-constrained market. ASML controls essential chip-manufacturing technology. GE Vernova powers the energy backbone that enables AI.

If AI demand remains durable and adoption continues to climb globally, these companies are positioned not just to participate, but potentially to thrive amid the bottlenecks shaping the next phase of the AI revolution.

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