The Gold Rush Playbook: 25 Public Companies Powering the AI Boom

This article is reprinted with permission from Esq. Wealth Management, Inc.

At EsqWealth, we’re constantly exploring strategic opportunities in sectors that may be on the verge of explosive growth. We believe one of those sectors is artificial intelligence (AI), and not just the headline-making applications like ChatGPT or autonomous cars, but also the real-world infrastructure that powers the entire AI ecosystem.

To borrow a timeless metaphor from business history: “The people who made the most money during the gold rush weren’t the ones digging for gold, they were the ones selling shovels.”

This principle is as true today as it was in 1849. While many investors desperately try to identify the next AI unicorn, our approach looks deeper. We aim to own the shovel sellers, the companies providing the critical infrastructure and services needed to fuel the AI boom.

Why This Strategy Works – Don’t Chase the Sparkle, Own the Companies Selling the Shovels

During the gold rush, the real fortunes weren’t made by those who struck gold; they were made by the merchants selling picks, shovels, and pants.

In the dot-com boom, everyone scrambled to invest in flashy internet startups, convinced that e-commerce would instantly change the world. And in a way, they were right, but many bet on the wrong part of the equation.

Companies like Webvan, eToys, and Pets.com burned through billions trying to build online empires before the infrastructure was ready. Meanwhile, FedEx and UPS, the unglamorous “shovel sellers” of that era, quietly delivered the goods. As e-commerce matured, it was the logistics giants, not the flashy dot-com darlings, that emerged as long-term winners. They didn’t need to predict which online store would dominate; they just got paid every time a package shipped.

Today’s AI boom should work the same way. Training AI requires enormous cloud power, cutting-edge chips, sprawling data centers, and enough electricity to light a city. That energy has to be delivered and regulated, which is where transformers and switchgear come in.

And what’s all that power fueling? GPUs (graphics processing units), the specialized chips built for the heavy duty number crunching behind AI.

• Certain companies make the GPUs (Nvidia and AMD). Even if one AI application fades, they can still sell chips to gaming, scientific research, and other high performance computing markets.
• Others provide advanced chips and networking technology to move data at lightning speed (Broadcom), which are also essential in cloud computing, telecom, and enterprise networks.
• Some build the servers that house these chips (Super Micro), used not only in AI data centers but in finance, healthcare, and countless other industries.
• Others enable ultra-fast switching between processors (Arista Networks), a need that extends well beyond AI into general cloud and enterprise operations.
• Some manufacture the transformers and substations that deliver high voltage, reliable energy into those data centers (GE Vernova), the same gear that powers factories, hospitals, and cities.
• Others operate cooled, high powered facilities where AI systems run around the clock (CoreWeave and Applied Digital), but which can also host cloud services or other compute heavy workloads.
• Some keep it all interconnected with global scale data center infrastructure (Digital Realty and Equinix), serving thousands of businesses regardless of which AI apps succeed or fail.

Like the shovel sellers of the gold rush, these companies don’t rely on guessing who will strike AI gold, they profit by supplying the infrastructure that will still be in demand for farmers, builders, scientists, and enterprises long after the latest AI sparkle fades.

25 Companies That Are Supplying The Tools for the AI Boom

In searching for the shovel sellers of the AI gold, we didn’t just chase names making headlines, we studied what’s powering those headlines. We focused on the key players that comprise the foundational layers that build, store, and scale AI capabilities. We also evaluated key metrics including the current price, earnings per share, the price-to-earnings ratio, and dividend yield.

Here are the 25 shovel sellers we evaluated, organized by category and listed alphabetically within each core area of the AI ecosystem:

AI “Shovel Sellers” Summary Table

Category	Companies Reviewed	Key Role in AI Ecosystem
Semiconductors	AMD (AMD), Intel (INTC), Micron (MU), Nvidia (NVDA), Super Micro (SMCI), Taiwan Semi (TSM)	Design and manufacture GPUs, AI accelerators, and memory essential for model training and inference
Hardware and Compute Backbone	Arista Networks (ANET), Broadcom (AVGO), CommScope (COMM), CoreWeave (CRWV), Dell (DELL), GE Vernova (GEV), IBM (IBM)	Build and deploy AI data centers, networking gear, power systems, and compute clusters
Digital Platforms and Software	Adobe (ADBE), Alphabet (GOOG), Apple (AAPL), Meta (META), Microsoft (MSFT), Palantir (PLTR), ServiceNow (NOW), Snowflake (SNOW)	Provide tools and environments for AI development, deployment, productivity, and enterprise integration
Digital Infrastructure and REITs	American Tower (AMT), Crown Castle (CCI), Digital Realty (DLR), Equinix (EQIX)	Own and operate the physical infrastructure—towers, fiber, and data centers—that power AI connectivity and compute

Below, we look at how each company stacks up, why it made our list, where the opportunities lie, and what risks investors should keep in mind.

1. Semiconductors

Semiconductors are the brains behind AI. They include chips that do the heavy lifting, like training models, running algorithms, and processing huge amounts of data. Companies in this group make GPUs, which are especially good at handling AI tasks. Others build memory chips that help store and move data quickly, or networking chips that connect servers and data centers so everything runs smoothly.

These firms are essential to AI because without fast, powerful chips, even the best software can’t perform. They’re the foundation of everything from smart assistants to self-driving cars.

AMD (AMD) – Price: ~$169.03 | EPS: ~$1.67 | P/E: ~101× | Div Yield: None

AMD has been gaining traction with its Instinct MI300 GPUs, a direct challenger to Nvidia’s dominance. It has secured design wins with major cloud providers and is beginning to carve out a meaningful share.

We included AMD despite the absence of a dividend because, like many companies driving AI forward, it is in a reinvestment phase. The company is allocating cash flow toward research and development, advanced packaging, and expanded production capacity to meet rising AI workloads. While we typically emphasize consistent dividend payers in many portfolios, firms fueling the AI boom are a different case. Their reinvestment today is what enables future growth, and this same reasoning applies to several other companies on our list that also do not currently pay dividends.

• Opportunities: Growing enterprise adoption, leadership in AI model training, and diversification beyond gaming into cloud and data center markets.
• Risks: High valuation and reliance on cyclical markets like PCs and memory, which could weigh on results if demand slows. Recent earnings volatility and margin pressure from competitive pricing may also impact near-term performance.

Intel (INTC) – Price: ~$24.93 | EPS: –$4.77 | P/E: N/A | Div Yield: None

Intel is in the middle of a major turnaround under new leadership. The company is shifting toward manufacturing chips for other designers (a foundry model) while advancing its own AI-focused Gaudi 3 processors, already adopted by cloud providers like IBM to power large-scale AI models. It has established partnerships with MediaTek and Arm. Additionally, it is investing an initial $20 billion to construct two leading-edge chip factories in Ohio.

In addition, the U.S. government recently acquired 9.9% equity stake in Intel making taxpayers one of the company’s largest shareholders. While the stake is passive, the move signals deeper federal involvement in semiconductor strategy and has raised concerns about international backlash and shareholder dilution.

• Opportunities: Potential resurgence as a competitive foundry player, expansion into AI chip markets, and strategic wins in enterprise and edge computing.
• Risks: Deep current losses, suspended dividend, large-scale layoffs, and intense competition from entrenched leaders like TSM, AMD, and Nvidia. The government stake may also trigger adverse reactions from foreign customers and regulators, potentially complicating Intel’s global sales strategy.

Micron Technology (MU) – Price: ~$117.07 | EPS: ~$5.55 | P/E: ~21× | Div Yield: ~0.39%

Micron makes the high-speed memory and storage that AI systems need in massive amounts. This includes high-bandwidth memory (HBM)—a type of chip that moves data extremely quickly—and more common memory types like DRAM (used for short-term data storage while a computer is running) and NAND (used for long-term storage, like in solid-state drives). As AI models become larger and more complex, they need more and faster memory, which could drive strong demand for Micron’s products.

• Opportunities: Direct beneficiary of the AI data surge, leadership in next-generation HBM, and growth potential as memory demand rises across AI, cloud, and 5G markets.
• Risks: Memory chip prices can swing sharply depending on supply and demand. Micron has faced oversupply (“inventory gluts”) and falling prices—especially in NAND—which can squeeze profits and slow recovery. HBM supply constraints and rising competition from SK Hynix and Samsung may also impact pricing power.

NVIDIA (NVDA) – Price: ~$180.45 | EPS: ~$3.11 | P/E: ~58× | Div Yield: ~0.02%

Nvidia is the top producer of GPUs (graphics processing units)—specialized chips designed for extremely fast, complex calculations. GPUs are the engine behind modern artificial intelligence, used both to train AI models (teaching them how to “think”) and to run those models once they’re built. Nvidia chips power everything from large language models like ChatGPT to the real-time decision-making in self-driving cars.

• Opportunities: Nvidia has become the backbone of AI infrastructure, with massive demand for its data center products and growing adoption in “edge computing” (bringing AI processing closer to where data is created). The company’s market value has surged more than 1,000% since its 2022 lows, reflecting its dominant position.
• Risks: The stock is priced for perfection, so any slowdown in AI-related spending or new competition could trigger sharp declines. Nvidia also faces regulatory and antitrust pressures, particularly in China, that could limit growth. Export restrictions and rising competition from custom ASICs may also challenge its long-term dominance.

Super Micro Computer (SMCI) – Price: ~$43.97 | EPS: ~$1.68 | P/E: ~26× | Div Yield: None

Super Micro builds advanced servers—the big, powerful machines that hold and connect the chips used in artificial intelligence. Think of them as the “engine rooms” where AI work gets done. While companies like Nvidia make the processors, Super Micro designs the custom systems that link them together, keep them cool, and make sure they run efficiently at a huge scale.

• Opportunities: Super Micro is becoming a go-to supplier for hyperscalers—massive cloud providers such as CoreWeave, Oracle, and DataVolt—who are racing to build the next wave of AI-ready data centers. Its speed in delivering complex, tailored systems gives it a competitive edge.
• Risks: The company doesn’t pay a dividend, instead reinvesting profits into expanding its manufacturing and cooling capabilities. While growth has been rapid, it’s exposed to risks like heavy dependence on a few major customers and the ups and downs of AI-related demand. Recent earnings misses and margin compression may also signal execution risk as demand scales.

Taiwan Semiconductor Manufacturing Company (TSM) – Price: ~$238.27 | EPS: ~$9.14 | P/E: ~26× | Div Yield: ~1.40%

TSM is the world’s top manufacturer of the tiny, high-performance chips that power advanced technology, including artificial intelligence. Companies like Nvidia, Apple, and AMD design these chips, but TSM is the one that actually makes them. Its ability to produce extremely small, powerful, and energy-efficient chips—at massive scale—gives it a unique and critical role in the AI supply chain.

• Opportunities: No other manufacturer matches TSM’s combination of scale, precision, and quality. That dominance gives it strong pricing power and makes it a key partner for nearly every major chip designer in the world.
• Risks: TSM is based in Taiwan, which introduces geopolitical risk—especially given tensions in the region. Any disruption could impact the global tech supply chain, making this a stock where political developments can matter as much as earnings reports. U.S. efforts to localize chip production and rising competition from Samsung and Intel Foundry Services may also challenge its long-term dominance.
• Hardware Backbone

AI doesn’t just need powerful chips—it also needs the physical systems that move data, deliver electricity, and keep servers running smoothly. This includes high-speed networks, energy-efficient power setups, and specialized computers built for heavy workloads. Some companies focus on hyperscale infrastructure, which means massive data centers that support cloud giants like Microsoft and Google. Others work on edge infrastructure, which brings computing closer to users (like placing mini data centers near cities) to reduce delays and speed up AI responses.

Arista Networks (ANET) – Price: ~$136.23 | EPS: ~$2.55 | P/E: ~53× | Div Yield: None

Arista is a leader in high-performance networking equipment, best known for its switches and software that connect the thousands of GPUs in modern AI data centers. As AI workloads explode, data needs to move between processors at lightning speed and with minimal delay — exactly where Arista’s products shine.

• Opportunities: Arista’s Ethernet switches and cloud networking software are widely used by hyperscalers such as Microsoft, Meta, and Google. With AI training clusters requiring massive east-west data traffic, demand for Arista’s low-latency, high-throughput gear is likely to remain strong. Its software-driven approach also creates sticky, recurring customer relationships.
• Risks: The company’s fortunes are closely tied to spending cycles of a few large cloud providers. Any slowdown in hyperscale data center build-outs, or increased competition from Cisco or new entrants, could pressure growth. Arista also trades at a premium valuation, reflecting high investor expectations for continued AI tailwinds. Recent margin compression and elevated inventory levels may also signal near-term volatility.

Broadcom Inc. (AVGO) – Price: ~$308.65 | EPS: ~$2.73 | P/E: ~113× | Div Yield: ~0.79%

Broadcom earns a place on our list for providing the high-speed connections and custom chips that keep AI data flowing smoothly. While it’s not always mentioned alongside Nvidia or AMD, Broadcom is a behind-the-scenes powerhouse—designing networking chips and custom ASICs (application-specific integrated circuits) that are built for very specific tasks, like moving data quickly between processors in giant AI data centers.

Its technology—such as switch fabrics (systems that route data between many processors) and SerDes (serializer/deserializer circuits that convert data for fast transmission)—is essential for AI clusters at companies like Meta, Google, and Microsoft.

• Opportunities: Broadcom’s deep relationships with big tech companies, plus its recent VMware acquisition, give it a strong foothold in both hardware and software. It also offers a steadily rising dividend and diversified revenue streams.
• Risks: The stock price already reflects high expectations for AI growth, so any slowdown in demand or delays in enterprise adoption could weigh on returns.

CommScope (COMM) – Price: ~$15.60 | EPS: $0.44 | P/E: N/A | Dividend Yield: None

CommScope is a global leader in network connectivity solutions, providing the fiber, cabling, antennas, and wireless infrastructure that form the physical backbone of modern communication networks. Its portfolio spans everything from data center interconnects to 5G small-cell deployments—critical enablers for the low-latency, high-bandwidth demands of AI-driven applications.

• Opportunities: The August 2025 sale of its Connectivity and Cable Solutions unit to Amphenol for $10.5 billion in cash will allow CommScope to significantly reduce its $9 billion debt load, streamline operations, and focus on core segments like Access Network Solutions and Ruckus. Strengthened finances and entrenched relationships with carriers, cloud providers, and enterprises position it to benefit as AI adoption fuels demand for faster, more reliable connectivity.
• Risks: The divestiture removes one of the company’s most profitable divisions, which could constrain future revenue growth. CommScope also remains exposed to cyclical telecom spending patterns, which can pressure results during downturns.

CoreWeave (CRWV) – Price: ~$102.79 | EPS: –$2.32 | P/E: N/A | Div Yield: None

CoreWeave has emerged as the leading GPU-native cloud provider purpose-built for AI workloads. With 33 data centers and more than 400,000 NVIDIA GPUs already deployed—expected to surpass 750,000 by year-end—the company is rapidly becoming a foundational pillar of enterprise AI infrastructure. Its specialized architecture delivers up to 35× faster and 80% cheaper inference than AWS or Google Cloud, making it a preferred partner for OpenAI, Mistral, and IBM.

• Opportunities: Strategic alliances with Dell and NVIDIA have accelerated CoreWeave’s rollout of Blackwell-powered clusters, while a $6 billion Pennsylvania data center investment and $25.9 billion backlog point to sustained demand. Its ability to deliver purpose-built, cost-efficient AI compute at scale positions it as a critical player in the infrastructure race.
• Risks: Heavy reliance on multi-billion-dollar debt facilities and aggressive capital expenditures could strain cash flows if AI demand growth slows or competition erodes pricing power. Recent volatility in private valuations and lack of profitability may also raise concerns as post-IPO lockup restrictions expire, increasing float and insider selling risk.

Dell Technologies (DELL) – Price: ~$134.05 | EPS: ~$6.39 | P/E: ~20.98× | Div Yield: ~1.58%

Dell is a longtime leader in servers, storage, and enterprise IT, with a growing footprint in rack-scale AI deployments. Its PowerEdge XE9680 and XE9680L servers are optimized for high-density GPU workloads, supporting up to eight NVIDIA H100 or H200 GPUs, AMD MI300X, or Intel Gaudi 3 accelerators. These systems deliver turnkey infrastructure to hyperscalers and Tier 2 cloud providers, enabling generative AI, simulation-heavy workloads, and advanced data analytics at scale. Dell’s scale, diversified revenue base, and robust free cash flow position it as a stable player in the AI hardware market.

• Opportunities: Continued demand for GPU-rich infrastructure, expansion into AI-optimized server lines, and strong relationships with enterprise and cloud customers could drive growth. Its ability to integrate GPUs from multiple vendors offers flexibility that appeals to a wide customer base.
• Risks: Margin pressure in the Client Solutions Group and fierce competition from Hewlett Packard Enterprise (HPE) and Super Micro Computer (SMCI) could limit upside. Any slowdown in enterprise IT spending or hyperscaler investment cycles could weigh on results. Supply chain constraints and rising component costs may also impact margins in the Infrastructure Solutions Group.

GE Vernova (GEV) – Price: ~$633.69 | EPS: ~$4.11 | P/E: ~154× | Div Yield: ~0.16%
Spun off from General Electric in April 2024, the company operates in three segments—Power, Wind, and Electrification. The Electrification unit is the most relevant for AI, producing high-voltage transformers, switchgear, and substations that deliver and regulate electricity for AI data centers, EV charging networks, and industrial systems. The company’s $128.7B backlog reflects strong demand for energy infrastructure. While not a traditional data center operator, GE Vernova’s inclusion reflects the growing importance of grid modernization in enabling compute at scale.

• Opportunities: A massive backlog, rising electrification demand, and leadership in mission-critical grid equipment give GE Vernova strong visibility into future revenues. Its role in enabling AI-driven energy demand could make it a long-term infrastructure winner.
• Risks: The stock trades at a steep valuation (~150× TTM P/E), making it vulnerable to pullbacks. Persistent losses in the Wind segment, policy shifts in key markets, and execution risk in converting backlog to revenue could weigh on results. Dividend yield has declined slightly, and payout remains minimal relative to earnings and cash flow.

IBM (IBM) – Price: ~$245.73 | EPS: ~$6.19 | P/E: ~39.70× | Div Yield: ~2.74%
IBM has shifted its focus toward hybrid cloud and AI solutions, especially for regulated industries like finance, healthcare, and government. Its watsonx platform helps companies build, run, and manage AI models securely and in compliance with industry rules. Meanwhile, Red Hat OpenShift AI lets customers run AI applications across multiple environments—whether in the cloud (like Azure or Oracle Cloud) or in their own data centers. IBM’s reliable dividend and strong cash flow appeal to conservative investors, and its $7.5 billion generative AI business signals growing demand.

• Opportunities: Deep expertise in regulated sectors, strong AI governance tools, and hybrid cloud flexibility position IBM as a trusted partner for large enterprises. Expanding the watsonx ecosystem and leveraging Red Hat’s infrastructure could fuel long-term growth.
• Risks: Legacy infrastructure and uneven performance in consulting and software could slow progress. Competition from hyperscalers and newer AI providers may challenge IBM’s ability to maintain market share despite recent margin expansion.
• Digital Platforms and Software

These companies don’t build the physical parts of AI like chips or data centers. Rather, they create the software and digital tools that help people use AI. Some offer productivity platforms like Microsoft Office or Adobe Creative Cloud, now enhanced with AI features. Others provide data platforms that help businesses organize and analyze information using AI.

A few companies focus on open-source AI, giving developers free tools to build their own models. Others specialize in enterprise AI, helping large organizations automate tasks, improve customer service, or make smarter decisions. While they don’t sell the “shovels,” these firms help people use them more effectively and they’re often the ones turning AI into real business results.

Adobe (ADBE) – Price: ~$353.96 | EPS: ~$15.61 | P/E: ~22.68× | Div Yield: None
Adobe has integrated generative AI directly into its Creative Cloud suite through its Firefly platform, giving marketers, designers, and agencies new AI-driven tools across Photoshop, Illustrator, Express, and Premiere Pro. These capabilities enable faster, more flexible creative workflows for both individuals and large enterprises. With an unmatched position in creative software, Adobe is well placed to monetize AI features through its subscription model.

• Opportunities: Firefly could deepen user engagement and increase subscription revenue, while enterprise adoption of generative design tools expands Adobe’s addressable market.
• Risks: Competition from nimble startups and open-source AI tools may pressure pricing power and slow growth if innovation cycles lag behind rivals. Adobe’s lack of a dividend and premium valuation may also deter income-focused investors.

Alphabet (GOOG) – Price: ~$212.37 | EPS: ~$9.39 | P/E: ~22.62× | Div Yield: ~0.40%
Alphabet is a leader in advanced AI through its Gemini platform, DeepMind research arm, and the integration of AI across Google Search, Cloud, and Workspace productivity tools. The company is investing heavily in AI infrastructure and innovation, prioritizing long-term growth over near-term dividends. Its world-class research capabilities and massive user base give it a strong advantage in training, deploying, and monetizing AI systems at scale.

• Opportunities: Expanding AI monetization in search, cloud services, and enterprise productivity could unlock significant revenue growth. Alphabet’s scale and proprietary data provide a durable competitive moat.
• Risks: Regulatory scrutiny, ongoing antitrust actions, and potential slowdowns in ad revenue growth could weigh on results. Increased AI competition from Microsoft, OpenAI, and others may pressure market share.

Apple (AAPL) – Price: ~$232.56 | EPS: ~$6.59 | P/E: ~35.29× | Div Yield: ~0.45%
Apple earns its place for its privacy-focused, on-device AI approach and the rollout of Apple Intelligence across iOS, Siri, and its custom silicon chips. This hybrid model—combining on-device processing with Private Cloud Compute—could be a major differentiator in a world increasingly concerned with data security. With a massive global installed base, even small AI enhancements can reach hundreds of millions of users almost instantly.

• Opportunities: Seamless AI integration into iPhones, Macs, and iPads could drive upgrade cycles and deepen customer loyalty. The closed ecosystem gives Apple control over both hardware and software optimizations.
• Risks: Continued reliance on hardware sales for growth makes Apple vulnerable to cyclical consumer demand. Competition in AI-enabled devices is intensifying, and regulatory scrutiny over its App Store and ecosystem practices remains a risk.

Meta Platforms (META) – Price: ~$751.11 | EPS: ~$27.58 | P/E: ~27.23× | Div Yield: ~0.28%
Meta earns its spot for its leadership in open-source AI through the LLaMA model suite, which powers AI across its social platforms and advertising infrastructure. The company’s recent $6 million startup initiative with AWS and the launch of LLaMA 4 reinforce its goal of making open models a foundational part of the AI ecosystem. The introduction of quarterly dividends signals a more mature, shareholder-friendly posture, though Meta’s aggressive reinvestment into infrastructure and software remains its primary growth driver.

• Opportunities: Open-source leadership could drive developer adoption and ecosystem lock-in. AI-driven ad targeting and content moderation improvements may boost monetization across Facebook, Instagram, Threads, and WhatsApp.
• Risks: Expensive metaverse investments could weigh on near-term returns. Regulatory headwinds—including EU fines and an FTC antitrust trial—pose risks to its ad model and platform operations.

Microsoft (MSFT) – Price: ~$509.64 | EPS: ~$13.63 | P/E: ~37.39× | Div Yield: ~0.66%
Microsoft stands out for its end-to-end AI platform strategy, spanning infrastructure (Azure), productivity (Copilot), and developer tools (GitHub). The company is embedding AI across Office, Dynamics, and Teams, making AI a core feature of its enterprise ecosystem. Its scale, deep integration across products, and unmatched enterprise relationships give it a formidable competitive advantage.

• Opportunities: Continued Azure adoption, expansion of AI-infused productivity tools, and leadership in enterprise-grade AI security and compliance could drive durable growth.
• Risks: A premium valuation leaves little room for execution missteps. Intensifying competition from Amazon, Google, and open-source AI models could challenge growth and pricing power.

Palantir Technologies (PLTR) – Price: ~$158.12 | EPS: ~$0.30 | P/E: ~527× | Div Yield: None
Palantir earns its spot for leadership in operational AI through its Artificial Intelligence Platform (AIP), which enables enterprises to deploy LLM-powered agents across finance, healthcare, logistics, and defense. Its builder tools—AIP Logic, Agent Studio, and AIP Evals—allow clients to go from zero to production-grade AI workflows in days. With 432 U.S. commercial customers and 71% year-over-year revenue growth in Q1 2025, Palantir is showing that AI can be deployed securely and at scale in highly regulated environments.

• Opportunities: Expanding commercial adoption, deeper integration with major cloud platforms, and its strong track record in government contracts provide growth momentum.
• Risks: An extremely high valuation leaves little margin for error, and heavy reliance on government contracts could limit diversification if commercial expansion slows.

ServiceNow (NOW) – Price: ~$928.60 | EPS: ~$7.99 | P/E: ~116.22× | Div Yield: None
ServiceNow is included for its AI-first enterprise workflow platform, which drives automation across IT, HR, and customer service for more than 85% of the Fortune 500. Its Now Assist suite is on track to reach $1 billion in annual contract value by 2026, highlighting its growing AI momentum in enterprise markets. The company’s strategy of reinvesting earnings is a calculated move to cement long-term leadership in AI-driven automation.

• Opportunities: Expanding adoption of AI-powered workflow tools, strong enterprise retention, and deep integration across critical business functions support durable growth.
• Risks: A premium valuation leaves little room for execution missteps, and any slowdown in enterprise IT spending or contract growth could lead to sharp stock corrections.

Snowflake (SNOW) – Price: ~$241.00 | EPS: –$4.21 | P/E: N/A | Div Yield: None
Snowflake is included for its transformation into an AI-native data cloud platform. Its Cortex AI suite enables enterprises to build AI-powered applications, analyze unstructured data, and deploy large language models (LLMs) securely within Snowflake’s environment. Strategic acquisitions like Applica and Neeva have expanded its capabilities in document intelligence and AI-native search, while partnerships with Meta, Anthropic, and Mistral strengthen its position as a foundational layer for AI-driven analytics.

• Opportunities: Integration of compute, storage, and model orchestration under one platform gives Snowflake a competitive edge in performance and security. Its expanding AI ecosystem could drive deeper enterprise adoption.
• Risks: A premium valuation makes the stock sensitive to growth slowdowns, while competition from Databricks and open-source stacks could erode market share.
• Digital Infrastructure and Data Center REITs

Real estate investment trusts (REITs) own and operate the buildings and land that support digital infrastructure—like data centers and cell towers. These companies lease space to cloud providers, telecoms, and AI firms that need reliable power, cooling, and connectivity. REITs also pay regular dividends, making them attractive to investors who want income while gaining exposure to the AI buildout.

American Tower (AMT) – Price: ~$202.56 | EPS: ~$5.35 | P/E: ~37.86× | Div Yield: ~3.35%
American Tower is included for its role in enabling real-time AI applications through its 5G and edge data center infrastructure. The company owns and leases cell towers worldwide and operates Access Edge Data Centers designed for low-latency AI workloads. Its portfolio includes six edge sites, three metro data centers, and the CoreSite acquisition, which enhances its cloud connectivity and colocation capabilities.

• Opportunities: Growing demand for edge computing and AI-driven mobile applications could boost leasing activity. Its recurring revenue model and global scale provide long-term stability.
• Risks: Significant capital expenditure requirements and foreign exchange exposure could impact margins. Rising competition in edge data services may pressure pricing power. Edge relevance (placing compute and storage closer to end users to reduce latency) may take time to fully materialize, especially as AI workloads remain concentrated in centralized hyperscale environments. CoreSite integration remains a key execution risk, requiring alignment across legacy tower operations and data center.

Crown Castle (CCI) – Price: ~$97.74 | EPS: –$9.00 | P/E: N/A | Div Yield: ~4.24%
Crown Castle is included for its small-cell and fiber infrastructure that supports decentralized AI and 5G connectivity in dense urban markets. The recent $8.5B sale of its metro fiber assets to Zayo and EQT highlights their strategic importance to the AI ecosystem. The company is now refocusing on tower assets, with plans to reduce its dividend and launch a $3B share repurchase program. Crown Castle still owns more than 40,000 towers and continues to drive 5G densification through amendment activity and selective small-cell deployments.

• Opportunities: 5G expansion and the integration of edge AI services could create new leasing and amendment revenue streams.
• Risks: Dividend reduction could pressure income-focused investors, while competition from other tower operators may limit pricing power. Execution risk in pivoting away from fiber (such as losing enterprise connectivity clients or underutilizing existing metro assets) could impact long-term growth and reduce strategic optionality in supporting AI workloads. The shift from fiber to towers may also reduce exposure to enterprise AI infrastructure, narrowing its strategic footprint.

Digital Realty Trust (DLR) – Price: ~$168.57 | EPS: ~$3.80 | P/E: ~44.36× | Div Yield: ~2.88%
Digital Realty is included for its global hyperscale data center footprint and expansion into AI-ready infrastructure. The company supports AI adoption through both hyperscale and enterprise facilities, offering low-latency connectivity and scalable compute environments. Its consistent dividend makes it appealing to income investors seeking exposure to AI-driven cloud growth.

• Opportunities: Increasing demand from hyperscalers, AI training clusters, and enterprise clients transitioning to AI workloads.
• Risks: Elevated debt levels and multi-year construction timelines could constrain growth and limit flexibility in scaling AI-ready capacity. Competition from agile private operators like CoreWeave and Applied Digital (who specialize in GPU-dense deployments) may pressure pricing and accelerate innovation cycles.

Equinix (EQIX) – Price: ~$786.35 | EPS: ~$10.20 | P/E: ~77.09× | Div Yield: ~2.37%
Equinix is included for its leadership in interconnection-focused colocation, critical for AI workloads that demand ultra-low latency and high-bandwidth data exchange. As the largest co-location provider globally, Equinix serves hyperscalers, telecoms, and enterprises needing proximity to data sources and AI models.

• Opportunities: Rising AI traffic and bandwidth needs could strengthen its core interconnection business.
• Risks: High capital intensity and activist investor pressure to limit AI-specific capex could slow AI-related expansion. REIT constraints may also limit flexibility in pursuing aggressive AI infrastructure investments.

Final Note for Investors
By embracing the shovel seller strategy (investing in the infrastructure powering AI’s rapid expansion) we aim to capture long term structural growth while reducing the volatility that often comes with chasing speculative tech. This approach allows us to thoughtfully balance upside potential, income generation, and risk management. Rather than searching for a single breakthrough company, we focus on the broader ecosystem that enables AI to scale and succeed. Whether a portfolio like this aligns with your overall financial plan depends on your unique goals, tax profile, and appetite for innovation. At EsqWealth, we are happy to explore how a carefully constructed allocation in this space could complement your broader investment strategy—whether through a taxable account, retirement portfolio, or trust structure.

The information above is not intended to and should not be construed as specific advice or recommendations for any individual. The opinions voiced are for general information only and are not intended to provide, and should not be relied on for tax, legal, or accounting advice. To discuss specific recommendations for any unique situation, please feel free to contact us.