What Really Powers ChatGPT? The Hidden AI Stack Most Investors Have Never Heard Of

What You'll Learn

By the end of this article, you'll understand:

Why the most interesting AI companies are not the ones you see on the news
How the AI industry actually works, from raw electricity to your screen, in plain English
Which publicly listed companies operate at each layer of this hidden stack, in India and the USA
A framework for thinking about these companies as a curious investor

The Gold Rush Nobody Talks About

Picture this scene. It is the spring of 1849 in California. Word has spread that gold has been discovered at Sutter's Mill. Within months, 300,000 people flood into the state from across the world, dreaming of striking it rich.

Most of them went broke.

The ones who got rich were not the miners. They were the people selling picks and shovels. Levi Strauss didn't mine gold. He sold tough denim jeans to the miners who kept wearing through their clothes. Wells Fargo didn't pan for gold. It offered banking and shipping services to the thousands of people pouring into the state. These "picks and shovels" businesses made fortunes while the miners gambled everything on a hole in the ground.

Fast forward to today. AI is the new gold rush. Everyone is rushing in. OpenAI, Google, Anthropic, and Meta are the miners, competing furiously to discover the next breakthrough. Most investors watch this race and ask: "Which AI company will win?"

That is actually the wrong question. Or at least, it is not the only question.

Because behind every single AI interaction (every ChatGPT answer, every image generated by DALL-E, every code suggestion from Copilot) there is a vast, invisible supply chain that most people never think about. Electricity generated by nuclear plants. Server rooms cooled by liquid flowing over chips. Miles of fiber optic cables carrying data across continents. Security software watching for hackers in real time.

The companies building this infrastructure are publicly listed. Many of them are in businesses that sound utterly unglamorous: power utilities, cable manufacturers, thermal management systems. And yet they are as essential to the AI era as picks and shovels were to the Gold Rush.

NVIDIA is the most famous example of this "picks and shovels" thesis. As we explored in our deep-dive on NVIDIA, the company doesn't build AI applications. It builds the chips (GPUs) that every AI application runs on. NVIDIA's stock price reflected this insight brilliantly.

But the stack goes much deeper than chips.

This article takes you through all eight layers, from electricity to your screen, and introduces the publicly listed companies operating at each level in India and the USA.

The AI Iceberg — What You See vs. What Actually Exists

Here is the single most important mental model in this entire article.

When most people think about the AI industry, they picture the tip of an iceberg: ChatGPT, Gemini, Copilot, the apps they use every day. But like an iceberg, roughly 90% of the AI industry is hidden below the surface. It is a vast, interconnected infrastructure that most consumers never see.

The AI Stack Iceberg — 8 hidden layers beneath the apps you use every day

Layer	What It Does	Where It Sits
Apps	What users see and interact with	ChatGPT, Gemini, Copilot, Siri
Layer 1: Chips (GPUs)	The brain that runs AI calculations	NVIDIA, AMD
Layer 2: Servers	The physical machines that hold the chips	Dell, HPE, Supermicro
Layer 3: Power	The electricity that runs everything	Nuclear plants, utilities
Layer 4: Cooling	Prevents chips from melting	Liquid cooling systems
Layer 5: Cables and Networking	Moves data between machines at high speed	Fiber optic cables, connectors
Layer 6: Data Centers	The buildings that house it all	Physical facilities, real estate
Layer 7: Cybersecurity	Protects the system from hackers	Security software platforms
Layer 8: Software and Services	Helps businesses use AI	IT services, specialized software

Every single one of these layers has publicly listed companies operating in it. Most investors know about Layer 1 (NVIDIA). Many have heard of some Layer 7 companies (CrowdStrike). But Layers 3, 4, 5, and the Indian equivalents of Layers 5-8 are almost never part of the conversation.

Let's walk through each one.

Before we dive in, here are three terms you'll see throughout this article:

GPU (Graphics Processing Unit): Originally designed to render video game graphics, a GPU can do millions of mathematical calculations simultaneously. AI models require exactly this kind of parallel computing, which is why GPUs (not regular computer processors) became the engine of the AI revolution.

Data center: A large building (sometimes the size of a warehouse or several city blocks) filled with thousands of computer servers. Think of it as the "factory" where AI thinking actually happens. When you ask ChatGPT a question, the answer is computed inside a data center somewhere.

Hyperscaler: The nickname for the world's largest cloud computing companies: Amazon (AWS), Microsoft (Azure), and Google (Cloud). They operate the biggest data centers on the planet, and when they spend money on AI infrastructure, entire industries feel the impact.

The USA — Where the Invisible Infrastructure Lives

Layer 3: Power — The Nuclear Revival Nobody Expected

Here is a fact that stops most people cold when they first encounter it: a single large AI data center can consume as much electricity as a small city.

Not metaphorically. Literally. The projections are staggering: the global AI industry is expected to spend 700 billion dollars on data center infrastructure in 2026 alone. Each new AI facility being built requires hundreds of megawatts of power. (Megawatt: a unit of electrical power equal to one million watts, enough to power roughly 750 to 1,000 homes continuously.)

Now here is the challenge that most people miss: AI workloads run 24 hours a day, 7 days a week, without pause. Unlike your home appliances, which you turn on and off, a data center never sleeps. This means AI companies need what energy professionals call baseload power, meaning electricity that is always available, regardless of weather, time of day, or season.

Solar panels only work when the sun shines. Wind turbines only spin when the wind blows. Nuclear power plants, on the other hand, run at full capacity more than 90% of the time. They are the ideal power source for the AI era.

This realization has triggered something remarkable in the United States: the revival of nuclear energy.

Vistra Corporation (NYSE: VST) is one of the largest independent power producers in the USA, operating nuclear, natural gas, and solar plants. In early 2026, Vistra signed a 20-year power purchase agreement (PPA, meaning a long-term contract to buy electricity at a fixed price) with Meta (the parent company of Facebook and Instagram) to supply 2,609 megawatts of nuclear power starting in late 2026. That is enough electricity to power approximately two million homes, dedicated entirely to Meta's AI data centers.

Constellation Energy (NASDAQ: CEG) operates America's largest fleet of nuclear power plants, with 21 reactors across 14 facilities. In one of the most remarkable business stories of recent years, Microsoft agreed to pay Constellation Energy to restart the Three Mile Island nuclear reactor in Pennsylvania, a plant that had been shut down for years, specifically to power Microsoft's AI data centers. The restarted reactor (835 megawatts) is expected to come online in 2027 under a 20-year agreement. This is the first time in history that a retired US nuclear plant has been brought back to life for a single corporate customer.

Eaton Corporation (NYSE: ETN) plays a different but equally essential role. Once a data center is connected to the power grid, the electricity still needs to be managed, converted, and distributed safely to thousands of servers. Eaton makes the switchgear, transformers, and uninterruptible power supplies (backup systems that kick in instantly if the grid fails) that sit inside every data center. Their Electrical Americas segment achieved record operating margins of 27.4% in 2025, a reflection of how essential and non-commoditized their products are.

Why this matters as an investor: Getting permission to build a new nuclear power plant in the United States takes 15 years or more. The companies that already own operating nuclear fleets have an advantage that literally cannot be replicated quickly. In understanding economic moats, we explore why this kind of structural barrier is so valuable for long-term investors.

Layer 4: Cooling — The Problem That Could Break Everything

Here is something remarkable about modern AI chips: they generate extraordinary amounts of heat.

A traditional server rack in a data center (the kind that powered the internet of 2010) produces roughly 5 to 10 kilowatts of heat. A single server rack equipped with the latest AI chips (NVIDIA's H100 or B200 GPUs) produces 50 to 100 kilowatts of heat. That is 10 times more heat from the same amount of physical space.

Traditional air conditioning (the kind that blows cold air through raised floor tiles) simply cannot remove heat fast enough. If you tried to cool today's AI data centers with old-fashioned air conditioning, the chips would throttle their performance or fail entirely.

The solution is liquid cooling: instead of blowing cold air over hot chips, you run pipes of chilled water (or a specialized cooling fluid) directly over the surface of the chips. This removes heat far more efficiently. But designing, manufacturing, and installing these systems at scale is enormously complex, and only a handful of companies in the world can do it reliably.

Vertiv Holdings (NYSE: VRT) is the dominant player in this space. Vertiv designs and manufactures the power, cooling, and monitoring systems that sit inside data centers, from the liquid cooling units that attach directly to server racks to the software that monitors temperature and power consumption across an entire facility in real time. Vertiv's equipment is present in virtually every major data center on the planet.

The numbers tell a striking story. In Q4 of 2025, Vertiv reported 252% year-over-year order growth. Their total order backlog reached 15 billion dollars. Their book-to-bill ratio (a measure of how fast new orders are arriving versus how fast they're being shipped) hit 2.9. This means for every one dollar of equipment shipped, 2.90 dollars of new orders are coming in. Demand is far outrunning supply.

Why this matters as an investor: Before a hyperscaler like Amazon or Microsoft can use Vertiv's cooling equipment in their data centers, they must go through an extensive qualification and testing process that typically takes 2 to 3 years. Once a company is a qualified supplier and has its systems installed in a live facility, replacing them is extremely disruptive and costly. This creates a powerful form of customer lock-in, which investors call switching costs.

Layer 5: PCBs and Connectors — The Unsexy Infrastructure Inside Every Server

Open up any AI server and look inside. Before you can see a single GPU chip, you'll notice green boards covered in tiny circuits, plus dozens of cables connecting every component to every other component.

PCB (Printed Circuit Board): The green board inside almost every electronic device that provides the electrical connections and mechanical support for all the components. Without a PCB, a GPU chip has nowhere to sit and no way to receive power or communicate with other parts.

TTM Technologies (NASDAQ: TTMI) is one of the largest manufacturers of printed circuit boards in North America, with a growing focus on the complex, high-density boards required for AI servers. Their data center computing segment grew 57% year-over-year in late 2025, with management guiding for further 66% growth in early 2026.

Amphenol Corporation (NYSE: APH) makes the connectors and cable assemblies that link every component inside an AI server: chips to boards, boards to power supplies, servers to networking equipment. These connectors may seem small and simple, but in high-density AI servers running at extreme speeds and temperatures, the specifications are extraordinarily demanding. Amphenol has spent decades building expertise in this area and maintains deep relationships with every major server manufacturer.

Why this matters as an investor: The components made by companies like TTM and Amphenol are not commodities. Hyperscalers qualify specific suppliers for specific designs, and these qualification processes take months to years. Once qualified, these suppliers receive steady, predictable orders for the life of a data center design, typically 3 to 5 years.

Layer 7: Cybersecurity — The Invisible Tax on Every AI Deployment

The more AI that gets deployed, the more software is running, the more data is being processed, and the more entry points exist for hackers to exploit.

Security professionals use the term attack surface to describe the total number of ways a hacker could potentially get into a system. Every new AI application, every new API (application programming interface, which is a connection point that lets different software systems talk to each other), and every new data pipeline expands this attack surface.

What makes this particularly challenging today is that AI itself can be used by hackers. AI tools can now scan software for vulnerabilities, write exploit code, and launch attacks at a speed and scale no human team could match. This is creating an arms race: defenders need AI-powered security tools just to keep up with AI-powered attacks.

The market is responding accordingly. Global cybersecurity spending is projected to reach 240 billion dollars in 2026, growing three to four times faster than the broader technology industry.

CrowdStrike (NASDAQ: CRWD) protects businesses from hackers using an AI-powered platform that monitors activity across every device, server, and cloud workload a company uses. Their business model is subscription-based: customers pay an annual fee (measured as ARR, or Annual Recurring Revenue, which is the total value of subscription contracts active at any given moment). CrowdStrike's ARR reached 5.25 billion dollars in early 2026, growing 24% year over year.

What makes CrowdStrike technically distinctive is that their software operates at the "kernel level," the deepest layer of a computer's operating system, giving them visibility into threats that other security tools might miss. Their platform also benefits from network effects: the more devices it monitors, the more threat intelligence it collects, which makes it better at detecting new attacks for all customers.

Palo Alto Networks (NASDAQ: PANW) is the largest pure-play cybersecurity company by market capitalization. While CrowdStrike focuses primarily on endpoint security (protecting individual devices and servers), Palo Alto takes a broader approach covering network security, cloud security, and AI-specific threat protection. Their next-generation security ARR reached 6.3 billion dollars in Q2 of FY2026, growing 33% year over year.

SentinelOne (NYSE: S) built its entire security platform around AI from day one, unlike older security companies that added AI features to existing products. Their Purple AI product, which automates security investigations that would otherwise take a human analyst hours, achieved above 50% attach rates in Q4 2025, meaning more than half of new customers are choosing to add this AI layer to their security subscription.

Why this matters as an investor: Cybersecurity spending is what financial analysts call "non-discretionary." A company that has deployed AI across its operations cannot cut its security budget without risking a catastrophic breach. This stickiness (the fact that customers cannot easily switch off their security tools) creates the kind of recurring, predictable revenue that investors value highly.

India — The AI Infrastructure Story the World Is Sleeping On

India is in the middle of a data center construction boom that most global investors have not yet noticed.

In 2024, India had approximately 950 megawatts of total data center capacity, already a significant amount, comparable to many developed markets. By 2030, that number is projected to reach 8,000 megawatts. That is an 8x increase in six years.

The catalyst is a combination of AI adoption, digital services growth, and India's data localization rules (which require certain types of data about Indian citizens to be stored within India). Global technology giants including AWS, Microsoft, Google, Adani, and Reliance are all investing heavily in India's data center market.

In early 2026, OpenAI announced a partnership with India's Tata Group to build AI-focused data centers starting at 100 megawatts and scaling toward 1 gigawatt. TCS (the Tata Group's IT arm) announced it would invest up to 7 billion dollars in this HyperVault data center unit.

This is not a distant future story. The construction is underway. And the Indian public markets have companies at every layer of this buildout. Most investors outside India have never heard of them.

Layer 5 (India): Cables and Fiber — The Arteries of India's AI Buildout

Every data center needs two types of cables: power cables (to bring electricity from the grid into the facility) and data cables / optical fiber (to move information between servers at extremely high speeds).

Optical fiber refers to cables made of glass or plastic that transmit data as pulses of light rather than electrical signals. Because light travels faster than electricity and doesn't degrade over distance, optical fiber is the backbone of modern digital infrastructure, connecting servers within a data center, connecting data centers to each other, and connecting the internet across continents.

India's cable industry was built primarily for housing construction, industrial power distribution, and telecom networks. Data centers are a new and rapidly growing customer segment that demands higher-specification cables in enormous quantities.

Polycab India (NSE: POLYCAB) is India's largest manufacturer of wires and cables, with a market share above 25% in the organized domestic market. What began as a company supplying copper wires to housing projects and factories is now entering the data center cabling segment. Their distribution network of more than 4,000 dealers and 200,000 retail touchpoints gives them a logistics advantage that is extremely difficult for smaller competitors to replicate.

KEI Industries (NSE: KEI) focuses on higher-voltage industrial and infrastructure cables, the kinds used in power plants, large commercial buildings, and now data centers. Where Polycab is stronger in the retail and housing segment, KEI has built deeper relationships with large industrial and infrastructure clients, making them a natural supplier as those same clients build data centers.

Sterlite Technologies (NSE: STRTECH) has a specific and valuable niche: they manufacture optical fiber preforms (the raw material from which optical fiber cables are made), as well as the cables themselves. As India builds out its data center capacity, Sterlite stands to benefit at the supply chain level. International technology media including LiveMint have specifically identified Sterlite as a potential beneficiary of the AI data center wave in India.

Why this matters as an investor: Here is a key insight about cable companies and AI infrastructure. Unlike the race between AI software companies, where it is genuinely uncertain whether OpenAI, Google, or some new entrant will "win," cable demand is agnostic to the winner. Whoever builds the data centers will need cables. This makes cable companies a more "infrastructure-like" exposure to the AI theme, regardless of which AI company ultimately dominates.

Layer 6 (India): Electronics Manufacturing — India as the World's New Factory

When most people think of AI manufacturing, they think of NVIDIA's chips being made in Taiwan or South Korea. But the physical assembly of the servers, routers, and devices that carry AI to end users is rapidly moving to India.

EMS (Electronics Manufacturing Services): Companies that manufacture electronic products on behalf of other companies (called OEMs, or Original Equipment Manufacturers) who design the products but outsource the actual making. Think of it this way: when you buy a Samsung phone, Samsung designed it, but a factory physically assembled it. EMS companies are that factory.

India's government has recognized the strategic opportunity here. The PLI scheme (Production Linked Incentive) offers cash incentives to manufacturers who produce electronics in India above a certain threshold. In the Union Budget of 2026, the government announced a Rs 40,000 crore (crore: one crore equals ten million; Rs 40,000 crore equals approximately 4.7 billion US dollars) expansion of the Electronics Component Manufacturing Scheme (ECMS), directly targeting the kind of components that go into AI servers and devices.

The Apple-India manufacturing story is the most visible headline. But the deeper supply chain story is who makes the PCBs, connectors, and sub-assemblies. That is where the interesting companies live.

Dixon Technologies (NSE: DIXON) is India's largest contract electronics manufacturer. They assemble mobile phones under brands like Samsung and Motorola, televisions, LED lights, washing machines, and more. Dixon is now positioning itself to manufacture AI-related hardware components as global electronics supply chains increasingly route through India. Their model benefits from the same structural tailwind driving global electronics companies to diversify away from China.

Kaynes Technology (NSE: KAYNES) operates in a more specialized segment: they manufacture printed circuit boards and electronics assemblies for defense, aerospace, medical equipment, and industrial automation, industries where precision and certification matter enormously. As AI gets embedded into defense systems, industrial robots, and medical devices, Kaynes's specialized capabilities become more valuable.

Amber Enterprises (NSE: AMBER) primarily manufactures components for air conditioners, which positions them adjacent to the data center cooling space. More interestingly, Amber has been building capabilities in electronics manufacturing services beyond HVAC, and their existing thermal management expertise has potential applications in data center cooling systems.

Why this matters as an investor: Government policy in India is rarely a short-term factor. The PLI and ECMS schemes are multi-year programs designed to build entire supply chains. Companies that are early and well-positioned within these programs often benefit from first-mover advantages with global OEM customers, and those customer relationships tend to be sticky once established. For a deeper look at how this kind of structural advantage works, see our guide on understanding economic moats.

Layer 3 (India): Power Utilities — The Grid Meets the GPU

The same power challenge facing US data centers exists in India, but with an additional complexity. India's electricity grid has historically been stressed, with regular shortfalls in certain states. Adding 8,000 megawatts of new data center demand (equivalent to several large power plants) to a grid that already struggles in peak season is a genuine infrastructure challenge.

This challenge is also an opportunity for India's power sector.

A gigawatt (GW) equals 1,000 megawatts, enough electricity to power roughly 750,000 to 1,000,000 average Indian homes. India's planned data center buildout to 8 GW by 2030 represents a massive new source of electricity demand.

Tata Power (NSE: TATAPOWER) is an integrated power utility that generates, transmits, and distributes electricity across multiple states. Tata Power's CEO has publicly stated that they expect 1 gigawatt of new power demand from AI data center capacity. Given that Tata Group itself is building a 1 GW data center in partnership with OpenAI, Tata Power is uniquely positioned as both a supplier and a beneficiary of India's AI infrastructure buildout.

NTPC Limited (NSE: NTPC) is India's largest power generation company, government-owned, operating coal, gas, hydro, solar, and wind plants. NTPC has historically supplied power to state distribution companies under long-term agreements. Data centers, which need 99.999% uptime (meaning they can tolerate less than five minutes of downtime per year), represent a new category of premium, reliable customer that NTPC is well-positioned to serve. Indian financial media has specifically highlighted NTPC as a power sector beneficiary of the AI data center wave.

Why this matters as an investor: India's power sector has a structural characteristic worth understanding. Building a new power plant (especially a large thermal, nuclear, or hydro plant) takes 7 to 10 years from planning to operation. The companies that already have generation capacity in place today are in a strong position as new demand arrives, because supply cannot be created quickly. For a comparable US dynamic, recall how nuclear plant owners like Constellation benefited from the same scarcity logic.

Layer 8 (India): Mid-Tier IT Services — The Quiet Outperformers

Everyone in India knows TCS. Most people are familiar with Infosys and Wipro. These are the tier-1 IT services giants, companies valued between 50 billion and 100+ billion US dollars, employing hundreds of thousands of people, serving clients across every continent.

Here is the challenge with giant companies: growing fast is mathematically harder at scale. To grow 10% annually, TCS needs to add roughly 2 to 3 billion dollars in new revenue every year. For a company adding 5,000 new employees each quarter, every new project must be enormous to move the needle.

Mid-tier IT companies face no such constraint. Their smaller size means even a handful of significant AI-related contracts can meaningfully shift their growth rate. And because they are often built around one or two industry specializations rather than serving everyone, they can go deeper with clients in ways that generalist giants cannot always match.

As we explored in our TCS analysis, TCS's horizontal breadth is part of its moat. But that same breadth can make it slower to specialize. Mid-tier companies have the opposite dynamic: narrow focus, faster speed.

Persistent Systems (NSE: PERSISTENT) builds and manages software for banks, healthcare companies, and technology firms. In FY2026, Persistent reported revenue of 1.65 billion dollars, growing 17% year over year, and has delivered growth for 24 consecutive quarters. They have publicly targeted 2 billion dollars in revenue by FY2027. What makes Persistent's AI story credible: approximately 82% of their revenue comes from clients who are actively using Persistent's AI tools (platforms called SASVA 3.0 and iAURA 2.0), rather than simply discussing AI pilots.

Mphasis (NSE: MPHASIS) focuses almost exclusively on banking and financial services. In Q2 FY2026, 42% of their new Total Contract Value (TCV, which is the total value of all contracts signed during a period) wins contained AI components. Their banking and financial services pipeline grew 45% year over year driven by AI-related demand. Mphasis benefits from the fact that financial services firms face enormous pressure to adopt AI for fraud detection, risk modeling, and customer service, areas where Mphasis has deep domain expertise.

Oracle Financial Services Software (NSE: OFSS) occupies a particularly interesting niche: they sell the core banking software that runs inside banks across the world, handling everything from account management to regulatory compliance and fraud detection. Their product is not glamorous. It does not show up in headlines. But once a bank has deployed OFSS software, replacing it costs tens of millions of dollars and years of disruption. This stickiness is reflected in OFSS's financial profile: a 40.6% EBITDA margin (EBITDA stands for Earnings Before Interest, Taxes, Depreciation, and Amortization; it is a measure of operating profitability. A 40%+ margin means nearly half of every rupee of revenue becomes profit before taxes), zero debt, and a 3.8% dividend yield.

Why this matters as an investor: The key difference between mid-tier IT companies and generalist giants is focus. A bank that has embedded Persistent Systems' AI tools into its core software workflows faces real switching costs, not because it is locked in contractually, but because the effort of finding an alternative, re-training staff, and migrating data is enormous. This is the same "switching cost" moat we see in cybersecurity, applied to a different industry.

How to Think About These Companies as an Investor

This is not a "buy these stocks" article, and deliberately so. What it is, is a map. A map of where the AI value chain actually runs, and which publicly listed companies are positioned at each point on that map.

When you encounter any company claiming to benefit from AI, here are three questions worth sitting with before you dig deeper:

Question 1: Is AI genuinely new demand for this company, or is it just re-labeling old demand?

A cooling company that is building entirely new liquid cooling products for GPU clusters is seeing genuinely new demand. An IT services company that is renaming existing projects as "AI projects" without any change in the actual work is not. The test is simple: can you find specific AI revenue numbers, not just AI mentions in press releases? Companies like Vertiv (orders up 252%), Persistent Systems (82% of revenue from clients using AI tools), and Mphasis (42% of new TCV with AI components) pass this test. Others may not.

Question 2: Does the company have switching costs with its customers?

Switching costs are what make a business hard to displace even when competitors offer better or cheaper products. Vertiv's cooling systems take 2 to 3 years to qualify before installation, and once installed, they are not going to be ripped out mid-operation. OFSS's core banking software is deeply embedded in bank operations. CrowdStrike's kernel-level software is deeply integrated into every device it protects. High switching costs create revenue predictability and pricing power.

Question 3: Is the moat widening or narrowing as AI spending grows?

A nuclear power plant cannot be replicated in five years. That moat widens as AI demand grows. A PCB manufacturer with years of hyperscaler qualification experience has a widening moat as new entrants face the same multi-year qualification process. Conversely, a company with no technical differentiation in a commodity business may find its moat narrowing as more competitors enter. Our guide to understanding economic moats covers this framework in detail.

The red flag to watch for: Companies that add "AI" to their marketing without any change in their actual business. Ask: what percentage of revenue is traceable to AI demand specifically? Learning how to read annual reports will help you find the honest answers buried in financial disclosures.

A note on valuation: Many of the companies discussed in this article trade at lower price multiples than headline AI names like NVIDIA or Alphabet. Whether a lower multiple represents an opportunity or a value trap depends entirely on the quality of the underlying business and the durability of the AI tailwind. Our Valuation 101 guide and guide to P/E ratios walk through how to approach this question.

A note on concentration risk: Understanding that the AI "stack" has eight layers rather than one is itself a useful insight for thinking about portfolio diversification. Exposure concentrated entirely in chip companies is very different from exposure spread across power, cooling, cables, and software.

Key Takeaways

The AI industry is an iceberg. The apps consumers see represent roughly the top layer; beneath them lies a vast infrastructure of power, cooling, cables, security, and software that most people never think about.
In the USA, four non-obvious layers stand out. Nuclear and power utilities (Vistra, Constellation, Eaton) providing the electricity AI demands. Liquid cooling companies (Vertiv) solving a thermal problem that has no alternative solution. PCB and connector manufacturers (TTM Technologies, Amphenol) supplying the unsexy but essential components inside every AI server. Cybersecurity companies (CrowdStrike, Palo Alto Networks, SentinelOne) providing the non-discretionary protection that AI expansion requires.
In India, the story is equally compelling but far less discussed. Cable manufacturers (Polycab, KEI, Sterlite) are building the arteries of India's 8x data center expansion. Electronics manufacturers (Dixon, Kaynes, Amber) are positioning India as the global alternative to China. Power utilities (Tata Power, NTPC) face structurally new demand. Mid-tier IT companies (Persistent Systems, Mphasis, OFSS) are growing faster than the giants they are often overshadowed by.
The best analytical question is not "which AI company will win?" The AI picks-and-shovels companies benefit regardless of whether OpenAI, Google, or some future entrant wins the AI race. As long as AI infrastructure spending continues to grow, the layers that support it grow with it.
Switching costs and moats are the key filters. Companies with deep customer relationships, long qualification cycles, or embedded software are harder to displace than companies selling commodity products or easily substituted services.
Watch for the AI veneer problem. Many companies will claim AI benefits without being able to show AI-specific revenue growth. The ones worth serious research are those with measurable, specific numbers, not just strategic positioning language.
This article is educational and does not constitute investment advice. The companies mentioned are discussed to illustrate industry dynamics and business models. Every investment decision requires individual research, understanding of personal financial circumstances, and ideally guidance from a qualified financial advisor. Always do your own research before making any investment decision.

Disclaimer: This analysis is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. All data cited is from publicly available sources as of April 2026. Markets are forward-looking and uncertain; past industry growth does not guarantee future returns. Consult a SEBI-registered advisor (for Indian markets) or a licensed financial advisor (for US markets) before making investment decisions.