AI Infrastructure Investing: Nvidia, OpenAI, Anthropic and the Future Race

Artificial intelligence remains one of the central themes of the global technology market. But in 2026, investors are increasingly looking beyond AI products and applications, toward the infrastructure without which the industry cannot grow. This means chips, data centers, cloud platforms, data-storage systems, and energy.

This market is already contested by the world's largest companies: Nvidia, OpenAI, Anthropic, Microsoft, Amazon, Google, Oracle, and new players from the data-center sector. For investors, AI is gradually turning into an infrastructure trend that could shape how capital is allocated across the technology sector in the coming years.

The market increasingly values a company's ability to build computing capacity, secure access to chips, sign long-term cloud-infrastructure contracts, and supply power to new data centers. The quality of the AI product still matters, but the decisive advantage is becoming access to the infrastructure that product runs on.

That is why investing in AI is increasingly seen as a distinct infrastructure trend. An AI company today is more than a software developer. It is part of a large chain that includes chips, servers, data centers, cloud platforms, energy, data-storage systems, and cybersecurity.

What Is Happening in the Artificial Intelligence Market Now

After the launch of generative AI, the market quickly moved past the experimentation stage to large-scale adoption. Companies use AI agents, corporate assistants, and tools for automating customer support, code development, analytics, and data processing. The wider these services spread, the higher the demands on the infrastructure that powers them: servers, chips, data centers, cloud platforms, and electricity.

That is why, in 2026, competition in the AI sector increasingly revolves around access to computing power. For OpenAI, Anthropic, and other major AI companies, what matters now is not only users and the product lineup, but also long-term cloud-infrastructure contracts, access to AI chips, and the ability to scale workloads quickly. Nvidia keeps its role as the key chip supplier, while cloud platforms and data centers are becoming a strategic resource for the whole industry.

Against this backdrop, rising AI investment is becoming one of the main drivers of the technology market. Capital is flowing ever more actively into chips, data centers, cloud platforms, and energy – the infrastructure that determines how fast AI services can scale. For investors, this turns AI into a separate infrastructure cycle, where most of the value forms around access to computing power and a company's ability to monetize that demand.

Why the Growth of AI Investments Affects the Market

AI requires large outlays before the business model is fully mature. Data centers must be built, chips bought, electricity reserved, data networks developed, and engineering teams hired – all in advance.

That is why AI investment affects several industries at once:

• semiconductors
• cloud services
• data centers
• energy
• cooling systems
• fiber-optic networks
• cybersecurity
• private equity and the pre-IPO market

Where investors once looked mainly at the stocks of AI companies, the focus has now broadened. Interest extends to the entire infrastructure that keeps artificial intelligence running.

This makes the market more complex. Some companies earn from selling chips, others from renting out computing power, others from cloud services, and others from storing and processing data. As a result, AI is becoming not a single sector but a technology layer that influences asset values across different parts of the economy.

What Is Included in AI Infrastructure

AI infrastructure is the set of technological and physical resources without which AI models cannot be trained or used. For an investor, it's important to understand that this market consists of several layers.

Data Centers

The data center has become one of the key assets of the AI economy. It is the physical site that houses servers, graphics processors, cooling systems, data storage, and network equipment. Data centers provide the computing power that AI models and enterprise services run on.

Investment in data centers is rising because training and running AI models require ever more electricity and computing resources. That is why the largest tech companies are aggressively expanding their infrastructure. One of the most visible examples is Stargate, a large-scale network of AI data centers in the US being developed by OpenAI, Oracle, and SoftBank. Oracle is also sharply increasing its capital spending on cloud and AI infrastructure, while Microsoft, Amazon, Google, and Meta keep building new sites for growing AI workloads.

In the US, demand is especially high in regions with access to power, land, and network infrastructure. Globally, interest is growing in sites across Europe, the Middle East, and Asia, where governments and corporations are building their own AI infrastructure, including local data storage and running models within their own jurisdictions.

For investors, data centers have become a separate area: through operators' stocks, REITs, infrastructure funds, private-equity funds, and stakes in private companies.

Chips and Hardware

The main beneficiary of the AI cycle is Nvidia. The company holds a key position in the market for graphics processors used to train and run AI models.

But the market isn't limited to one company. AMD, Broadcom, Marvell, Micron, TSMC, ASML, and makers of network equipment are also part of the AI chain. Demand is growing for GPUs, specialized accelerators, memory, optics, server racks, cooling systems, and network components.

Stocks of semiconductor AI companies have become one of the most prominent instruments for investors who want exposure to rising investment in AI technology. At the same time, the high valuations of these companies call for a cautious approach: the market is already pricing in much of the future growth.

Cloud Platforms

Microsoft Azure, Amazon Web Services, Google Cloud, and Oracle Cloud have become the main suppliers of computing power for AI companies and enterprise clients.

OpenAI is closely tied to Microsoft's cloud infrastructure. Anthropic is building partnerships with major compute providers. Oracle is sharply increasing its capital spending on AI infrastructure and trying to take a stronger position in the cloud-computing market.

Cloud platforms benefit from the fact that many companies don't want to build their own data centers. They rent capacity, pay for access to models, and use AI as a service. For the market, this creates long-term revenue for cloud providers and makes them an important part of AI infrastructure.

Data Storage and Processing Systems

AI models work with enormous volumes of data. So demand is rising for storage systems, fast data transfer, databases, labeling tools, information processing, and cybersecurity.

Companies that help store, structure, and protect data are also becoming part of the AI chain. This is especially important for the corporate sector, where adopting AI comes with requirements around confidentiality, security, and regulatory compliance.

The Largest AI Companies and Their Role in the Market

The largest AI companies can be divided into several groups.

• The first group is the model developers. These include OpenAI, Anthropic, xAI, Google DeepMind, Meta AI, and others. They create demand for computing power and set the technological agenda.
• The second group is the infrastructure providers. Nvidia, AMD, TSMC, Broadcom, Oracle, Microsoft, Amazon, and Google supply chips, cloud, data centers, and networking solutions.
• The third group is applied AI companies. They use models in specific industries: finance, healthcare, cybersecurity, legal services, education, logistics, marketing, and manufacturing.

For investors, it's important to distinguish these layers. Model developers can grow fast but require enormous spending. Infrastructure companies often have clearer revenue but already trade at a high premium. Applied AI companies can show strong growth if their product solves a specific business problem and scales without an excessive rise in costs.

How Investors Use AI

A separate area is AI for stock investing. Banks, funds, and private investors use artificial intelligence to analyze news, financial reports, market data, customer behavior, and macroeconomic signals.

AI helps process information faster, spot anomalies, compare companies, and build scenarios. But it doesn't remove the need for analysis. A model can be a useful tool, but the decision to buy or sell an asset stays with the investor.

In practice, AI is most often used for three tasks:

• initial analysis of companies and sectors
• monitoring news and risks
• assessing portfolio concentration and volatility scenarios

So AI is becoming not only an investment target but also a tool for analyzing the market.

How to Invest in AI

AI investment can be approached through several formats. Each has its own logic, accessibility, and level of risk.

AI Company Stocks

The most straightforward way is buying shares of public companies tied to AI. These can be Nvidia, Microsoft, Alphabet, Amazon, Meta, Oracle, AMD, Broadcom, TSMC, and other companies from the technology and infrastructure chain.

The advantage of this approach is liquidity. Shares can be bought and sold on the exchange. The main risk is high volatility and inflated market expectations.

ETFs and Funds

ETFs offer diversified exposure to the AI sector. Such funds can include shares of chipmakers, cloud providers, software companies, data centers, and cybersecurity.

This format reduces the risk of picking a single company, but the investor still depends on the overall dynamics of the technology market. In the Regolith catalog, such ETFs are available from $50 – for example, AIQ (AI and technology), SOXX (semiconductors and AI infrastructure), SMH (chipmakers), or BOTZ (robotics, automation, and AI).

Investments in Private AI Companies

The private market gives access to companies before they go public. These can be model developers, AI infrastructure, cloud services, cybersecurity, robotics, or applied AI platforms.

The potential here is higher, but liquidity is lower. Such deals require more thorough due diligence on valuation, the company's stage, the investors in the round, and exit prospects.

Pre-IPO Deals in the AI Sector

Pre-IPO is a format for participating in companies that have already gone through several growth stages and may be preparing for the public market. In the AI sector this format is especially interesting, because many of the largest AI companies remain private.

OpenAI, Anthropic, Databricks, and other companies are regularly discussed by the market as potential candidates for future IPOs. On Regolith, pre-IPO positions are available in technology companies, including in the AI sector – for example, Dataminr, an AI data-analytics platform.

AI Investments in the USA and Worldwide

AI investment in the US remains the largest in the world. The American market combines venture capital, public technology companies, developed cloud infrastructure, the largest chipmakers, and broad access to capital.

But AI investment is growing worldwide, not only in the US. Europe is developing its own AI infrastructure and tightening regulation. The Middle East is investing in data centers, energy, and sovereign AI. Asia remains a key region for component manufacturing, cloud services, and applied adoption.

Global competition is intensifying. Countries and corporations realize that AI infrastructure is becoming a strategic resource, comparable in importance to energy, telecommunications, and financial infrastructure.

What Risks Are Associated with AI Investments

Market growth doesn't cancel out the risks. On the contrary, the faster valuations rise, the more important it is to watch the fundamentals.

Risk of Overvalued Companies

Many AI companies are valued on the basis of future growth that still has to be confirmed by revenue and profit. If the pace of monetization falls short of expectations, the market can reprice them quickly.

Market Volatility

AI company stocks are sensitive to earnings reports, capital-expenditure forecasts, chip-related news, regulation, and interest rates. Even strong companies can make sharp moves on the market.

Regulatory Restrictions

AI touches data, security, copyright, and national infrastructure. Regulators in the US, Europe, and other regions may impose restrictions that affect AI companies' business models.

Portfolio Concentration Risk

Investors often concentrate in a few of the most popular AI assets. This increases the portfolio's dependence on a single sector and a limited number of companies. A more resilient approach involves diversifying across chips, cloud, data centers, software, and the private market.

How Regolith Helps Investors Access Investment Opportunities in AI Companies

AI infrastructure is becoming one of the main investment areas for the years ahead. But access to the most interesting companies often appears before the IPO, while they are still private and out of reach for most retail investors.

Regolith analyzes private-market deals and helps investors access companies in fast-growing technology sectors, including AI, infrastructure, fintech, Web3, and private markets.

This approach lets you consider not only the public stocks of AI companies but also private-market opportunities: pre-IPO, secondary deals, funds, and stakes in companies that may go public in the future.

In practice, these are different entry points for different budgets and horizons: from thematic AI ETFs (AIQ, SOXX, and others) with a threshold from $50 to pre-IPO positions in private technology companies, such as Dataminr.

At the same time, the key principle stays the same: what matters is not the popularity of the AI theme itself, but the quality of the specific company – its valuation, the investors in its capital, the business model, the competitive position, and the potential path to liquidity.

AI infrastructure has already become one of the main markets of the new technology cycle. For investors, it's a chance to take part in the growth of an industry shaping the future economy of data, computing, and automation.

This material is for informational purposes only and does not constitute investment advice.