AI Investing After Earnings Season: Demand, Power, and the Next Layer of Growth

May 27, 2026

Michael Landsberg’s BNN Bloomberg appearance centered on a strong earnings season, but one of the clearest takeaways was about artificial intelligence. The AI trade is not fading. It is widening.

Landsberg summed up the theme directly:

“AI is still very much a big theme and should be invested in accordingly.”

That does not mean every AI-related investment deserves the same attention. It means investors may need to think about AI in a broader way. The first phase of AI investing has been tied heavily to chips, computing power, and the companies building the core tools. The next phase may involve power generation, data centers, cybersecurity, industrial capacity, cloud infrastructure, and businesses that support AI adoption behind the scenes.

The bigger question is no longer whether AI matters. It is where AI spending is turning into earnings, backlog, pricing power, and long-term business demand.

Why AI Remains a Serious Investing Theme

AI expectations are already high. That creates a strange market setup. A company can report strong growth, strong demand, and strong earnings, yet investors may still react with less excitement than expected.

Landsberg described that investor reaction with a sports comparison:

“It’s really reminiscent of almost like watching Tiger Woods at his prime.”

The point is simple. When the market gets used to strong AI-related growth, it takes even stronger results to surprise investors. That does not mean demand is weak. It means expectations have moved higher.

That matters because AI investing is now moving from excitement to proof. Investors are asking better questions:

• Is demand showing up in earnings?
• Is there a backlog?
• Can supply keep up?
• Is pricing power still present?
• Is the business tied to a real need in the AI buildout?
• Is the valuation already pricing in too much future growth?

Those questions are important because AI is no longer just a theme. It is becoming a capital spending cycle.

AI Spending Is Becoming Too Large to Ignore

One reason AI remains central to markets is the scale of spending. Gartner forecast worldwide AI spending at nearly $1.5 trillion in 2025, with global spending expected to pass $2 trillion in 2026. Gartner also forecast generative AI spending at $644 billion in 2025, up 76.4% from 2024.

IDC’s forecast also points to strong growth. IDC projected worldwide AI spending, including AI-enabled applications, infrastructure, and related IT and business services, to reach $632 billion by 2028, with a 29.0% compound annual growth rate from 2024 through 2028.

How Is AI Impacting the World of Investing?

AI is changing investing by widening the way investors evaluate growth. The discussion is no longer limited to technology companies or software tools. It now includes earnings strength, capital spending, infrastructure demand, power needs, cybersecurity, and portfolio concentration.

For investors, the AI story raises a practical question: where is demand turning into real business results?

AI Is Changing Where Investors Look for Growth

AI is pushing investors to look beyond consumer-facing tools. The spending is moving into the systems that make AI possible:

• Computing capacity
• Data centers
• Electricity
• Cooling systems
• Cloud infrastructure
• Cybersecurity
• Automation tools
• Enterprise software
• Industrial equipment
• AI-enabled business services

That is why Landsberg’s comments matter. He did not frame AI as a narrow trade. He pointed toward adjacent areas that may benefit from the AI buildout.

He said:

“I think you need to continue to diversify.”

That line is important. A narrow AI portfolio may work during one phase of the cycle, but AI adoption can spread across many parts of the economy. As spending broadens, investors may need to look at where demand is building outside the obvious areas.

AI Is Making Earnings Quality More Important

AI can create excitement, but earnings still matter. Investors should separate companies that only talk about AI from companies that are getting paid because of AI demand.

Useful signs include:

• Revenue tied to AI-related demand
• Backlog
• Margin strength
• Customer renewals
• Pricing power
• Long-term contracts
• Capacity constraints
• Clear capital spending plans from customers

A company may be connected to AI, but that connection needs to become financial performance. That is where earnings season becomes useful. It gives investors a way to separate market enthusiasm from business results.

AI Is Raising the Value of Infrastructure

AI is digital, but its requirements are physical. It needs electricity, real estate, cooling, servers, networking equipment, and data center capacity.

That is why Landsberg’s comment stands out:

“Anywhere you can get power is going to be positive for the AI story.”

This is one of the clearest signals that AI investing is changing. Investors are not only looking at software or chips. They are looking at the systems required to keep AI running.

How Much Money Is Invested in AI?

How much money is invested in AI depends on what gets counted. Some figures track private funding. Others include corporate spending, mergers and acquisitions, research and development, data infrastructure, computing hardware, software, skills, and organizational investment.

That is why AI investment is difficult to reduce to one number. OECD.AI notes that AI investment data can vary because some figures reflect actual spending, while others include commitments, budget plans, or broader categories such as R&D and infrastructure. It also frames AI as a general-purpose technology, meaning its investment footprint extends beyond software into skills, data, hardware, and organizational systems.

Stanford HAI’s 2025 AI Index offers another useful view. Corporate AI investment reached $252.3 billion in 2024, while private investment rose 44.5% from the prior year. Generative AI alone attracted $33.9 billion in private investment, up 18.7% from 2023 and more than 8.5 times higher than 2022 levels.

For investors, the takeaway is not just the size of the number. It is where the money is going. AI spending is moving into computing capacity, data infrastructure, power needs, cybersecurity, and business systems. That supports Landsberg’s broader point: AI demand is not only about applications. It is about capacity.

The Next Layer of AI Investing May Be Power and Infrastructure

AI requires energy. That is why data centers have become part of the AI investment discussion.

The International Energy Agency projects global electricity consumption from data centers to reach about 945 TWh by 2030. It also projects data center electricity consumption to grow about 15% per year from 2024 to 2030, far faster than overall electricity demand from other sectors.

Goldman Sachs Research forecast global power demand from data centers to rise 165% by 2030 compared with 2023. The same research noted that U.S. data center construction spending tripled over three years.

That supports the AI-adjacent investment idea. Landsberg described one part of the opportunity this way:

“This is kind of an AI adjacent play.”

AI-adjacent investing focuses on companies that may not be pure AI businesses, but still benefit from AI adoption. Examples can include firms tied to power, data center infrastructure, cooling, networking, cybersecurity, cloud systems, and industrial capacity.

The key is not to force every company into the AI category. The key is to ask whether AI demand creates a real business tailwind.

Cybersecurity Is Part of the AI Investment Story

AI adoption can also increase the need for cybersecurity. As more companies use AI across workflows, customer service, finance, logistics, research, and internal operations, the risk surface can expand.

Landsberg named cybersecurity as one of the themes connected to the broader AI story:

“Obviously AI being a theme, cyber security.”

That link makes sense. AI can improve productivity, but it can also create new risks around data access, identity, fraud, model misuse, and vendor exposure. For investors, cybersecurity may become a practical part of AI adoption rather than a separate technology category.

The question is whether cybersecurity spending keeps moving from optional to necessary as AI becomes more embedded in business operations.

AI Adoption Is Broad, But Business Value Still Takes Work

AI use has spread quickly. McKinsey’s 2025 survey found that 88% of respondents said their organizations use AI in at least one business function, up from 78% a year earlier. Yet only 39% reported EBIT impact at the enterprise level.

That gap matters for investors.

AI adoption alone is not the same as profit growth. Many companies are still testing tools, redesigning workflows, and figuring out where AI improves productivity. This is why investors should watch for proof, not just adoption.

A stronger AI investment case may include:

• AI tied to measurable revenue
• AI tied to lower costs
• AI tied to better margins
• AI tied to customer retention
• AI tied to faster production or service delivery
• AI tied to recurring demand

Without those signs, AI may be more of a spending line than an earnings driver.

Competition and Supply Constraints Still Matter

Landsberg also acknowledged that competition will come. That is a normal part of a fast-growing market. High demand attracts capital, and capital attracts new suppliers.

The important question is whether the market grows fast enough to absorb new competition. If demand remains above supply, strong companies may still benefit. If supply catches up too quickly, pricing power can weaken.

This is why backlog and capacity matter. A backlog can signal demand, but it can also invite new entrants. Investors should watch whether backlog converts into revenue, whether customers keep spending, and whether pricing stays firm as competition builds.

How Investors Can Think About AI Without Chasing Hype

AI may remain a strong theme, but discipline matters. Landsberg’s comment about diversification is important because crowded trades can create risk. When too many investors own the same narrow slice of the market, expectations can become hard to meet.

A practical AI investing framework should focus on four questions.

1. Is AI Demand Showing Up in Earnings?

Look for revenue growth, margins, backlog, and guidance. AI demand should be visible in the numbers, not only in management commentary.

2. Is the Business Necessary to the AI Buildout?

Companies tied to electricity, data centers, cooling, cloud infrastructure, cybersecurity, automation, and industrial capacity may benefit if their products or services solve real AI bottlenecks.

3. Is the Valuation Already Pricing In Too Much Growth?

Even a strong business can be a poor investment at the wrong price. Investors should compare earnings growth with valuation, cash flow, and future demand.

4. Is the Portfolio Too Concentrated?

AI can be a useful theme, but too much exposure to one part of the market can raise risk. A broader approach may include direct AI exposure, infrastructure, cybersecurity, and other AI-adjacent areas.

Final Thoughts: AI Investing Is Moving Into Its Next Phase

The AI story is no longer only about one part of the technology market. It is becoming a broader earnings, infrastructure, power, and cybersecurity story.

Landsberg’s key message was clear:

“AI is still very much a big theme and should be invested in accordingly.”

The next step for investors is to think beyond the headline. AI spending is large, adoption is spreading, and power demand is rising. But the better investment decisions may come from asking where AI demand becomes real revenue, where infrastructure limits create pricing power, and where portfolio exposure needs to be managed with discipline.

AI may still be one of the defining investment themes of this earnings cycle. The opportunity now requires a wider lens.