Investing in AI: Assessing the investment opportunities

Artificial Intelligence (AI) has moved beyond speculation and now shapes how companies build products, run operations, and compete. You see that shift in how businesses invest in AI tools and the computing power that supports them, which many analysts link to recent technology stock gains. AI is already influencing earnings and business performance across multiple sectors, even as the story continues to evolve quickly.

The collision of a powerful new technology with uncertain outcomes often creates  a bumpy ride for markets. Investors have reacted to rapid increases in capital spending, limited visibility into future profitability, and the possibility that AI disrupts subscription software business models. AI capabilities keep expanding at an accelerating pace, and those surprises can move markets before investors fully digest economic and corporate implications.

AI focused companies have delivered strong profit growth in recent years, and forecasts still point to meaningful expansion. The Bloomberg AI Index, comprised of 50 stocks, posted annualized earnings growth above 20% in recent years, far exceeding the S&P 500. Expectations call for Bloomberg AI Index earnings growth of 39% in 2026 versus 12.6% for the S&P 500, which helps explain why investors remain engaged even during pullbacks.

News headlines trigger market jitters

Technology stocks began 2026 on a weaker note after several years of leadership supported in part by heavy AI spending. After rising 24% in 2025, the S&P 500 technology sector is down 5.5% through February 9, 2026, driven largely by a 16.2% decline in software stocks. The Bloomberg AI Index, comprised of 50 AI companies, rose over 34% in 2025 and is down 2.6% year-to-date. Yet the broader S&P 500 is up 1.8% year to date as other areas offset technology’s weakness, showing leadership has broadened rather than vanished.

If investors already pay up for future success, one new headline can spark quick selling as people cut risk first and sort out implications later. Evidence that the future is more uncertain cause investors to quickly pull back on exposures out of fear their original analysis is incorrect. That “sell first, analyze later” pattern becomes more common when the payoff timeline is unclear and confidence hinges on future proof that has not arrived yet.

However, the current environment also differs from the classic pattern people associate with an equity market bubble. Typical bubble behavior tends to involve companies associated with a theme performing well for a time, regardless of fundamental merits, often with companies exhibiting both strong and weak fundamentals performing well. Yet today you can still see investors differentiating stronger businesses from weaker ones. Markets have also demanded more compensation to fund riskier technology borrowers in recent months, reinforcing that investors are differentiating rather than buying everything with an AI label.

AI spending growth is real, but the payoff may take time

Demand for AI computing power continues to accelerate as consumers and businesses use technology to improve processes, gain speed, scale, and efficiency, and achieve better outcomes. Several of the world’s largest cloud providers (companies that supply computing resources—such as data storage, processing power, networking, and software—over the internet instead of through on‑premises hardware) are investing heavily in AI infrastructure and data centers to meet that demand. Amazon, Microsoft, Alphabet, Oracle, and Meta are expected to deploy approximately $650 billion in 2026, up 70% from an estimated $380 billion in 2025.

That spending can benefit companies that supply chips and essential components, but it also raises a practical question: when does the payoff become clearer? Returns on AI investments remain works in progress and may take several quarters (or years) before visibility improves. Until that visibility improves, you should expect volatility as markets try to connect today’s spending with tomorrow’s profits.

Over the past few months, investors have moved away from the idea that “every tech stock is a winner” toward a narrower set of opportunities across the AI investment universe. That shift reflects uncertainty about which business models benefit most as AI impacts diverge across industries. In plain terms, markets are starting to separate “AI excitement” from “AI results.”

AI is reshaping the software business

AI is pushing companies that sell software subscriptions to adapt faster, because new tools can replicate or replace parts of what traditional software packages do. Anthropic’s Claude Cowork product expansion served as a proximate trigger for the recent software sell-off. On February 4, 2026, Anthropic released plugins that included tools for contract analysis and document generation, finance and data marketing capabilities that overlap with specialized analytics software. Additionally, their open-source licensing lowers barriers to adoption and intensifies competition.

When prices already assume a lot of future success, headlines like these can feel like a shock even if the longer-term implications take time to unfold. You can see why investors worry that AI could compress pricing power and reduce switching costs that previously protected incumbent businesses. At the same time, it is illogical to assume that every company will replace every layer of mission critical enterprise software, so disruption is more likely to be selective than universal.

This is why the recent pullback does not automatically mean the software industry is collapsing. Instead, it can signal that investors are repricing companies based on how well they can defend their advantages and adapt their products. The key question becomes simple: who can prove they still deliver distinctive value when AI becomes widely available?

Investor playbook: Diversify broadly and focus on AI’s building blocks

In a high volatility environment, you can reduce regret by starting with broad diversification rather than trying to pick a single winner. A diversified allocation to global companies helps when leadership rotates and when outcomes vary across industries. This approach also fits periods when markets react quickly to headlines and later reassess with more complete information.

From there, you can focus on “AI building blocks” that support adoption across many possible outcomes. Data infrastructure—data capture, storage, processing, analytics, security, and electrification—matters regardless of which AI application dominates next. You can also look for software companies with clear strengths in privacy, security, governance, and compliance, because businesses often need trusted systems before they scale new tools.

Even after a pullback, the outlook for the Information Technology sector can remain constructive when earnings rise and economic conditions stay supportive. Greater visibility into the return on AI spending—and clearer evidence about which companies have durable advantages, especially unique data they own—can act as catalysts in coming weeks and quarters. Talk with a wealth professional if you have questions about technology sector investments, your personal financial circumstances or investment portfolio.

AI basics in plain language

Scientists define Artificial Intelligence as simulating human intelligence in machines and computers, and the field includes several related approaches. Machine learning trains systems to perform specific tasks using data and algorithms, often uncovering patterns people miss. Deep learning uses multiple neural network layers to support voice and vision tasks, such as voice assistants and driver assistance features that “see” the road ahead.

Generative AI (GenAI) goes a step further by mastering language and creating new content such as text, code, audio, and video. Agentic AI extends GenAI by adding systems that plan, reason, and act across multi step tasks with limited oversight. AI’s rapid evolution helps explain why adoption can feel faster and broader than earlier technology waves.

Industry leaders describe the moment in sweeping terms that capture both excitement and urgency. “AI will likely become the biggest, the best, and most important of technology revolutions,” according to Sam Altman, CEO, OpenAI. Artificial intelligence “is one of the most profound technologies we are working on, as important or more than fire and electricity,” notes Sundar Pichai, CEO, Alphabet, while Jensen Huang, CEO, NVIDIA adds: “You’re not going to lose your job to AI, but you’re going to lose your job to somebody who uses AI.”

AI opportunities and risks: What to watch as AI adoption accelerates

AI spending growth has two engines: it can help companies grow revenue and improve efficiency by automating work and sharpening decisions. Organizations across nearly every sector are investing to unlock new revenue streams, streamline workflows, address talent shortages, and cut operational costs. For example, Amazon’s CEO has emphasized that the company expects to reduce its corporate workforce as it integrates more generative AI tools and agents, while encouraging employees to learn and experiment to do more with leaner teams.

You should also keep AI’s current limits in view, because they shape how quickly adoption can scale. AI engines do not possess human-like comprehension, consciousness, or common sense, and models may confidently produce false information—so verification still matters. AI systems can also inherit bias from training data, require substantial computing and energy resources, and raise intellectual property and job displacement concerns.

Finally, high valuations can create near term risk even when the long-term opportunity remains real. Many AI company valuations already reflect optimistic expectations that may not align with current profitability. If promised changes do not materialize in the coming months, sentiment could cool, even though modest market pullbacks and industry consolidation are normal dynamics.

FAQs

Why do markets sometimes drop quickly after AI headlines?

When prices are already high, markets can react sharply because investors have less patience for uncertainty. In that environment, new headlines can trigger a “sell first, analyze later” move as people reduce risk quickly and reassess once more details emerge. Elevated valuations can add to that sensitivity, especially when the payoff from large AI spending remains hard to measure in real time.

Does the 2026 pullback mean AI was a “bubble”?

A bubble typically lifts nearly anything connected to a theme regardless of fundamental merits, and recent market performance has not reflected that phenomenon. Instead, investors have separated stronger business models from riskier ones, and borrowing costs have reflected that differentiation for lower quality technology borrowers. You can interpret this as repricing and sorting, not a blanket rejection of AI’s longer-term potential.

What areas can matter even if the “winners” change over time?

AI adoption depends on dependable building blocks—trusted data, secure systems, and reliable infrastructure—not just one product or one company. Data capture, storage, processing, analytics, security, and electrification support many AI outcomes because they make tools usable at scale. Privacy, governance, and compliance also matter because many organizations require clear guardrails before they expand new technology across sensitive workflows.