Investing in AI: Assessing the investment opportunities

Artificial Intelligence (AI) has moved beyond speculation and now influences how companies build products, run operations, and compete. Businesses are increasing investments in AI tools and the computing power required to supports them, and analysts often link that spending to recent technology stock price gains. AI is already shaping earnings and business performance across multiple sectors, even as the investment story continues to evolve quickly.

Powerful new technologies rarely follow a straight path, and AI is no exception. Investors must weigh rising capital spending, unclear profit timelines and the possibility that AI will disrupt established software businesses. At the same time, rapid advances in AI capabilities can move markets before investors fully assess the broader economic and corporate implications.

AI focused companies have delivered strong profit growth in recent years, and forecasts still point to continued expansion. The Bloomberg AI Index, which includes 50 companies, has grown earnings at more than 20% annually in recent years, well above the S&P 500. Expectations call for 28% earnings growth for the AI Index in 2026 versus 21.6% for the broader market, helping explain why investor interest remains firm even during pullbacks.

AI stocks remain volatile as investors test expectations

Technology stocks started 2026 with some weakness after several years of strong performance but regained momentum in early April. After rising 25% in 2025, the S&P 500 technology sector is up another 17% through May 15, 2026, with semiconductor stocks leading the rally. The Bloomberg AI Index rose over 34% in 2025 and has gained 22% year-to-date, compared with a 9% increase for the broader S&P 500.

High expectations can heighten sensitivity to new information. When investors already price in strong future growth, a single headline can trigger quick selling. That “sell first, analyze later” pattern becomes more common when visibility into future profits remains limited and confidence depends on results that have not yet emerged.

The current environment still looks different from a traditional investment “bubble”. In a typical bubble, companies tied to a popular theme rise together regardless of business quality. Today, investors are more selective, continuing to differentiate between stronger and weaker companies, while requiring higher compensation to fund riskier businesses.

AI infrastructure spending is accelerating

Demand for AI computing power continues to expand as companies look to improve efficiency, increase speed and scale operations. Large cloud providers, which deliver data storage, processing power and software over the internet, are investing heavily in data centers and infrastructure to meet that demand. Amazon, Microsoft, Alphabet, Oracle, and Meta expect to spend approximately $740 billion in 2026, up roughly 80% from $411 billion in 2025.

That scale of investment supports a wide range of businesses that supply semiconductors, networking equipment, power systems and other critical components. It also highlights a key question for investors: when will this spending translate into profit growth? For now, returns on AI investments remain uncertain and may take several quarters or years to fully materialize.

AI is reshaping the software business

Investors have started to shift away from the idea that all companies benefit equally from AI. Instead, markets are focusing on which business models can convert AI innovation into durable results. This shift reflects growing recognition that AI impacts industries differently and that outcomes will vary across companies.

AI is pushing software companies to adapt quickly, as new tools can replicate or replace parts of traditional software offerings. Anthropic’s Claude Cowork release helped drive recent volatility after introducing tools for tasks such as contract analysis and document generation, along with an open approach that lowers barriers to adoption. In a YouTube interview, CFO Krishna Rao described the strategy clearly, noting, “Most of what we’re building is platform,” referring to systems customers can build upon and extend.

When expectations are high, developments like these can prompt sharp market reactions even if long-term impacts remain uncertain. Investors are assessing whether AI could pressure pricing models and make it easier for customers to switch providers, which could weaken competitive advantages. At the same time, it is unlikely that AI will replace every layer of business-critical software, suggesting disruption will be selective rather than widespread.

Recent market weakness reflects this reassessment rather than a breakdown in the industry. Investors are repricing companies based on their ability to adapt, differentiate and maintain value as AI capabilities expand. The central question remains, which companies can continue to prove their relevance as AI becomes more widely available?

AI basics in plain language

Artificial intelligence refers to technology that enables machines to perform tasks that typically require human intelligence. These systems use data to identify patterns, make predictions and support decision-making across a wide range of applications. A basic understanding of how AI works can help investors better evaluate opportunities and risks.

Machine learning allows systems to improve over time by learning from large data sets rather than relying on fixed institutions. Deep learning builds on this approach using more complex models that support tasks such as voice recognition and image analysis. Generative AI extends these capabilities by creating new content, including text, code, images and audio.

More advanced systems, sometimes called agentic AI, can plan, reason and complete multi-step tasks with limited human input. The pace of innovation continues to accelerate, which helps explain why adoption appears faster and broader than previous technology cycles. Industry leaders highlight the significance of this shift, with OpenAI CEO Sam Altman stating that “AI will likely become the biggest, the best, and most important of technology revolutions,” while Alphabet CEO Sundar Pichai and NVIDIA CEO Jensen Huang, CEO, NVIDIA emphasize both its transformative scale and growing impact on the workforce.

AI investment opportunities and risks to watch

Companies invest in AI for two primary reasons – to grow revenue and improve efficiency. Organizations across many industries are using AI to streamline workflows, automate repetitive tasks and uncover new ways to serve customers. In fact, generative AI could allow teams to do more with fewer resources over time, while also requiring employees to adapt and learn new skills.

Despite its potential, AI also comes with important limitations. These systems do not possess human judgment and can produce inaccurate results, which means oversight remains essential. AI also depends on large amounts of computing power and energy and raises ongoing questions about data privacy, intellectual property rights and workforce disruption.

High valuations introduce additional near-term risk. Many AI companies already reflect strong future expectations, which may not align with current earnings performance. If progress falls short of expectations, investor sentiment could weaken even as the long-term opportunity remains intact, reinforcing that pullbacks and industry consolidation are normal dynamics.

How to invest in AI with a diversified approach

A diversified approach can help investors navigate the uncertainty that comes with rapid technological change. Rather than focusing on a single company, spreading exposures across industries and regions can help manage shifting leadership and uneven outcomes. This approach also reduces the risk of reacting to short-term market swings driven by headlines.

Investors can also focus on the foundational elements that support AI adoption across multiple use cases. These can include data storage and processing, cybersecurity, energy infrastructure and governance tools that help companies deploy AI safely and effectively. Businesses with strengths in these areas may benefit regardless of which specific applications or platforms emerge as leaders.

Even after periods of volatility, the outlook for technology and AI remains constructive when earnings growth continues and economic conditions support investment. Greater clarity around returns on AI spending and stronger evidence of durable competitive advantages could serve as catalysts in in the months ahead. Pullbacks and consolidation are part of the process as markets sort through winners and long-term opportunities.

Talk with a wealth professional if you have questions about technology sector investments, your personal financial circumstances or investment portfolio.

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 early 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.