How to invest in AI technology in 2025?

What is artificial intelligence?

AI is the science of teaching machines to think, learn, and make decisions like humans—only faster, and sometimes better. It’s not one thing. It’s a bunch of technologies working together: generative AI, machine learning, natural language processing, neural networks, and more. Together, they help machines analyze data, recognize patterns, and take actions without being explicitly told what to do every time.

The idea’s been around since the 1950s. But what’s changed in the last few years is scale. More data. More computing power. Better algorithms. That’s why you’re now seeing AI generate images, write code, predict trends, drive cars, and even help doctors diagnose diseases.

In 2025, AI isn’t just software. It’s infrastructure. It’s embedded in tools you use every day—even when you don’t realize it. Think Google Search, ChatGPT, your bank’s fraud detection system, and the recommendation engine on Netflix. And if you’re thinking about investing in AI, understanding how these pieces fit together is your starting point.

The AI stack: Layers of innovation

Think of AI like a cake. Not the kind you eat—more like the kind with layers that all work together to make the whole thing function. If you’re investing in AI, it helps to know what these layers are. Why? Because each layer has different types of companies—and different types of opportunities.

1. Infrastructure layer

   This is the foundation. Chips, servers, and cloud platforms. It’s where all the heavy lifting happens. Companies like NVIDIA, AMD, and TSMC build the hardware that powers AI training and deployment. Then you’ve got Amazon Web Services, Microsoft Azure, and Google Cloud, which rent out the computing power to everyone else. If you’re bullish on the growth of AI overall, this is one of the safest (and earliest) places to look.

2. Model layer

   Here’s where the brains live. These are the large language models (LLMs), neural networks, and other algorithms that actually “learn” from data. Think OpenAI’s GPT, Anthropic’s Claude, or Google DeepMind. These models don’t just do one task—they’re flexible, powerful, and still evolving fast. Most of them aren’t publicly traded, but the companies building or integrating them—like Microsoft or Meta—are.

3. Application layer

   This is what end-users actually see. AI tools that write emails, generate videos, forecast sales, or automate customer support.

   This layer moves the fastest. New startups pop up every week, but not all of them stick. Companies like Zoom, Adobe, Salesforce, and Notion are baking AI into their products to make them smarter, faster, and more competitive.

Each layer has a different risk/reward profile. Infrastructure tends to be stable and capital-heavy. The model layer is cutting-edge but mostly closed. The application layer is where innovation (and competition) explodes. Smart investors look at the whole stack—not just the shiny stuff on top.

Why invest in AI?

AI is bringing in a foundational shift to how humans work, like the internet or electricity. AI is already changing how companies operate, how people make decisions, and how products are built. It’s saving time, cutting costs, boosting productivity, and opening up completely new business models (even things like AI business consulting).

Massive market growth

The global AI market is expected to cross $4.8 trillion by 2033. But the real story isn’t just the number—it’s the speed. From 2023 to 2025 alone, we’ve seen AI adoption go from early buzz to full-blown infrastructure. And that momentum isn’t slowing down.

AI touches everything

AI isn’t one industry. It’s in every industry.

• In healthcare, it’s helping with diagnosis and drug discovery.
• In finance, it’s behind fraud detection and algorithmic trading.
• In retail, it powers recommendation engines and demand forecasting.
• In manufacturing, it drives automation and predictive maintenance.

In short, wherever data exists, AI can create value. And the companies that figure out how to use it best? They’ll likely lead the next decade.

Early days = big upside

We’re still early. Even though big names like Microsoft and NVIDIA have seen huge gains, there’s still room. Especially as smaller players and specialized startups enter the scene—or get acquired.

The earlier you understand where AI is going, the better your shot at riding the wave rather than chasing it.

Identify growth sectors within AI

AI has those three cake-like layers. It also has different sectors, like a pie. Some of those sectors are moving faster—and making more money—than others. So if you’re looking to invest smartly, it’s worth knowing where the real momentum is.

1. Generative AI

   This is basically the type of AI that creates. Text, images, videos, code, music. Tools like ChatGPT, Midjourney, and Runway fall here. But beyond the fun, generative AI is finding serious use cases in marketing, product design, gaming, and education.

2. Enterprise AI

   Behind the scenes, companies are using AI to make smarter decisions, automate workflows, and save costs. Think of software like Salesforce, SAP, or Palantir integrating AI for sales forecasting, risk modeling, or operations planning. This is a huge space. And it’s only getting bigger as businesses realize they need AI to stay competitive.

3. AI infrastructure

   Chips. Cloud. Compute. The demand for processing power is exploding. Companies like NVIDIA and TSMC are critical here, along with cloud giants offering AI platforms (AWS, Azure, Google Cloud). Infrastructure is the pick-and-shovel play—boring to some, but essential.

   

   Source: Unsplash

4. Healthcare AI

   From detecting tumors to designing new drugs, AI is making healthcare faster, cheaper, and more accurate.
   Startups and biotech firms using AI for diagnostics and clinical trials are gaining traction—and attention from big pharma.

5. Cybersecurity

   With more digital data, the risks are higher. AI is now at the front lines of identifying threats, responding in real-time, providing endpoint security, and predicting breaches before they happen. Companies like CrowdStrike and SentinelOne are using AI as a core part of their defense systems.

6. Robotics & manufacturing

   Think of factories with smart machines that learn and adapt. AI is powering everything from automated quality checks to predictive maintenance—cutting downtime and saving millions.

You don’t need to go all-in on every sector. But knowing where the growth is can help you spot better opportunities — and avoid the noise.

Ways to invest in artificial intelligence: Investment strategies for AI

You don’t have to be a tech expert to invest in AI. But you do need a game plan. Here are the main ways people are getting in—from easy ETF buys to higher-risk bets on tomorrow’s unicorns.

1. Direct stocks

This is the simplest route: invest in companies building or using AI at scale. You’ve got the obvious heavyweights—NVIDIA, Microsoft, Alphabet (Google), Amazon, Meta. These aren’t just tech giants; they’re AI giants too. Then there are mid-cap players like Palantir, C3.ai, and UiPath, which are more focused but also more volatile. If you’re into research and don’t mind some ups and downs, direct stocks let you bet on specific winners.

2. AI-focused ETFs

Want exposure without picking individual stocks? Exchange trade funds (ETFs) are your friend.

Funds like:

• Global X Robotics & Artificial Intelligence ETF (BOTZ)
• iShares Robotics and Artificial Intelligence ETF (IRBO)
• ROBO Global AI ETF (THNQ).

These bundle together dozens of AI-related companies, giving you built-in diversification. Great for long-term investors who want to ride the wave without stressing over daily stock movements.

3. Venture capital & private equity

This is where the high-risk, high-reward action is. Startups like Runway, Anthropic, and Mistral AI aren’t public (yet), but some VC funds and crowdfunding platforms let you invest early—if you qualify.

It’s harder to access, less liquid, and riskier. But if you’re bullish on the next OpenAI or Hugging Face, this is where they start.

Source: Unsplash

4. Indirect plays

Not every great AI investment screams “AI.”Some companies just benefit from AI adoption. For example:

• Cloud providers like Snowflake
• Chip manufacturers like ASML
• Enterprise software leaders like Adobe or ServiceNow.

These aren’t building the models, but they’re enabling them—and profiting along the way.The best strategy? Mix and match. Spread your risk. Cover different layers of the AI stack. And keep your time horizon long.

Monitor promising companies

As with all things tech, AI moves fast. The winners today might not be the winners tomorrow. But some names are already pulling ahead—and others are quickly catching up.

Here’s who to keep an eye on across the stack.

Big tech (Still dominating)

• NVIDIA: The king of AI chips. If AI is eating the world, NVIDIA is selling the silverware.
• Microsoft: Heavy investor in OpenAI. Deep AI integrations across Azure, Office, and GitHub.
• Alphabet (Google): Years of research at DeepMind and a strong play in cloud AI with Gemini.
• Amazon: AWS is quietly powering half the AI boom with compute and machine learning tools.
• Meta: Big on open-source AI and multimodal models. Plus, it’s baking AI into every app it owns.
• InData Labs: A global AI consulting firm delivering custom solutions in generative AI, NLP, predictive analytics across the globe.

These players have scale, money, and models—and they’re not slowing down.

Specialized players

• Palantir: Known for government and enterprise AI platforms. Leaning into AI decision-making tools.
• UiPath: Automation-first, with strong traction in enterprise RPA (robotic process automation).
• C3.ai: Enterprise-focused, but with a more volatile track record. Still one to watch.
• Snowflake: More of a data infrastructure company, but AI workloads run better on clean data—and they’re leaning in.

Private & emerging stars

• OpenAI: Not public yet, but its tech is everywhere thanks to Microsoft.
• Anthropic: Creator of Claude. Big on safety and alignment, and backed by Google and Amazon.
• Mistral AI: Europe’s open-source counterweight to OpenAI. Lean, fast, and innovative.
• Scale AI: Big in AI data infrastructure. Working with the U.S. government and enterprise clients.

Some of these are easy to invest in. Others you’ll just want to track—for now. But the deeper you watch the space, the faster you’ll spot breakout players before the market does.

Use AI-powered research & advisory tools

Tell you what: If you’re investing in AI, why not use AI to do it smarter? These days, you don’t need a team of analysts to stay ahead. You just need the right tools.

AI-enhanced research platforms

Platforms like Kensho, Alphasense, and YCharts use AI to break down earnings calls, SEC filings, and macroeconomic trends. Instead of digging through PDFs, you get insights in minutes—not hours. Great if you’re tracking multiple companies or trying to catch subtle signals before they hit the headlines.

Source: Unsplash

Retail investor tools with AI baked in

Apps like Seeking Alpha, Finchat, and even ChatGPT (with plugins) now offer summaries, financial comparisons, and stock screeners—all powered by large language models. It’s like having a junior analyst in your pocket—minus the payroll or costs incurred with an AI consultancy.

Smart alerts and news monitoring

Want to know when a company files a new patent or gets a government contract? AI tools can now watch these signals for you. Tools like Feedly AI or Quid make it easier to stay updated without doom-scrolling through Twitter or financial blogs.

Bottom line: AI isn’t just something to invest in. It’s something to invest with. If you’re still using spreadsheets and Google alone—you’re leaving insights on the table.

Risks and considerations

Like a new relationship, AI is exciting. But that doesn’t mean it’s risk-free.If you’re putting money into this space, it’s important to know what could go wrong—or just take longer than expected.

1. Hype ≠ Reality

   AI is powerful. But not every company slapping “AI” on its pitch deck is the next NVIDIA. Some are just riding the wave. Others may never turn a profit. Avoid chasing headlines. Look for solid fundamentals, real products, and revenue, not just vibes.

   

   Source: Unsplash

2. Regulatory uncertainty

   Governments are playing catch-up. The EU AI Act, U.S. executive orders, and new privacy laws are all in motion. Depending on how they shake out, some business models might get restricted—or shut down entirely.

   Pay attention to how companies talk about compliance. If they’re not ready for regulation, that’s a red flag.

3. Ethical and social risks

   Bias in algorithms. Job displacement. Deepfakes. Misuse of generative tools. These aren’t just tech problems—they’re business risks too. Reputational blowback or lawsuits can tank momentum fast. Smart investors look for companies that take AI safety, ethics, and transparency seriously.

4. Overvaluation and volatility

   Some AI stocks have already seen big jumps. That doesn’t mean they’ll keep going up in a straight line. Valuations can get disconnected from reality, especially in hot sectors.

   Expect volatility. Don’t panic during the dips. But also, don’t assume endless upside. In short: be excited, but be smart. A grounded AI strategy implementation will take you further than hype-driven bets.

Build a diversified AI portfolio

One golden rule of investing? Don’t put all your chips on one square. That applies to AI too. The AI ecosystem is broad, and the smartest play is spreading your risk across different layers, companies, and regions.

1. Mix big names with bold bets

Start with the anchors—established companies like Microsoft, NVIDIA, and Google. They’ve got deep pockets and real revenue. Then, layer in some mid-cap AI specialists like Palantir or UiPath if you’re comfortable with a bit more volatility. Also, allocate a small slice to emerging players or private funds—just know this is where risk and reward go hand in hand.

Source: Unsplash

2. Use AI-focused ETFs to smooth the ride

ETFs give you instant diversification. You get exposure to dozens of companies without having to pick them yourself. Some solid options include:

• BOTZ (Global X Robotics & AI)
• IRBO (iShares Robotics and AI)
• THNQ (ROBO Global AI).

Great for long-term investors who want broad exposure without micromanaging every holding.

3. Cover different parts of the stack

This includes:

• Infrastructure
• Software platforms
• Application-layer companies.

By investing across the full AI stack, you reduce your exposure to any one segment flopping or slowing down.

4. Think globally

The U.S. is leading, but not alone. European countries are also pushing open-source. Furthermore, China is building aggressively. Even India is quietly becoming a machine learning services hub.

Global diversification gives you more upside and helps you hedge against region-specific risks. The point isn’t to be everywhere—it’s to be intentional. Diversification keeps your downside in check while still giving you room to grow.

Stay informed: News, metrics & signals to watch

Besides monitoring those promising companies, it’s a good idea to stay plugged into what’s the latest scoop.

Keep an edge by:

1. Watching demand indicators

   • GPU sales from NVIDIA and AMD—a proxy for AI infrastructure growth.
   • Cloud revenue from AWS, Azure, and Google Cloud—tracks enterprise AI adoption.
   • Job postings for AI roles—signals which sectors and companies are hiring to scale.

   

2. Tracking company activity

   AI-related earnings call mentions—who’s walking the talk?
   Patents and R&D spending—who’s building, not just buying?
   Partnerships and acquisitions—who’s expanding their AI footprint?

3. Reading the right sources

   Skip the hype-heavy headlines. Go for:

   • The Rundown AI or Ben’s Bites (daily AI briefings)
   • CB Insights and PitchBook for funding and trend analysis
   • State of AI Report (annual deep dive into global progress).

4. Setting up smart alerts

   Use Feedly, Google Alerts, or Quid AI to monitor topics like “AI regulations,” “generative AI startups,” or specific companies and generative AI developments.

   Bonus tip: let ChatGPT (with browsing or plugins) summarize updates for you weekly. You don’t need to spend hours every day on this stuff. Just 15–20 minutes a week of focused reading puts you ahead of 90% of retail investors.

Wrapping up

AI is no longer optional. It’s not “emerging” tech—it’s here, baked into the systems, tools, and products we use every day. Investing in AI in 2025 requires an understanding of what’s fundamentally changing in the economy—and placing smart, intentional bets on that future.

You don’t need to pick the next OpenAI to win. You just need an artificial intelligence strategy that’s informed, diversified, and tuned into where the momentum’s headed.

Start with the basics. Stay curious. And remember: the best time to understand AI was yesterday. The second-best time is now.

Best ways to invest in AI right now

If you’re wondering about the best way to invest in AI now, the good news is—you’ve got options depending on your goals and risk appetite.

Investing in AI isn’t about just finding one hot stock. It’s about understanding how deeply AI is getting woven into technology, healthcare, finance, logistics—and betting smartly across that ecosystem. As for companies investing in AI, keep your eye on established names that are doubling down on AI research, model development, and infrastructure expansion.

Bottom line: investing in AI technology is like investing in electricity 100 years ago—you’re not just betting on one use case, but on a transformation that’s touching everything.