Should You Worry About an AI Bubble? 

By Cap Puckhaber, Reno, Nevada

I’m Cap Puckhaber, a marketing professional, amateur investor, part-time blogger and outdoor enthusiast. Today on SimpleFinanceBlog.com, we break down one of the hottest, and frankly, most confusing topics in the market: investing in AI. We’ll explore whether the current frenzy over artificial intelligence stocks is a massive bubble ready to pop or a genuine breakthrough you can’t afford to miss. AI is going crazy, and I’ve been wrestling with this myself. I don’t know if I’m too late based on NVIDIA’s current valuation and I should wait or should I jump in? Should I buy chipmakers, AI companies, Google, Amazon, Meta, Microsoft? It’s a storm of questions for anyone trying to plan for retirement, and I’m right there with you. Let’s get some clarity together.

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The AI Gold Rush: That Dot-Com Déjà Vu Feeling

Let’s be honest, the buzz around artificial intelligence is impossible to ignore. It feels like every company is scrambling to slap “AI” onto its products, and the stock market has responded with explosive growth for anything tied to the technology. For anyone who remembers the late 1990s, this feels eerily familiar. The dot-com bubble was fueled by a similar frenzy over a world-changing technology: the internet. Companies like Pets.com or Webvan were market darlings, soaring to incredible valuations with little more than a good idea and a website. We all know how that ended for many investors who jumped in without a solid strategy.

That’s the core of my anxiety, and maybe yours. I see the incredible potential of AI technology stocks, but I also see the sky-high valuations and the frantic hype. I look at a company like NVIDIA ($NVDA), which saw its stock surge over 230% in a single 12-month period, and it makes me wonder, “Is this sustainable, or is this 1999 all over again?” The parallels are hard to ignore. Back then, any company with “.com” in its name saw its stock price multiply. Today, any company that mentions “AI” in an earnings call gets a similar boost. The challenge is separating the revolutionary companies from the opportunistic ones. The big question we have to tackle is whether this is a sustainable technological revolution or just an overinflated bubble. The truth is, it’s probably a bit of both.

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The Two Sides of the AI Coin: A Modern Gold Rush

To make sense of the AI stock market, I find it helpful to use the old gold rush analogy of “picks and shovels.” When gold was discovered in California, a few prospectors got rich finding nuggets, but the people who made the most consistent money were the ones selling the picks, shovels, and blue jeans to all the miners. The same logic applies to AI investing today.

• The “Picks” (AI Applications): These are the prospectors. They’re the companies developing AI software, models, and direct-to-consumer applications. This includes AI-powered design tools, new search engines, or automated business software. These can be incredibly exciting investments because a single successful product could lead to massive growth. However, they also carry higher risk. For every successful application, many others will likely fail to gain traction.
• The “Shovels” (AI Infrastructure): These are the folks selling the equipment. They’re the companies providing the essential hardware and platforms that the entire AI industry is built on. This includes chipmakers, data center operators, and cloud computing giants. Their success isn’t tied to a single AI application but to the growth of the entire sector. This can often be a less volatile, though not risk-free, way to approach AI investing.

Thinking in this framework helps me decide where I’m willing to place my bets and how much risk I’m comfortable taking with my AI portfolio.

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Case Study 1: The “Shovel” Sellers – Building the AI Foundation

When people talk about investing in AI, the “shovel” sellers are the most obvious starting point. This is the foundational layer of the revolution, and it’s where some of the biggest money has been made so far.

The Chipmaker Frenzy: The Brains of the Operation

The chipmakers design the specialized processors that are the brains of every AI model. This is where the hype has been most intense.

NVIDIA ($NVDA) is the undisputed champion here. Its graphics processing units (GPUs) are the industry standard for training large language models, giving it an estimated 80-95% market share in this niche. But that dominance comes at a price. At times, NVIDIA’s forward price-to-earnings (P/E) ratio has soared past 70, a stark contrast to the S&P 500’s historical average of around 20. You can see its dramatic price history for yourself on a stock chart.

CHART LINK: https://www.macrotrends.net/stocks/charts/NVDA/nvidia/stock-price-history

Beyond the headliner, the ecosystem is vast and full of opportunity. Advanced Micro Devices ($AMD) is a major competitor, aggressively rolling out its own AI chips to challenge NVIDIA’s dominance. Then there’s Broadcom ($AVGO), which designs custom AI processors for giants like Google, a less flashy but highly profitable business. And we can’t forget the most critical player of all: Taiwan Semiconductor Manufacturing Co. ($TSM). TSM is the world’s largest contract chip manufacturer, physically producing the most advanced chips for nearly all of these companies, including NVIDIA and AMD. An investment in TSM is a bet on the entire semiconductor industry’s growth.

The Cloud Titans: Renting Out the Digital Real Estate

The other major “shovel” sellers are the cloud providers who rent out immense computing power. Training an AI model requires thousands of these specialized chips running for weeks or months, a cost that is prohibitive for all but the largest companies. This is where the big tech titans dominate.

Amazon’s ($AMZN) Amazon Web Services (AWS), Microsoft’s ($MSFT) Azure, and Google’s ($GOOGL) Cloud are in an arms race to provide the infrastructure for AI development. Investing in them is a bet on the widespread adoption of AI, as they profit from nearly every company that trains or deploys a model. Microsoft, in particular, has seen huge success by integrating its investment in OpenAI directly into its Azure services, creating a powerful incentive for businesses to use its cloud platform. You can see the market share breakdown in the link below.

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Case Study 2: The “Pick” Sellers – Putting AI to Work

If the infrastructure players are the “shovels,” the software companies are the “picks.” These are the firms using AI to create better products and services. This category is much broader and offers different kinds of opportunities.

Enterprise Software & Cybersecurity

Many established software companies are weaving AI into their core products, creating new revenue streams and wider competitive moats. Adobe ($ADBE) integrated its Firefly AI into Photoshop and its other creative suite products, changing workflows for millions of designers. Enterprise software leaders like Salesforce ($CRM) use their Einstein AI, which can perform over 1 trillion predictions per week, to make customer management smarter.

In cybersecurity, AI is a game-changer. The sheer volume of cyber threats makes it impossible for human analysts to keep up. Companies like Palo Alto Networks ($PANW) and CrowdStrike ($CRWD) rely heavily on AI to predict and stop sophisticated cyber threats in real-time, analyzing trillions of data points per day to identify patterns that would be invisible to humans.

The Data Platforms: Fuel for the AI Engine

AI is useless without massive amounts of clean, organized data. Companies that help manage and analyze this data are critical cogs in the AI machine. Snowflake ($SNOW) provides a cloud-based data platform that allows companies to store and analyze vast datasets, which is essential for building custom AI applications. Similarly, Datadog ($DDOG) offers monitoring services that ensure these complex AI systems are running smoothly. They represent another crucial, and often overlooked, part of the AI ecosystem.

Niche Innovators: The High-Risk, High-Reward Plays

Beyond the established giants, there are countless smaller companies using AI to disrupt specific industries. UiPath ($PATH) is a leader in robotic process automation, using AI to automate tedious office tasks. In the legal tech space, companies are using AI to analyze documents and predict case outcomes. These are the true “prospectors” of the AI gold rush. They carry significantly more risk—many will fail—but a single big success could deliver incredible returns. Investing here requires a strong stomach for volatility and a deep understanding of the specific market.

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My Actionable AI Investing Strategy (And Yours Too)

After looking at all these options, from the chipmakers to the software firms, it’s easy to feel overwhelmed. I’ve had to step back and build a clear, simple strategy to participate in the potential of AI without letting FOMO dictate my choices. This is the AI investment strategy I’m building for myself.

Step 1: Don’t Chase the Hype and Use Time to Your Advantage

The first rule is to stop chasing stocks that have already gone vertical. My anxiety about being “too late” for NVIDIA is a classic behavioral trap. Instead of buying at an all-time high, I can be patient or use dollar-cost averaging. This is a simple but powerful technique. By investing a fixed amount of money at regular intervals—say, $500 a month—I automatically buy more shares when the price is low and fewer when it’s high. This smooths out my average cost over time and removes the stress of trying to perfectly time the market. Over a year, that $6,000 investment will have been deployed across various price points, reducing my risk.

Step 2: Diversify, Diversify, Diversify

I don’t want to bet my retirement on a single horse. A diversified approach feels much safer. A great way to do this is with an Exchange-Traded Fund (ETF). Funds like the Global X Robotics & Artificial Intelligence ETF ($BOTZ) or the iShares Robotics and Artificial Intelligence Multisector ETF ($IRBO) hold a basket of dozens of AI-related companies from around the world. This provides instant diversification, so I’m not responsible for picking the individual winners and losers.

Before buying an ETF, I always look under the hood. You can find its full list of holdings on the fund provider’s website. For example, a moderate-risk strategy could involve allocating 60% of my AI funds to a broad ETF, 30% to diversified tech giants like Microsoft and Google, and a smaller, 10% position in a more volatile pure-play stock that I’ve researched heavily. This gives me broad exposure while limiting my risk from any single company’s failure. You can see an example of an ETF’s holdings breakdown at the link below.

CHART LINK

Step 3: Focus on Long-Term Fundamentals, Not Short-Term Noise

With AI algorithms making the market more chaotic in the short term, focusing on the long game is crucial. A company’s stock price can swing wildly from day to day, but over years, its value is tied to its actual business performance. I’m learning to look past the daily noise and focus on companies with strong balance sheets, consistent revenue growth exceeding 15% annually, and a clear, profitable vision for how AI fits into their business. A great resource for this kind of in-depth financial analysis is The Motley Fool, which provides detailed breakdowns of company fundamentals.

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The Invisible Risks: What Could Go Wrong?

A balanced view means looking at the potential downsides, and with AI, there are plenty. It’s not just about picking the wrong stock.

• Regulation is Coming: Governments around the world are scrambling to figure out how to regulate AI. New rules around data privacy, algorithmic bias, and national security could significantly impact the profitability of certain AI business models. This is an unpredictable risk that could materialize quickly.
• The AI “Winter”: The history of AI research isn’t a straight line up. It’s marked by periods of intense excitement and funding (like now), followed by “AI winters” where progress stalls, funding dries up, and interest wanes. Another winter is always a possibility, which could depress stock prices for years.
• Ethical Nightmares: What happens when an AI-powered medical device makes a mistake? Who is liable when a self-driving car crashes? These ethical and legal questions are far from settled, and a major public backlash against a specific AI application could have a chilling effect on the entire industry.

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Conclusion: So, Is the AI Bubble Going to Burst?

So, back to the billion-dollar question. Is AI a good investment, or is the bubble about to burst? I believe the answer is yes… to both.

Some parts of the AI market are almost certainly in a speculative bubble, driven by hype and unrealistic expectations. Some companies with “AI” in their name but no solid business plan will eventually fade away, and investors who piled in will lose money. That part of the bubble will burst.

However, the underlying technology of artificial intelligence is undeniably a breakthrough that will reshape our world for decades to come. It’s a genuine revolution, not just a passing fad. Companies that successfully integrate AI to create real value for their customers will become the titans of the next generation.

The key for investors like us isn’t trying to perfectly time the market or pick the single stock that will return 100x. It’s about building a resilient, diversified portfolio across the different layers of the AI industry—the shovels and the picks—that can capture the upside of this technological shift while protecting us from the inevitable volatility. For me, that means proceeding with informed caution, focusing on my long-term strategy, and resisting the urge to make emotional decisions based on fear or greed. The AI gold rush is here, but I’m focused on building a solid foundation, not chasing fool’s gold.

Frequently Asked Questions

1. Is NVIDIA overvalued right now? As of early 2026, NVIDIA ($NVDA) continues to dominate the AI hardware market with an estimated 90% to 92% market share in discrete GPUs. While its forward Price-to-Earnings (P/E) ratio has historically been high (often exceeding 70 during peak hype), some analysts argue its massive earnings growth—such as the record **$57 billion revenue** reported in late 2025—justifies a premium. Whether it is “overvalued” depends on if you believe competitors like AMD or custom silicon from Google/Amazon can chip away at its near-monopoly.

2. How can I invest in AI without picking individual stocks? For investors wary of individual stock volatility, Exchange-Traded Funds (ETFs) are a popular “safety net.” Funds like $BOTZ (Global X Robotics & AI) or $IRBO (iShares Robotics and AI) hold a basket of dozens of companies across the “picks and shovels” spectrum. This diversification ensures that even if one company fails, your entire retirement isn’t at risk.

3. What is the “Picks and Shovels” strategy in AI? This strategy involves investing in the infrastructure required for AI (the “shovels”) rather than just the applications (the “picks”).

• Shovels: Chipmakers (NVIDIA, TSM), Cloud providers (Microsoft Azure, AWS), and Data Centers.
• Picks: Companies building the software we use, like Adobe’s Firefly or Salesforce’s Einstein AI. Infrastructure is often seen as a safer bet because these companies profit regardless of which specific AI app becomes the “next big thing.”

4. What are the biggest risks to AI stock growth? The primary risks for 2026 include:

• Regulation: The EU AI Act (fully coming into effect in August 2026) and U.S. executive orders may increase compliance costs.
• Energy Constraints: AI data centers consume massive amounts of power; if the grid can’t keep up, growth could stall.
• Capability Plateaus: If new models stop showing massive productivity gains, the high “Capex” (spending) by big tech may be viewed as a waste by shareholders.

5. How does the AI boom compare to the 1990s Dot-Com bubble? The major difference today is profitability. In 1999, many companies had sky-high valuations with zero revenue. In contrast, today’s AI leaders like Microsoft, Meta, and NVIDIA are generating billions in actual cash flow. However, the “circularity” of the market—where AI companies buy from each other to inflate growth—is a red flag that many veteran investors are watching closely.

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