AI Bubble Risks: How to Build AI Strategy That Survives the Swings

Key Highlights:

• The latest events ignited global fears of an AI bubble, with sharp market swings and mixed expert opinions creating uncertainty for technology leaders.
• AI investment decisions are now harder than ever, as companies balance real opportunities with the risk of overspending in a volatile market.
• This article gives you 4 practical, grounded strategies to reduce risk and make more resilient AI decisions.

The past week have been unexpectedly tensing in the tech world. Markets swung, nerves spiked, and one question echoed everywhere: Are we in an AI bubble?

Nvidia’s sudden heights and lows - a sharp rise, a quick drop, and a partial recovery - only amplified the uncertainty. And for technology decision-makers under pressure to justify every investment, this is the last thing you needed. Because if there’s one thing we’ve been taught to trust, it’s the unstoppable momentum of AI. Yet last week proved something uncomfortable: even AI isn’t untouchable.

Let’s break down what happened, what the experts think, and - most importantly - how to navigate the chaos with smart, risk-aware decisions. AI may boom or cool, but there are ways to invest wisely regardless of how the next weeks unfold.

What Happened with the AI Landscape in the Last Month

You surely know the headlines already, but here’s a quick rundown to put everything in one place. It’s hard to describe the events - especially in the last week - because things shift at the speed of light.

• It all began when Nvidia - the AI champion - became the first company to hit a $5 trillion valuation. You would expect that to signal a bright AI future, but the excitement faded fast. Within a week, around November 7th, AI bubble fears spiked as the US market tumbled. And it wasn’t just Nvidia sliding more than 4% - several other big tech players showed similar strain, with Meta falling over 20% due to concerns about its AI spending, Microsoft dropping more than 13%, and AI-infrastructure-heavy companies like CoreWeave and Oracle taking even sharper hits.

• Nvidia briefly eased the panic with huge earnings beat and a firm rejection of the AI bubble narrative, which resulted in stocks rising.

• The calm didn’t last long. By November 20th, the volatility was back: the Nasdaq 100 hit its lowest since September, bringing AI fears back.

The AI chips are just one part of the story, though. Over the same weeks, two parallel realities became clear: AI technologies are improving rapidly, yet most companies are still struggling to turn that progress into day-to-day value. McKinsey reported that most businesses experimenting with AI still haven’t managed to scale it across the organizations. At the same time, the Washington Post noted that according to the U.S. Census Bureau data adoption is steadily growing, with more companies starting or planning to use AI in some form in 2026. This contrast only adds to the uncertainty: the capabilities are accelerating, but the real-world usage is still catching up.

Add to that a wave of warnings - SoftBank selling its entire $5.8B Nvidia stake, Google’s CEO urging caution, analysts questioning sustainability, and central banks like the Bank of England and Bank of America warning that AI investments may be “too much, too fast.”

With swings this sharp and signals this mixed, it’s no wonder everyone is starting to question whether the AI boom is turning into an AI bubble.

Are We in an AI Bubble?

A question worth millions ($5trillions, actually) of dollars. Opinions are split:

The Sceptics

Many see echoes of the dot-com era in today’s AI boom. Companies are investing enormous amounts in AI - often faster than they can explain where the future profits will come from. Some argue that the fact Nvidia keeps reporting unbelievable demand should add to the worry, not calm it. If chip sales are this hot, maybe the spending is simply running ahead of reality.

Michael Burry’s - the investor who predicted the 2007 house crash - viral post on X only fueled the doubts, claiming that big players like Meta, OpenAI, and Nvidia may be overstating earnings by stretching the assumed lifespan of their AI chips - a move he called “one of the more common frauds of the modern era”, and completely detached from reality.

The Optimists

They argue this moment looks nothing like the dot-com era. Today’s major AI players are profitable, cash-rich, and funding their expansion with real earnings. They also point out that AI is already generating revenue: enterprises are ramping up spending, cloud providers are seeing real returns, and demand for compute is so high that data centers are running at full capacity.

Some industry leaders even insist there’s no bubble at all, just a market struggling to keep up with rapid growth. ABB’s CEO - Morten Wierod said in front of Reuters that he doesn’t think we are in an AI bubble and the real constraint isn’t hype, but the shortage of people and resources needed to build all the new infrastructure. And as Jeff Bezos put it, big technological waves always fund everything - good ideas and bad, but after the dust settles, society benefits from the innovations.

What the AI Bubble Means for Technology Leaders: 4 Steps to Avoid Wasting Money

When markets jump around like this, CTOs and CIOs feel the pressure immediately. Boards and CFO start asking tougher questions about AI spending, and suddenly the risks are much harder to ignore. Regulations are shifting as well – for example, the U.S. AI Action Plan adding new expectations around data use and model transparency, while talent shortages and ethical concerns can quickly turn into operational headaches.

So while big players are playing their game, for you as a tech leader, the idea of an “AI bubble” shows up in a very practical way: it makes AI plans feel more fragile, because it depends on conditions that doesn’t hold steady. Here’s why, when a ROI conversation emerges, having a resilient AI strategy matters. Here are four steps on how to build one.

1. Move Away from Pilots and Start Thinking Strategically

The standard approach of many companies is to jump straight into AI by running small pilots everywhere. That move is what fuels the bubble – chaotic investments, without a clear reason behind them and even doubtful chances for ROI.

The solution to this problem is called a strategy. Take a step back and decide how AI investment will fit into the bigger picture. Talk to the teams to understand their challenges and define exactly what you want to improve (fewer errors, faster processing, lower costs). Write down the expected outcome. Map out where the AI solution will sit in your existing systems, which team will use every day, and what changes they will need to make. If a potential project does not fit into the goals your company or team set – then do not invest in it, no matter how tempting it looks.

2. Pick A Solution That Fits Your Industry

When the hype cools, the solutions that are far from being generic and universal and truly fit their industry have a chance to survive. Choose a tool that can work with your company’s specific processes, compliance rules, and the messy data your teams use every day. A good way to test this is simple: walk the solution through a real workflow from start to finish - your systems, your inputs, your edge cases. For example, during our work with Castle Trust Bank, we integrated AI-powered identity verification and developed a fraud-detection PoC, but they were both designed around the bank’s own approval flow, data sources, and the strict regulatory requirements that financial institutions must follow.

If the solution breaks, needs heavy customization, or can’t meet your regulatory requirements without major rebuilding, it’s not the right fit. Tools designed to match your industry are harder to knock over when the market shifts, and they produce value faster - a smart hedge against the uncertainty surrounding today’s AI boom.

3. Measure Value as Early as Possible

AI projects are much easier to manage when you know what “success” is supposed to look like. Before any work starts, pick up a few clear things you want to improve and formulate it in a measurable way- maybe it’s cutting the time of a task from 10 minutes to 3, reducing the number of manual steps, or speeding up customer responses. Write these goals down and treat them as your compass when you lose direction.

Once the first version is up and running, even if it’s only helping a small team, start checking those numbers right away. Keep them visible and follow them consistently. This lets you see what’s improving, what isn’t, and where you should adjust next. Early measurements protect you from sinking more money into projects that aren’t working and help you double down on the ones that are. In shaky markets, fast feedback is your safety net.

4. Choose Wisely Your Software Development Partner

Last, but not least, probably your most valuable weapon is the technology partner you are working with. In a moment where hype is loud and the risk of an AI bubble is on the table, companies naturally look for experienced teams who can guide them through the full journey of turning an idea into a working, scalable solution.

Most of the real effort lives in the details: the data flows, the workflows, the security setup, the integrations, and the user experience that makes the solution usable. That’s why experience matters so much. You want teams who have seen where things usually break, know how to work around constraints, and understand how to keep a system stable long after launch.

AI is only half the job, building it to work in your world is the other half. Every industry has its own rules and day-to-day challenges. When a partner understands the business specifics, as well as your firm’s objectives, the solutions they build tend to fit naturally into your company, even if the AI bubble suddenly pops, which mitigates the risk.

How Safe Is Your AI Strategy in a Possible Bubble?

The truth is, no one can say whether this is the peak of the AI boom or just another bump in a much longer climb. What we can say is that the companies who will come out ahead tomorrow are the ones making thoughtful, grounded decisions today.

This moment is a good reminder to pause and look at your AI plans with clear eyes. What kind of problems do you want to solve with AI? Do your projects have defined outcomes? And do you have a partner who can take you from an idea to something stable enough to survive market swings?

The real winners of this cycle won’t be the fastest movers, they’ll be the ones who build solutions that last. If you want help reviewing your approach or shaping your next step in those turbulent times, we’d be happy to talk.