We're living through perhaps the most hyped technology moment since the dot-com era. Every week brings new headlines about AI valuations, billion-dollar investments, and breathless predictions. Lately, the narrative has shifted to something darker: warnings of an "AI bubble" ready to burst.
Let me be clear: whether there's a bubble or not is irrelevant to your AI strategy.
I know this sounds counterintuitive. As leaders, we're trained to understand market dynamics, spot warning signs, and be cautious when instinctively everyone rushes forward. But fixating on bubble dynamics misses three fundamental truths that should actually guide your thinking about AI in your organization.
The Problem with "AI" in "AI Bubble"
When people talk about the "AI bubble," they're typically referring to valuations of generative AI companies, large language model startups, and the massive capital expenditures by tech giants on AI infrastructure. But here's the issue: "AI" is not a monolithic technology.
Calling everything "AI" is like calling everything on the internet "dot-com" (as we did in 1999). The term encompasses machine learning models that optimize supply chains, computer vision systems that detect manufacturing defects, natural language processing that routes customer inquiries, recommendation engines that personalize user experiences, and yes, the flashy large language models that can write essays, generate images and fill timelines with amazingly-realistic-tiktok-like-AI-generated-video's.
For many organizations, the highest-impact AI applications aren't the sexy ones making headlines. They're the "simple" analytical AI systems that have been quietly delivering value for years, predictive maintenance models, demand forecasting algorithms, fraud detection systems. These tools don't generate venture capital excitement, but they do drive real ROI.
This applies even to the most hyped category: large language models. The ability to generate coherent text and images isn't a temporary trick or market manipulation it's a genuine technological capability with clear business applications.
If there is a bubble in generative AI valuations, it doesn't deflate the value of applying AI to real business problems. Deploying computer vision to reduce quality control costs by 30% will still be as valuable to your organization as the day that the project was launched regardless of a potential AI bubble bursting. The hype cycle around AI doesn't invalidate the practical real world applications.
The Technology Isn't Going Anywhere
History offers us a crucial lesson here. The "dot-com" internet bubble of the late 1990s was spectacularly, catastrophically real. Trillions in market value evaporated. Companies with no revenue and no path to profitability collapsed overnight.
But the internet didn't disappear.
In fact, the internet became more transformative than imagined by the biggest advocates during the bubble. In 1999, the bullish case for the internet seemed almost absurd in its optimism. Yet today, that optimistic vision in hindsight was conservative. The internet reshaped retail, media, communication, education, and virtually every other sector of the economy, in ways that no one predicted back then.
The "dot-com" bubble was about valuations and business models[1], not about the underlying technology's potential. When the bubble burst, the infrastructure remained. The protocols kept working. The fiber optic cables stayed in the ground. The technology continued advancing.
AI is following a similar trajectory. Even if AI companies valuations are inflated, even if some business models prove unsustainable, the fundamental capabilities aren't going to vanish. Neural networks will still be able to recognize patterns. Models will still be able to generate text and images. Algorithms will still be able to optimize complex systems.
Consider three areas where these capabilities are already delivering measurable value: marketing, coding, and customer service. These aren't speculative use cases, they're happening now, and in organizations of all sizes. A market correction won't make these capabilities disappear. Moreover, the technology will continue improving. Models are becoming more capable, more efficient, and less expensive to run. The trajectory is clear: lower costs, better performance, and broader accessibility. Efficiency improvements compound. The GPT-5 level LLM's of today will seem primitive compared to what's available in three years, regardless of what happens to AI company stock prices.
The models you deploy this year will work next year, regardless of what happens to AI startup valuations. The efficiency gains you achieve through AI implementation won't evaporate if the market corrects. The technology is real, even if some of the financial expectations aren't.
We Don't Know What the Killer App Will Be Yet
Perhaps the most important reason to ignore bubble speculation is this: we're still in the earliest innings of understanding what AI will become.
Everything is still on the table. We don't know which use cases will prove most valuable. We don't know which industries will see the deepest transformation when the technology has matured. We don't know which business models will dominate. The term "AI" itself is so broad spanning content generation, decision support, automation, scientific discovery, and countless other applications, that identifying a single "killer app" is impossible and beside the point.
This uncertainty is precisely why you shouldn't wait for the market to settle before exploring AI in your organization. The companies that win won't be those who waited for certainty. They'll be the ones who experimented, learned, and developed organizational capabilities.
Your job as a leader isn't to predict market dynamics. It's to ask: "Where can AI create value in my organization?" That question has the same answer whether AI stocks are up or down.
What Actually Matters
Here's what I do know to be true: bubbles can only be confidently identified in hindsight. And, more importantly for your organization, it doesn't matter.
What matters is whether AI can help you serve customers better, operate more efficiently, or make better decisions. What matters even more is building the organizational capabilities, the data infrastructure, the talent, the culture of experimentation, that will let you leverage AI regardless of where the technology goes or what markets do.
The leaders who succeed with AI will be those who focused relentlessly on practical application, who started with clear problems rather than shiny solutions, who built learning organizations that adapt as the technology evolves. Not those who timed the market perfectly.
So ignore the bubble talk. Focus on building the capabilities to deliver value.
* I'm not an investment expert and don't claim to be able to predict what is and isn't a bubble, so don't take this as investment advice. [1] Some business models were simply years too early, while others were completely disconnected from reality, with business plans that did not even touch the concept of making a real profit and while spending extravagantly on marketing, parties and PR stunts.