Also the AI Investment Boom Won’t Last Forever

We are living through one of the most intense investment frenzies in recent memory. Artificial intelligence (AI), once confined to academic labs and sci-fi plots, has become the hottest commodity in tech. Billions of dollars are flooding into AI startups, semiconductor companies, and cloud infrastructure. Any company whispering the letters “A” and “I” can seemingly command attention — and valuation. Hence the parade of AI washers.

But as history has shown, investment manias don’t last. The telco and dot-com bubbles taught us that markets have a way of correcting inflated expectations. The crypto boom did the same, replacing wild speculation with regulatory scrutiny. Now, AI faces a similar inflection point.

The first warning signs are already here. The market is becoming saturated with startups offering indistinguishable tools — LLM-driven chatbots, content creation assistants, and software “copilots.” While many of these tools are genuinely useful, few offer real competitive advantages. It’s increasingly hard to tell which companies are building lasting infrastructure and which are just layering AI wrappers on top of existing solutions.

Meanwhile, the cost of building serious AI models is rising fast. Running large language models requires not only vast datasets but also access to extremely expensive compute — namely high-end GPUs from companies like NVIDIA, which are in short supply. Startups without deep pockets or cloud credits often hit a wall before their product reaches maturity. Over time, I would expect alternative AI chip providers to claim their share of the pie. More rivalry will start lowering hardware costs at the expense of NVIDIA’s valuation. For many business cases and for SMEs with less cash at hand, it’s worth the wait.

On top of this, regulators are catching up. The European Union’s AI Act is the first major piece of legislation aimed squarely at controlling the development and deployment of high-risk AI systems. The U.S. and other governments are also moving quickly to draft rules. While regulation is necessary to ensure safety and accountability, it inevitably raises compliance costs and slows iteration.

Another looming issue is environmental. The energy required to train and run massive AI models is staggering — and only growing. Even more so than for crypto mining. In a world increasingly focused on sustainability, AI’s carbon footprint will come under heavier scrutiny than the crypto industry. That could reshape how and where models are trained.

Perhaps most importantly, there’s the question of real-world adoption. Yes, businesses are experimenting with AI, and early results can be promising. But many companies are discovering that deploying AI tools at scale is complex, expensive, and sometimes underwhelming. Consumers, too, are still figuring out how— or if — AI improves their daily lives. Although the occasional professional – me for example – might still radiate enthusiasm and excitement, it might be too time-consuming for many to detect and navigate the common hallucinations.

All of this suggests that the current AI gold rush is unsustainable in its current form. But that’s not a bad thing. A cooling market would separate signal from noise, giving space to companies that are solving real problems rather than chasing hype.

We’ve seen this before. The internet didn’t disappear after the dot-com crash — it matured. Cloud computing didn’t vanish after early skepticism — it became essential infrastructure. AI is following the same arc. The boom may slow, but what comes next — practical, trusted, and embedded AI — will be far more valuable.

The frenzy may fade. The impact will not. And that’s exactly how real revolutions happen.