Beating the AI ​​bubble

You can’t help but feel uneasy when looking at market concentration. Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla now make up more than a third of the S&P 500, more than twice the level seen before the dot-com bust.

AI-related capital spending has outpaced the US consumer as the main driver of gross domestic product growth. OpenAI alone plans trillions in data-center investments while exiting 2025 with about $20 billion in annualized revenue. Of course, there are physical limitations to how fast we can build. Data centers require enormous energy, land, and skilled labor—more than trade schools produce today—a concern raised in the Trump administration’s US AI Action Plan.

On top of this is a web of circular financing among major players. Companies are using complex structures to fuel the investment wave, adding opacity and risk. Investors like Masayoshi Son and Michael Burry are heading for the exits. In a new Bank of America survey, 45% of investors cite an AI bubble as the top tail risk for the economy and markets. Many believe AI stocks are already in bubble territory.

When a bubble bursts, it is like a balloon losing air. Prices fall, investors pull back, and companies that depended on constant capital inflows often fail. The slowdown could ripple across the industry. But a burst forces a reset, where work with real value continues and the rest falls away.

There is one way out: real growth.

A record of breakthroughs (and setbacks)

There is some consensus among economists that artificial intelligence can become the next general-purpose technology. These are revolutionary innovations with widespread impact that by themselves enable new inventions and change most aspects of our day-to-day lives and work. They do not just improve one industry; they create new possibilities for others. We have seen this before with the steam engine, electricity, and, most recently, the internet.

Growth will require diffusion, a fancy way of describing how new tools and ideas spread to lots of people. New tools never disseminate evenly, and AI is no exception.

AI has been through more than 70 years of breakthroughs and setbacks since mathematicians and early computer scientists began imagining how machines might simulate human thinking. Breakthroughs were often followed by AI winters, when funding and enthusiasm receded.

But since late 2022, when generative AI hit the zeitgeist, we have been on a tear. ChatGPT became the fastest application to reach 100 million users in history and is already used by about 10% of the planet’s population. Can we continue?

The growth plan

Let’s break this out to understand the source of potential growth.

First, there is the consumer segment. For all the excitement around AI, many users still sit in the free bucket. The business challenge now is converting that into sustainable revenue.

Expect a shift from today’s generous “freemium” models toward tighter paywalls, bundled services, and even advertising-supported tiers—moves already being tested. For example, Canva raised its prices, bundling new AI features, which led to widespread backlash and a rollback of some of the changes. Notion moved key features behind higher-tier plans as it included built-in AI, sparking user criticism over value and fairness.

Some frontier labs are also exploring something Big Tech once swore off: hardware. To unlock new monetization paths, companies are designing devices such as wearables, home hubs, and the next generation of phones around their proprietary AI interfaces. OpenAI, in partnership with designer Jony Ive, is working on a family of devices that goes beyond phones and computers.

Second, there is enterprise adoption, arguably the most important frontier. Enterprises—large organizations that buy software and services for thousands of employees—pay, stick, and rarely churn when productivity improvements are demonstrable.

But this market is splitting in two. Smaller firms are moving fastest, using AI to level the playing field against incumbents. Norm Ai shows how smaller disruptors can move first, using AI agents to rethink legal work, even launching an AI-native law firm.

Large enterprises, by contrast, are cautious. Their concerns center on reputational risk, hallucinations, and product liability. Yet once they see quantifiable return on investment in a controlled domain, they will scale quickly and pay premium prices for reliability, compliance, and integration.

Barclays shows how major incumbents adopt more cautiously, using AI to support employees, speed service, and personalize banking while keeping humans in the loop. It is a quest for reimagining business workflows and integrating AI into them.

Third: There is the government, where modernization is both overdue and unavoidable. Cities and federal agencies are using AI to improve responsiveness, reduce backlogs, and redesign citizen services that have long suffered from paper-era processes.

As these systems prove they can cut wait times and improve accuracy, adoption will accelerate. For example, the United States Patent and Trademark Office launched its “Automated Search Pilot (ASAP!)” program to use AI in preexamination review, with plans to accept at least 1,600 applications across technology centers.

On the national security side, the stakes and budgets are higher. Defense agencies are deploying AI for threat detection, mission planning, and intelligence analysis, creating a fast-growing market for companies like Palantir and Anduril, whose surge in government and defense contracts shows the scale of demand.

These multiyear defense contracts secure growth over an extended period. A contract Palantir recently entered into with the US Army topped $10 billion over 10 years. Anduril’s programs exceed $1 billion, in multiple contracts, creating steady demand.

Finally, there’s global adoption. The geopolitical competition for AI markets is intense. As recently reported, even Silicon Valley companies are quietly reliant on Chinese AI components, while Washington, DC, is pushing to export an American AI stack as part of its industrial strategy. The geopolitical rivalry is as much about who defines the global interfaces, platforms, and rules as it is about the chips that power AI.

Growth is possible, though not guaranteed. It depends on turning early experiments into products people rely on every day. The real race is not about ever-larger models held by a few firms. It is about unleashing competition and letting a diverse market push new ideas into the world. Innovation spreads when many players build, test, and iterate. That is how bubbles become breakthroughs.

The moment is here. Let’s get to work.