The AI Race: Why the Real Risk Isn’t Technological, but Systemic

In a recent article titled “Here’s How the AI Crash Happens,” The Atlantic raises a question that few leaders are willing to ask out loud: what if the explosive growth of artificial intelligence isn’t just a technological revolution, but a fragile macroeconomic bet?

The piece describes a phenomenon that is now impossible to ignore — massive global investment in AI infrastructure: data centers, energy capacity, and specialized chips, concentrated in the hands of a few dominant players. Nvidia, in particular, has become so central to the global economy that the authors describe the United States as an emerging “Nvidia-state,” drawing a parallel with petro-states.

The question, then, is no longer whether AI will transform the economy.

It is at what cost — and who ultimately bears the risk.

Growth Driven by Infrastructure, Not Yet by Proven Value

The numbers are staggering: hundreds of billions in capital expenditures, energy consumption rivaling major cities, historic market valuations. And yet, a fundamental paradox remains.

Despite soaring stock prices, AI’s contribution to actual corporate profitability remains limited. Multiple studies suggest that for most organizations, generative AI has not yet produced measurable gains in productivity or bottom-line performance. In other words, infrastructure investment is racing far ahead of demonstrated economic value.

History offers familiar precedents. Canals, railroads, fiber-optic networks, and even the early cloud all experienced periods of overinvestment. But AI is different in one critical way: each incremental advance demands more capital, more energy, and more complexity — not less. Marginal returns are shrinking, while costs continue to rise.

A Rational Race — with Dangerous Collective Consequences

Why, then, does the acceleration continue?

Because at the individual firm level, the logic is rational. For Big Tech and leading AI labs, failing to invest aggressively is an existential risk. If one player achieves a decisive advantage — whether through superior models or control of critical infrastructure — it stands to capture an outsized share of future value. Everyone else becomes marginal.

What we are witnessing is a race in which everyone sees the wall approaching, but no one dares slow down — hoping that being first will somehow change the outcome.

Economists have a name for this dynamic: a tragedy of the commons, Silicon Valley–style.

The Real Fragility Lies in Finance, Not Algorithms

Where The Atlantic is particularly incisive is in its examination of the financial structures underpinning this race.

To avoid loading their balance sheets with visible debt, many companies are financing data centers through complex arrangements involving private equity, real estate structures, and securitized leases. These assets are then sliced, packaged, and sold as financial products — sophisticated, but opaque.

This is not subprime mortgages. But the underlying logic is familiar: complexity, leverage, and reliance on a single optimistic assumption — that demand for large-scale AI capacity will remain both constant and profitable.

The problem is that data centers depreciate quickly. Chips become obsolete in a few years. And there is no guarantee that AI use cases will scale economically at the same pace as the infrastructure built to support them.

Useful AI vs. Spectacular AI: A Strategic Distinction

One major source of confusion fueling today’s AI bubble is the failure to distinguish between useful AI and infrastructure-driven, spectacle AI.

Useful AI is quiet. It targets specific problems, reduces measurable costs, improves operational decisions, and consumes relatively modest resources. It creates real, defensible value.

Infrastructure AI is loud. Massive models, breathtaking demos, promises of general intelligence—paired with enormous capital requirements and uncertain returns.

Over time, economic history suggests that durable value rarely resides at the most visible center of technological hype. It emerges instead at the edges, where technology is applied pragmatically to real constraints.

What Boards and Executives Should Take Away

For non-tech companies, the implications are clear:

• AI is not a strategy in itself.

• The greatest risk is not “falling behind,” but investing without discipline.

• Strategic clarity, measurable ROI, and reversibility matter more than speed of adoption.

Competitive advantage will not come from access to the largest models, but from organizational maturity—the ability to absorb AI intelligently where it creates tangible value.

A Final Paradox

As The Atlantic ultimately suggests, all plausible scenarios involve disruption.

If AI fails to deliver on its promises, the financial consequences could be severe.

If it succeeds beyond expectations, the shock to labor markets, skills, and social systems could be just as profound.

Either way, the central issue is no longer technological. It is strategic, economic, and societal.

Perhaps that is the real lesson of this race toward the wall: the danger is not AI itself, but the speed at which we have turned it into a structural pillar of the economy before fully understanding its consequences.