I’ve spent the past few years deep inside the generative AI space, building platforms, running experiments, integrating models into real products. I’ve seen firsthand how transformative the technology can be when it’s applied to actual problems. But lately, I’ve also become increasingly uncomfortable with how AI is shaping the broader economy, or rather, how it seems to be creating its own parallel one.
The Market Floats, The Economy Stalls
It’s hard not to notice the surreal disconnect between today’s stock markets and the world most people live in. Growth is tepid, trade barriers are back, and consumer confidence is fragile. Yet equities are euphoric, especially anything labeled “AI.” The reality is that AI is now the only thing propping up the markets. Investors have decided that as long as AI exists, gravity doesn’t apply.
In India, funds are flowing almost exclusively into AI-linked sectors, leaving traditional industries behind. In the U.S., chipmakers and cloud providers have become the market’s lifeblood. Nvidia’s stock alone has risen nearly 40% this year, with CEO Jensen Huang pointing to “substantially” higher AI demand. These numbers look extraordinary until you realize they’re floating above an economy still struggling to translate generative AI’s promise into profitability.
The Snake Eating Its Own Tail
What worries me most is that much of the capital fueling this boom isn’t coming from outside demand, it’s circulating within the AI ecosystem itself.
Chip vendors invest in AI startups, which then spend that money on compute, cloud credits, or APIs from the same vendors. Giants like Nvidia, Microsoft, and Oracle are customers, suppliers, and investors in one another’s ecosystems. It’s a beautiful, self-reinforcing loop, but economically, it’s circular.
Morgan Stanley recently called this out as “circular financing,” where the same dollars keep recirculating between partners. The problem is that it creates a closed system: AI companies buying from AI companies to justify further AI investments. It feels, at times, like a snake biting its own tail.
The Myth That AI Is Untouchable
Tariffs, policy uncertainty, supply chain fragility, all of these factors should, in theory, weigh on valuations. But they don’t. Investors have convinced themselves that AI is somehow insulated from the real economy, that it can thrive independently of energy costs, regulation, or consumer demand. That’s magical thinking.
The Bank of England recently warned that a sudden shift in AI sentiment could cause a “sharp correction” in global markets. And it’s not hard to see why. Markets have priced AI not as a technology, but as a faith system, something inherently self-justifying.
When the Market Has to Meet the Economy
At some point, AI’s speculative economy will need to converge with the real one. The question is: when AI “meets the economy,” will its value hold?
So far, AI’s visible economic footprint remains small. The productivity impact is uneven. In some sectors, like code generation, marketing automation, and design - the gains are real. But in manufacturing, logistics, healthcare, and education, the impact is still largely theoretical.
There’s a growing academic movement trying to measure this gap, calling it the Capability Realization Rate, essentially, how much of AI’s promised potential has actually been realized. The findings suggest that valuations are outpacing results by an order of magnitude.
As someone who’s building in this space, I want AI to succeed but in a way that’s grounded in useful, measurable outcomes. Not because investors keep convincing each other it will.
The Inevitable Correction
When this circular economy hits the limits of its own abstraction, a correction must follow. Not necessarily a crash, but a forced recalibration. Earnings reports that miss inflated expectations, regulatory pressure, or a macro shock could all act as catalysts. Goldman Sachs already warned that AI valuations are “stretched” and could reprice sharply on any disappointment.
This'll be hard on everyone's portfolio, but necessary. It will bring the focus back to AI’s alignment with real-world value creation, where dollars spent on compute and models translate into better logistics, cleaner energy, or more efficient education. Until that happens, the market will remain trapped in its own feedback loop.
Sources: Reuters, Investopia, Barron's, arXiv