The AI Bubble: Innovation and Inflation of Expectations

 The AI Bubble: Innovation and Inflation of Expectations

By: Rishita Arora

 The latest obsession inflating global markets is Artificial Intelligence. From multi-million business models to two person AI startups. Money is flowing into the sector faster than the markets can process.

Corporations and Investors are racing alike to secure their place in what many are calling the 'AI gold rush’. But behind this dazzling growth lies a critical economic question: is this a genuine transformative growth like the Dot-com bubble of the late 1990s that turned many internet dreams into multi billion dollar valuations or just another inflated bubble like the Tulip Mania of 1630s which saw the ordinary Tulip bulbs fetch prices higher than real estates? 

The AI Rush of the 2020s

With today’s dazzling surge in AI, NVIDIA’s valuations have skyrocketed. Even the traditional startups and companies are rebranding themselves with the ‘AI’ label to attract investors. Likewise, the venture capitalists are pouring money into any company that mentions “Artificial Intelligence” in its pitch deck. Educational institutions, too, are introducing AI and machine learning courses to draw students similar to what firms once did with “.com” during the 1990s boom.

AI has become the hottest buzzword of the decade so much so that anyone not investing in it feels a growing sense of FOMO and this collective fear of missing out has blurred the line of rational investment judgment not just among individuals but also among major corporate organizations. However, the real question isn’t whether AI will change the world but whether this flow of investment truly matches the pace of actual innovation.

Why the bubble?

The sudden and explosive rise of Artificial Intelligence has created a market trend that increasingly looks unsustainable. What started as a genuine technological progress has turned into a financial loop. Beneath the impressive headlines, inflated numbers and billion-dollar valuations lies a fragile system built on circular investments and exaggerated expectations.

At the center of this storm stands NVIDIA the key supplier of semiconductor chips that power AI models across the globe. Companies like OpenAI rely heavily on these chips to operate, yet NVIDIA is not merely their supplier but also their investor. This creates a cycle as NVIDIA invests in these OpenAI with a condition that they will buy semiconductor chips from them. The result? OpenAI’s valuation increases and NVIDIA’s sales soar. Moreover, all these AI companies are now investing in each other, making revenues rise artificially and investors pouring in more money, mistaking inflated data for actual growth.

By the end of 2024, NVIDIA’s market capitalization had crossed $3.3 trillion, gaining much of global investors attention. However, on the inside the AI market is showing signs of strain, high dependence, inflated valuations, and a widening gap between innovation potential and economic reality.

Economics beneath the Algorithms

At its core, a bubble represents a misallocation of capital when investments are driven more by expectations and hype than by actual productivity or profit potential. In the current AI race, this misallocation is clearly visible. Huge sums are flowing into speculative projects that may not yet yield measurable returns. Many companies are rebranding themselves under the ‘AI Label’ despite lacking true commercial viability. This mirrors past episodes like the dot-com bubble, where investors prioritised potential over performance.

Meanwhile, the labor market is adjusting. AI is replacing certain cognitive tasks but also creating demand for new skills. While certain roles in customer support, content writing, and analytics face automation, entirely new opportunities are emerging in data labeling, model training, prompt engineering, and AI safety research.

It’s Schumpeter’s “creative destruction” theory unfolding in real time, innovation tearing down existing traditional systems to make way for new industries, skill sets, and even ways of thinking. Beneath all this lies John Maynard Keynes’ “animal spirits” theory which suggests that all decisions are not rational but driven by human emotions like optimism, fear and herd behavior etc. These psychological theories propel both investors and consumers to take risk. The confidence that AI has the potential to change every aspect of life from healthcare, education to finance fuels this risk taking.

Real-World Impacts on the Economy

The Real World impacts of the rise in the use of Artificial Intelligence is both positive and negative. On one hand, the AI companies have captured a significant portion of the market through open platforms like OpenAI, effectively locking in customers for the long term.

On the other hand, the gap between existing skill sets and the ability of workers to adapt to this major technological shift has led to widespread layoffs. Moreover, the huge capital inflows into the ‘AI Sector’ have created a hype that is not yet met with actual productivity gains. This has widened inequality among the firms, with early adopters and the big corporations benefitting the most while smaller ones struggle to catch up

It has also given rise to the problem of opportunity cost, the capital inflows poured into the ‘AI’ sector for speculative gains could have been redirected towards more productive projects.

Bubble or Restructuring?

No matter how cool this new trend of Artificial Intelligence might sound, the bubble burst (if it happens) would still be painful. Just like in the case of the ‘Dot-com’ bubble not all companies who adopted the”.com” label to attract investors managed to survive in the long term. This eventually led to business failures and loss of investor confidence.

However, that period also gave rise to Multi billion dollar companies like Google, Amazon, eBay.

Similarly the current hype surrounding the ‘AI’ sector might not last forever. But it could potentially pave the way for a major technological transition.

As of 2025, AI adoption is still uneven. Many businesses are experimenting, but few have managed to convert these experiments into sustainable profits. However, we’re yet to see what this revolution unfolds into!

Written & Published by: Rishita Arora

Credits: Inspired by insights shared by CA Sarthak Ahuja

Image Courtesy: Pinterest