The economics of AI: Who gains, who loses, and what comes next

Artificial intelligence, or AI, refers to technologies capable of performing tasks that traditionally require human intelligence – learning, problem-solving and decision-making. Once confined to laboratories and niche applications, AI is now becoming embedded in everyday life. Tools such as ChatGPT are widely used for studying, travel planning and idea generation, signalling a growing dependence on algorithmic assistance.

At the core of these systems are Large Language Models, trained on vast volumes of data to predict the most probable sequence of words. While the output appears purely digital, the process is underpinned by heavy physical infrastructure and capital investment. Training and operating AI models require large-scale data centres powered by specialised Graphics Processing Units, or GPUs, designed for parallel computation. The construction of a single advanced data centre can cost tens of billions of dollars.

The financial winners of the AI boom

Among the biggest beneficiaries of this surge is Nvidia, a leading manufacturer of GPUs. The company's revenue jumped to $130 billion in the 2025 financial year from $61 billion a year earlier, marking a 114 percent increase. Its market capitalisation crossed the $5 trillion mark in early November 2025, reaching levels rarely seen in corporate history.

Nvidia is not alone. Technology giants such as Meta, Oracle, OpenAI and Amazon have also seen sharp rises in valuation and earnings, with several doubling or even tripling their market capitalisation amid the AI frenzy.

Startups, hype and capital misallocation

The boom has also extended to smaller firms. Numerous AI startups have raised billions of dollars on the back of expectations rather than proven revenue models, raising concerns about capital misallocation. A recent study by the Massachusetts Institute of Technology found that around 95 percent of businesses investing in AI have yet to generate profits, despite an estimated combined expenditure of roughly $40 billion.

Builder.ai, a UK-based startup once valued at $1.5 billion, is a frequently cited example. The company filed for bankruptcy after disclosures showed that much of its "AI-powered" software development relied on human labour outsourced across different countries. Similar failures across the sector have already wiped out tens of billions of dollars, pointing to speculative excess.

Circular financing and bubble risks

Another emerging concern is circular financing within the AI ecosystem, where capital flows repeatedly among the same firms. For instance, a chipmaker may invest in an AI company, which then uses that funding to purchase more chips from the original investor.

Analysts warn that such arrangements can artificially inflate demand. Veritas Investment Research has identified between 80 and 100 such circular deals involving Nvidia alone. Critics argue this self-reinforcing loop resembles classic asset bubbles, where prices rise to unsustainable levels without corresponding consumer demand.

Can AI justify current valuations?

For AI to justify these valuations, proponents argue that the technology must advance towards Artificial General Intelligence – systems capable of human-like reasoning across a wide range of tasks. Several technology executives believe that with sufficient data and computing power, AGI is achievable.

However, scepticism remains. Yann LeCun, Meta's chief scientist, has argued that large language models are unlikely to lead to AGI, noting that language is primarily a tool for communication rather than reasoning. Without a breakthrough, critics contend, the current valuation surge may be difficult to sustain.

Time, cost and inequality

Investment in US data centres has surged dramatically, rising from about $200 million per month in 2015 to more than $3 billion a month in 2025. This investment reportedly accounted for nearly two-thirds of US GDP growth in the first half of 2025.

Yet the benefits are unevenly distributed. High-income investors and technology executives have gained disproportionately, while income inequality has widened. Moreover, GPUs typically depreciate within five years. Unlike 19th-century railway infrastructure that lasted decades, sustaining the AI boom would require repeated trillion-dollar reinvestments simply to replace ageing hardware.

Why it matters for Bangladesh

A collapse of the AI bubble could trigger a recession in the United States with global spillover effects. According to estimates by IMF First Deputy Managing Director Gita Gopinath, such a downturn could erase $20 trillion in US household wealth and another $15 trillion abroad.

For Bangladesh, the risks are significant. A slowdown in Western economies could reduce demand for garments, cutting export earnings and raising unemployment. At the same time, economic stress in the Middle East could weaken remittance inflows, placing pressure on foreign exchange reserves and import capacity.

There are, however, areas for preparation. Greater awareness of AI tools could improve productivity across professions, particularly as routine tasks such as data entry and preliminary screening become automated. Public policy could also play a role through training programmes and targeted grants for AI startups, coupled with robust due diligence to avoid wasteful investment.

Whether the AI industry can navigate these structural challenges remains uncertain. For countries like Bangladesh, the task is not to ride the hype, but to understand the risks – and prepare accordingly.

Ayman Hossain is an A-Level candidate and a keen follower of global affairs and economics.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.