AI spending is soaring, but revenue is lagging: Here’s why Indian investors should pay attention

Artificial intelligence (AI) has become the biggest driver of global stock markets, with companies spending hundreds of billions of dollars to build AI infrastructure. While investor excitement remains high, experts believe the economics behind the AI ​​boom are far less convincing than the market suggests.

The gap between AI spending and the revenue it currently generates raises important questions for investors, especially those invested in global equity funds with significant exposure to US tech stocks, says Ametra PMS Co-Founder and CIO Karan Aggarwal.

AI spending is racing ahead of revenue

“The difference between AI capex of around $725 billion and current AI revenue of only $50-60 billion is significant because it means that for every $10 invested annually, the revenue earned is less than $1. What matters is that it’s not profit, it’s revenue,” Aggarwal said.

Simply put, companies are investing heavily today in the hope that AI will become highly profitable years from now. It remains unclear whether these returns will eventually occur.

Cash flows coming under pressure

The AI ​​race is also becoming expensive for the companies leading it. Aggarwal notes that most large hyperscalers use almost all of their free cash flow to fund AI expansion, while some have taken on debt or raised new equity to continue investing.

“Currently, AI capex accounts for 94% of hyperscalers’ free cash flow, and that number is expected to grow to 157% by 2030. Hyperscalers will become debt-laden companies burning cash on unknown and unproven growth paths,” he said.

For investors, this means that today’s gains may increasingly be sacrificed for uncertain long-term returns.

Adoption rising but monetization remains weak

AI is being adopted across all industries, but generating meaningful revenue from it has proven much harder.

Citing an MIT NANDA study reported by Fortune, he said 95% of productive AI pilots fail to reach production. The consumer story is equally bleak. According to NPR, only 3% of consumers actually pay for AI services.

He also highlighted Sequoia Capital’s forecasts showing the AI ​​revenue gap growing from $125 billion in 2024 to approximately $600-700 billion today, highlighting how infrastructure spending continues to outpace demand.

A lesson from the dot-com era

Aggarwal believes the current environment bears similarities to the internet boom of the late 1990s.

“It’s very similar to the internet bubble of the early 2000s, when the internet was widely adopted but almost 80% of internet companies went bankrupt,” he said. Even the internet boom took almost a decade for companies to start generating meaningful returns.

He also warns that AI remains a “winner takes all” industry. In order for trillions of artificial intelligence capital to be thrown into useless garbage, a low-cost and equally effective model must be taken from China.

AI rally poses risks for Indian investors

Many Indian investors build assets abroad through global mutual funds, ETFs or international feeder funds. The challenge, Aggarwal says, is that these portfolios are increasingly concentrated in a handful of large U.S. tech companies.

It notes that the top 10 companies account for more than 40% of the S&P 500’s market capitalization, compared with the historical average of 20-25%, while most global equity funds have almost 70% exposure to the US.

Some emerging market funds also have around 45% exposure to Taiwan and South Korea, with the three companies accounting for almost 32% of the portfolio weight.

Aggarwal also believes that US stock valuations leave little room for disappointment. The US market capitalization/GDP ratio is above 220, well above the historical comfort range of 80-120; The price/earnings ratio of the S&P 500 is close to 30, the level last seen in the dot-com era.

“With the global equity boom revolving around the adoption of artificial intelligence and unrealistic income expectations around the largest capex cycle in human history, there is little margin for error. Any setback in expectations could trigger a 2000 downturn,” he said.

“Considering the precedent of major crashes in the US markets over the last 25 years – be it 2000 or 2008 – Indian markets may also see sharp valuation pressures due to bearish global cues that suggest Indian investors should remain cautious against negative global trends,” Aggarwal noted.

Key takeaways for investors

“When it comes to global investing, it is time for a more nuanced approach to geographic diversification, focusing on value plays across a broad universe of emerging and developed markets,” Aggarwal said.

He added that AI-led enthusiasm has increasingly concentrated global markets, making broad-based index investing alone a less effective strategy for global diversification.