General Discussion, page-4904

• Tech giants set to invest over $1 trillion in AI infrastructure

• Experts express skepticism about AI’s cost-effectiveness

• Despite differing views, Goldman Sachs analysts remain optimistic about AI

Tech giants are expected to spend over US$1 trillion on artificial intelligence infrastructure in the coming years, which includes investments in data centres, chips, and power grids.

The Magnificent 7, which refers to seven tech stocks closely related to AI, have also contributed a significant portion of the stock market’s returns in recent years, including in 2024.

But despite this massive outlay and relentless rally, the future return on investment remains uncertain.

Goldman Sachs has recently published a report outlining its insights on the potential of AI, highlighting the differing expert opinions on AI for both the economy and investors.


Skeptical views on AI
Daron Acemoglu, an MIT professor, is among the skeptics.

He believes that AI will have limited economic benefits over the next decade. According to him, only a small fraction of tasks—or around a quarter of AI-exposed tasks—will be cost-effective to automate.

This implies that AI will impact less than 5% of all tasks, leading to a modest 0.5% increase in productivity and a 0.9% boost in GDP over ten years.

Acemoglu also questions the belief that AI technology will improve rapidly and become more cost-effective. He argues that advances in AI models are unlikely to occur as quickly or be as impressive as many expect.

“Given the focus and architecture of generative AI technology today, truly transformative changes won’t happen quickly and few – if any – will likely occur within the next 10 years,” said Acemoglu.

He also doubts that AI will create new tasks and products at a significant rate, which is crucial for substantial economic impact.

Jim Covello, Head of Global Equity Research at Goldman Sachs, shares similar doubts.

He argues that AI is not designed to solve the complex problems that would justify its high costs.

He points out that past revolutionary technologies, like the internet, provided low-cost solutions that disrupted existing high-cost solutions.

In contrast, AI remains expensive and may not see significant cost reductions.

Covello isn’t the only one who thinks companies are spending too much on AI.

David Cahn from Sequoia Capital says tech companies need to earn US$600 billion per year to justify their current AI investments. This figure is about six times higher than his best-case revenue projection for the AI industry.

However, Cahn says that if the AI industry is indeed a bubble, its burst might not be as severe as the dot-com crash.

He suggests that today’s tech giants have more cash reserves than companies did back in 2000, potentially cushioning the impact.



Optimistic perspectives on AI
On the other hand, some analysts at Goldman Sachs remain optimistic.

While current AI automation isn’t cost-effective, GS senior global economist Joseph Briggsestimates that generative AI will ultimately automate 25% of all work tasks and raise productivity by 9% and GDP growth by 6.1% cumulatively over the next decade.

Kash Rangan and Eric Sheridan, also analysts at Goldman Sachs, share this enthusiasm.

They argue that, despite high spending on AI infrastructure, current investment levels aren’t irrational.

Sheridan notes AI spending relative to revenues mirrors past tech investment cycles, and companies linking AI investments to revenue growth are being rewarded.

Rangan adds that the returns from this AI investment cycle look more promising due to leading companies with low capital costs and vast distribution networks driving the investments.

However Toshiya Hari, a GS’ semiconductor analyst, points out that chip shortages will limit AI expansion in the near term.

The demand for critical components like High-Bandwidth Memory technology and Chip-on-Wafer-on-Substrate packaging exceeds supply, hindering progress, he says.

Meanwwhile, Carly Davenport and Alberto Gandolfi, utilities analysts at Goldman Sachs, predict that the proliferation of AI will drive a surge in power demand, stressing an already aged and underprepared power grid.



What this means for markets and AI-related stocks
GS senior US equity strategist Ryan Hammondsays he sees more room for the AI theme to run and expects AI beneficiaries to broaden out beyond just Nvidia, and particularly to what looks set to be the next big winner: Utilities.

That said, looking at the bigger picture, GS senior multi-asset strategist Christian Mueller-Glissmann finds that only the most favourable AI scenario, in which AI significantly boosts profitability without raising inflation, would result in above-average returns over the S&P 500.

Meanwhile, Covello cautions that the AI bubble could take a long time to burst; and in the meantime, AI infrastructure providers might continue to benefit.

He’s sceptical about AI’s ability to enhance company valuations, as any efficiency gains could be offset by increased competition and unclear revenue pathways.

“Many people attempt to compare AI today to the early days of the internet,” Covello said.

“But even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions.

“Amazon could sell books at a lower cost than Barnes & Noble because it didn’t have to maintain costly brick-and-mortar locations.

“Fast forward three decades, and Web 2.0 is still providing cheaper solutions that are disrupting more expensive solutions, such as Uber displacing limousine services.

“While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable.

“The less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do,” said Covello.