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Goldman Sachs queries if AI will displace low-wage jobs

Despite massive investment in AI, sustainable business models have yet to emerge, a Goldman Sachs research paper has said.

Goldman Sachs estimates that around $1 trillion will be spent on AI infrastructure – including data centres, semi-conductors and grid upgrades.

The report states that it is questionable whether generative AI will bring about financial returns for investors, even for a so-called “killer application”.

Investment in the technology is leading to power and hardware shortages as national grids are overhauled.

"So, the crucial question is: What $1tn problem will AI solve?

“Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my 30 years of closely following the tech industry," said head of global equity research Jim Covello.

"Many people attempt to compare AI today to the early days of the Internet. But even in its infancy, the Internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions," he said.

"The market is too complacent about the certainty of cost declines," he added.

“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,” he said.

Investor enthusiasm

Covello said that investor enthusiasm was likely to fade if use-cases did not become more apparent in the next 12-18 months.

“The more time that passes without significant AI applications, the more challenging the AI story will become,” he stated.

Covello added that continuing corporate profitability was key to sustained experimentation with projects that had a negative return on investment (ROI).

Life-changing inventions such as the internet enabled low-cost solutions to disrupt high-cost solutions even in its infancy, unlike costly AI tech today, he said.

“As long as corporate profits remain robust, these experiments will keep running. So, I don’t expect companies to scale back spending on AI infrastructure and strategies until we enter a tougher part of the economic cycle, which we don’t expect anytime soon."

Kash Rangan of GS added: "Every computing cycle follows a progression known as IPA - infrastructure first, platforms next, and applications last. The AI cycle is still very much in the infrastructure build-out phase, so finding the killer application will take more time, but I believe we’ll get there."

Irrespective of long-term success, the AI build-out will have a near-term impact on the grid, the paper states, with US electricity demand to rise at a 2.4% compound annual growth rate and data centres doubling their electricity use by 2030.