Goldman Sachs says AI is too expensive and unreliable — firm asks if massive investments in ‘overhyped’ AI processing will ever pay off

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Goldman Sachs says AI is too expensive and unreliable — firm asks if massive investments in ‘overhyped’ AI processing will ever pay off

Corporations and investors have been spending billions of dollars on building AI. The current LLM models we use today, like GPT-4o, already cost hundreds of millions of dollars to train, and the next generation models are already underway going up to a billion dollars. However,

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, one of the leading global financial institutions, is asking whether these investments will ever pay off.

Sequoia Capital, a venture capital firm, recently looked at AI investments and they computed that the entire industry needs to make $600 billion annually just to break even on their initial expenditure. So, as massive corporations like Nvidia,

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are spending huge amounts of money to gain a leg up in the AI race, Goldman Sachs interviewed several experts asking whether investments in AI will actually pay off.

The expert opinions in the Goldman Sachs report are currently divided into two: one group is skeptical about its group, saying that AI will only deliver limited returns to the ********* economy and that it won’t solve complex problems more economically than current technologies. On the other hand, the opposing view says that capital expenditure cycle on AI technologies seem promising and are similar to what prior technologies went through.

MIT Professor Daron Acemoglu estimates that generative AI’s impact on the economy will be limited, contributing only to around a 0.5% increase in productivity and a 1% addition to GDP output. This is in sharp contrast to estimates of Goldman Sach’s own economists, which suggested a 9% jump in productivity and 6.1% increase in GDP. He also said that even though AI technologies will eventually evolve and become less costly, he isn’t convinced that the current trend of dumping more data and computing power at AI models will allow us to hit our vision of artificial general intelligence more quickly.

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 “Human cognition involves many types of cognitive processes, sensory inputs, and reasoning capabilities. Large language models (LLMs) today have proven more impressive than many people would have predicted, but a big leap of ****** is still required to believe that the architecture of predicting the next word in a sentence will achieve capabilities as smart as HAL 9000 in 2001: A Space Odyssey,” say Acemoglu. “It’s all but certain that current AI models won’t achieve anything close to such a feat within the next ten years.”

The contrarian view on the report comes from Kash Rangan and Eric Sheridan, who are both Senior Equity Research Analysts at Goldman Sachs. They say that even though returns on AI investments are taking longer than expected, it should eventually pay off. Rangan says, “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 buildout phase, so finding the ******* application will take more time, but I believe we’ll get there.”

 “This capex (capital expenditure) cycle seems more promising than even previous capex cycles because incumbents — rather than upstarts — are leading it, which lowers the risk that technology doesn’t become mainstream,” Sheridan added. “Incumbents [like

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and

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] have access to deep pools of capital, an extremely low cost of capital, and massive distribution networks and customer bases, which allows them to experiment with how the capital dollars could eventually earn a return.”

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Despite these two contrarian views, Goldman Sachs recognized the two challenges facing AI — the availability of chips and power. The AI GPU crunch seems to be over, especially as Nvidia could now deliver chips with a lead time of two to three months, instead of the 11 months that it used to take.

However, data center power consumption is now becoming the major limiting factor, especially as AI GPUs are becoming increasingly power hungry. In fact, a single modern AI GPU could use up to 3.7 MWh of power annually, with all the GPUs sold just last year consuming enough electricity to power more than 1.3 million average ********* households. Major corporations have even now started looking at modular nuclear power plants just to ensure that their massive AI data centers could get the power it requires.

Only history can tell us whether AI will ***** like the internet and e-commerce, or if it will be bust like 3D TVs, virtual reality, and the metaverse. But whatever the case, we expect to see AI development continue. As Goldman Sachs puts it, “We still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubble take a long time to burst.”

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