Experts at Goldman Sachs have said the benefits of Generative Artificial Intelligence (Gen AI) are little or non-existent when compared to the investments in the technology.
This revelation was made in the Goldman Sachs Global Macro Research, where industry experts shared concerns on the profitability of Gen AI globally.
According to research, tech giants are set to spend over $1 trillion on AI capex in coming years, alongside investments in data centres, chips, and other AI infrastructure.
However, Goldman Sachs argued that, beyond reports of efficiency gains among developers, there has been little to show for it. They added that Nvidia, the chipmaker reaping the most benefits of Gen AI, has seen its stocks decline.
Daron Acemoglu, Institute Professor at MIT, estimated that only a quarter of AI-exposed tasks would be cost-effective to automate within the next 10 years, implying that AI will impact less than 5 percent of all tasks.
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“Generative AI has the potential to fundamentally change the process of scientific discovery, research and development, innovation, new product and material testing, etc., as well as create new products and platforms.
“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,” Acemoglu said.
He added that AI technology will instead primarily increase the efficiency of existing production processes by automating specific tasks or by making workers who perform these tasks more productive.
Jim Covello, Head of Global Equity Research at Goldman Sachs, argued that Gen AI technology must be able to solve complex problems to earn an adequate return on the $1 trillion estimated cost of running it.
“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.
“We estimate that the AI infrastructure buildout will cost over $1 trillion in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve?”
He added that AI’s cost may never decline enough to make automating a large share of tasks affordable, given the high starting point and the complexity of building critical inputs—like GPU chips—which may prevent competition.
Joseph Briggs, Goldman Sachs Global economist, estimated that Gen AI would ultimately automate 25 percent of all work tasks.
Despite his optimism, he acknowledged that automating some AI-exposed tasks isn’t cost-effective today. He argued that the large potential for cost savings and the likelihood that costs will decline over the long run—as is often, if not always, the case with new technologies—should eventually lead to more AI automation.
