Goldman Sachs recently released a report titled "Gen AI: Too Much Spend, Too Little Benefit?" Their analysis explores the current state of generative AI and its potential economic impacts. It examines the substantial investments being made in AI infrastructure — estimated at over $1 trillion — and questions whether this spending will yield commensurate benefits.
GenAI has generated enormous hype and investment over the past 18 months, with $12 billion of funding in Q1 2024 alone. Many see it as a revolutionary technology that could transform industries and boost economic productivity. However, as the initial excitement fades, more skeptical views are emerging about GenAI's near-term potential and long-term use cases.
The report represents a notable shift in perspective from a major financial institution. It suggests that the economic and productivity gains from GenAI over the next decade may be much more limited than many have predicted. This could have major implications for tech companies and investors who have bet heavily on AI's potential.
Jim Covello, Goldman Sachs' Head of Global Equity Research, argues that the common belief that AI technology will become cheaper over time may be misguided:
“The idea that technology typically starts out expensive before becoming cheaper is revisionist history. E-commerce… was cheaper from day one, not ten years down the road. But even beyond that misconception, the tech world is too complacent in its assumption that AI costs will decline substantially over time. Moore’s law in chips that enabled the smaller, faster, cheaper paradigm driving the history of technological innovation only proved true because competitors to Intel, like Advanced Micro Devices, forced Intel and others to reduce costs and innovate over time to remain competitive.Today, Nvidia is the only company currently capable of producing the GPUs that power AI. Some people believe that competitors to Nvidia from within the semiconductor industry or from the hyperscalers—Google, Amazon, and Microsoft— themselves will emerge, which is possible. But that's a big leap from where we are today given that chip companies have tried and failed to dethrone Nvidia from its dominant GPU position for the last 10 years.The starting point for costs is also so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable. People point to the enormous cost decline in servers within a few years of their inception in the late 1990s, but the number of $64,000 Sun Microsystems servers required to power the internet technology transition in the late 1990s pales in comparison to the number of expensive chips required to power the AI transition today, even without including the replacement of the power grid and other costs necessary to support this transition that on their own are enormously expensive.”
Covello’s concerns raise meaningful questions about the sustainability of the current AI investment boom if costs remain prohibitively high for many potential applications. As for the broader implications of generative AI spending and progress, it extends beyond the tech sector itself. For companies investing heavily in AI, there's a growing need to recalibrate expectations about short-term returns, given the substantial costs involved in development and implementation.
The job market, while not facing immediate widespread disruption, may see gradual shifts, particularly in creative and entry-level positions vulnerable to AI-driven commoditization. The tech industry faces the risk of an AI investment bubble should the technology fail to deliver on its promises, which could have ripple effects throughout the sector. Infrastructure, particularly energy grids, is under increasing strain from the power demands of AI systems, underscoring the necessity for significant upgrades to support continued AI growth. A report from Sequoia describes the gap between AI investment and potential AI revenue from monetizable use cases as “AI’s $600B Question.”
One potential silver lining of the slower-than-anticipated progress in AI capabilities is that it could provide society with additional time to deal with ethical concerns and develop appropriate regulatory frameworks. While GenAI remains a promising and disruptive technology, a more cautious and measured approach to AI investment and adoption may be warranted. The coming years will be crucial in determining whether the massive spending on AI will ultimately pay off or lead to a costly bubble like we’ve seen in the past.
