Introduction

Generative Artificial Intelligence (AI) has become the latest buzzword across industries. Many believe it will revolutionize businesses. However, serious questions hang over its uses and impact. Recently, a Goldman Sachs report raised significant concerns about the economic viability of generative AI. This blog post delves into the key points of this report and explores the potential and challenges of generative AI.

The Hype Around Generative AI

Generative AI is being hailed as the next big thing in technology. A KPMG survey in the US found that executives expect generative AI to have an enormous impact on business. Yet, most say they are unprepared for immediate adoption. This gap between expectation and readiness raises questions about the future of AI in business.

Goldman Sachs’ Concerns

Goldman Sachs’ report questions the massive spending on AI. Tech giants and other companies are set to spend over $1 trillion on AI capital expenditure in the coming years. However, there is little to show for it so far. The report asks if this large spend will ever pay off.

Experts’ Opinions

• Many experts doubt any revolutionary impact of AI in the short term.
• Some experts are more optimistic about AI’s economic potential.
• They believe AI could ultimately generate returns beyond its current phase.

Despite these concerns, there is still room for the AI theme to run. This could be either because AI starts to deliver on its promise or because bubbles take a long time to burst.

Productivity and Generative AI

Daron Acemoglu, an Institute Professor at MIT, shared his insights in an interview with Goldman Sachs. He argued that the upside to US productivity and growth from generative AI technology over the next decade will likely be more limited than many expect. Acemoglu estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years, impacting less than 5% of all tasks.

He also questions whether AI adoption will create new tasks and products. Acemoglu estimates that total factor productivity effects within the next decade should be no more than 0.66%, translating into a 0.9% GDP impact over the decade.

Acemoglu’s Caution

Acemoglu cautions against too much optimism and hype. He believes it may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and troubleshooting capabilities that human capital provides.

Return on Investment

Jim Covello, Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn’t capable of doing, and may never be.

Cost Concerns

Covello estimates that the AI infrastructure buildout will cost over $1 trillion in the next several years alone. This includes spending on data centers, utilities, and applications. The crucial question is: What $1 trillion problem will AI solve?

Replacing low-wage jobs with tremendously costly technology is the opposite of prior technology transitions. Covello doesn’t think that technology costs will decline dramatically as technology evolves. This is due to the lack of competition, as Nvidia is the only company currently capable of producing the GPUs that power AI. The starting point for costs is so high that even if they decline, they would have to do so dramatically to make automating tasks with AI affordable.

Comparisons with the Internet

Many people attempt to compare AI today to the early days of the internet. However, even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions. AI technology is exceptionally expensive, and to justify those costs, it must be able to solve complex problems, which it isn’t designed to do.

Conclusion

Generative AI holds immense potential, but significant challenges remain. The hype around AI must be tempered with realistic expectations. Companies need to carefully consider the return on investment before diving into AI adoption. The future of AI will depend on its ability to solve complex problems and deliver tangible benefits. Until then, the $1 trillion question mark over generative AI will continue to loom large.

Stay tuned for more updates on the evolving landscape of generative AI and its impact on various industries.