Can Profitability be Achieved by AI Genesis Enterprises?

In the rapidly evolving world of technology, a significant debate is unfolding around the potential return on investment (ROI) of generative AI. A report by Jim Covello, Head of Global Equity Research at Goldman Sachs, sheds light on this issue, raising concerns about the immense cost of AI infrastructure buildout and the uncertain ROI.

Covello estimates that the AI infrastructure buildout will cost over $1 trillion in the next few years, encompassing spending on data centers, utilities, and applications. This astronomical figure has been fuelled by the growing investment in generative AI by the IT industry, with cloud infrastructure capital expenditure increasing from $32 billion in 2013 to $119 billion in 2020.

However, the potential ROI from generative AI remains uncertain, leading to a growing debate about its real-world business application. The MIT report indicates that 95% of organizations are seeing no or very limited returns from their internal generative AI pilot programs. This uncertainty has led some experts to question whether the generative AI market will ever be able to make money.

The playbook for AI startups, such as OpenAI and Anthropic, resembles that of hyperscale platforms like Uber, with a potential future increase in prices to return capital to investors. Yet, the skepticism around the ability of generative AI to help companies increase revenue and profit is becoming more common.

If businesses begin demanding concrete returns on their AI investment and find them lacking, a significant market correction could follow, potentially affecting the global economy negatively. Covello's 2024 report suggests that AI technology is exceptionally expensive and must solve complex problems to justify its costs.

The report also mentions that AI companies continue to swear by the scaling myth, claiming they need more data, training, and investor money to deliver on their promises. This relentless pursuit of perfection, however, raises questions about the feasibility of achieving sustainable ROI.

History suggests that bubbles burst when the gap between investment and practical returns becomes unsustainable, as seen in the dot-com crash of 2001. With the current state of generative AI, the question remains: What $1 trillion problem will AI solve?

Covello asks this question, believing that replacing low-wage jobs with expensive technology is not a typical technology transition. Uber, for instance, delayed the moment of price increases for years by collecting funds with the promise of autonomous driving, but this did not materialize.

All current generative AI services, including ChatGPT, Claude, Cursor, Microsoft Copilot, and Google Gemini, are heavily subsidized by investors' or companies' money. The $1 trillion question is what happens when financial realities can no longer be delayed, and investors and companies realize that the chasm between costs and applications can't be filled.

Sam Altman, CEO of OpenAI, admits that there is a bubble surrounding the potential and applications of generative AI. As the AI industry continues to grow, it is crucial to address these concerns and work towards creating a sustainable and profitable AI ecosystem.