Is AI Worth The Cost? Uber And Nvidia Executives Unsure Of Returns On Investments

From Uber's Andrew Macdonald to Nvidia's Bryan Catanzaro, top business leaders have raised concerns over rising costs linked to artificial intelligence. Many believe that it is becoming “harder to justify” the expense of large-scale AI systems as companies invest heavily in computing power.

Author Ed Zitron, in his popular newsletter 'Where's Your Ed At' shared concerns over the high cost of AI development and its uncertain economic viability. His article argued that, at present, most AI companies are losing large amounts of money, while only hardware and data centre firms benefit from the boom. 

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“Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027, meaning that they need to make at least three trillion dollars in AI specific revenue just to break even, and $6 trillion or more for AI to be anything other than a wash,” Zitron said in the newsletter published last week.

What CEOs Say:

He is not alone in sharing this concern. In January, a Business Insider report cited a PwC survey highlighting that CEOs do not think AI is paying off yet. The survey of 4,500 CEOs found that many executives are still unsure about the short-term financial returns from AI investments.

“More than half of the CEOs surveyed, 56%, said AI hasn't produced revenue or cost benefits for their businesses to date. Some reported benefits for either revenue or costs: around a third said their revenue was up in the last year, and 26% said they were seeing lower costs from AI. But only 12% said they had both decreased costs and increased revenue using AI in the last 12 months,” the report stated.

In a YouTube podcast with Rapid Response on May 23, Uber Chief Operating Officer Andrew Macdonald also said that it was becoming harder to justify AI costs. Notably, Uber Chief Technology Officer, Praveen Neppalli Naga, went viral after telling The Information in an April interview that Uber had already blown through its Claude Code budget for 2026.

In early May, Nvidia Vice President of Applied Deep Learning, Bryan Catanzaro, said that AI's reported productivity gains are coming at a very high cost. He said that for his team, the cost of computing power is far greater than the cost of employees.

Why Are Experts Wary?

The list of skeptical voices on AI investments includes MIT's Professor Daron Acemoglu, who recently argued his case in an article titled “The Simple Macroeconomics of AI”. He estimated that AI can cost-effectively automate only about one-quarter of tasks it can technically handle, which translates to roughly 5% of all tasks overall.

With this low base, the total productivity gain from AI will be 0.5% in the US and will contribute just 0.9% of US GDP in 10 years. One can assume it will be lower in a low-cost country like India, he said.

“Many tasks that humans currently perform, for example in the areas of transportation, manufacturing, mining, etc., are multifaceted and require real-world interaction, which AI won't be able to materially improve anytime soon. So, the largest impacts of the technology in the coming years will most likely revolve around pure mental tasks, which are non-trivial in number and size, but not huge, either,” he said.

In a similar view, Goldman Sachs Head of Global Equity Research Jim Covello, recently pointed out that for AI investments to deliver strong returns, the technology must be able to solve complex, high-value problems.

According to him, truly life-changing innovations, like the internet, work by enabling low-cost alternatives to expensive systems. He also doubts that AI costs will fall quickly enough to make automation economically efficient. This is due to the complexity of producing key inputs like GPU chips and limited competition in the GPU market. At the same time, companies are under pressure to adopt AI, but clear revenue models are still missing, he argued.

Strategy Remains A Key Challenge

A separate EY survey highlighted challenges in achieving returns from AI. It found that the companies are missing out on about 40% of potential productivity gains due to weak strategy and execution.

According to PwC, these challenges have also resulted in declined confidence among CEOs about revenue growth. Only 30% of those surveyed said they are very or extremely confident about revenue growth over the next 12 months. This is lower than 38% in last year's report and much lower than the peak of 56% recorded in 2022.

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The lack of strategy may be a reason why the companies do not clearly know how much AI will cost them each quarter, Zitron's newsletter said. As a result, spending becomes experimental and open-ended, he argued. This may also explain the boosted reported revenues for firms like Anthropic, as companies allow engineers to freely use AI tools, driving higher usage, rising costs and inflated revenue figures.

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