AI’s purported productivity gains are coming at a cost greater than companies would typically pay humans, according to Nvidia (NASDAQ:NVDA) Vice President of Applied Deep Learning Bryan Catanzaro.
“For my team, the cost of compute is far beyond the costs of the employees,” Axios quoted Catanzaro as saying last week.
And Catanzaro’s experience is not be unique. Uber Chief Technology Officer Praveen Neppalli Naga told The Information early last month that the popular ride-hailing platform has already burned through its AI budget for the year.
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However, companies do not appear to be dissuaded by the cost of these AI tools. In fact, they appear to see it as a flex.
Nvidia CEO Jensen Huang in March said he would be concerned if his engineers earning $500,000 a year did not at least use up to $250,000 in AI tokens. About 11% of Uber’s code is now written by AI, Neppalli Naga is quoted as saying by The Information, adding that the plan is for AI agents supervised by other AI agents to replace software engineers.
In a viral LinkedIn post last month, startup Swan AI CEO Amos Bar-Joseph boasted that his four-person team had reached a $113,000 monthly AI bill.
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The revelation that the cost of relying on AI may outpace the cost of human labor comes against a backdrop of big tech companies actively dropping employees while embracing AI tools.
Meta Platforms (NASDAQ:META) is set to lay off about 8,000 employees this month. Microsoft (NASDAQ:MSFT) has reportedly offered buyouts to nearly 9,000 employees.
Meanwhile, academic studies and reporting question whether the adoption of AI tools is yielding productivity gains. A number of Amazon employees were recently quoted by The Guardian as saying that, in some instances, the use of AI actually hurt productivity.
As companies continue to invest heavily in artificial intelligence, questions around cost efficiency and long-term returns are becoming increasingly important for investors evaluating the sector. Rising compute expenses and uncertainty around productivity gains are leading to a more nuanced view of how different companies within the AI ecosystem may perform over time.
Platforms like Public allow investors to explore these broader themes through diversified portfolios that include exposure to major technology companies and other sectors influenced by the growth of AI. By investing across a range of asset classes in one place, users can position themselves around long-term structural shifts in the market rather than short-term cost fluctuations.
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