IBM CEO says there is ‘no way’ spending trillions on AI data centers will pay off at today’s infrastructure costs

  • IBM’s CEO did some math on data centers and said it’s “impossible” to be profitable at current costs.

  • Arvind Krishna told Decoder: “$8 trillion in capital spending means you need about $800 billion in profits to pay interest.”

  • Krishna is skeptical that current technology can achieve AGI, putting the possibility at between 0-1%.

In the race for AGI, artificial intelligence companies are investing billions of dollars in data centers. IBM CEO Arvind Krishna has some thoughts on the math behind these bets.

Data center spending is increasing. In Meta’s recent earnings call, words like “capacity” and artificial intelligence “infrastructure” were used frequently. Google just announced that it wants to eventually build them in space. The question remains: Will the revenue generated by the data center be enough to justify all capital expenditures?

On the “Decoder” podcast, Krishna concluded that it may be “impossible” for these companies to generate returns on data center capital expenditures.

Kirshner, who said his napkin calculations are based on today’s costs “because anything in the future is speculative,” said it would cost about $80 billion to fill a 1-gigawatt data center.

“Well, that’s today’s numbers. So, if you’re committing to 20 to 30 gigawatts, that’s $1.5 trillion in capital expenditures from one company,” he said.

Krishna also mentioned the depreciation of AI chips within data centers as another factor: “You have to use it within five years, because by then, you have to throw it away and refill it,” he said.

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Artificial intelligence stocks have tanked recently after investor Michael Burry took aim at Nvidia over concerns about its depreciation.

“If I look at the total commitments in the world chasing AGI in this space, those announcements seem to be in the 100-gigawatt range,” Krishna said.

The cost per 100 gigawatts is $80 billion, which puts Krishna’s computing commitment price at about $8 trillion.

“I don’t think you’re going to get a return because $8 trillion in capital spending means you need about $800 billion in profits to pay interest,” he said.

Reaching this gigawatt number will require significant investment from AI companies and will require outside help. OpenAI CEO Sam Altman recommended in an October letter to the White House Office of Science and Technology Policy that the United States add 100 gigawatts of energy capacity annually.

“Decoder” host Nilay Patel noted that Altman believes OpenAI can provide a return on its capital expenditures. OpenAI has committed to spending approximately $1.4 trillion in various transactions. Here Krishna says that he is different from Ultraman.

“It’s a belief,” Krishna said. “That’s what some people like to pursue. I understand that from their perspective, but that’s not the same as agreeing with them.”

Krishna clarified that he does not believe current technology will allow us to achieve general artificial intelligence, which is widely believed to be an as-yet-unrealized technological breakthrough when it comes to completing complex tasks better than humans. He estimates the probability of achieving this goal without further technological breakthroughs to be 0-1%.

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Several other prominent leaders are skeptical of the acceleration of general artificial intelligence. Marc Benioff said he was “very skeptical” of the push for AGI, comparing it to hypnosis. Google Brain founder Andrew Ng said AGI was “overhyped,” and Mistral CEO Arthur Mensch said AGI was a “marketing move.”

Even if AGI is the goal, scaling computing may not be enough. OpenAI co-founder Ilya Sutskever said in November that the era of scaling is over and that even scaling LLM 100 times would not bring about radical change. “We’re back in the research days, with just mainframe computers,” he said.

Krishna, who began his career at IBM in 1990 and was eventually named CEO in 2020 and chairman in 2021, did praise the current AI toolset.

“I think it’s very clear that this will unlock trillions of dollars in productivity for businesses,” he said.

But Krisha said AGI will require “more technology than the current LLM path.” He proposed the integration of hard knowledge and LL.M. as a possible future path.

How likely is it to reach AGI? “Even so, I’m still a ‘maybe,'” he said.

Read the original article on Business Insider

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