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AI

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

By Henry Chandonnet
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IBM CEO Arvind Krishna is pictured.
[ ] IBM CEO Arvind Krishna was skeptical of the "belief" that data
center spending could be profitable. Riccardo Savi/Getty Images for
Concordia Annual Summit
2025-12-02T10:30:10.362Z
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  * IBM's CEO walked through some napkin math on data centers-- and
    said that there's "no way" to turn a profit at current costs.
  * "$8 trillion of CapEx means you need roughly $800 billion of
    profit just to pay for the interest," Arvind Krishna told
    "Decoder."
  * Krishna was skeptical of that current tech would reach AGI,
    putting the likelihood between 0-1%.

AI companies are spending billions on data centers in the race to AGI
. IBM CEO Arvind Krishna has some thoughts on the math behind those
bets.

Data center spending is on the rise. During Meta's recent earnings
call, words like "capacity" and AI "infrastructure" were frequently
used. Google just announced that it wants to eventually build them in
space. The question remains: will the revenue generated from data
centers ever justify all the capital expenditure?

On the "Decoder" podcast, Krishna concluded that there was likely "no
way" these companies would make a return on their capex spending on
data centers.

Couching that his napkin math was based on today's costs, "because
anything in the future is speculative," Kirshna said that it takes
about $80 billion to fill up a one-gigawatt data center.

"Okay, that's today's number. So, if you are going to commit 20 to 30
gigawatts, that's one company, that's $1.5 trillion of capex," he
said.

Krishna also referenced the depreciation of the AI chips inside data
centers as another factor: "You've got to use it all in five years
because at that point, you've got to throw it away and refill it," he
said.

Investor Michael Burry has recently taken aim at Nvidia over
depreciating concerns, leading to a downturn in AI stocks.

"If I look at the total commits in the world in this space, in
chasing AGI, it seems to be like 100 gigawatts with these
announcements," Krishna said.

At $80 billion each for 100 gigawatts, that sets Krishna's price tag
for computing commitments at roughly $8 trillion.

"It's my view that there's no way you're going to get a return on
that, because $8 trillion of capex means you need roughly $800
billion of profit just to pay for the interest," he said.

Reaching that number of gigawatts has required massive spending from
AI companies -- and pushes for outside help. In an October letter to
the White House's Office of Science and Technology Policy, OpenAI CEO
Sam Altman recommended that the US add 100 gigawatts in energy
capacity every year.

"Decoder" host Nilay Patel pointed out that Altman believed OpenAI
could generate a return on its capital expenditures. OpenAI has
committed to spending some $1.4 trillion in a variety of deals. Here,
Krishna said he diverged from Altman.

"That's a belief," Krishna said. "That's what some people like to
chase. I understand that from their perspective, but that's different
from agreeing with them."

Krishna clarified that he wasn't convinced that the current set of
technologies would get us to AGI, a yet to be reached technological
breakthrough generally agreed to be when AI is capable of completing
complex tasks better than humans. He pegged the chances of achieving
it without a further technological breakthrough at 0-1%.

Several other high-profile leaders have been skeptical of the
acceleration to AGI. Marc Benioff said that he was "extremely
suspect" of the AGI push, analogizing it to hypnosis. Google Brain
founder Andrew Ng said that AGI was "overhyped," and Mistral CEO
Arthur Mensch said that AGI was a "marketing move."

Even if AGI is the goal, scaling compute may not be the enough.
OpenAI cofounder Ilya Sutskever said in November that the age of
scaling was over, and that even 100x scaling of LLMs would not be
completely transformative. "It's back to the age of research again,
just with big computers," he said.

Krishna, who began his career at IBM in 1990 before rising to
eventually be named CEO in 2020 and chairman in 2021, did praise the
current set of AI tools.

"I think it's going to unlock trillions of dollars of productivity in
the enterprise, just to be absolutely clear," he said.

But AGI will require "more technologies than the current LLM path,"
Krisha said. He proposed fusing hard knowledge with LLMs as a
possible future path.

How likely is that to reach AGI? "Even then, I'm a 'maybe,'" he said.

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  * OpenAI
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