HomeThe $3 Trillion Question: Can AI Answer It?
Three years ago, Sequoia partner David Cahn became one of the first to quantify the impact of Silicon Valley’s massive spending on AI infrastructure.
In 2023, he responded to Nvidia’s reported $50 billion annual GPU revenue. Using that figure, plus the implied costs of running data centers and operator margins, he calculated that $200 billion in revenue would be needed to recoup the initial investment.
He framed it as a challenge, urging entrepreneurs to develop AI products and services that could leverage that infrastructure and generate revenue. Fast forward three years of hyperscaling, and Cahn now has a new estimate for AI infrastructure spending in 2026: $1.5 trillion.
All told, he estimates the AI industry must generate $3 trillion in revenue to justify all those chips and data center expenses. And that’s likely an underestimate—rising memory costs and greater use of specialized or inference-specific chips will push the figure higher. “Recently,” he writes, “the required revenue per GW of CapEx has sharply increased due to these bottleneck dynamics and rising costs of construction.”
On the other side of the ledger, Anthropic is believed to have reached $60 billion in annual recurring revenue (ARR), while OpenAI reportedly earned $13 billion in 2025 (though in November 2025 it claimed $20 billion ARR) and is likely earning more this year. Still, a significant gap remains.
One person tracking that gap is Torsten Slok, chief economist at Apollo, the giant asset manager. In a recent note, he observes that the hyperscalers—Google, Meta, Microsoft, and Amazon—are all forecasting substantial acceleration in free cash flow by 2028. In other words, they anticipate a payoff from all the chips they purchased.
Image Credits:Torsten Slok/Apollo / Torsten Slok/Apollo
What if they don’t? Slok highlights a risk currently visible in AI usage: more organizations are shifting to cheaper open-weight models, often from China, rather than those created by frontier labs, and overall token prices are declining. According to CEO Sam Altman, OpenAI’s latest model is 54% more token-efficient for coding tasks. That benefits users worried about the cost of their AI agents, but it could hurt companies building token factories if users don’t dramatically increase their overall token consumption.
Image Credits:Torsten Slok/Apollo / Torsten Slok/Apollo
Slok worries that if hyperscalers fail to meet their cash flow targets, the market reaction could be severe—
“with so much riding on so few names,” he writes, “a slower payoff wouldn’t just be a sector problem, it would risk tipping the economy into recession and the S&P 500 into a correction.”
Just something to keep in mind as you steer your AI agents toward cheaper tokens.
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Three years ago, Sequoia partner David Cahn became one of the first to quantify the impact of Silicon Valley’s massive spending on AI infrastructure.
In 2023, he responded to Nvidia’s reported $50 billion annual GPU revenue. Using that figure, plus the implied costs of running data centers and operator margins, he calculated that $200 billion in revenue would be needed to recoup the initial investment.
He framed it as a challenge, urging entrepreneurs to develop AI products and services that could leverage that infrastructure and generate revenue. Fast forward three years of hyperscaling, and Cahn now has a new estimate for AI infrastructure spending in 2026: $1.5 trillion.
All told, he estimates the AI industry must generate $3 trillion in revenue to justify all those chips and data center expenses. And that’s likely an underestimate—rising memory costs and greater use of specialized or inference-specific chips will push the figure higher. “Recently,” he writes, “the required revenue per GW of CapEx has sharply increased due to these bottleneck dynamics and rising costs of construction.”
On the other side of the ledger, Anthropic is believed to have reached $60 billion in annual recurring revenue (ARR), while OpenAI reportedly earned $13 billion in 2025 (though in November 2025 it claimed $20 billion ARR) and is likely earning more this year. Still, a significant gap remains.
One person tracking that gap is Torsten Slok, chief economist at Apollo, the giant asset manager. In a recent note, he observes that the hyperscalers—Google, Meta, Microsoft, and Amazon—are all forecasting substantial acceleration in free cash flow by 2028. In other words, they anticipate a payoff from all the chips they purchased.
Image Credits:Torsten Slok/Apollo / Torsten Slok/Apollo
What if they don’t? Slok highlights a risk currently visible in AI usage: more organizations are shifting to cheaper open-weight models, often from China, rather than those created by frontier labs, and overall token prices are declining. According to CEO Sam Altman, OpenAI’s latest model is 54% more token-efficient for coding tasks. That benefits users worried about the cost of their AI agents, but it could hurt companies building token factories if users don’t dramatically increase their overall token consumption.
Image Credits:Torsten Slok/Apollo / Torsten Slok/Apollo
Slok worries that if hyperscalers fail to meet their cash flow targets, the market reaction could be severe—
“with so much riding on so few names,” he writes, “a slower payoff wouldn’t just be a sector problem, it would risk tipping the economy into recession and the S&P 500 into a correction.”
Just something to keep in mind as you steer your AI agents toward cheaper tokens.
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