At least $3.5 trillion revenue per year is required to fund the AI data centers per Prof. Van Nieuwerburgh

Reddit r/ArtificialInteligence News

Summary

A commentary highlighting Prof. Van Nieuwerburgh's research that AI data centers require at least $3.5 trillion in annual revenue to service their debt, raising doubts about whether OpenAI, Anthropic, and others can generate returns fast enough to avoid a bubble.

It looks like that the market doesn't like all the debt data centers are raising. I saw a video on this professor's research that we'll need $3.55T a year to fund the AI data centers (cost around $10T). That's more than 10% of US annual GDP that OpenAI, Anthropic, etc have to make a year for the debt to be solvent. Personally think it'll take many decades. The internet for example took at least 20 years since early 90s to have around 10% impact on our annual GDP. What do you think? Can we have a soft landing? Source: https://theinference.org/indexes/value-chain?course=applications https://www.brookings.edu/wp-content/uploads/2026/09/4c_Van-Nieuwerburgh.pdfhttps://www.youtube.com/watch?v=e5YuA53OFQ0
Original Article

Similar Articles

Can AI answer the $3 trillion question?

TechCrunch AI

Analysis of AI infrastructure spending, with Sequoia's David Cahn calculating $1.5 trillion in spending for 2026 and a required $3 trillion in revenue to justify it, while Apollo's Torsten Slok warns of recession risk if hyperscalers fail to meet cash-flow goals.

AI Is Too Expensive

Hacker News Top

The article argues that AI is too expensive to be economically viable for most companies, with hyperscalers spending trillions on data centers but failing to generate proportionate AI revenue. It suggests only hardware suppliers like NVIDIA benefit from the current AI bubble.

Spending on ai datacenters vs revenue

Reddit r/ArtificialInteligence

An analysis comparing spending on AI data centers with the revenue they generate, likely discussing financial sustainability and market implications.