Analyst: AI buildout needs $2.5T annual revenue, equal to 250 million jobs
TechRadar reports that an analysis by Marin Ivezic, founder of Applied Quantum, concludes AI would need roughly $2.5 trillion in annual revenue to justify current infrastructure investment, while optimistic estimates put current AI revenues at $150 billion to $250 billion a year. Assuming a $100,000 cost per full-time employee, Ivezic calculates that meeting that target would require replacing or delivering the productivity of 250 million full-time jobs, while the US has only about 159 million nonfarm jobs. He also describes a “50-Cent Rule” for infrastructure providers and compares the spending to telecom carriers that laid fiber during the dot-com boom and could not recover their investment.
The analysis puts a number on the return needed from AI capital spending and argues that infrastructure providers could be left holding the costs.
- AI needs to replace 250 million jobs in order to make a return on the current $2.5 trillion investment
- AI is getting cheaper and more capable, but the underlying hardware is not
- Infrastructure providers for the AI boom could be left holding the bag
Since the release of ChatGPT in late 2022, AI developers have entered into a frenzied arms race of hyperscaler data center build outs, silicon development, and huge power grid expansions.
The level of investment made by AI developers is unlike anything that has been seen (or spent) before. But a key question that IT leaders, investors, and enthusiasts keep asking is how will all this investment be paid for?
New analysis by Marin Ivezic, Founder of Applied Quantum, has found that the math really isn’t adding up. In order to justify the colossal investment, AI would need to generate approximately $2.5 trillion in annual revenue. Current estimates aren’t even close.
AI must replace 250 million jobs
Optimistic estimates put AI’s current revenues at between $150 billion and $250 billion per year, which is a multi-trillion shortfall.