AI has a $2.5 trillion problem and replacing 250 million jobs is the only way to solve it

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.

Ivezic theorizes that AI would need to replace or deliver the equivalent productivity gains of 250 million full-time jobs in order to meet the $2.5 trillion revenue requirement - assuming each full-time employee costs $100,000.

The $2.5 trillion investment can be broken down into the $1.5 trillion already spent on hardware investments since 2022, with a subsequent $1 trillion being spent on additional investments every year.

But there is one small problem: the US only has 159 million nonfarm jobs.

The infrastructure providers are also having difficulties reckoning with spending.

High-performance AI chips are made obsolete around every five years, but the data centers infrastructure they are installed in is expected to last around 20 years - resulting in enormous depreciation costs.

This problem is what Ivezic calls the ‘50-Cent Rule’; providers must generate around 50 cents in annual revenue for every single dollar they invest.

This is compounded by AI models that become cheaper to use but deliver far superior capabilities over previous models.

GPT-4-class capability models for example have dropped in price 100-fold since 2022, but the costs borne by infrastructure providers have not dropped at all - and are rising instead - making it even harder for them to make a return on their initial investment.