AI Will Make Code Cheap, But It Won’t Shrink Most Engineering Teams

Rajesh Rajagopalan is CTO of Winter.

As a leader in a technology company or role, you may be asking yourself: If agents are writing code, we’ll need a fraction of the engineers, right?

It’s a fair question. But my honest answer, for most enterprises, is probably not. Here’s why, and where I could be wrong.

The same assumption came up about coal.

In 1865, the English economist William Stanley Jevons published The Coal Question. Britain feared running out of coal, and many assumed more efficient engines would solve it. Jevons pointed out the opposite: As coal-powered work got cheaper, it spread into more industries, and the total coal use kept climbing. In his words: “It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth.”

That’s the Jevons paradox. Make something cheaper to use, and if enough demand is waiting, you end up using more of it.

I think code is going through the same thing.

It’s already happening one layer down. This summer, AI token prices fell by more than one-third while GPU rental prices held, according to market data compiled by Citadel Securities. Cheaper inference didn’t mean less compute. People used more of it.​​

The Forgotten Project Backlog

Every enterprise I’ve worked with has two backlogs of projects. There’s an official one. Then, there’s a much bigger one that never got written down because nobody thought these were worth the money or time​.

• Needed Internal Tools That Were Put On The Back Burner: When you have approvals that run through a chain of emails or reconciliations that someone does by hand every month-end, you need a better process. But everyone is used to the manual fixes, so they make do.

• Modernization Efforts That Are Always Pushed Back To “Next Year”: You might have a core system on a database that’s full of stored procedures written by people who left a decade ago, but nobody wants to touch it.