CEO and co-founder of Nvidia, Jensen Huang, expects engineers at the company to spend half an annual wage’s worth of AI tokens to get their job done more efficiently. Featuring on the All-In Podcast on the final day of GTC this March, Huang spent some time discussing token allocation among employees, noting that the company is “trying to” spend two or three billion dollars on AI tokens for the engineering team.

Nvidia is the biggest company in the world by market cap, and is easily the biggest success story in the AI boom. So, naturally, it’s no surprise to see executives like Jensen Huang always banging on about how great AI is. The latest Nvidia GTC last week provided us with more AI developers to look forward to, including something for gamers – DLSS 5.

Nvidia engineers should use AI tokens worth half their annual salary

According to Huang, the company has 43,000 employees, and he estimates that around 38,000 of those are engineers. Simply put, that’s a lot of people working with AI tools at any given time and eating up a considerable amount of the AI token budget – but Jensen’s okay with that. He says he would be “deeply alarmed” if someone earning $500,000 annually was only using $5,000 worth of tokens each year.

“Let’s say you have a software engineer or AI researcher, and you pay them $500,000 a year… At the end of the year, I’m going to ask him: how much did you spend in tokens? And [if] that person said $5,000, I will go ape ‘something else’. If that $500,000 engineer did not consume at least $250,000 worth of tokens, I’m going to be deeply alarmed.

This is no different than one of our chip designers who says, guess what, I’m just gonna use paper and pencil, I don’t think I’m gonna need any CAD tools.”

Nvidia CEO & Co-founder, Jensen Huang

The Nvidia boss continues by comparing the use of AI to the industrial revolution, adding, “I think that every engineer is going to have a hundred agents”. He says, “it’s just a new way of doing computer programming” with more focus on writing ideas, architectures, and specifications – basically crafting a fool-proof plan to feed into AI and then adjust the results. This essentially gives engineering teams more time to be more creative.

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Jack Goodall

At PC Guide, Jack is mostly responsible for reporting on hardware deals. He also specializes in monitors, TVs, and headsets and can be found putting his findings together in a review or best-of guide.