Nvidia CEO Jensen Huang said the demand for computing resources is “exploding” due to rapid advances in artificial intelligence models, and there is “fierce competition” to the next frontier in technology.
At Nvidia’s live event in Las Vegas on Monday, Huang discussed the company’s various developments heading into 2026, noting the intense competition in the artificial intelligence space.
Commenting on the growth of AI since it first hit the market, Huang said everyone has been fighting to be the first to reach the next level of technology.
“The amount of computation required for AI is exploding, and the demand for Nvidia GPUs is exploding,” he said, adding: “The demand for Nvidia GPUs is exploding. We’re growing models by a factor of 10, an order of magnitude every year, so demand is exploding.”
“Everybody’s trying to get to the next level, somebody’s trying to get to the next level. So it’s all about computing. The faster you can compute, the faster you can get to the next level of the next frontier.”
Jensen Huang talks about AI and next-generation chips. sauce: Nvidia
Demand could cause more Bitcoin miners to move to AI
The rapid growth and adoption of AI has already seen many Bitcoin (BTC) mining companies fully or partially pivot to this sector in recent years.
Related: Here’s how an AI model predicts Bitcoin and altcoin price ranges in 2026.
This is partly due to the increasing difficulty of mining Bitcoin over time, but on the other hand, AI also presents opportunities for miners to make the most of their resources and potentially earn greater returns outside of BTC.
Increasing demand for AI computing power could make the switch to AI computing even more attractive for Bitcoin miners.
During his speech, Nvidia’s CEO also touched on the company’s next-generation Rubin Vera chips, which he said are currently in “full production” and on schedule.
Huang said the combination of Rubin and Vera, which are designed to work together, can deliver five times the artificial intelligence computing performance compared to previous models.
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