They says it’s because of continual learning. Here is why it’s wrong -
https://www.theinformation.com/articles/artificial-intelligence-will-change-2026
1) Jevon’s paradox - NVDA just co-published a paper helping on the groundbreaking research on continual learning. They were the co-authors of the paper. A company positioned to lose from a technology does not actively research/promote it. Link to the paper below:
https://arxiv.org/pdf/2512.24880.pdf
2) Deepseek published a mHC paper this weekend. It was a breakthrough in hyper connected residual architectures training at scale in a more stable way. If model companies can train larger models at scale and reduce training instability, it can enable larger and larger training runs which were impossible before
3) You may have heard endless reasons of why Groq was a good/bad acquisition for NVDA. My take is - while you may have a few other use cases, I think Jensen did it for edge inferencing in robotics and other physical AI use cases. I have an entire write up on this. Can’t share here right now, but suffice to say it’s much more bullish than bearish for NVDA.