By Sharon Goldman
Publication Date: 2026-04-09 16:24:00
Welcome to Eye on AI, with AI reporter Sharon Goldman. The pro-Iran meme machine trolling Trump with AI Lego cartoons…Amazon’s Andy Jassy defends Amazon’s $200 billion spending spree...OpenAI pauses Stargate U.K. data center, citing energy costs.
It’s been another one of those wild weeks in AI, with Anthropic electing not to release its new Claude Mythos model because of concerns about the cybersecurity risks it poses (and forming a coalition to use a preview version of the model to bolster cybersecurity defenses); Meta releasing its first AI model since hiring Alexandr Wang; and mounting expectations about OpenAI’s upcoming new “Spud” model.
Most of these AI models run on Nvidia GPUs, the sophisticated and expensive AI chips (at over $30,000 a pop) that power their training and output. But across the industry, access to those chips remains a bottleneck. OpenAI president Greg Brockman, for example, has said allocating GPUs at OpenAI is “pain and suffering.”
This week, at the HumanX conference in San Francisco, I discovered that even inside Nvidia, GPUs are scarce.
I sat down with Bryan Catanzaro, who leads applied deep learning research at Nvidia, overseeing teams working on AI-driven graphics, speech recognition, and simulation. Catanzaro was also among the first, back in the early-to-mid 2010s, to notice researchers snapping up Nvidia GPUs to train AI models—a signal that helped push CEO Jensen Huang to double down on AI, setting the…
