Press gets it wrong again – Google, Meta, and Broadcom are not competing against NVIDIA!

The press again misinterpreted today’s announcements from Google, Meta, and Broadcom, framing them as direct challenges to Nvidia’s leadership in AI compute. Headlines emphasized an expanded TPU roadmap, a deeper Google–Meta alignment around shared inference infrastructure, and Broadcom’s 8 percent stock surge—while Nvidia traded down roughly 2%. 

Yet, the reporting repeated an old analytical mistake: conflating the market for inference silicon with the market for training silicon. Google and Meta’s new deployments, and Broadcom’s strengthened role as a TPU supplier, all sit squarely in the inference domain and do not encroach on Nvidia’s unchallenged position in training large-scale models.

Context from earlier analysis
This misunderstanding is not new! In my February 15 article, “DeepSeek, Marvell, and Broadcom: No Threat to Nvidia’s Training Monopoly,” I showed why training and inference remain fundamentally different markets defined by different memory architectures, compiler stacks, and economic constraints.

That argument resurfaced again on October 28 in my article, “Press Gets It Wrong Again – Qualcomm Chips Not Competing Against Nvidia,” where I detailed how Qualcomm’s AI200 and AI250 were wrongly portrayed as threats to Nvidia. 

Today’s reaction to the Google-Meta announcement, and Broadcom’s expanded TPU roadmap repeats the same analytical error. Google, Meta, and Broadcom are scaling inference—high-throughput, cost-efficient model serving—not training, where Nvidia’s B100 and H200 remain irreplaceable.

Again, these TPU chips are used in data centers for Inference, and DO NOT compete against Nvidia, which makes GPU chips for Training. There is a difference and the press need to be educated, just like I’m trying to educate readers of my articles.

Product overview
Google and Meta emphasized shared infrastructure priorities: lowering inference cost, improving energy efficiency, and enabling global-scale model serving for LLaMA, Gemini, and internal generative workloads. Broadcom’s expanded TPU engagement directly supports these goals. 

TPUs are engineered for inference: high-throughput serving, embedding workloads, and fine-tuned execution of already-trained models. Their internal memory and interconnect designs optimize efficiency, not the bandwidth, multi-node scaling, and compiler maturity required for frontier-model training. Today’s announcement introduced no training-capable hardware. It expanded the scale and operational efficiency of inference silicon.

-- Dr. Robert Castellano, Semiconductor Deep Dive, USA.