Google Cloud Next 2026 opens today in Las Vegas — and Google brought the goods. Three announcements take center stage, signaling where Google wants to go in the AI race: the new Gemini 3.2, the Ironwood TPU, and a massive push into agentic AI.
Ironwood TPU v7: Google’s answer to Nvidia
The new Ironwood TPU (internally TPU v7) is Google’s most powerful chip to date — and it’s built specifically for inference, not training. That’s a deliberate strategic shift: while the world is still hoarding GPUs for training, Google is betting that the future lies in inference.
The numbers are impressive: 4.6 petaflops of FP8 compute, 192 GB of HBM3E memory, manufactured on a 3-4nm process node. In pods of 9,216 chips, they deliver 7.4 TB/s memory bandwidth — enough for models with 10 trillion parameters.
Anthropic already signed a multi-gigawatt deal for these chips. Meta has a multi-year, multi-billion-dollar contract too.
Gemini 3.2: More context, less latency
Gemini 3.2 aims to push the context window beyond one million tokens, reduce inference latency, and cut enterprise AI costs. More details are expected throughout the conference.
Agentic AI becomes enterprise strategy
Google is positioning agentic AI as a core enterprise building block. Workspace Studio will feature autonomous agents automating workflows — from data analysis to customer communication. The goal: premium subscriptions and cost reduction for routine tasks.
What this means
Cloud Next is traditionally an enterprise conference, but this year it’s also a statement of intent. With Ironwood, Google positions itself as a Nvidia alternative for inference. With Gemini 3.2, they want to compete at the model frontier. And with the agentic AI push, Google is targeting exactly the segment where Anthropic has been gaining ground with Cowork and Claude Code.
For us users, the takeaway is straightforward: infrastructure is getting better, models are getting cheaper, and the big players are investing billions in the chips that power our AI tools. That’s not bad news.
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