The AI Infra race is entering a new phase
Published on: 08/11/2026
By Keshav Malhotra
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For years, the focus was on training: 𝒍𝒂𝒓𝒈𝒆𝒓 𝒎𝒐𝒅𝒆𝒍𝒔, 𝒃𝒊𝒈𝒈𝒆𝒓 𝒄𝒍𝒖𝒔𝒕𝒆𝒓𝒔, 𝒎𝒐𝒓𝒆 𝑮𝑷𝑼𝒔.
But as AI moves into real products, enterprise workflows, and agentic systems, the heavier load is shifting to inference.
The challenge is no longer just GPU access. It is GPU efficiency.
In this newsletter, we look at three approaches emerging across the stack:
📀 Hardware built for agentic workloads.
📀 Data pipelines that keep GPUs fed.
📀 Scheduling layers that improve utilisation across multi-model workflows.