Buying GPUs Isn't Enough: AI Infra's Real Battleground

Opening

Dear reader, let me start with one number: ₩690 trillion (~$496B).

That’s the amount five Big Tech companies have pledged to pour into infrastructure in 2026 — roughly 30% of Korea’s annual GDP. And yet there’s a company that can’t spend all that money even if it wants to: Microsoft. CEO Satya Nadella admitted it himself — GPUs are piling up in warehouses with no power to plug them into. There’s $80 billion worth of backlogged Azure¹ orders, and physically, there’s no way to run the servers.

In the last week of February 2026, Big Tech announcements came in waves: a Meta-Nvidia GPU partnership on February 17, a Meta-AMD deal on February 24, and Google’s Texas data center announcement the same day. Most coverage focused on “another GPU deal.” I think these announcements are telling a different story.

That’s what today’s issue is about — not who bought the most GPUs, but where the real bottleneck actually lies.

The Structure of the AI Infrastructure War: Three Axes

Building AI infrastructure requires three things at once.

The first is high-compute chips — AI-specific semiconductors like GPUs and TPUs². The second is power infrastructure — the data centers and electricity needed to actually run those chips. The third is consumable memory chips: semiconductors like HBM³, DRAM, and NAND that hold data while AI models are computing.

The problem is that no company currently dominates all three at once. So what’s actually happening is this: everyone is pouring in ₩690 trillion (~$496B) while racing to shore up whichever axis they’re weakest on.

Let’s break this down one axis at a time.