The Ledger

Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.

clear all ✕

why aren't all 6 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable

Assertion Not checkable as stated
Patel: Google is internally discussing selling physical TPU hardware to buyers
“I think Google's even discussing it. Internally, I think it would require a big reorg of culture and a big reorg of like how Google Cloud works and how the TPU team works and how the JAX software team and XLA software teams work.”
Dylan Patel Aug 18, 2025 ▶ 22:54 Dylan Patel on GPT-5’s Router Moment, GPUs vs TPUs, Monetization
Opinion
Google TPU Data Centers Require Double the Equity Check of NVIDIA GPUs
“TPUs are the second most financeable. It probably takes, I don't know, double the equity check at least. And then the rates on the rest of it are higher.”
Gavin Baker Aug 31, 2026 ▶ 1:04:01 Why AI Demand Is Outrunning Compute Supply
Assertion Supported
David George: Google's 7-to-8-year-old TPUs maintain 100% utilization
“Seven to eight year old TPUs, Google actually disclosed this, seven to eight year old TPUs actually have 100% utilization.”
David George Feb 9, 2026 ▶ 33:49 AI Markets: Deep Dive with a16z's David George
Assertion Supported
Vahdat: Google TPUs are 10x to 100x more energy efficient than CPUs
“TPU, I'll use that example again because I know it best for certain computation, is somewhere between 10 and a hundred times more efficient per watt, and it's this watt that really matters than a CPU.”
Amin Vahdat Oct 29, 2025 ▶ 12:52 Building the Real-World Infrastructure for AI, with Google, Cisco & a16z
Insight
Moe: LLM serving differs fundamentally from traditional ML workloads
“Serving large language model is a fundamentally different problem. Because serving it requires to run it on accelerators like GPUs or TPUs, and it is a computationally intensive process that will require a lot of engineering and ensuring that for each request,…”
Simon Moe Aug 5, 2026 ▶ 1:53 How Open Source Became AI's Backbone | Inferact with a16z
Assertion Not checkable as stated
Vahdat: Google's seven- and eight-year-old TPUs maintain 100% utilization
“Our seven and eight year old TPUs have a hundred percent utilization.”
Amin Vahdat Oct 29, 2025 ▶ 4:04 Building the Real-World Infrastructure for AI, with Google, Cisco & a16z
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