WhatA marketplace and orchestration layer that helps companies dynamically allocate AI training and inference workloads across Google TPU pods, NVIDIA GPU clusters, and other accelerators based on cost, latency, and model architecture fit.
SignalThe AI compute landscape is splitting into two dominant paths — buying from NVIDIA or renting from Google — and most companies lack the expertise to evaluate which is optimal for their specific workloads, leaving money on the table.
Why NowGoogle's TPU 8t represents a generational leap in scale (121 ExaFlops per superpod) while NVIDIA continues dominating purchased hardware, creating a genuine two-horse race that makes cross-platform optimization newly valuable.
MarketMid-to-large enterprises spending $1M-$100M+ annually on AI compute; TAM is a slice of the $150B+ AI infrastructure market; competitors like CoreWeave and Lambda focus on GPU-only, leaving the cross-platform arbitrage gap open.
MoatProprietary benchmarking data on workload-to-hardware fit across architectures, accumulated through thousands of customer deployments, creating a data flywheel no new entrant can replicate.
Our eighth generation TPUs: two chips for the agentic eraView discussion ↗ · Article ↗ · 437 pts · April 22, 2026
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