Every major hyperscaler is now building custom AI accelerators: Google (TPU, the most mature program with 1.6M+ Trillium chips in 2026), Amazon (Trainium, 500K+ Trn2 chips for Anthropic's Project Rainier, now a multi-billion-dollar business), Microsoft (Maia 200, 3nm inference chip with 10+ PFLOPS FP4 serving GPT-5.2 and Copilot), Meta (MTIA 300-500, four generations in 24 months on RISC-V, hundreds of thousands deployed for inference), and OpenAI (Titan ASIC with Broadcom, targeting 90% inference cost reduction, deploying H2 2026). Combined custom silicon represents ~10-15% of the AI accelerator market by revenue in 2026, projected to reach 15-25% by 2030. However, all hyperscalers are SIMULTANEOUSLY increasing NVIDIA GPU orders -- the trajectory is additive, not substitutive.
NVIDIA's training share exceeds 90%, while inference share is 60-75% and declining. Google uses TPUs for nearly all internal AI compute; AWS runs majority of Bedrock inference on Trainium; Meta and Microsoft are inference-first with custom silicon while relying on NVIDIA for training. The key dynamic: NVIDIA's absolute revenue continues growing ($150B+ data center in 2026E) even as percentage share declines from 87% peak to ~75%, because the total addressable market is expanding from $115B (2024) to $200B+ (2026E)..
Competitive pressure is real but bounded
Custom ASICs and AMD offer cheaper alternatives for specific workloads, but only a handful of companies can afford multi-billion-dollar chip programs. The competitive threat is structural but limited in scope.
What percentage of Google's total AI compute fleet (by FLOPS or by server count) is TPU vs NVIDIA GPU? Google does not publicly disclose this split.