Source: OpenAI recently began renting Google's TPUs to power ChatGPT, marking its first significant use of non-Nvidia chips; Meta also considered using TPUs

OpenAI, one of the world's biggest customers of Nvidia artificial intelligence chips, recently began renting Google's AI chips …

The Information 2025-06-28

Context & Ripple Effects

OpenAI's reported TPU rental is an early sign that Google’s in-house accelerator can serve external frontier-model workloads, not only Google’s own products. OpenAI shortly afterward publicly added Google Cloud for ChatGPT and its API, creating a broader Google Cloud supply relationship.

The subsequent arc strengthens the signal: Google moved to pitch TPUs to outside customers, while Meta later committed to rent Google TPUs for new-model development. This report is the initial competitive validation behind that commercialization push.

First-order effects

OpenAI gains a significant non-Nvidia compute option for ChatGPT, while Google gets a high-profile external workload for its TPUs.
Nvidia’s position at OpenAI is no longer exclusively defined by chip supply, even though the report still identifies OpenAI as a major Nvidia customer.

Second-order effects

Google has stronger evidence to market TPU capacity to other large AI buyers; Meta’s reported consideration makes it an immediate prospective customer rather than a purely internal Google platform.
Model developers can use multi-supplier sourcing as leverage in capacity negotiations and can place workloads across differing accelerator stacks, at the cost of supporting more than one software and operations environment.

Third-order effects

If major model labs continue adopting TPUs alongside GPUs, AI compute could become a more heterogeneous capacity market rather than one centered on a single accelerator vendor.
The durable competitive variable shifts beyond hardware availability toward cloud integration and developer-framework compatibility—an issue underscored by Google’s later effort to improve TPU support for PyTorch.

The trend: This is one data point in the commercialization of alternative AI accelerators as frontier-model providers diversify compute supply.

Discussion

@timkellogg.me Tim Kellogg on bluesky
when openai dropped its prices, some attributed it to this — chat, what do we think? — feels like you can't do an 80% drop that fast, you'd have to move most traffic. the gap must have already existed [embedded post]
@mick @mick on bluesky
Google flexing AI up and down the stack. Just as ChatGPT eats into search, TPUs and Gemini should be a concern to AWS/Azure. [embedded post]
@quinnypig.com Corey Quinn on bluesky
OpenAI chose TPUs. Meta's sniffing around TPUs. — Meanwhile, AWS is duct-taping together press releases about Trainium like it's a group project no one else showed up for. — Nobody's using your chips, Jassy. Not even you. [embedded post]
@ramahluwalia @ramahluwalia on x
OPENAI TAPS GOOGLE TPU The Information reports OpenAI has tapped Google for TPUs to lower inference costs. How does Google not exceed Cloud growth expectations? Someone at AWS is not happy. [image]
@danielnewmanuv Daniel Newman on x
$GOOGL will provide TPU to OpenAI for inference. Like I have said. There is plenty of room for custom AI chips. Not everything will be a win/loss for $NVDA as it won't ever have 100% of the market. Google has proven it can train and deliver a really high quality model with Ge…
@simonw Simon Willison on x
Could this be how they managed to drop the price of o3 while maintaining the same performance?
@jukanlosreve Jukan Choi on x
Wasn't this the scenario we could already anticipate when reports came out that OAI was using Google Cloud? I'm curious to see how much the TPU order volume will increase — it's exciting.
r/singularity r on reddit
The Information (hard paywall): Google Convinces OpenAI to Use TPU Chips in Win Against Nvidia

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