AI inference technology rarely attracts the kind of attention that training breakthroughs do, but it’s here — in the milliseconds between a user’s request and a machine’s response — that artificial intelligence becomes infrastructure.
At Google Cloud Next 2025, the company introduced Ironwood, its seventh-generation custom Tensor Processing Unit (TPU), designed not to build AI models, but to run them. Alongside it came a quieter, arguably more telling update: a reengineered Cloud WAN backbone, now optimised to move inference data across geographies with markedly lower latency. Taken together, these two announcements reveal less about Google’s ambition to compete in AI — and more about the way it intends to operationalise it.
The shift underway is not simply technological. It’s philosophical. As AI continues its seep into everyday processes — customer service, logistics, fraud detection, software development — the spotlight moves from what the models can do to where and how they’re being run. Inference, in this context, becomes something like electricity: ambient, essential, and best when unnoticed. Ironwood is Google’s latest attempt to wire the grid.
Inference as infrastructure
To understand the stakes, one has to understand what inference is — and isn’t. If training is about possibility, inference is about delivery. The term refers to the act of applying a trained model to a new piece of input, generating a result in real time.
In practice, it means a user asks a question of Gemini, and that query travels through a network of systems, chips, and interconnects before returning an answer. It happens in seconds, but it’s computationally expensive — especially at the scale demanded by enterprise applications.
Ironwood is a response to this pressure. Google claims it offers 2x performance-per-watt efficiency over its predecessor, and up to 10x performance when scaled across clusters. This matters not just for speed, but for cost and energy load — two factors that determine whether companies will keep AI in the lab or embed it across their operations.
But performance is only half the story. Making inference viable at scale also requires moving data efficiently. That’s where Cloud WAN enters.
The role of the network
Inference isn’t bound to a single geography. A user in Nairobi might hit a model housed in Frankfurt, depending on latency and load balancing. What matters is not proximity, but responsiveness.
Google’s Cloud WAN upgrade targets this directly. According to the company, the new architecture reduces latency by up to 40% and introduces more intelligent routing based on application type. While that sounds like back-end housekeeping, it’s precisely this kind of low-level optimisation that will define the next stage of AI rollout: not flashy models, but invisible improvements to the substrate that runs them.
In the context of AI inference technology, Google’s strategic advantage has always been its control over the stack — from data centres and custom chips to software orchestration and global fibre. Ironwood and Cloud WAN simply reinforce the idea that performance at the edge is inseparable from the choices made at the core.
The Nvidia factor
There is another, more commercial dimension to Ironwood’s emergence. Right now, Nvidia occupies a dominant position in the AI ecosystem. Its GPUs, software libraries, and developer tooling define how models are trained and deployed across industries. This reliance has become a strategic vulnerability for hyperscalers like Google, Amazon, and Microsoft, who each pay a premium for access to the hardware that makes AI possible.
Custom silicon is one route out. While Nvidia continues to lead on training workloads, inference offers a narrower set of compute demands — making it a more tractable target for homegrown chips. Ironwood, like Amazon’s Inferentia and Microsoft’s Maia, is meant to chip away at that dependence.
But Google’s approach differs in its integration. With the TPU stack already embedded in its AI services, and the networking layer under its own control, it’s arguably in a stronger position to deliver inference not just as a service, but as a seamless layer of computing.
The everyday edge
The implications stretch far beyond Google’s bottom line. A more efficient inference system means more accessible AI. It means lower latency in translation apps. It means generative tools inside enterprise software that respond without lag. It means medical diagnostics in rural clinics with limited bandwidth.
Inference isn’t just what happens after training. It’s the interface through which AI becomes real — useful, habitual, even banal. The fact that Ironwood was announced with little spectacle only reinforces its role. It isn’t meant to impress. It’s meant to disappear into everything.