Google Cloud Next: Ironwood, A2A, and a very cheap Gemini

Google Cloud Next happened this week. Three things from it will still matter in a year.

Ironwood#

The seventh-generation TPU, and the first Google has explicitly positioned for inference rather than training. Large HBM capacity per chip, high interconnect bandwidth, deployable in pods of thousands.

The strategic point is not the specs. It is that Google is the only hyperscaler with a mature, decade-old, production-proven alternative to buying Nvidia. AWS has Trainium and Inferentia, which are real but younger. Microsoft has Maia, which is very young. Google has been running TPUs in production since 2015 and has an entire compiler stack (XLA) and framework story (JAX) built around them.

That translates directly into pricing freedom. When Google prices Gemini aggressively, they are not eating a margin on someone else's silicon.

A2A#

Agent2Agent: a protocol for agents from different vendors to discover each other and collaborate on tasks. Announced with a long list of partner companies.

The mental model is that MCP connects an agent to tools and data, while A2A connects an agent to other agents. An agent publishes an "agent card" at a well-known URL describing its capabilities; other agents discover it and delegate tasks over a JSON-RPC-ish protocol with support for long-running work and streaming updates.

My honest read: the problem is real, the timing is early, and the number of partner logos on the announcement slide is inversely correlated with how much production usage a protocol has on day one. Multi-agent systems in 2025 mostly do not work well enough to need a standard for interoperating — the hard part is that agents fail in the middle of tasks, not that they cannot find each other.

But standards need to exist before they are needed, and having the conversation now beats having it in 2027 with four incompatible implementations.

Gemini 2.5 Flash#

The cost-efficient reasoning model, with a controllable thinking budget. You set how many tokens the model may spend reasoning, including zero.

config = types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(thinking_budget=1024)
)

This is the right API shape and I expect everyone to converge on it. The decision of how much to think is workload-specific, it is the primary cost lever in a reasoning model, and hiding it inside the model's own judgment takes control away from the person paying the bill.

Set it to zero for classification. Set it high for planning. Measure the quality difference on your own task, because the curve is steeper for some tasks than others and there is no general answer.

the rest#

A great deal of "agentic" branding applied to existing products, an agent development kit, and a marketplace. Cloud vendor conferences have a genre and this one was firmly in it.

The signal-to-slide ratio was better than most.