The reality is that for large scale AI deployment there's only one criterion that matters: what is the total cost of ownership? If TPUs are 1/30th the total perf but 1/50th the total price, then they will be bought by customers. Basically that simple.
Most places using AI hardware don't actually want to expend massive amounts of capital to procure it and then shove it into racks somewhere and then manage it over its total lifetime. Hyperscalers like Google are also far, far ahead in things like DC energy efficiency, and at really large scale those energy costs are huge and have to be factored into the TCO. The long dominant cost of this stuff is all operational expenditures. Anyone running a physical AI cluster is going to have to consider this.
The walled garden stuff doesn't matter, because places demanding large-scale AI deployments (and actually willing to spend money on it) do not really have the same priorities as HN homelabbers who want to install inefficient 5090s so they can run Ollama.
At large scales why shouldn't it matter whether you're beholden to Google's cloud only vs having options to use AWS or Oracle or Azure etc. There's maybe an argument to be made about price and efficiency of Google's data centers, but Google's cloud is far from notably cheaper than alternatives (to put it mildly) so that's a moot point if there's any efficiencies to be had Google's pocketing it themselves. I just don't see why anyone should care about this chip except Google themselves. It would be a different story if we were talking about a chip that had the option of being available in non-Google data centers.
Most places using AI hardware don't actually want to expend massive amounts of capital to procure it and then shove it into racks somewhere and then manage it over its total lifetime. Hyperscalers like Google are also far, far ahead in things like DC energy efficiency, and at really large scale those energy costs are huge and have to be factored into the TCO. The long dominant cost of this stuff is all operational expenditures. Anyone running a physical AI cluster is going to have to consider this.
The walled garden stuff doesn't matter, because places demanding large-scale AI deployments (and actually willing to spend money on it) do not really have the same priorities as HN homelabbers who want to install inefficient 5090s so they can run Ollama.