After Google's recent AI announcements, one thing stood out to me.
A lot of the discussion online is about Gemini's capabilities, but Google also spent considerable time talking about TPUs, AI Hypercomputer, networking, data infrastructure, and enterprise deployment.
It made me wonder whether the long-term competitive advantage for businesses is shifting.
Choosing between GPT, Gemini, Claude, or another model is becoming easier every year.
Building reliable AI systems—with clean data, governance, monitoring, security, and integrations—still seems to be the hard part.
For those working on enterprise AI:
Where do you spend more engineering effort today?
- Choosing and evaluating models?
- Building the surrounding infrastructure?
I'm interested in hearing from people who've deployed AI in production.
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