Google says Go is ideal for AI-assisted engineering. Are you using it in production ML/AI?
I recently read Google’s post arguing that Go may be an ideal language for AI-assisted software engineering:
https://developers.googleblog.com/why-go-is-an-ideal-language-for-ai-assisted-software-engineering/
Their argument is that as coding agents generate more code, the bottleneck shifts from writing code to reviewing, validating and maintaining it.
And Go has some interesting properties in that context: strict typing, fast compiler feedback, gofmt, a relatively small language surface, and a standardized toolchain.
That made me curious about a slightly different question: does the same argument make Go more attractive for ML/AI engineering?
Python obviously dominates training, experimentation and most of the ML ecosystem. But a lot of production code around models is not really ML-specific: serving, APIs, orchestration, retrieval, feature services, queues, monitoring, infra, etc.
So for people here working with ML/AI systems: do you use Go alongside Python today?
If yes, what parts of the stack are written in Go, and where has it actually been a better choice than Python? If not, what keeps you on a Python-only stack?
And more broadly: have coding agents made you more willing to introduce Go into a Python-heavy ML stack than you would have been a few years ago?