// HACKER NEWS — CYBERSECURITY
AI and Infrastructure Engineering
There’s a push right now for whole companies to adopt AI wholesale - dump
every bit of context into it, write an AGENTS.md or INSTRUCTIONS.md in
every repo so any project is discoverable and contributable by an agent, not
just a human. Slightly funny, if you think about it: I’ve never once gotten
a human teammate to actually read the README, and now we’re all writing
better docs than we ever did, just aimed at a robot instead. The question
that comes with it is the obvious one: does this make engineering redundant? Once all the context about a stack and its
infrastructure is written down somewhere an agent can read it, are we next?
I don’t think that’s quite the right question, because we’ve already lived
through a version of it.
Did Kubernetes kill Ansible? Kind of. I haven’t written an Ansible playbook
in years - if you handed me one right now I’d be squinting at the module
syntax like I’d never seen it before - not because configuration management
stopped mattering, but
because Kubernetes made server management easy enough that we stopped
building our own node images at all - we just use whatever the cloud
provider hands us, an AWS AMI built for us, no questions asked. And when did
I last SSH into a node to debug something? Mostly never. If a node’s acting
up, I kill it and hope the replacement doesn’t have the same problem. The
next layer up went the same way: run a container on ECS Fargate, in a
Lambda, or on Cloudflare Containers, and I genuinely don’t know or care what
node it landed on - but that doesn’t mean nobody’s orchestrating it, it
means I still decided that workload should be a container in the first
place, what image it runs, what it’s allowed to talk to, how it scales, what
happens when it fails. Kubernetes and serverless containers didn’t remove
that layer of decisions, they moved the unit of work up from “the machine”
to “the workload,” and everything below that layer got quietly automated
away.
Nobody would say Kubernetes, or Fargate, or Cloudflare’s container platform,
replaced infrastructure engineers. Each one replaced a specific layer of
manual work - hand-building images, hand-patching boxes, knowing which node
a workload landed on - and the engineers moved up to the layer above it
every time. I think AI is doing the same thing again, one layer higher.
I use Claude daily to generate Helm charts and write Terraform modules. The
part it actually removed from my day isn’t the thinking - it’s the
lookup work. I don’t read through the AWS provider’s changelog to figure out
what changed between v5 and v6 anymore; I describe what I want, in whatever
shape I want the module or chart to end up, and Claude produces a version of
it. It takes iteration to get it into the shape I’d actually ship, but once
it’s there, it becomes the example for next time - especially with an
AGENTS.md in the repo pointing at it.
The same thing happened one level down a while ago: I don’t hand-write raw
Kubernetes YAML any more than I hand-write Ansible modules - that’s what
Helm charts are for. Increasingly, I don’t hand-write the Helm chart either.
I direct what it should do, and Claude writes it.
I still need to know what a good Terraform module or a well-structured Helm
chart looks like. I still need to be able to SSH into a node when something
genuinely goes wrong and killing the pod isn’t an option - the layer above
doesn’t remove the layer below, it just moves how often you have to touch
it. And I’m still the one deciding the actual shape of things: what the
final version of a module looks like, what’s maintainable a year from now,
how a chart should be deployed and versioned. AI does the time-consuming
part. I still give the direction.
The honest tradeoff: I’m faster at building and debugging things than I was
two years ago, and I’m also visibly rustier at the fundamentals underneath
that speed. My HCL syntax recall isn’t what it used to be. Four years ago I
hand-wrote a nested for loop - four levels deep, tagging sub