iliaxj

The article doesn't seem to take his train of thought quite far enough.

If AI suddenly makes it possible for a law firm to be run with a skeleton crew, then what's stopping all those people you fired from starting new law companies, where AI also does most of the work, and competing with you for the same market?

And ultimately, if AI gets to be so good that it can competently do a lawyer's job, what reason do big law firms even have to exist? Who is going to hire them if they can just hire AI?

The companies that are rushing so hard to replace their workers don't realise that AI is eventually going to replace them too.

I foresee a wave of entrepreneurship coming. AI will empower more people to provide useful services directly to other people, with less middlemen and menial work, and more direct problem solving.

Sometimes it doesn't though. Sometimes the engine seizes because a piece of tubing broke and you left your coolant down the road two turns ago. Or you steer off a cliff because there was coolant on the road for some reason. Or the meat sack in front of the wheel just didn't get enough sleep and your response time is degraded and you just can't quite get the thing to feel how you usually do. Ultimately the failure rate is low enough to trust your life on it, but that's just a matter of degree.

We use a single codebase to deploy to multiple environments. The setup looks like this:

  - root
  -- /envs
  --- / dev.tfvars
  --- / prod.tfvars
  - main.tf

When it gets deployed by the CICD, the right tfvars file is passed in via the -var-file parameter. A standard `env` var is also passed in, and used as a basis for a naming convention. Backend is also set by the pipeline.

The rationale here is that our environments should be almost the same between them, and any variations should be accomplished by parameterization.

Modules are kept either in separate repos, if they need to be shared between many workspaces, or under the `modules` subfolder.