Building software is becoming easier. Running it reliably isn't.

AI can now generate code, build features, write tests, and even assemble complete applications much faster than before.

But production doesn't care how quickly the code was generated.

Once real customers start using the system, the difficult questions begin:
* What happens when the AI produces inconsistent results?
* How do we handle failures across dynamic workflows?
* How do we monitor a system that can change its behaviour?
* How do we control infrastructure and model costs as usage grows?
* How do we recover when something fails in production?

This is where I think the next engineering challenge will be.

AI is reducing the cost of creating software.

It isn't reducing the complexity of operating software.

In some cases, it may actually increase it.

The more dynamic the system becomes, the more important reliability patterns, observability, testing, failure handling and clear architecture become.

For SaaS companies adopting AI, the question shouldn't only be:

"How quickly can we build this?"

It should also be:
"Can we operate this reliably when 10x more customers start using it?"

That's where the real engineering work begins.