> And it doesn't freak you out that you're relying on thousands of lines of code that you've never looked at?

I was a product manager for 15 years. I helped sell products to customers who paid thousands or millions of dollars for them. I never looked at the code. Customers never looked at the code. The overwhelming majority of people in the world are constantly relying on code they've never looked at. It's mostly fine.

> How do you verify the end result?

That's the better question, and the answer is a few things. First, when it makes changes to my ad accounts, I spot check them in the UI. Second, I look at ad reporting pretty often, since it's a core part of running my business. If there were suddenly some enormous spike in spend, it wouldn't take me long to catch it.

It's thousands of lines of variation on my own hand-tooling, run through tests I designed, automated by the sort of onboarding docs I should have been writing years ago.

Why wouldn't you test? That sounds like a bad thing.

Me? I use AI to write tests just as I use it to write everything else. I pay a lot of attention to what's being done including code quality but I am no more insecure about trusting those thousands of tested lines than I am about trusting the byte code generated from the 'strings of code'.

We have just moved up another level of abstraction, as we have done many times before. It will take time to perfect but it's already amazing.

Do you trust the assembly your compiler puts out? The machine code your assembler puts out? The virtual machine it runs on? Thousands of lines of code you've never looked at...

I've been doing agentic work for companies for the past year and first of all, error rates have dropped to 1-2% with the leading Q3 and Q4 models... 2026's Q1 models blowing those out the water and being cheaper in some way

but second of all, even when error rates were 20%, the time savings still meant A Viable Business. a much more viable business actually, a scarily crazy viable business with many annoyed customers getting slop of some sort, with a human in the loop correcting things from the LLM before it went out to consumers

agentic LLM coders are better than your co-workers. they can also write tests. they can do stress testing, load testing, end to end testing, and in my experience that's not even what course corrects LLMs that well, so we shouldn't even be trying to replicate processes made for humans with them. like a human, the LLM is prone to just correct the test as the test uses a deprecated assumption as opposed to product changes breaking a test to reveal a regression.

in my experience, type errors, compiler errors, logs on deployment and database entries have made the LLM correct its approach more than tests. Devops and Data science, more than QA.