If you can define your problem well then you can write tests up front. An ML person would call tests a "verifier". Verifiers let you pump compute into finding solutions.

I'm not sure we write good tests for this because we assume some kind of logic involved here. If you set a human to task to write a procedure to send a 'forgot password' email, I can be reasonably sure there's a limited number of things a human would do with the provided email address, because it takes time and effort to do more than you should.

However with an LLM I'm not so sure. So how will you write a test to validate this is done but also guarantee it doesn't add the email to a blacklist? A whitelist? A list of admin emails? Or the tens of other things you can do with an email within your system?

Will people be willing to make their full time job writing tests?

As well, just because it pasts a test doesn't mean it doesn't do wonky, non-performant stuff. Or worse, side effects no one verified. Plenty often the LLM output will add new fields I didn't ask it to change as one example.

We’ll just have an LLM write the tests.

Now we can work on our passion projects and everything will just be LLMs talking to LLMs.

I hope sarcasm.

They probably won't. But it doesn't matter. Ultimately, we'll all end up doing manual labor, because that is the only thing we can do that the machines aren't already doing better than us, or about to be doing better than us. Such is the natural order of things.

By manual labor I specifically mean the kind where you have to mix precision with power, on the fly, in arbitrary terrain, where each task is effectively one-off. So not even making things - everything made at scale will be done in automated factories/workshops. Think constructing and maintaining those factories, in the "crawling down tight pipes with scewdriver in your teeth" sense.

And that's only mid-term; robotics may be lagging behind AI now, but it will eventually catch up.