“Bad” seems extreme. The only way to pass the litmus test you’ve described is for a tool to be 100% perfect, so then the graph looks like 99.99% “bad tool” until it reaches 100% perfection.

It’s not that binary imo. It can still be extremely useful and save a ton of time if it does 90% of the work and you fix the last 10%. Hardly a bad tool.

It’s only a bad tool if you spent more time fixing the results than building it yourself, which sometimes used to be the case for LLMs but is happening less and less as they get more capable.

If you show me a tool that does a thing perfectly 99% of the time, I will stop checking it eventually. Now let me ask you: How do you feel about the people who manage the security for your bank using that tool? And eventually overlooking a security exploit?

I agree that there are domains for which 90% good is very, very useful. But 99% isn't always better. In some limited domains, it's actually worse.

Counterpoint.

Humans don't get it right 100% or the time.

That is a true and useful component of analyzing risk, but the point is that human behaviour isn't a simple risk calculation. We tend to over-guard against things that subjectively seem dangerous, and under-guard against things that subjectively feel safe.

This isn't about whether AI is statistically safer, it's actually about the user experience of AI: If we can provide the same guidance without lulling a human backup into complacency, we will have an excellent augmented capability.

That's my thinking. If I need to check up on the work, then I'm equally capable of writing the code myself. It might go faster with an LLM assisting me, and that feels perfectly fine. My issue is when people use the AI tools to generate something far beyond their own capabilities. In those cases, who checks the result?

Perfection is the enemy of good.

I wouldn't go that far, but I do believe good tool design tries to make its failure modes obvious. I like to think of it similar to encryption: hard to do, easy to verify.

All tools have failure modes and truthfully you always have to check the tool's work (which is your work). But being a master craftsman is knowing all the nuances behind your tools, where they work, and more importantly where they don't work.

That said, I think that also highlights the issue with LLMs and most AI. Their failure modes are inconsistent and difficult to verify. Even with agents and unit tests you still have to verify and it isn't easy. Most software bugs are created from subtle things, often which compound. Which both those things are the greatest weaknesses of LLMs: nuance and compounding effects.

So I still think they aren't great tools, but I do think they can be useful. But that also doesn't mean it isn't common for people to use them well outside the bounds of where they are generally useful. It'll be fine a lot of times, but the problem is that it is like an alcohol fire[0]; you don't know what's on fire because it is invisible. Which, after all, isn't that the hardest part of programming? Figuring out where the fire is?

[0] https://www.youtube.com/watch?v=5zpLOn-KJSE