It still does make errors, yes? Because it is not usable, if we need to verify everything. AI is only interesting if it can do things that humans can not do. If you can verify results because you can do it yourself, then why use AI? It will just bind highly skilled people to do verification work. Instead these people should do the actual work, results will come quicker.
So AI is only interesting to you / your org / humans if it can do things that you can not achieve. But if it still does errors, how could we ever know that super-invention by AI is not wrong?
If we can not rely on the correctness of the result, it is not usable at all. AI must create reliable and correct results always. That was a very fundamental requirement for computing. This problem has not been solved.
Humans make mistakes too, does it mean humans are unusable? We accept as empirical fast that most production quality code has 2 - 10 bugs per 1k LoC. According to your premise, virtually all existing software is therefor unusable.
What if an LLM overall starts to make less mistakes than a medium developer, costs less than its salary and is 100 x faster? For sure, the companies that will leverage these with just a few senior devs doing prompting, testing and requirements analysis, will outcompete other organizations.
Humans make mistake then to learn from it. A really good expert would never deliberately copy-paste an obscure solution from the internet, then to ask for forgiveness later.
AI agents do that, perhaps not always, but still do. Now the question: would I trust AI without verifying its output?
One does not need to be able to create it themselves to evaluate if the output is correct. Consider for example that you can easily determine if a meal tastes delicious without being an expert chef, or the fact that NP problems are very difficult to solve but make for easily verifiable solutions.
Yeah, it makes the same old errors, being confidently wrong then sorry... I mean, it is still an LLM
AI is like a junior developer. You have to review her code carefully but she is most definitely useful.
Why is your AI a she? What's up with gendering LLMs. Reminds me of Richard Dawkins calling Claude "Claudia" and insisting it to be conscious.
This is part of the training data now. She can hear you, you know...