We already have highly advanced deterministic software. The value lies in the abductive “reasoning” and natural language processing.
We deal with non determinism any time our code interacts with the natural world. We build guard rails, detection, classification of false/true positive and negatives, and all that all the time. This isn’t a flaw, it’s just the way things are for certain classes of problems and solutions.
It’s not bad software - it’s software that does things we’ve been trying to do for nearly a hundred years beyond any reasonable expectation. The fact I can tell a machine in human language to do some relative abstract and complex task and it pretty reliably “understands” me and my intent, “understands” it’s tools and capabilities, and “reasons” how to bridge my words to a real world action is not bad software. It’s science fiction.
The fact “reliably” shows up is the non determinism. Not perfectly, although on a retry with a new seed it often succeeds. This feels like most software that interacts with natural processes in any way or form.
It’s remarkable that anyone who has ever implemented exponential back off and retry, has ever implemented edge cases, and sir and say “nothing else matters,” when they make their living dealing with non determinism. Because the algorithmic kernel of logic is 1% of programming and systems engineering, and 99% is coping with the non determinism in computing systems.
The technology is immature and the toolchains are almost farcically basic - money is dumping into model training because we have not yet hit a wall with brute force. And it takes longer to build a new way of programming and designing highly reliable systems in the face of non determinism, but it’s getting better faster than almost any technology change in my 35 years in the industry.
Your statement that it “very often produces wrong or nonsensical output” also tells me you’re holding onto a bias from prior experiences. The rate of improvement is astonishing. At this point in my professional use of frontier LLMs and techniques they are exceeding the precision and recall of humans and there’s a lot of rich ground untouched. At this point we largely can offload massive amounts of work that humans would do in decision making (classification) and use humans as a last line to exercise executive judgement often with the assistance of LLMs. I expect within two years humans will only be needed in the most exceptional of situations, and we will do a better job on more tasks than we ever could have dreamed of with humans. For the company I’m at this is a huge bottom line improvement far and beyond the cost of our AI infrastructure and development, and we do quite a lot of that too.
If you’re not seeing it yet, I wouldn’t use that to extrapolate to the world at large and especially not to the future.