The current state of so-called AI does not provide much meaningful assistance in software development beyond basic tasks such as explaining workflows, breaking down thought processes, and performing simple conversions. I believe that generative AI, in its current form, is not true artificial intelligence. Rather, it is a sophisticated prediction engine that lacks genuine reasoning or understanding.
True AI should be capable of comprehending problems and devising its own solutions, rather than merely generating statistically likely outputs. Until AI reaches that level of cognitive ability, its applications in the real world remain limited, and much of what we see today is largely hype.
Tokenization and embeddings merely help models predict the most probable next token, a process that is executed at scale using vast computational resources. This is not intelligence but large-scale probabilistic prediction. The terminology used in computer science, especially in recent years, can often be misleading.
I think this comment underlines the biggest difference between people that say AI is a transformative tool and people that say it is nowhere close to working as expected.
I never expect some magic "understanding" to ever arrive, but doing remedial pattern matching is already a hugely valuable power that frees up humans to do more interesting work. This is how I use current AI - spitting out 5 line functions I could spend 5 minutes writing that he can do in 3 seconds and take me 10 seconds to review. Like "check for circular references" or "use Django ORM to write a query for all categories that have this flag for users that have this permission".
It doesn't "write the app" or solve difficult problems for me (unless it is some configuration issue). I can paste in a error code and save myself a few minutes of manual debugging. If I add a new parameter to a function it prefills the correct type definition and things like that. These are all micro-improvements but add up to a lot of saved time. Some people have success with editing across files but I rarely even try that - it excels at solving discrete, repeatable bits of work with tidy solutions so I use it for that.
Until AI can return "I don't know" or, better, "did you want it this way or that way?" it will be severely limited. Yes, it acts like a junior dev in some ways, but a junior dev that never asks any questions, which is not the junior dev you ever want to give important work.
Do we really want this? As soon as possible, employers will fire software engineers and replace them with AI. I’m positive they will not care about what AI can do, only how many salaries they can eliminate and still achieve the same results. You and I will not be the inheritors of AI.
I think that by the time AI can genuinely replace software engineers, a lot else in society will change.
It's hard to predict what it will look like. I could write both utopian and dystopian narratives and I can pretty much guarantee they'll both be wrong. Not "in the middle" but something unexpected, the way nobody predicted cat videos or doomscrolling.
But you are almost certainly right that we will not be the inheritors.
Yes, because employers will also be replaced by AI. Technology penetration won’t stop at some arbitrary boundary, it will go all the way through to logical conclusion. We have a chance at qualitatively better world, but we’ll need to act and push for new economic systems - when the time comes.
Maybe I read too much science fiction, but my first thought when speaking about "true AI" isn't the worry that a lot of us will get fired, it's the worry that we'll have created an army of digital slaves.
"Army of digital slaves" doesn't really sound that bad when I think about it. As long as it's your army and not your adversary's army... In what ways do you think "an army of digital slaves" is bad?
True AI should be capable of comprehending problems and devising its own solutions, rather than merely generating statistically likely outputs. Until AI reaches that level of cognitive ability, its applications in the real world remain limited, and much of what we see today is largely hype.
Tokenization and embeddings merely help models predict the most probable next token, a process that is executed at scale using vast computational resources. This is not intelligence but large-scale probabilistic prediction. The terminology used in computer science, especially in recent years, can often be misleading.