It is the last narrative that some of Wall Street believes and has enough mediocre or senile coders to promote it.
That narrative will implode like Sora later this year.
That narrative will implode like Sora later this year.
Then of course the hype collapsed and now even the usecases where VR shines are deemed a flop. But no, it's exceptionally good at simulation (racing/flight) and visualising complex designs while 3D designing.
I see the same with generative AI and LLM. It's really good with programming. It's definitely good at making quick art drafts or even final ones for those who don't care too much about the specifics of the output. I use it a lot for inspiration.
But it's not good for everything that it's trying to be sold as. Just like the VR craze they're dragging it by the hairs into usecases where it has no business being. A lot of these products are begging to die.
For example an automation tool using real world language. For that it's a disaster, it's inconsistent and constantly confuses itself. It's the reason openclaw is a foot bazooka. It's also not very great at meeting summaries especially those where many speakers are in a room on the same microphone.
I don't think AI will disappear but a realignment to the usecases where it actually adds value, yes I hope that happens soon.
It is astonishingly poor at this. My intuition was that it should be good at this (it is basically a translation problem right? And LLMs are fundamentally translation systems) but the practical results are so poor. Not just mis-identifying speakers (frequently saying PersonX responded to PersonX) but managing complete opposite conclusions from what was actually said.
I'm genuinely intrigued as to what approaches have been taken in this space and what the "hard problem" is that is stopping it being good.
And yeah the transcription quality also drops a lot. Where humans are still quite capable at reading it. Sometimes when I read the transcript I'm quite surprised it manages to make any intelligble minutes out of it at all.
I just don't understand how Microsoft place this feature as a minute-taking replacement when it's not ready for really super common usecases.
Generating pointless AI videos for pocket change or ad revenue is a loser in comparison.
However, I don't know a single developer who pays "thousands of dollars a month", not sure how you'd end up like that.
The top down push for AI is in line with the age old traditions of replacing highly skilled and highly compensated trade workers with automation. The writing is on the wall if folks care to look; many just don't want to. This has happened 1000 times before and it'll keep happening in the name of "progress" in capitalist systems for as long as there are "inefficiencies" to "resolve." AI is meant as our replacement, not as an extension of our skill as it happens to align with today.
Its increasingly obvious that the next phase in the evolution of the average programmer role will be as technical requirements writers and machine generated output validators, leaving the actual implementation outsourced to the machine. Even in that new role, there is no secret sauce protecting this "programmer" from further automation. Technical product managers eventually fall to automation given enough time and money poured into the automation of translating fuzzy, under specified ideas into concrete bulleted requirements where they can simply review the listed output, make minor tweaks and hit "send" to generate the list of jira-like units of work to farm out to a fleet of agents wearing various hats (architect, programming, validator, etc.)
The above is very much in progress already, and today I'm already spending the majority of my time reviewing the output of said AI "teams", and let me tell you: it gets closer and closer to "good enough" week by week. Last year's models are horse shit in comparison to what I'm using today with agentic teams of the latest frontier models (Opus 4.6 [1m] currently, with some Sonnet.)
Maybe we're at a plateau and the limitations inherent in GenAI tech will be insurmountable before we get to 100% replacement. But it literally won't matter in the end as "good enough" always prevails over the perfect, and human devs are far from perfect already.
I have been producing software (at fang scale) for several decades now, and I've been closely monitoring GenAI systems for coding specifically. Even just a few months ago I'd get a verbose, meandering sprawl of methods and logic scattered with the actual deliverables outlined in the prompt from these systems. Sometimes even with clear disregard of the requirements laid out, or "cheating" on validation via disabling tests or writing ones that don't actually do anything useful. Today I'm getting none of that. I don't know what changed, but I somehow get automated code with good separation of concerns, following best practices and proven architectural patterns. Sure, with a bunch of juniors let loose with AI you get garbage still, but that's simply a function of poor delegation of work units. Giving the individual developer and the AI too much leeway in the scope of changes is the bug there. Division of work into small enough units is the key and always has been for the de-skilling portion of automating away skilled human labor for machines. We're just watching Marxist theory on capitalist systems play out in real time in a field generally thought to be "safe." It certainly won't be the last.
No they aren't. Any decently skilled human blows them out of the water. They can do better than an untrained human, but that's not much of an achievement.
Source?
No, by far no. I’m by all accounts “decently skilled human”, at least if we go by our org, and it blows anyone out of the water with some slight guidance.
And the most important part: it doesn’t get tired, it doesn’t have any mood swings, its performance isn’t affected by poor sleep, party yesterday or their SO having a bad day.
Even with years as a principal engineer at a company with high coding standards and engineering processes?
Modern models like Opus / Gemini 3 are great coding companions; they are perfectly capable of building clean code given the right context and prompt.
At the end of the day it’s the same rule of garbage in -> garbage out, if you don’t have the right context / skills / guidance you can easily end up with bad code as you could with good code.
I don't tell the agents to "just go do it", as that tends to go off the rails for complex tasks. Emulating real world software development processes in meat space with your AI "team" seems to approximate similar outcomes.
I usually start by having the agents construct a plan document which I iterate on and build up well before writing code. This is a living document, not a final design (yet.) If I run into context window issues I just shut them down and rebuild from the document. I farm out research and data gathering tasks to build it up. Once all the findings are in I have the architect take a stab at the technical system design before the break down and delegation work begins. By then the units of work are small and manageable.
So a good PM running 1-3 teams, will only need 1-3 agentic ai teams instead.