Comment by _pdp_
6 hours ago
I was skeptical until 3-4 months ago, but my recent experience has been entirely different.
For context: we're the creators of ChatBotKit and have been deploying AI agents since the early days (about 2 years ago). These days, there's no doubt our systems are self-improving. I don't mean to hype this (judge for yourself from my skepticism on Reddit) but we're certainly at a stage where the code is writing the code, and the quality has increased dramatically. It didn't collapse as I was expecting.
What I don't know is why this is happening. Is it our experience, the architecture of our codebase, or just better models? The last one certainly plays a huge role, but there are also layers of foundation that now make everything easier. It's a framework, so adding new plugins is much easier than writing the whole framework from scratch.
What does this mean for hiring? It's painfully obvious to me that we can do more with less, and that's not what I was hoping for just a year ago. As someone who's been tinkering with technology and programming since age 12, I thought developers would morph into something else. But right now, I'm thinking that as systems advance, programming will become less of an issue—unless you want to rebuild things from scratch, but AI models can do that too, arguably faster and better.
It is hard to convey that kind of experience.
I am wondering if others are seeing it too.
I'm seeing it too, but there's a distinction I think matters: AI isn't replacing the thinking, it's shifting where the bottleneck is. You mention systems are self-improving and code quality has increased dramatically. But the constraint isn't execution anymore. It's judgment at scale. When AI collapses build time from weeks to hours, the new bottleneck becomes staying current with what's actually changing. You need to know what competitors shipped, what research dropped, what patterns are emerging across 50+ sources continuously. Generic ChatGPT can't do that. It doesn't know what YOU care about. It starts from scratch every time. The real question is how do you build personal AI that learns YOUR priorities and filters the noise? That's where the leverage is now.
Excited for the future :)
> You need to know what competitors shipped, what research dropped, what patterns are emerging across 50+ sources continuously. Generic ChatGPT can't do that.
You're saying that a pattern recognition tool that can access the web can't do all of this better than a human? This is quintessentially what they're good at.
> The real question is how do you build personal AI that learns YOUR priorities and filters the noise? That's where the leverage is now.
Sounds like another Markdown document—sorry, "skill"—to me.
It's interesting to see people praising this technology and enjoying this new "high-level" labor, without realizing that the goal of these companies is to replace all cognitive labor. I strongly doubt that they will actually succeed at that, and I don't even think they've managed to replace "low-level" labor, but pretending that some cognitive labor is safe in a world where they do succeed is wishful thinking.
Agreed. I don’t know if it will create or eliminate jobs but this is certainly another level from what we’ve seen before.
Since last 2 months, calling LLMs even internet-level invention is underserving.
You can see the sentiment shift happening last months from all prominent experienced devs to.
Yeah, the latest wave of Opus 4.5, Codex 5.2, Gemini Pro 3 rendered a lot of my skepticism redundant as well. While I generally agree with the Jevon's paradox line of reasoning, I have to acknowledge it's difficult to make any reasonable prediction on technology that's moving at such immense speed.
I expected the LLM's would have hit a scaling wall by now, and I was wrong. Perhaps that'll still happen. If not, regardless of whether it'll ultimately create or eliminate more jobs, it'll destabilize the job market.
My guess: projects "learn" every time we improve documentation, add static analysis, write tests, make the API's clearer, and so on. Once newly started agents onboard by reading AGENTS.md, they're a bit "smarter" than before.
Maybe there's a threshold where improvements become easy, depending on the LLM and the project?
As a hobbyist programmer, I feel like I've been promoted to pointy-haired boss.