AI is literally just legalized theft of other people’s work. It won’t credit them to any capacity.
Also, whilst it is adding some productivity, it’s also being used by absolute idiots who have no idea what they’re doing spreading bad info and causing other people more work.
Every idiot is typing everything into chatgpt, getting a bad answer which is obvious to anyone half trained and then promoting it like it’s correct just because AI said it
The most interesting part of this isn’t that it slowed them down when they expected to be faster, it’s that even after it slowed them down, they couldn’t tell and were fooled that they had been faster.
My theory about this is that LLMs were tasked with giving useful output, but they couldn’t do that, because they have no fidelity, so instead they found a shortcut, which was to trick people into thinking they were being useful. They found the same loophole that conmen have used for millenia, and automated it. It’s the AI alignment problem, only for some reason people aren’t talking about it, maybe because they don’t want to believe that we’re this easily manipulated.
There’s no reason to believe LLMs have gotten any better at actually doing useful work in the meantime in the absence of any objective measure of it. I think the best explanation for their “improvement” is that they have simply gotten better at fooling us.
That’s from almost a year ago. I’m sure it was accurate at the time, but LLMs got a lot more useful around December 2025 or so. Tooling for them has also evolved a lot since then.
Unfortunately, I don’t know what data I can share, so I’ll err on the side of caution and share none 🙂. But I suppose I can share a little more general insight:
Modern “agentic” (yeah I’m tired of that word too) techniques, patterns, and tools, paired with modern LLMs allow for much more autonomy than what was available a year ago.
Are agents faster than skilled engineers per task? No, not in most cases. But they allow engineers to scale horizontally, knocking out many tasks in parallel.
That’s the performance gain: Foster autonomy for horizontal scaling. Build/optimize projects’ AGENTS.md and SKILL.md files[1].
Agents can work for some long runs (some engineers even run them overnight), given a safe environment/project with guardrails — mostly the same guardrails that human engineers have had for years: Statically typed languages, TDD, good test coverage, code reviews (both agents and human[2]), CI pipelines, etc.
They still need human engineers to operate them; the workflow is just different now, and there’s a learning curve for it.
Whether we like it or not (personally, I miss the old days), this is just how it is now. We have not even reached the peak yet. This is the least autonomous that agents will ever be.
The bigger the repo, the more important this probably is. Structure them so they don’t bloat the context windows with unnecessary info. ↩︎
I usually wait for the AI agent review cycles to settle first — no need to spend human engineering time on potential slop that will probably get fixed autonomously. ↩︎
AI is literally just legalized theft of other people’s work. It won’t credit them to any capacity.
Also, whilst it is adding some productivity, it’s also being used by absolute idiots who have no idea what they’re doing spreading bad info and causing other people more work.
Every idiot is typing everything into chatgpt, getting a bad answer which is obvious to anyone half trained and then promoting it like it’s correct just because AI said it
Is it though? Like what’s the evidence of that? If it just feels like it must be true, I have some bad news about that:
https://arstechnica.com/ai/2025/07/study-finds-ai-tools-made-open-source-software-developers-19-percent-slower/
The most interesting part of this isn’t that it slowed them down when they expected to be faster, it’s that even after it slowed them down, they couldn’t tell and were fooled that they had been faster.
Look at the graph, especially the last two lines:
https://cdn.arstechnica.net/wp-content/uploads/2025/07/aicodingchart-1024x507.png
My theory about this is that LLMs were tasked with giving useful output, but they couldn’t do that, because they have no fidelity, so instead they found a shortcut, which was to trick people into thinking they were being useful. They found the same loophole that conmen have used for millenia, and automated it. It’s the AI alignment problem, only for some reason people aren’t talking about it, maybe because they don’t want to believe that we’re this easily manipulated.
There’s no reason to believe LLMs have gotten any better at actually doing useful work in the meantime in the absence of any objective measure of it. I think the best explanation for their “improvement” is that they have simply gotten better at fooling us.
That’s from almost a year ago. I’m sure it was accurate at the time, but LLMs got a lot more useful around December 2025 or so. Tooling for them has also evolved a lot since then.
Data or you’re just getting fooled better.
Unfortunately, I don’t know what data I can share, so I’ll err on the side of caution and share none 🙂. But I suppose I can share a little more general insight:
Modern “agentic” (yeah I’m tired of that word too) techniques, patterns, and tools, paired with modern LLMs allow for much more autonomy than what was available a year ago.
Are agents faster than skilled engineers per task? No, not in most cases. But they allow engineers to scale horizontally, knocking out many tasks in parallel.
That’s the performance gain: Foster autonomy for horizontal scaling. Build/optimize projects’
AGENTS.mdandSKILL.mdfiles[1].Agents can work for some long runs (some engineers even run them overnight), given a safe environment/project with guardrails — mostly the same guardrails that human engineers have had for years: Statically typed languages, TDD, good test coverage, code reviews (both agents and human[2]), CI pipelines, etc.
They still need human engineers to operate them; the workflow is just different now, and there’s a learning curve for it.
Whether we like it or not (personally, I miss the old days), this is just how it is now. We have not even reached the peak yet. This is the least autonomous that agents will ever be.
The bigger the repo, the more important this probably is. Structure them so they don’t bloat the context windows with unnecessary info. ↩︎
I usually wait for the AI agent review cycles to settle first — no need to spend human engineering time on potential slop that will probably get fixed autonomously. ↩︎
The plague of work chats now:
Here’s what ChatGPT/copilot had to say:
People can ask for themselves, you answering that way adds no value. Just say you don’t know.
In group chats, keep your mouth shut and let people that actually know answer. Don’t drown out the actual expert answers.
And holy hell the ones that will die on the hill that they are right because chatgpt agreed with them even when they are totally wrong…