I think its the same disease that makes people make shitty, unoptimized, bloated apps because modern client machines ahve so much ram. But that wont work AI agents. Not until tokens become dirt cheap anyway. Until then we'll need apps with more efficient usage patterns
I think the push from management for us to use AI has made it so we don’t have to be efficient with our consumption, so now we write md files which we feed to Claude in a loop instead of python and bash scripts to do routine tasks.
We're all being measured to AI usage, so...
Instead of doing a grep | uniq | awk which would give me an answer in 100 milliseconds for free, I launch a prompt to spend 30 seconds on it and will cost some actual money.
I hope we get over this phase of the hype soon. I want and will use AI as a tool, but it's just another (good) tool in the toolbox.
When I need to do a one-off investigation, it's great to use AI and spend 5-10 minutes querying and get my answer for $5 or so, instead of having to spend 2-3 hours writing a script which I'll discard. That's a great use case.
But using AI for routine processing done daily where writing a script would be amortized over thousands of runs, it's insane. I'd rather use AI to write the script and then don't need the AI anymore, the script will be faster and free. Oh but then my AI usage in the executives report drops. Can't have that. Waste away.
I'd argue it will be even worse, you still remember how to do it, next gen of SDEs won't even try.
Part of that is the ridiculous belief that they can create "AGI" by just glueing together enough LLMs.
Presumably it's also financial viability. You can't charge thousands a month without replacing those "highly trained engineers" with a bunch of kids in the developing world.
That's marketing. It's up to management to decide to fall for it blindly or look at it with healthy skepticism. It seems a lot of them chose the former.
It's worse than that, in many cases management actively rewards inefficiency. It's like Friedman's "why not spoons?"
For an engineer paid $100/hr to write a 150 line Python script and test it to the same extent could take a few hours, so the total costs rise meaningfully.
Many, if not most, medium to hard problems require at least 100–150 lines of code. With experience, you can write it, bugfree, in 15 minutes.
It's about thinking ahead and experience.
So... if you spend $3m to replace a $1m team... you are betting on that $3m cost coming down. It's a proof of concept. The first step is to find out if agents can do the job at all. At this point you are hoping future versions will get more efficient.
Trying to make something efficient before you know that it is even possible is hard.
Drop-in, profitable on day-1 isn't what the frontier looks like.
There's already many things that can be done now to bring down token use. Better planning, tests, Language severs, MCP compression. Don't use claw, teams, swarms, Ralph loop, scheduled tasks unless there is a clear use case.
The point is that efficiency comes after, not before.
Yes there is, because I have made it, basically which archives archive.is pages to archive.org (I have listed it way too many times but feel free to find it in my submissions)
https://web.archive.org/web/20260427063707/https://serjaimel...
hope this helps ya.
you have there more options, seems removepaywall.com option works as well
because companies will need
“proof of productivity gains or metrics that show a clear return for all this AI investment.”
which in my opinion is simply not true. I haven’t seen any good study that showed AI to actually improve productivity overall. It massively helps in some areas, but then promptly gets stuck in others. So you still need an expert to guide it.
AI is overhyped, but on the other hand, I think it would be difficult to deny the significant productivity increases when used appropriately.
For some tasks, it's huge. Some tasks that I might've spent 8 hours on, I can do in 20 minutes. That's very real and huge.
At the same time, that's not the average that I experience. Some things are pretty much a wash. Others might be 2x or 3x faster which is quite nice, but short of the hype. And some things can be very clearly slower with AI. Also some things are more unreliable with AI.
We need to get to a maturity point where we realize it's just another tool. An incredibly powerful one for many tasks, yes. But it's not magically the right tool for everything and not always the right answer.
I think we have all heard of (or are living through) mandates to prove that AI makes us more productive, or else...
We'll see how many of these actually works out.
After I discovered how to use git worktrees in Codex to work in three conversations in parallel, I am able to build apps with a scope that simply was not realistic before.
There was one feature/screen that Codex built in a single 5k LOC file.
It was still perfectly capable of developing the feature and it was working as expected.
I had it break it down into multiple files, but if I wouldn’t have seen it during the MR review, I would not have noticed. The large file did not seem to degrade the performance of the agent.
You might think that this would lead to a mess with merge conflicts, but the agent can resolve them automatically.
I added an instruction to AGENTS.md so that before handoff it fetches and rebases, resolving conflicts if needed plus rerunning the tests.
It gets worse when you look at LLM (or even any other kind of AI) benchmarks, they tend to cap out around 110% of human performance.
The more that LLM services try and creep towards profitability, the more features they are paywalling behind higher tiers, the more some lazy junior dev is going to look like a better value proposition.
And when some of the CTO's they have pushed LLMs on to go looking for cost savings, some of them are going to look at opex instead of capex and in house the LLMs using open models.
The only real question to my mind is whether the air will be let out of the AI balloon slowly, or if it escapes in one big pop.
