I see people highly trained engineers spend hundreds of thousand of tokens doing what can reliably be accomplished with 150 lines of python.
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.
> 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.
It's not just push from mangement; AI firms themselves really aggressively market this idea of AI replacing everything. It's not "allowed" to be a mere tool, useful for some but not other tasks, it's gotta (be able to) do everything.
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.
> AI firms themselves really aggressively market this idea of AI replacing everything. It's not "allowed" to be a mere tool, useful for some but not other tasks, it's gotta (be able to) do everything.
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.
> 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.
It's worse than that, in many cases management actively rewards inefficiency. It's like Friedman's "why not spoons?"
They optimize because their work requires them to. 100k tokens is a few bucks and a couple minutes, then 15 more minutes to verify that the output does what it's intended for reasonably well, so it's more like $50 in total cost.
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.
There are no specialized factories for every product in the world.
Pillows are wildly different. Every pillow you've ever owned has a different shape, fabric, fill. You could build a robot for any specific pillow. The tech exists. Nobody does it. Why?
A Chinese factory can train sweatshop workers in two weeks on a new pillow design. A dedicated machine costs millions and can't pivot.
Human labor wins not on capability. The machines exist. It wins on flexibility per dollar. And the ratio still favors humans by an order of magnitude in most categories.
Agent replacements are the dedicated machine. Their real cost isn't tokens. It's tokens plus the engineer wrapping them, plus orchestration, plus the supervisor, plus the eval pipeline, plus the rebuild every time a model version subtly changes behavior. The team you replaced could pivot in two weeks. The agent stack can't.
This is a good point and imo there is an interesting tension between adaptability and specialization.
Stable environments naturally drive populations towards more specialized actors in niches as they benefit from efficiency. Think of leverage in the financial economy or the dinosaurs.
When a big system disruption inevitably arrives, you better hope you still have some depth around with adaptable general populations that can survive the crisis and occupy the new environment. Think of Minsky moments and the K-Pg event for the dinosaurs 66 million years ago.
Another example would be stem cells vs organs and their specialized cell types.
It seems to me like you need enough regular change to avoid overspecialization and preserve the ability to survive large changes.
“When AI labs raise prices, big spending on AI could shift from a flex to a liability.”
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.
> I haven’t seen any good study that showed AI to actually improve productivity overall.
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.
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.
You obviously are not reviewing the generated code in any detail before merging it. This is not sustainable for the project as it will grow to be too large for what it needs to be.
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.
I had that conversation with our AI VP recently. At a certain cost entertaining humans will be more cost effective from a financial and energy perspective. Especially on a global scale.
The way these agents are being used now is crazy in how inefficient the token utilization is. Reading from ill structured knowledge dumps meant for humans, ralph wiggum loops. Just crazy iterations for simple things.
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
Human workers get more expensive with time, often engage in politics, and withhold knowledge. Just go to any company that has lived long enough to devolve into an enterprise behemoth.
People are willing to accept the fact that the token price will come down or efficiency will go up even more! Meanwhile, they are sure of the cost of human workers from decades of data we’ve had.
Companies might bet that it is safer to base their businesses on more fungible explicated domain knowledge rather than knowledge that is siloed in human brains.
In a sense, everyone is a startup now... At least, every serious user of agents.
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.
If we want to be like everyone else then yes it's true. However that business may or may not survive when token costs go up (or is fashionable to say now, "rug pull"). If you can be token efficient now, the path to profitability is much clearer.
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.
> Is there an alternative to archive.(is|ph|whatever)?
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)
personally, I don't think so. The link itself is actually a static website which anyone can host combined with piping server (actually I recommend doing so if someone wishes to also use my project to host it on their own github pages)
If Archive.org has any trouble with content owners, then archive.org has a proper mechanism iirc if content owners wish to remove the content.
Currently, only I use this myself to share links of archive.(is|ph|today etc) which people share on hackernews, and I convert it to archive.org
Personally I made this project because I was a bit sick of captchas and I saw many people who couldn't access archive.is or who were hesitant to do so, so I decided to made it.
Edit: that being said, I am not a lawyer and I am more than happy to help anybody/everybody interested
1. Build-out and Competition: (current phase) Multiple AI companies write down massive debt while building data centres and offering sweetheart deals to customers in an attempt to dominate the market. The financial numbers will be silly by design in this phase because it's all predicated on obliterating/outlasting the competition so you can move on to...
2. Enshittification and Exploitation: With most competition wiped out, the survivors will have to pay their debts. A chainsaw will be applied to every corner that can be cut (and many that shouldn't). Prices will be jacked up mercilessly.
3. Maturity: Eventually, once debts are paid down, the technology will reach the point where it's cheap and omnipresent. It might be good. It might not be. e.g. Web search is "mature", but it kind of sucks right now.
AI users are going to become more efficient in how they use it and they're also going to learn when AI is appropriate to use and when it isn't. AI itself will likely improve long-term, but it may get worse at times. It's definitely going to get much more expensive. The math is going to change during each of these phases. Businesses who torch their human capabilities and become dependent on AI during Phase 1 are headed for rough sailing in Phase 2.
And that 20-30% real unemployment from arbitrary, consultant-led mass firings creates zillions of people who primarily consume only minimal goods and services after no longer having disposable income or retirement capital to plow into financialized investments. Also, a surplus of idle, disgruntled people hasn't led to many historically positive outcomes.
On related note, my clients told me because of AI advancement in the field they wanna lower my fees by 10-20%, told them I lowered them already considering I am paid in USD losing money on weaker exchange rate than when we signed contract + there was pretty significant cummulative inflation in those years since signing and me not raising rates (Chinese clients don't realize there is world of inflation outside China apparently), so I am already earning like 10-18% less depending on client.
The best part for their AI argument lowering fees - the AI is crap, it can help with QA, but still 98% of reports are false positive and can't really do almost any task.
So told them to feel free to replace me with AI if they think AI can do my job and send me only tasks AI can't do, but keep my rates same (the reality is AI by itself can't do any of my tasks) and still didn't warn them about introducing new rush/holiday fees I am not charging yet and are included in the rate + new AI fee for tasks AI simply can't do. Only result will be, maybe I will get less tasks, but I will make sure to charge more for those AI can't do.
Thats the thing right, like how many tokens can you extract from an employee per day? In API cost terms, LLMs cant currently cut it.
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.
I see people highly trained engineers spend hundreds of thousand of tokens doing what can reliably be accomplished with 150 lines of python.
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.
> 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.
It's not just push from mangement; AI firms themselves really aggressively market this idea of AI replacing everything. It's not "allowed" to be a mere tool, useful for some but not other tasks, it's gotta (be able to) do everything.
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.
> AI firms themselves really aggressively market this idea of AI replacing everything. It's not "allowed" to be a mere tool, useful for some but not other tasks, it's gotta (be able to) do everything.
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.
> 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.
It's worse than that, in many cases management actively rewards inefficiency. It's like Friedman's "why not spoons?"
They optimize because their work requires them to. 100k tokens is a few bucks and a couple minutes, then 15 more minutes to verify that the output does what it's intended for reasonably well, so it's more like $50 in total cost.
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.
Yeah they rise from $100/hr to $150/hr.
There are no specialized factories for every product in the world. Pillows are wildly different. Every pillow you've ever owned has a different shape, fabric, fill. You could build a robot for any specific pillow. The tech exists. Nobody does it. Why?
A Chinese factory can train sweatshop workers in two weeks on a new pillow design. A dedicated machine costs millions and can't pivot. Human labor wins not on capability. The machines exist. It wins on flexibility per dollar. And the ratio still favors humans by an order of magnitude in most categories.
Agent replacements are the dedicated machine. Their real cost isn't tokens. It's tokens plus the engineer wrapping them, plus orchestration, plus the supervisor, plus the eval pipeline, plus the rebuild every time a model version subtly changes behavior. The team you replaced could pivot in two weeks. The agent stack can't.
Flexibility per dollar is the gap.
This is a good point and imo there is an interesting tension between adaptability and specialization.
Stable environments naturally drive populations towards more specialized actors in niches as they benefit from efficiency. Think of leverage in the financial economy or the dinosaurs.
When a big system disruption inevitably arrives, you better hope you still have some depth around with adaptable general populations that can survive the crisis and occupy the new environment. Think of Minsky moments and the K-Pg event for the dinosaurs 66 million years ago.
Another example would be stem cells vs organs and their specialized cell types.
It seems to me like you need enough regular change to avoid overspecialization and preserve the ability to survive large changes.
A machine can absolutely be reprogrammed and your pillow example fails. Variants are made all the time.
Slopidly slop slop
Show me the prompt that produces the pillow metaphor.
“When AI labs raise prices, big spending on AI could shift from a flex to a liability.”
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.
> I haven’t seen any good study that showed AI to actually improve productivity overall.
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.
Yup.
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.
I use a.i to build my startup and it massively helps but i still spend hours reviewing and fixing what is genertes.
And that expert will not have their knowledge from learning through AI
why not?
At this point it’s undeniable for my use cases.
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.
Three? Across how many projects?
One, thus the git worktrees.
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.
You obviously are not reviewing the generated code in any detail before merging it. This is not sustainable for the project as it will grow to be too large for what it needs to be.
I will see if that becomes a blocker.
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.
I had that conversation with our AI VP recently. At a certain cost entertaining humans will be more cost effective from a financial and energy perspective. Especially on a global scale.
The way these agents are being used now is crazy in how inefficient the token utilization is. Reading from ill structured knowledge dumps meant for humans, ralph wiggum loops. Just crazy iterations for simple things.
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
Human workers get more expensive with time, often engage in politics, and withhold knowledge. Just go to any company that has lived long enough to devolve into an enterprise behemoth.
People are willing to accept the fact that the token price will come down or efficiency will go up even more! Meanwhile, they are sure of the cost of human workers from decades of data we’ve had.
Right so lets fire them all especially the ones with domain knowlege
Companies might bet that it is safer to base their businesses on more fungible explicated domain knowledge rather than knowledge that is siloed in human brains.
In a sense, everyone is a startup now... At least, every serious user of agents.
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.
If we want to be like everyone else then yes it's true. However that business may or may not survive when token costs go up (or is fashionable to say now, "rug pull"). If you can be token efficient now, the path to profitability is much clearer.
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.
If token cost goes up, then the efficiency gains come from using fewer tokens... which is likely possible.
The point is that efficiency comes after, not before.
seems like what you're suggesting to token efficiency is to simply use less of it?
Less or be more productive with same amount?
Almost everyone needs a worker-owned co-op to capture more of the value they create.
https://archive.ph/2PsPD
Is there an alternative to archive.(is|ph|whatever)? The Kremlin sympathizer admin is still blocking my country at both DNS and Cloudflare level.
> Is there an alternative to archive.(is|ph|whatever)?
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.
Could that bring archive.org in trouble with the content owners?
personally, I don't think so. The link itself is actually a static website which anyone can host combined with piping server (actually I recommend doing so if someone wishes to also use my project to host it on their own github pages)
If Archive.org has any trouble with content owners, then archive.org has a proper mechanism iirc if content owners wish to remove the content.
Currently, only I use this myself to share links of archive.(is|ph|today etc) which people share on hackernews, and I convert it to archive.org
Personally I made this project because I was a bit sick of captchas and I saw many people who couldn't access archive.is or who were hesitant to do so, so I decided to made it.
Edit: that being said, I am not a lawyer and I am more than happy to help anybody/everybody interested
Why not use a DNS server that actually is compatible? Why give away even more data to Cloudflare? The choice is yours.
https://byebyepaywall.com/en/
you have there more options, seems removepaywall.com option works as well
[dead]
Inference cost is dropping 30x this year (see GTC keynote) So imo this is a meaningless blip.
The following phases are likely:
1. Build-out and Competition: (current phase) Multiple AI companies write down massive debt while building data centres and offering sweetheart deals to customers in an attempt to dominate the market. The financial numbers will be silly by design in this phase because it's all predicated on obliterating/outlasting the competition so you can move on to...
2. Enshittification and Exploitation: With most competition wiped out, the survivors will have to pay their debts. A chainsaw will be applied to every corner that can be cut (and many that shouldn't). Prices will be jacked up mercilessly.
3. Maturity: Eventually, once debts are paid down, the technology will reach the point where it's cheap and omnipresent. It might be good. It might not be. e.g. Web search is "mature", but it kind of sucks right now.
AI users are going to become more efficient in how they use it and they're also going to learn when AI is appropriate to use and when it isn't. AI itself will likely improve long-term, but it may get worse at times. It's definitely going to get much more expensive. The math is going to change during each of these phases. Businesses who torch their human capabilities and become dependent on AI during Phase 1 are headed for rough sailing in Phase 2.
AI is a scam. It can't replace workers because you pay more, for lower quality. CEOs who fall for the scam are just gullible hype-riders.
Any second now they will also find out that AI won't buy the products you create either.
And that 20-30% real unemployment from arbitrary, consultant-led mass firings creates zillions of people who primarily consume only minimal goods and services after no longer having disposable income or retirement capital to plow into financialized investments. Also, a surplus of idle, disgruntled people hasn't led to many historically positive outcomes.
On related note, my clients told me because of AI advancement in the field they wanna lower my fees by 10-20%, told them I lowered them already considering I am paid in USD losing money on weaker exchange rate than when we signed contract + there was pretty significant cummulative inflation in those years since signing and me not raising rates (Chinese clients don't realize there is world of inflation outside China apparently), so I am already earning like 10-18% less depending on client.
The best part for their AI argument lowering fees - the AI is crap, it can help with QA, but still 98% of reports are false positive and can't really do almost any task.
So told them to feel free to replace me with AI if they think AI can do my job and send me only tasks AI can't do, but keep my rates same (the reality is AI by itself can't do any of my tasks) and still didn't warn them about introducing new rush/holiday fees I am not charging yet and are included in the rate + new AI fee for tasks AI simply can't do. Only result will be, maybe I will get less tasks, but I will make sure to charge more for those AI can't do.
Thats the thing right, like how many tokens can you extract from an employee per day? In API cost terms, LLMs cant currently cut it.
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.
when will they learn that its people that make a company, not machines.
somewhere near the 90 minute mark, IIRC: https://www.imdb.com/title/tt0070723/
https://en.wikipedia.org/wiki/Jevons_paradox
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