Right now the AI race has a lot of similarities to the dotcom bubble. The subject is packed with risky loans based on huge debts. Those huge debts are expecting to be paid as AI becomes profitable, but AI companies are largely loosing money.
All those loans and infrastructure create the burden of sunk costs leading to a desperate need to succeed.
The people feeling that desperation are the same people who own the largest marketing, news, and social media networks in the world.
As a result, there’s a lot of hype around AI. A lot of “kool-aid,” and everyone wants you to drink it. If you drink the kool-aid, that means you’re also bought into the problem. You also need it to succeed, thus making their problem into your problem.
I explained to him that mature, professional use of AI is going to wind up following a similar path to data engineering. It’ll start with bullshit standards, “prompt engineers” and the like, but eventually SE disciplines are going to define who makes best use of AI. You’re going to have niche use cases for daemon AIs, local LLMs, and remote models. You’ll have stronger frameworks around session management, context management, agent permissions, …
It’s not going to be like this forever, “dump all your shit into our web upload and let the AI figure everything out in one go.” It’s going to become more fragmented, bounded, dare I say deterministic… orchestratable.
Then I told my boss, it would be better if he could frame his excitement around these future use cases… so we can skip the kool-aid stage and get right into the good stuff.
He agreed, until about a week passed. Then it was AI hype again.
The 3rd or 4th “industry expert” tells them that things are “moving fast” and things that were impossible months ago are now reality. It’s designed to make them distrust their own subject-matter experts. They thing, ohh POTV, they’re just not educated and up to speed.
Yeah. Local LLM stuff is great when you want to shove a huge pile of documentation into a model trainer and make a more intelligent search. Two of my vendors have implemented it, and it’s more useful than a traditional indexing search tool, though you do have to verify the results (which is not much more effort since with a search you’d have to skim the document to find the info it matched anyway).
But for general “do everything” tool, yeah no. It can’t read and understand your entire database, codebase, business process, etc.
Honestly, I’ve had a rather interesting experience with AI. I was very adverse to LLM usage at first. Later I sort of figured out that I was more adverse to the energy around AI than I am AI itself.
I knew the models sucked at large tasks. Trying to get an edge on the matter though, I started asking myself, how can I get the model to perform better? I figured I could pass over the AI hate stage and get right into the AI professional stage… at least a head start.
So I began experimenting with local LLMs, LLM harnesses, and various governance tools like jai. I decided against Claude Code and Cortex because they’re provider specific — instead using OpenCode so that I can use whichever model I desire. Then I began building out a SKILL.md repository for tightly scoped tasks like change-review, security-analysis, refactor, architecture-review, grill-me, feature-design, …
I’m still thinking through some of the project needs. I want something that lets an agent work, while treating the agent as a kind of helpful adversary. You should be able to configure workloads that designate models, context, available tooling, skills, permissions, session length, inference level, acceptance criteria, and human-review stages. It would also allow for session switching, model switching, agent deliverable handoff to another agent, … not to mention, your VCS should know and respond appropriately if an agent ever pushes code. Don’t trust it by default.
These workloads should be version controllable, benchmarked, …
Anyway, a lot of that is speculative. Just where I’m at now, controlling context and skills manually, I’m already seeing much better results.
And no, I don’t have the AI do everything. I just find smarter ways to decompose “everything” into much smaller tasks that are easier to review and scrutinize.
But also, I push for local model usage in my organization. I don’t want my success to mean success for the AI companies. Fuck the AI companies.
I was forced to dogfood it. I found that for my specific needs, it made me super productive. I generally make Claude write Ansible jobs, I store all my secrets in a vault that it never gets access to.
It can do tremendous amounts of work at my command in relative safety as long as i provide it protected tools.
Now, that said, I burn a hell of a lot of tokens moving at that speed. When the ass falls out of the market, i’ll still have all the ancible stuff I can reuse.
Neither Claude code neither codex is actually vendor specific, they just don’t tell you that you can config other providers, including local
However opencode is pretty nice too, so if you like it, use that. I personally find that opencode with GLM 5.2 or Kimi K2.7 isn’t actually that great, it’ll hallucinate more than Claude code or Codex with their respective first party models. I think it’s the models themselves rather than opencode itself though, as when I use GPT for planning and hand it off to deepseek flash to do the actual work, it’s more or less fine.
Not impossible, but also the thinking process reduces the need for something like that. Anything in the context gets regurgitated back and forth multiple times so the model has multiple opportunities to flag something as being weird.
Incidentally if you actually want to do something like that yourself, I believe OpenRouter has a feature for that. But of course it means you can essentially add up each model’s costs and that’s your new cost per prompt (not exact since output token count will differ, but on average).
That’s because they bought into the pitch that AI will replace employees (or at least large number of employees) they understand that they will still need to build tooling that would facilitate that and believe that once other companies will say they eliminated employees this way the companies that are “are left behind” will be stuck still needing employees that will catch up to this plan and refuse to help company to get there.
Yeah, that isn’t how this works. You don’t want to be the one using the software while it’s still in beta. Wait until the dust settles before committing to anything. Besides which the super-urgent "You have to buy now!" FOMO sales pitch is a classic strategy for scammers.
All your competition is going AI. They’re be producing 10x the work with mouth breathing morons at the keys, while you’re stuck paying millions to subject matter experts.
They’re scared ot death that the tenuous hold they have on their market segment will be severed if their competition outflanks them in this, so FUD wins.
This isn’t just in industry or tech. I work in the academy. You would be shocked how many people from administrators all the way on down truly believe this. That, without any proof, this technology is going to make everybody a billion times more productive and that any graduates who don’t have this is a foundational skill will surely not survive in the future workforce.
students using AI to write thier papers, LORs, or projects, and professors using it to screen out AI themselves, or using it to make lectures. this is for university level, and then you have students unable to write a paper themselves, and teacher even more removed from teaching a class.
they will just lay off more people to stave off the debt, and then to hold the industry to gether outsource, and hire only some senior devs while ignoring entry or juniour level people.
And we hope they go broke, dont pay their bills, cause a panic sell on AI services, which causes private equity to panic sell everything… which pops the bubble… and leads to the literal version of ‘its raining men’ on wall street as executives and profiteers have their horde of ill gotten gains evaporate in seconds.
The headline combined with the quote just make me laugh so much, I love it
This is what happens when the people in charge of everything are entirely separated from reality.
Those same idiots have been in charge of everything for decades, blindly doing whatever suited them.
They got duped and didn’t have the technical competence to see it or trust their staff to negotiate it.
Every IT / Developer out there knew it was a bad idea. The C-Staff was sold by the billionaires that you will go AI or you will be left behind.
My own CEO is simultaneously telling us to use AI for as much as we can and telling us to reduce costs as much as possible.
yep. these mba types have gutted half of id, and are changing id - ID - to unreal engine projects.
fucking hell
That’s sacrilege.
I told my boss this:
I explained to him that mature, professional use of AI is going to wind up following a similar path to data engineering. It’ll start with bullshit standards, “prompt engineers” and the like, but eventually SE disciplines are going to define who makes best use of AI. You’re going to have niche use cases for daemon AIs, local LLMs, and remote models. You’ll have stronger frameworks around session management, context management, agent permissions, …
It’s not going to be like this forever, “dump all your shit into our web upload and let the AI figure everything out in one go.” It’s going to become more fragmented, bounded, dare I say deterministic… orchestratable.
Then I told my boss, it would be better if he could frame his excitement around these future use cases… so we can skip the kool-aid stage and get right into the good stuff.
He agreed, until about a week passed. Then it was AI hype again.
also the environmental damage, noise pollution,etc its causing that the dot com dint cause.
The 3rd or 4th “industry expert” tells them that things are “moving fast” and things that were impossible months ago are now reality. It’s designed to make them distrust their own subject-matter experts. They thing, ohh POTV, they’re just not educated and up to speed.
Yeah. Local LLM stuff is great when you want to shove a huge pile of documentation into a model trainer and make a more intelligent search. Two of my vendors have implemented it, and it’s more useful than a traditional indexing search tool, though you do have to verify the results (which is not much more effort since with a search you’d have to skim the document to find the info it matched anyway).
But for general “do everything” tool, yeah no. It can’t read and understand your entire database, codebase, business process, etc.
Honestly, I’ve had a rather interesting experience with AI. I was very adverse to LLM usage at first. Later I sort of figured out that I was more adverse to the energy around AI than I am AI itself.
I knew the models sucked at large tasks. Trying to get an edge on the matter though, I started asking myself, how can I get the model to perform better? I figured I could pass over the AI hate stage and get right into the AI professional stage… at least a head start.
So I began experimenting with local LLMs, LLM harnesses, and various governance tools like
jai. I decided against Claude Code and Cortex because they’re provider specific — instead using OpenCode so that I can use whichever model I desire. Then I began building out a SKILL.md repository for tightly scoped tasks likechange-review,security-analysis,refactor,architecture-review,grill-me,feature-design, …I’m still thinking through some of the project needs. I want something that lets an agent work, while treating the agent as a kind of helpful adversary. You should be able to configure workloads that designate models, context, available tooling, skills, permissions, session length, inference level, acceptance criteria, and human-review stages. It would also allow for session switching, model switching, agent deliverable handoff to another agent, … not to mention, your VCS should know and respond appropriately if an agent ever pushes code. Don’t trust it by default.
These workloads should be version controllable, benchmarked, …
Anyway, a lot of that is speculative. Just where I’m at now, controlling context and skills manually, I’m already seeing much better results.
And no, I don’t have the AI do everything. I just find smarter ways to decompose “everything” into much smaller tasks that are easier to review and scrutinize.
But also, I push for local model usage in my organization. I don’t want my success to mean success for the AI companies. Fuck the AI companies.
I was forced to dogfood it. I found that for my specific needs, it made me super productive. I generally make Claude write Ansible jobs, I store all my secrets in a vault that it never gets access to.
It can do tremendous amounts of work at my command in relative safety as long as i provide it protected tools.
Now, that said, I burn a hell of a lot of tokens moving at that speed. When the ass falls out of the market, i’ll still have all the ancible stuff I can reuse.
Neither Claude code neither codex is actually vendor specific, they just don’t tell you that you can config other providers, including local
However opencode is pretty nice too, so if you like it, use that. I personally find that opencode with GLM 5.2 or Kimi K2.7 isn’t actually that great, it’ll hallucinate more than Claude code or Codex with their respective first party models. I think it’s the models themselves rather than opencode itself though, as when I use GPT for planning and hand it off to deepseek flash to do the actual work, it’s more or less fine.
I suspect behind the scenes, the first parties are sending your requests to multiple targets and sending you back quorum.
Not impossible, but also the thinking process reduces the need for something like that. Anything in the context gets regurgitated back and forth multiple times so the model has multiple opportunities to flag something as being weird.
Incidentally if you actually want to do something like that yourself, I believe OpenRouter has a feature for that. But of course it means you can essentially add up each model’s costs and that’s your new cost per prompt (not exact since output token count will differ, but on average).
The “you’ll be left behind” nonsense makes me laugh. Left behind from what exactly? Lol
That’s because they bought into the pitch that AI will replace employees (or at least large number of employees) they understand that they will still need to build tooling that would facilitate that and believe that once other companies will say they eliminated employees this way the companies that are “are left behind” will be stuck still needing employees that will catch up to this plan and refuse to help company to get there.
Yeah, that isn’t how this works. You don’t want to be the one using the software while it’s still in beta. Wait until the dust settles before committing to anything. Besides which the super-urgent "You have to buy now!" FOMO sales pitch is a classic strategy for scammers.
The sales pitch is:
All your competition is going AI. They’re be producing 10x the work with mouth breathing morons at the keys, while you’re stuck paying millions to subject matter experts.
They’re scared ot death that the tenuous hold they have on their market segment will be severed if their competition outflanks them in this, so FUD wins.
This isn’t just in industry or tech. I work in the academy. You would be shocked how many people from administrators all the way on down truly believe this. That, without any proof, this technology is going to make everybody a billion times more productive and that any graduates who don’t have this is a foundational skill will surely not survive in the future workforce.
students using AI to write thier papers, LORs, or projects, and professors using it to screen out AI themselves, or using it to make lectures. this is for university level, and then you have students unable to write a paper themselves, and teacher even more removed from teaching a class.
That is a fine hypothesis, but that has nothing to do with how I run my classes. Or anyone in my department for that matter.
“it’s technology and science, it must be good!”
Man, you would be shocked how resistant to tech and innovation some of us are unless, I guess, you promise the world with your racist chatbot.
That’s a great point.
on the grift since its basically a rehashed version of crypto.
Someone else can output more slop than us!
And faster slop! Turbo slop even
Except this time they’ll have a hard time blaming the devs and other workers.
I mean they’ll sneak around it, but maybe just maybe the blame will not be distributed? Lol who am I kidding.
they will just lay off more people to stave off the debt, and then to hold the industry to gether outsource, and hire only some senior devs while ignoring entry or juniour level people.
The’re apex predators, they don’t blame anyway, just mass layoffs due to non-profitability ()
non-profitability(){ if CEO_makes_less_money_than_they_want(); return true; return true anyway because fuck the proliariat }if (bankrupt) find_new_ceo_job(parents_network);
Yeah, I’m glad you woke up there. They will always find a way to blame the developers.
Ed Zitron wrote a whole thing about these people. Calls them “Business Idiots”
And we hope they go broke, dont pay their bills, cause a panic sell on AI services, which causes private equity to panic sell everything… which pops the bubble… and leads to the literal version of ‘its raining men’ on wall street as executives and profiteers have their horde of ill gotten gains evaporate in seconds.
… too much?
Damn, I was almost there. Keep going
everyone will just end using deepseek, assuming they arnt charging.
I’m thinking the last scene in Fight Club. Yeah, that would be the one you’re looking for.
These execs were the ones we were supposed to replace with AI.