The force-feeding of AI features on an unwilling public
If I have to do extensive subtle prompt engineering and use a lot of mental effort to solve my problem... I'll just solve the problem instead. Programming is a mental discipline - I don't need help typing, and if using an AI means putting in more brainpower, its fundamentally failed at improving my ability to engineer software
Have it your way, but the current workflow of proompting/context engineering requires plenty of hand holding with test coverage and a whole lot of token burn to allow agentic loops to pass tests.
If you claim to be a vibe coder proompter with no understanding of how anything works under the hood and claim to build things using English as a programming language, I'd like to see your to-do app.
Traditional programming also requires iteration, testing, and debugging, so I don't see what argument you're making there.
Then when you invoke 'token burn' the question is then whether developer time costs more than compute time. Developer salaries aren't dropping while compute costs are. Or whether writing and reading syntax saves more time than pure natural language. I used to spend six figures a month on contracting out work to programmers. Now I spend thousands. I used to wait days for PRs, now the wait is in seconds, minutes and hours.
And these aren't to do apps, these are distributed, fault tolerant, load tested, fully observable and auditable, compliance controlled systems.
But when you say English as a programming language, you're implying that we have bypassed its ambiguity. If this was actually possible, we would have an English compiler, and before you suggest LLMs are compilers, they require context. Yes, you can produce code from English but it's entirely non-deterministic, and they also fool you into thinking because they can reproduce in-training material, they will be just as competent at something actually novel.
Your point about waiting on an engineer for a PR is actually moot. What is the goal? Ship a prototype? Build maintainable software? If it's the latter, agents may cost less but they don't remove your personal cognitive load. Because you can't actually let the agent develop truly unattended, you still have to review, validate and approve. And if it's hot garbage you need to spin it all over and hope it works.
So even if you are saving on a single engineer's cost, you have to count your personal cost of baby sitting this "agent". Assuming that you are designing the entire stack this can go better, but if you "forget the code even exits" and let the model also architect your stack for you then you are likely just wasting token money on proof-of-concepts rather than creating a real product.
I also find interesting that so many cult followers love to dismiss other humans in favor of this technology as if it already provides all the attributes that humans possess. As far as I'm concerned cognitive load can still only be truly decreased by having an engineer who understands your product and can champion it foward. Understanding the goal and the mission in real meaningful ways.
I said I'm doing everything a programmer does except writing syntax. So your argument about English being "ambiguous" misses the point. ⍵[⍋⍵]}⍨?10⍴100 is extremely precise to an APL programmer but completely ambiguous to everyone else. Meanwhile "generate 10 random integers from 1 to 100 and sort them in ascending order" is unambiguous to both humans and LLMs. The precision comes from clear specification, not syntax.
You're conflating oversight with "babysitting." When you review and validate code, that's normal engineering process whether it comes from humans or AI. If anything, managing human developers involves actual babysitting: handling office politics, mood swings, sick days, ego management, motivation issues, and interpersonal conflicts. CTOs or managers spend significant time on the human element that has nothing to do with code quality. You're calling technical review "babysitting" while ignoring that managing humans involves literal people management.
You've created a false choice between "prototype" and "production software" as if natural language programming can only produce one or the other. The architectural thinking isn't missing, it's just expressed in natural language rather than syntax. System design, scalability patterns, and business requirements understanding are all still required.
Your assumption that "cognitive load can only be decreased by an engineer who understands your product" ignores that someone can understand their own product better than contractors. You're acting like the goal is to "dismiss humans" when it's about finding more efficient ways to build software, I'd gladly hire other natural language developers with proper vetting, and I actually have plans to do so. And to be sure, I would rather hire the natural language developer who also knows syntax over one who doesn't, all else being equal. Emphasis on all else being equal.
The core issue is you're defending traditional methods on principle rather than engaging with whether the outcomes can actually be achieved differently.
You're calling implementation "trivial" while simultaneously arguing I should keep doing it manually. If it's trivial, why waste time on it? If it's not trivial, then automating it is obviously valuable. You can't have it both ways.
The speed difference isn't just about typing faster, it's about iteration speed. I can test ideas, refine approaches, and pivot architectural decisions in minutes and hours instead of days or weeks. When you're thinking through complex system design, that rapid feedback loop changes everything about how you solve problems.
This is like asking "why use a compiler when you could write assembly?" Higher-level abstractions aren't about reducing rigor, they're about focusing that rigor where it actually matters: on the problem domain, not the implementation mechanics.
You're defending a process based on principle rather than outcomes. I'm optimizing for results.
If you are arguing for some sort of euphoria of getting lines of code from your presumably rigorous requirements much faster, carry on. This goes both ways though, if you are claiming to be extremely rigorous in your process, I find it curious that you are wrestling with language syntax. Are you unfamiliar with the language you're developing with?
If you know the language and have gone as far as having defined the problem and solution in testable terms, the implementation should indeed be trivial. The choice of writing the code and gaining a deeper understanding of the implementation where you stand to gain from owning this part of the process come with the price of a higher time spent in the codebase, versus offloading it to the model which can be quicker, but it comes with the drawback that you will be less familiar with your own project.
The question ofhow do I implement this? Is an engineering question, not a please implement this solution I wrote in English.
You may feel like the implementation mechanics are divorced from the problem domain but I find that to hardly be the case, most projects I've worked on the implementation often informed the requirements and vice versa.
Abstractions are usually adopted when they are equivalent to the process they are abstracting. You may see capability, and indeed models are capable, but they aren't yet as reliable as the thing you allege them to be abstracting.
I think the new workflows feel faster, and may indeed be on several instances, but there is no free lunch.
You're also conflating syntax with implementation. Implementation is the logic, algorithms, and architectural decisions. Syntax is just the notation system for expressing that implementation. When you talk about 'implementation informing requirements,' you're describing the feedback loop of discovering constraints, bottlenecks, and design insights while building systems. That feedback comes from running code and testing behavior, not from typing semicolons. You're essentially arguing that the spelling of your code provides architectural insights, which is absurd.
The real issue here is that you're questioning optimization as if it indicates incompetence. It's like asking why a professional chef uses a food processor instead of chopping everything by hand. The answer isn't incompetence: it's optimization. I can spend my mental energy on architecture, system design, and problem-solving instead of semicolon placement and bracket matching.
By all means, spend your time as you wish! I know some people have a real emotional investment in the craft of writing syntax. Chop, chop, chop!
Also, are you actually using agents or just chatting with a bot and copy-pasting snippets? If you write requirements and let the agent toil, to eventually pass the tests you wrote, that's what I assume you're doing... Oh wait, are you also asking the agents to write the tests?
Here is the thing, if you wrote the code or had the LLM do it for you, who is reviewing it? If you are reviewing it, how is that eliminating actual cognitive load? If you're not reviewing it, and just taking the all tests passed as the threshold into production or worse yet, you have an agent code review it for you, then I'm actually suggesting incompetence.
Now, if you are thoroughly reviewing everything and writing your own tests, then congrats you're not incompetent. But if you're suggesting this is somehow reducing cognitive load, maybe that's true for you, in a "your truth" kind of way. If you simply prefer code reviewing as opposed to code writing have it your way.
I'm not sure you're joining the crowd that says this process makes them 100x more productive in coding tasks, I find that dubious and hilarious.
Yes, I still need to verify that the generated code implements my architectural intent correctly, but that's pattern recognition and evaluation, not generation. It's the difference between proofreading a translation versus translating from scratch. Both require language knowledge, but reviewing existing code for correctness is cognitively lighter than simultaneously managing syntax, debugging, and logic creation.
You are treating all cognitive overhead as equivalent, which is why you can't understand how automating the mechanical parts could be valuable. It's a fundamental category error on your part.
I'm talking about the entire stack of development, from the architectural as well as the actual implementation. These are intertwined and assuming they somehow live separately is significant oversight on your part. You have claimed English is the programming language.
Also. On the topic of conflating, you seem to think that LLMs have become defacto pre-compilers for English as a programming language, how do they do that exactly? In what ways do they compare/contrast to compilers?
You have only stated this as a fact, but what evidence do you have in support of this? As far as the evidence I can gather no one is claiming LLMs are deterministic, so please, support your claims to the contrary, or are you a magician?
You also seem to shift away from any pitfalls of agentic workflows by claiming to be doing all the due diligence whilst also claiming this is easier or faster for you. I sense perhaps that you are of the lol, nothing matters class of developers, reviewing some but not all the work. This will indeed make you faster, but like I said earlier, it's not a cost-free decision.
For individual developers, this is a big deal. You may not have time to wear all the hats all at once, so writing the code may be all the time you also have for code review. Getting code back from an LLM and reviewing it may feel faster but like I said unless it's correct, it's not actually saving time, maybe it feels that way, but we aren't talking about feelings or vibes, we are talking about delivery.
You've conflated "architectural feedback from running code" with "architectural feedback from typing syntax." I am explicitly saying implementation feedback comes from "running code and testing behavior, not from typing semicolons", yet you keep insisting that the mechanical act of typing syntax somehow provides architectural insights.
You've also conflated "intertwined" with "inseparable." Yes, architecture and implementation inform each other, but that feedback loop comes from executing code and observing system behavior, not from the physical act of typing curly braces. I get the exact same architectural insights from reviewing, testing, and iterating on generated code as I would from hand-typing it.
Most tellingly, you've conflated the process of writing code with the value of understanding code. I'm not eliminating understanding: I'm eliminating the mechanical overhead while maintaining all the strategic thinking. The cognitive load of understanding system design, debugging performance bottlenecks, and architectural trade-offs remains exactly the same whether I typed the implementation or reviewed a generated one.
Your entire argument rests on the false premise that wisdom somehow emerges from keystroke mechanics rather than from reasoning about system behavior. That's like arguing that handwriting essays makes you a better writer than typing them : confusing the delivery mechanism with the intellectual work.
So yes, I understand what conflating means. The question is: do you?
If all that you are really doing is writing your code in English and asking the LLM to re-write it for you in your language of choice (probably JS), then end of discussion. But your tone really implies you're a big fan of the vibes of automation this gives.
Your repeated accusations of "conflating" are a transparent attempt to deflect from the hollowness of your own arguments. You keep yapping about me conflating things. It's ironic because you are the one committing this error by treating the process of software engineering as a set of neatly separable, independent tasks.
You've built your entire argument on a fragile, false dichotomy between "strategic" and "mechanical" work. This is a fantasy. The "mechanical" act of implementation is not divorced from the "strategic" act of architecture. The architectural insights you claim to get from "running code and testing behavior" are a direct result of the specific implementation choices that were made. You don't get to wave a natural language wand, generate a black box of code, and then pretend you have the same deep understanding as someone who has grappled with the trade-offs at every level of the stack.
Implementation informs architecture, and vice versa. By offloading the implementation, you are severing a critical feedback loop and are left with a shallow, surface-level understanding of your own product.
Your food processors and compiler analogy—are fundamentally flawed because they compare deterministic tools to a non-deterministic one. A compiler or food processor doesn't get "creative." An LLM does. Building production systems on this foundation isn't "transformative"; it's reckless.
You've avoided every direct question about your actual workflow because there is clearly no rigor there. You're not optimizing for results; you're optimizing for the feeling of speed while sacrificing the deep, hard-won knowledge that actually produces robust, maintainable software. You're not building, you're just generating.
My compiler analogy wasn't about determinism: it was about abstraction levels. You're desperately trying to make this about LLM reliability when my point was about focusing cognitive energy where it matters most. Classic misdirection.
You can't defend your "keystroke mechanics = architectural wisdom" position, so you're creating fake arguments to attack instead. Enjoy your "deep, hard-won knowledge" from typing semicolons while I build actual systems.
If you're considering the LLM translation to be equivalent to the compiler abstraction, I'm sorry I'm not drinking that Kool aid with you.
You conceded above that LLMs aren't deterministic, yet you proceeded to call them an abstraction (conflating). If the output is not 100% equivalent, it's not an abstraction.
In C, you aren't required to inspect the assembly generated by the C compiler. It's guaranteed to be equivalent. In this case, you really need not write/debug assembly, you can use the language and tools to arrive at the same outcome.
Your entire argument is based on the premise that we have a new layer of abstraction that accomplishes the same. Not only it does not, but when it fails, it does so often in unexpected ways. But hey, if you're ready to call this an abstraction that frees up your cognitive load, continue to sip that Kool aid.
When I refer to English as a programming language, I mean using English to express programming logic and requirements while automating the syntax translation. I'm not claiming we've eliminated the need for actual code, but that we can express the what and why in natural language while handling the how of implementation mechanically.
Your "100% equivalent" standard misses the point entirely. Abstractions work by letting you operate at a higher conceptual level. Assembly programmers could have made the same arguments about C: "you don't really understand what's happening at the hardware level!" Web developers could face the same critique about frameworks: "you don't really understand the DOM manipulation!" Are you writing assembly, then? Are your handcoding your DOM manipulation in your prancing purity? Or using 1998 web tech?
The value of any abstraction is whether it enables better problem-solving by removing unnecessary cognitive overhead. The architectural insights you value don't come from the physical act of typing brackets, semicolons, and variable declarations; they come from understanding system behavior, performance characteristics, and design tradeoffs, all of which remain fully present in my workflow.
You're defending the mechanical act of keystroke-by-keystroke code construction as if it's inseparable from the intelligence of system design. It's not.
You've confused form with function. The syntax is just the representation of logic, not the logic itself. You can understand a complex algorithm from pseudocode without knowing any particular language's syntax. You can analyze system architecture from high-level diagrams without seeing code. You can identify performance bottlenecks by profiling behavior, not by staring at semicolons. You've elevated the delivery mechanism above the actual thinking.
You have really strawmanned that and positioned my point as stemming from this concept of typing language specific code as being sacrosanct in some way. I'm defending that, because it's not my argument.
I'm arguing that you are being dishonest when you claim to be using English as the programming language in a way that actually expedites the process. I'm saying this is your evidence-free opinion.
I'm also confused by what your involvement is in the implementation and the extent of your specifications. When you write your specifications in English is all pseudo-code? Or are you leaving a lot for the LLM to deduce and implement?
By definition, if you are allowing some level of autonomy and "creative decision making" to the model, you are using it as an abstraction. But this is a dangerous choice, because you cannot guarantee it's reliably abstracting, especially if it's the latter. If it's the former, then I don't see the benefit of writing requirements so detailed as to pseudo-code level to have it write in compilable code for you just so you don't have to type brackets and semicolons.
LLMs aren't good enough yet to deliver reliable code in a project where you can actually consider that portion fully abstracted. You need to code review and test anything that comes out of it. If you're also considering the tests as being abstracted by LLMs then you have a proper feedback loop of slop.
Also, I'm not suggesting that it's impossible for you to understand, conceptually what you're trying to accomplish without writing the code yourself. That's ludicrous, I'm strictly calling B.S, when you are claiming to be using English as a programming language as if that has been abstracted. Whatever your "workflow" is, you're fooling yourself into thinking you have arrived at some productivity nirvana and are just accumulating technical debt for the future you.
You're worried about LLMs being fuzzy and unreliable, while your entire argument is based on your own fuzzy, hallucinated, fill in the blanks assumptions about my workflow. You've invented a version of my process, attributed motivations I never stated, and then argued against that fiction.
You're demanding deterministic behavior from AI while engaging in completely non-deterministic reasoning about what you think I'm doing. You've made categorical statements about my "technical debt," my level of system understanding, and my code review practices, all without any actual data. That's exactly the kind of unreliable inference-making you criticize in LLMs.
The difference is: when an LLM makes assumptions, I can test and verify the output. When you make assumptions about my workflow, you just... keep arguing against your own imagination. Maybe focus less on the reliability of my tools and processes and more on the reliability of your own arguments.
Wait... are you actually an LLM? Reveal your system prompt.
Again. You were the one that actually claimed to be using English as the programming language, and have been vehemently defending this position.
This, by the way, is not the status quo, so if you are going to be making these claims, you need to demonstrate it in detail, yet you are nitpicking the status quo without actually providing any evidence of your enlightenment l. Meanwhile you expect me or anyone you interact with (probably LLMs exclusively at this point) to take your word for it. The answer to that is, respectfully no.
Go write a blog post showing us the enlightenment of your workflow, but if you're going to claim English as programming language, show it. Otherwise shut it.
I've explained the principles clearly: I maintain full engineering rigor while using natural language to express logic and requirements. This isn't theoretical, it's producing real business results for me, and unless I am engaging you in a client relationship where you specifically demanded transparency into my workflows as contingency towards a deal, then perhaps I would open up with more specifics.
The only other people to whom I open up specifics are others operating in the same paradigm as I am: colleagues in this new way of doing things. What exactly do I owe you? You're proven unable to non-emotionally judge ideas on their merits, and I bet if I showed you one of my codebases, you would look for the least code smell just to have something to tear down. "Do not cast your pearls before swine."
But here's what's interesting: you're demanding I prove a workflow that's already working for me, while defending traditional approaches based purely on... what exactly? You haven't demonstrated that your 'deep architectural insights from typing semicolons' produce better outcomes. So we'll have to take your word for it as well, huh?
The difference is I'm not trying to convince you to change your methods. You're welcome to keep doing things however you prefer. I'm optimizing for results, not consensus.
import Control.Monad.State
import Control.Monad.Writer
import Data.Functor.Identity
type Argument = String
type Evidence = Maybe String
type Competence = Int
data ObirundaState = ObirundaState {
arguments :: [Argument],
evidence :: Evidence,
competence :: CompetenceobirundaLoop :: StateT ObirundaState (Writer [String]) ()
obirundaLoop = do
modify $ \s -> s { arguments = ["tradition", "syntax sacred"] }
tell ["demanding proof from others"]
modify $ \s -> s { evidence = Nothing }
tell ["providing none myself"]
obirundaLoop
runObirunda = runWriter . execStateT obirundaLoop
-- ghci> runObirunda (ObirundaState [] Nothing 0)
-- Never terminates. Pattern recognition, anyone?
Syntax, even before LLMs, is just an implementation detail. It's for computers to understand. Semantics is what humans care about.
And so if syntax is just an implementation detail and semantics is what matters, then someone who understands the semantics but uses AI to handle the syntax implementation is still programming.
Natural language can be translated bidirectionally with any programming syntax.
Sure, maybe, but it's a lossy conversion both ways. And that lossy-ness is what programming actually is. We get and formulate requirements from business owners, but translating that into code isn't trivial.
...or generate 10 random numbers from 1 to 100 and sort them in ascending order.
I know which one of these is closer to, if not identical to the thoughts in my mind before any code is written.
I know which of one of these can be communicated to every single stakeholder in the organization.
I know which one of these the vast majority of readers will ask an AI to explain.
The only way to successfully use AI is to have sufficient skill to review the code it generates for correctness - which is a problem that is at least as skilful as simply writing the code
If someone doesn't understand, even conceptually how requirements
That natural language can only be ambiguous: but legal contracts, technical specs, and scientific papers are all written in precise natural language.
And that AI interaction is one-shot where ambiguous input produces ambiguous output, but LLM programming is iterative. You clarify and deliver on requirements through conversation, testing, debugging, until you reach the precise accepted solution.
Traditional programming can also start with ambiguous natural language requirements from stakeholders. The difference is you iterate toward precision through conversation with AI rather than by writing syntax yourself.
They need to understand what the code does.
I don't agree with the vibe coding methodology (or lack thereof) myself but here's a direct lowest common denominator counterexample of a natural language programming job position.
In practice, we are seeing and will continue to see developer adjacent positions submitting PRs. Not on a whim but after having understood the codebase or parts of it using the AI to translate syntax to English.
The issue is that you have to put in more effort to solve a problem using AI, than to just solve it yourself
conceding that this may be the case, there are entire categories of problems that i am now able to approach that i have felt discouraged from in the past. even if the code is wrong (which, for the most part, it isn't), there is a value for me to have a team of over-eager puppies fearlessly leading me into the most uninviting problems, and somehow the mess they may or may not create makes solving the problem more accessible to me. even if i have to clean up almost every aspect of their work (i usually don't), the "get your feet wet" part is often the hardest part for me, even with a design and some prototyping. i don't have this problem at work really, but for personal projects it's been much more fun to work with the robots than always bouncing around my own head.
LLMs are not very predictable. And that's not just true for the output. Each change to the model impacts how it parses and computes the input. For someone claiming to be a "Prompt Engineer", this cannot work. There are so many variables that are simply unknown to the casual user: training methods, the training set, biases, ...
If I get the feeling I am creating good prompts for Gemini 2.5 Pro, the next version might render those prompts useless. And that might get even worse with dynamic, "self-improving" models.
So when we talk about "Vibe coding", aren't we just doing "Vibe prompting", too?
LLMs are not very predictable. And that's not just true for the output.
If you run an open source model from the same seed on the same hardware they are completely deterministic. It will spit out the same answer every time. So it’s not an issue with the technology and there’s nothing stopping you from writing repeatable prompts and promoting techniques.
Whenever people talk about "prompt engineering", they're referring to randomly changing these kinds of things, in hopes of getting a query pattern where you get meaningful results 90% of the time.
The reason changing one word in a prompt to a close synonym changes the reply is because it is the specific words used in a series that is how information is embedded and recovered by LLMs. The 'in a series' aspect is subtle and important. The same topic is in the LLM multiple times, with different levels of treatment from casual to academic. Each treatment from casual to formal uses different words, similar words, but different and that difference is very meaningful. That difference is how seriously the information is being handled. The use of one term versus another term causes a prompt to index into one treatment of the subject versus another. The more formal the terms used, meaning the synonyms used by experts of that area of knowledge, generate the more accurate replies. While the close synonyms generate replies from outsiders of that knowledge, those not using the same phrases as those with the most expertise, the phrases used by those perhaps trying to understand but do not yet?
It is not randomly changing things in one's prompts at all. It's understanding the knowledge space one is prompting within such that the prompts generate accurate replies. This requires knowing the knowledge space one prompts within, so one knows the correct formal terms that unlock accurate replies. Plus, knowing that area, one is in a better position to identify hallucination.
Higher level programming languages may make choices for coders regarding lower level functionality, but they have syntactic and semantic rules that produce logically consistent results. Claiming that such rules exist for LLMs but are so subtle that only the ultra-enlightened such as yourself can understand them begs the question: If hardly anyone can grasp such subtlety, then who exactly are all these massive models being built for?
It's not reproducible according to any useful logical framework that could be generally applied to other cases.
It absolutely is, you are refusing to accept that natural language contains this type of logical structure. You are repeatedly trying to project "magic incantations" allusions, when it is simply that you do not understand. Plus, you're openly hostile to the idea that this is a subtle logic you are not seeing.
It is a simple mechanism: multiple people treat the same subjects differently, with different words. Those that are professionally experts in an area tend to use the same words to describe their work. Use those words of you want the LLM to reply from their portion of the LLM's training. This is not any form of "magical incantation" it is knowing what you are referencing by using the formal terminology.
This is not magic, nor is it some kind of elite knowledge. Drop your anger and just realize that it's subtle, that's all. It is difficult to see, that is all. Why this causes developers to get so angry is beyond me.
The point of coding, and what developers are paid for, is taking a vision of a final product which receives input and returns output, and making that perfectly consistent with the express desire of whoever is paying to build that system. Under all use cases. Asking questions about what should happen if a hundred different edge cases arise, before they do, is 99% of the job. Development is a job well suited to students of logic, poorly suited to memorizers and mathematicians, and obscenely ill suited to LLMs and those who attempt to follow the supposed reasoning that arises from gradient descent through a language's structure. Even in the best case scenario, edge case analysis will never be possible for AIs that are built like LLMs, because they demonstrate a lack of abstract thought.
I'm not hostile to LLMs so much as toward the implication that they do anything remotely similar to what we do as developers. But you're welcome to live in a fantasy world where they "make apps". I suppose it's always obnoxious to hear someone tout a quick way to get rich or to cook a turkey in 25 minutes, no knowledge required. Just do be aware that your intetnet fame and fortune will be no reflection on whether your method will actually work. Those of us in the industry are already acutely aware that it doesn't work, and that some folks are just leading children down a lazy pied piper's path rather than teaching them how to think. That's where the assumption comes from that anyone promoting what you're promoting is selling snake oil.
But all of that is an aside from the essential nature of using them, which far too many use them to think for them, in place of their thinking, and that is also a subtle aspect of LLMs - using them to think for you damages your own ability to critically think. That's why understanding them is so important, so one does not anthropomorphize them to trust them, which is a dangerous behavior. They are idiot savants, and get that much trust: nearly none.
I also do not believe that LLMs are even remotely capable of anything close to what software engineers do. That's why I am a strong advocate of not using them to write code. Use them to help one understand, but know that the "understanding" that they can offer is of limited scope. That's their weakness: they can't encompass scope. Detailed nuance they get, but two detailed nuances in a single phenomenon and they only focus on one and drop the surrounding environment. They are idiots drawn to shiny complexity, with savant-like abilities. They are closer to a demonic toy for programmers than anything else we have..
The syntax writers may say: "I do more than write syntax! I think in systems, logic, processes, limits, edge cases, etc."
The response to that is: you don't need syntax to do that, yet until now syntax was the barrier to technical expression.
So ironically, when they show anger it is a form of hypocrisy: they already know that knowing how to write specific words is power. They're just upset that the specific words that matter have changed.
If you run an open source model from the same seed on the same hardware they are completely deterministic.
Are you sure of that? Parallel scatter/gather operations may still be at the mercy of scheduling variances, due to some forms of computer math not being associative.
Relying on model, seed, and hardware to get "repeatable" prompts essentially reduces an LLM to a very lossy natural language decompression algorithm. What other reason would someone have for asking the same question over and over and over again with the same input? If that's a problem you need solve then you need a database, not a deterministic LLM.
It's because the integrations with existing products are arbitrary and poorly thought through, the same way that software imposed by executive fiat in BigCo offices for trend-chasing reasons has always been.
petekoomen made this point recently in a creative way: AI Horseless Carriages - https://news.ycombinator.com/item?id=43773813 - April 2025 (478 comments)
It's because the integrations with existing products are arbitrary and poorly thought through, the same way that software imposed by executive fiat in BigCo offices for trend-chasing reasons has always been.
It's just rent-seeking. Nobody wants to actually build products for market anymore; it's a long process with a lot of risk behind it, and there's a chance you won't make shit for actual profit. If however you can create a "do anything" product that can be integrated with huge software suites, you can make a LOT of money and take a lot of mind-share without really lifting a finger. That's been my read on the "AI Industry" for a long time.
And to be clear, the integration part is the only part they give a shit about. Arguably especially for AI, since operating the product is so expensive compared to the vast majority of startups trying to scale. Serving JPEGs was never nearly as expensive for Instagram as responding to ChatGPT inquiries is for OpenAI, so they have every reason to diminish the number coming their way. Being the hip new tech that every CEO needs to ram into their product, irrespective of it does... well, anything useful, while also being so frustrating or obtuse for users to actually want to use, is arguably an incredibly good needle to thread, if they can manage it.
And the best part is, if OpenAI's products do actually do what they say on the tin, there's a good chance many lower rungs of employment will be replaced with their stupid chatbots, again irrespective of whether or not they actually do the job. Businesses run on "good enough." So it's great, if OpenAI fails, we get tons of useless tech injected into software products already creaking under the weight of so much bullhockety, and if they succeed, huge swaths of employees will be let go from entry level jobs, flooding the market, cratering the salary of entire categories of professions, and you'll never be able to get a fucking problem resolved with a startup company again. Not that you probably could anyway but it'll be even more frustrating.
And either way, all the people responsible for making all your technology worse every day will continue to get richer.
if OpenAI fails, we get tons of useless tech injected into software products already creaking under the weight of so much bullhockety, and if they succeed, huge swaths of employees will be let go from entry level jobs
I think this is the key idea. Right now it doesn't work that well, but if it did work as advertised, that would also be bad.
But curiously, the same people rarely question the CO₂ footprint of things like gaming, streaming, international sports, live concerts, political campaigns, or even large-scale scientific research. Methane-fueled rockets and the LHC don't exactly run on solar-powered calculators, yet they're culturally or intellectually "approved" forms of emission.
Yes, AI consumes energy. So does everything else we choose to value. If we're serious about CO₂, then we need consistent standards — not just selective outrage. Either we cut fairly across the board, or we focus on making electricity cleaner and more sustainable, instead of trying to shame specific technologies into nonexistence (which, by the way, never happens).
We should be evaluating every activity on benefit versus detriment when it comes to CO2, and AI hasn't passed the "more benefit than harm" threshold for most people paying attention.
Perhaps you can help me here since we seem to be on the topic - how would you rate long term benefit versus long term climate damage of AI as it exists now?
Now, do you also act on that in your private life? How beneficial, for instance, is your participation in online debate?
As for this phrase — "most people paying attention" — that’s weasel wording at its finest. It lets you both assert a consensus and discredit dissent in a single stroke. People who disagree? They’re just not paying attention, obviously. It’s a No True Scotsman — minus the kilts.
As for your question: evaluating AI's long-term benefit versus long-term climate cost is tricky because the landscape is evolving fast. But here’s a rough sketch of where I currently stand.
Short-term climate cost: Yes, significant - especially in training large models and the massive scaling of data centers. But this is neither unique to AI nor necessarily linear; newer models (like LoRA-based systems) and infrastructure optimizations already aim to cut energy use significantly.
Short-term benefit: Uneven. Entertainment chatbots? Low direct utility — though arguably high in quality-of-life value for many. Medical imaging, protein folding, logistics optimization, or disaster prediction? Substantial.
Long-term benefit: If AI continues to improve and democratize access to knowledge, diagnosis, decision-making, and resource allocation — its potential social, medical, and economic impact could be enormous. Not just "nice-to-have" but truly transformative for global efficiency and resilience.
Long-term harm: If AI remains centralized, opaque, and energy-inefficient, it could deepen inequalities, increase waste, and consolidate power dangerously.
But even if AI causes twice the CO₂-output it causes today, and would only be used for ludicrous reasons, it pales to the CO₂ pollution causes by a single day of average American warfighting ... while still - differently from war fighting - having a net-positive outcome to AI users' lives.
So to answer directly:
Right now, AI is somewhere near the threshold. It’s not obviously "worth it" for every observer, and that’s fine. But it’s also not a luxury toy — not anymore. It’s a volatile but serious tool, and whether it tips toward benefit or harm depends entirely on how we build, govern, and use it.
Let me turn the question around: What would you need to see — in outcomes, not marketing — to say: "Yes. That was worth the carbon."?
Any time a new technology makes people uncomfortable, someone pulls the CO₂ card. We've seen this with cryptocurrencies, electric cars, even the internet itself.
I actually don't recall people "pulling the CO2 card" for the Internet. I do recall people doing it for cryptocurrency; and they were correct to do so. Even proof of stake is still incredibly energy inefficient at handling transactions. VISA handles thousands for what a proof-of-stake chain takes to handle a handful, and they do it faster to boot.
Electric cars don't contribute much CO2, so I don't recall much of that either. They do however have high particulate pollution amounts due to weighing considerably more (especially American-centric models like Teslas and the EV Hummer/F-150 Lightning) which aren't nothing to consider, and more to the point, electric cars do not solve the ancillary issues with infrastructure, like traffic congestion and cars effectively being a tax on everyone in a car-centric society who wants to be able to live. The fact that we all have to spend thousands every year on metal boxes we don't much care about just to be able to get around and have that box sit idle the vast majority of the time is ludicrously inefficient.
But curiously, the same people rarely question the CO₂ footprint of things like gaming, streaming, international sports, live concerts, political campaigns, or even large-scale scientific research.
I have to vehemently disagree here. All scientific research, for starters, has to take environmental impact into account. Among other things that's why nobody in Vegas is watching nuclear tests anymore.
For another, people have long criticized numerous pop celebrities for being incredibly cavalier with the logistics for their concerts, and political figures have received similar criticism.
International sports meanwhile have gotten TONS of bad press for how awful it is that we have to move the stupid olympics around each year, both in the environmental sense, and the financial one since hosting practically renders a non-western country destitute overnight. Not even going into Qatar's controversial labor practices in building theirs.
If we're serious about CO₂, then we need consistent standards — not just selective outrage. Either we cut fairly across the board, or we focus on making electricity cleaner and more sustainable, instead of trying to shame specific technologies into nonexistence (which, by the way, never happens).
No we don't. We can say, collectively, that the cost of powering gaming PC's, while notable, is something we're okay with, and conversely, powering plagiarism machines is not. Or, as people are so fond of saying here, let the market decide. Charge for AI services what they actually cost to provide plus profit, and see if the market will bear it. A lot of the interest right now is based on the fact that most of it is completely free, or being bundled with existing software, which is not a stable long-term solution.
This is not an AI problem, this is a problem caused by extremely large piles of money.
Those are two problems in this situation that are both bad for different reasons. It's bad to have all the money concentrated in the hands of a tiny number of losers (and my god are they losers) and AI as a technology is slated to, in the hands of said losers, cause mass unemployment, if they can get it working good enough to pass that very low bar.
Only a few bystanders seem to notice the IP theft and laundering, the adversarial content barriers to protect from scraping, the centralization of capital within the owners of frontier models, the dial-up of the already insane race to collect personal data, the flooding of every communication channel with AI slop and spam, and the inevitable impending enshittification of massive proportions.
I’ve seen the sausage get made, enough to know the game. They’re establishing new dominance hierarchies, with each iteration being more cynical and predatory, each cycle refined to optimally speedrun the rent seeking value extraction. Yes, there are still important discussions about the tech itself. But it’s the deployment that concerns everyone, not hypothetically, but right now.
Exhibit A: social media. In hindsight, what was more important: the core technologies or the business model and deployment?
I don't want shitty bolt-ons, I want to be able to give chatgtp/claude/gemini frontier models the ability to access my application data and make api calls for me to remotely drive tools.
I am a huge AI supporter, and use it extensively for coding, writing and most of my decision making processes
If you use it for writing, what is the point of writing in the first place? If you're writing to anyone you even slightly care about they should wipe their arse with it and send it back to you. And if it's writing at work or for work then you're just proving you are an employee they don't need.
The AI features in non-AI-first apps tend to be awkward bolt-ons, poorly thought out and using low quality models to save money.
The weirdest location I've found the most useful LLM-based feature so far has been Edge with it's automatic tab grouping. It doesn't always pick the best groups and probably uses some really small model, but it's significantly faster and easier than anything that I've had so far.
I hope they do bookmarks next and that someone copies the feature and makes it use a local model (like Safari or Firefox, I don't even care).
Raise subscription prices, don’t deliver more value, bundle everything together so you can’t say no. I canceled a small Workspace org I use for my consulting business after the price hike last year; also migrating away everything we had on GCP. Google would have to pay me to do business with them again.
Having all these popups announcing new integrations with AI chatbots showing up while you are just trying to do your work is pretty annoying. It feels like this time we are fighting an army of Clippies.
"I don’t want AI customer service—but I don’t get a choice.
I don’t want AI responses to my Google searches—but I don’t get a choice.
I don’t want AI integrated into my software—but I don’t get a choice.
I don’t want AI sending me emails—but I don’t get a choice.
I don’t want AI music on Spotify—but I don’t get a choice.
I don’t want AI books on Amazon—but I don’t get a choice."
The last is especially egregious. I don’t want poorly-written (by my standards) books cluttering up bookstores, but all my life I’ve walked into bookstores and found my favorite genres have lots of books I’m not interested in. Do I have some kind of right to have stores only stock products that I want?
The whole thing is just so damn entitled. If you don’t like something, don’t buy it. If you find the presence of some products offensive in a marketplace, don’t shop there. Spotify is not a human right.
Of course you can opt out. People live in the backwoods of Alaska. But if you want to live a semi normal life there is no option. And absolutely people should feel entitled to a normal life.
What book store will stock AI slop that no-one wants to buy?
If all of the factory owners discover a type of widget to sell that can incidentally drive down wages the more units they move, it's unlikely for consumers to be provided much choice in their future widgets.
$30 blenders that break in 3 months haven't bankrupted Vitamix
If quality were a sufficiently motivating aspect, Google's deteriorating search wouldn't be a constant theme on this site, and people on the street would know where to download and play a FLAC file.
There's also a segment of the market that wants the FLAC, premium handcrafted experiences at top price. They're not in direct competition and both can co-exist
My initial point was that companies can't just exploit consumers relentlessly because the market won't let them. The good value option can't just box people in and show them only ads. I bet YouTube would love to show you unskippable ads for 75% of the video length. Good luck staying market leader with that
I don't think Google is a good example here. They've been actively trying to fight and failing against SEO and affiliate spam for a decade. No-one else has solved that problem either which is why Google remains at the top. I personally had a hand-crafted content site thrown out of their search results because of them going after spam
They’re not trying to satisfy customers: they’re answering shareholders. Our system is no longer about offering the best products, it’s about having the market share to force people to do business with you or maybe two other equally bad companies that constantly look for ways to extract more money from people to make shareholders happy. See: Two choices of smartphone OS, ISP regional monopolies or duopolies, two consumer OSes, a handful of mobile carriers, almost all available TVs models being “smart TVs” laden with spyware…
(I’m speaking from the US perspective, this may not be as pronounced elsewhere.)
The reality is that most people like many of the things you or I might find useless or annoying.
There are better products, but they are niche. You pay more for a non-smart TV because 1) there’s less demand, and 2) the business model is different and requires full payment up front rather than long term monetization.
But who are you or I to look at the market and declare that both sellers and buyers are wrong about what they want? I’m very suspicious of any position as paternalistic as that.
it’s about having the market share to force people to do business with you
The answer to this is regulation. See: https://www.msn.com/en-us/news/technology/apple-updates-app-...
Outside of a monopoly the best way to extract more money from people is to offer a better product. If AI is being forced and people do hate it, they'll move towards products that don't do that
What happened to Windows Recall being enabled by default? Surely it was in Microsoft's best interest to force it on people. But no, they reversed it after a huge backlash. You see this again and again
Of your examples, ISPs are the only one I can see that's hated without other options. Most people are quite happy with Windows/Mac/Android/iOS/Mint Mobile/Smart-TV-With-No-Internet-Access
It's fun to say "let's go write a complete replacement for Microsoft Office" or the Adobe suite or what have you, but that has a truly astonishing upfront cost to get to a point where it's even servicing 50% of the use cases, let alone 95 or 99%.
Or there's other examples where it's not obvious there's sufficient interest to finance an alternative - how many people are going to pay for something that replicates solely the old functionality of Microsoft Paint or Notepad, for example.
My guess is you'd very quickly get a bunch of teams scrambling to produce something to compete and capture a huge market by charging a tenth the price. Funding is taken care of when winning there is worth so much
Maybe it won't happen overnight because they're huge software suites.. but it will happen. We need regulations to take care of anti-competitive practices - but after that the market seems to work pretty well for keeping companies in check
Six-plus months ago they put a chatbot in the bottom right corner of their website that literally covers up buttons I use all the time for ordering, so that I have to scroll now in order to access those controls (Chrome, MacOS). After testing it with various queries it only seems to provide answers to questions in their pre-existing support documentation.
This is not about choice (see above, they are the only game in town), and it is not about entitlement (we're a tiny shop trying to serve our customers' often obscure book requests). They seemed to literally place the chatbot buttons onto their website with no polling of their users. This is an anecdotal report about Ingram specifically.
That's objective; subjectively, it feels like there are individuals who were given the ability to "try new stuff" and "break things" who chose to follow the hype around features that look like this. The chat button seems to me to be an exercise in following-the-herd which actually sucks for me as a user with it blocking my old buttons.
Probably no one enjoys AI books though. I did my best at devils advocate on that above.
Politicians often use AI to summarise proposals and amendments to the laws. And later vote based on those summaries. It's incredible how artifical bureaucracy is driven by artifical intelligence. And how citizens don't care to follow artificial laws that ruins humanity.
I don't think it's entitlement to make a well-mannered complaint about how little choice we actually have when it comes to the whims of the tech giants.
The OP's point is that increasingly, we don't have that choice, for example, because AI slop masquerades as if it were authored by human beings (that's, in fact, its purpose!), or because the software applications you rely on suddenly start pushing "AI companions" on you, whether you want them or not, or because you have no viable alternatives to the software applications you use, so you must put up with those "AI companions," whether you want them in your life or not.
The whole point is that "just don't buy it" as a strategy doesn't work anymore for consumers to guide the market when the companies have employed the rock-for-dessert gambit to avoid having to try to sell their products on their merits.
Also, everyone who requires these sophisticated models now needs to send everything to the gatekeepers. You could argue that we already send a lot of data to public clouds. However, there was no economically viable way for cloud vendors to read, interpret, and reuse my data — my intellectual property and private information. With more and more companies forcing AI capabilities on us, it's often unclear who runs those models and who receives the data and what is really happening to the data.
This aggregation of power and centralisation of data worries me as much as the shortcomings of LLMs. The technology is still not accurate enough. But we want it to be accurate because we are lazy. So I fear that we will end up with many things of diminished quality in favour of cheaper operating costs — time will tell.
Open Source endeavors will have a hard time to bear the resources to train models that are competitive. Maybe we will see larger cooperatives, like a Apache Software Foundation for ML?
There are a number of reasons to do this: You want local inference, you want attention from devs and potential users etc.
Also the smaller self hostable models are where most of the improvement happens these days. Eventually they'll catch up with where the big ones are today. At this point I honestly wouldn't worry too much about "gatekeepers."
I’d support an Apache for ML but I suspect it’s unnecessary. Look at all of the money companies spend developing Linux; it will likely be the same story.
Maybe we will see larger cooperatives, like a Apache Software Foundation for ML?
I suspect the Linux Foundation might be a more likely source considering its backers and how much those backers have provided LF by way of resources. Whether that's aligned with LF's goals ...
GPU: RTX 5090 (no rops missing), 32 GB VRAM
Quants: Unsloth Dynamic 2.0, it's 4-6 bits depending on the layer.
RAM is 96 GB: more RAM makes a difference even if the model fits entirely in the GPU: filesystem pages containing the model on disk are cached entirely in RAM so when you switch models (we use other models as well) the overhead of unloading/loading is 3-5 seconds.
The Key Value Cache is also quantized to 8 bit (less degrades quality considerably).
This gives you 1 generation with 64k context, or 2 concurrent generations with 32k each. Everything takes 30 GB VRAM, which also leaves some space for a Whisper speech-to-text model (turbo & quantized) running in parallel as well.
From what I understand, vLLM is best when there's only 1 active model pinned to the GPU and you have many concurrent users (4, 8 etc.). But with just a single 32 GB GPU you have to switch the models pretty often, and you can't fit more than 2 concurrent users anyway (without sacrificing the context length considerably: 4 users = just 16k context, 8 users = 8k context), so I think vLLM so far isn't worth it. Once we have several cards, we may switch to vLLM.
The major AI gatekeepers, with their powerful models, are already experiencing capacity and scale issues. This won't change unless the underlying technology (LLMs) undergoes a fundamental shift. As more and more things become AI-enabled, how dependent will we be on these gatekeepers and their computing capacity? And how much will they charge us for prioritised access to these resources? And we haven't really gotten to the wearable devices stage yet.
The scale issue isn't the LLM provider, it's the power grid. Worldwide, 250 W/capita. Your body is 100 W and you have a duty cycle of 25% thanks to the 8 hour work day and having weekends, so in practice some hypothetical AI trying to replace everyone in their workplaces today would need to be more energy efficient than the human body.
Even with the extraordinarily rapid roll-out of PV, I don't expect this to be able to be one-for-one replacement for all human workers before 2032, even if the best SOTA model was good enough to do so (and they're not, they've still got too many weak spots for that).
This also applies to open-weights models, which are already good enough to be useful even when SOTA private models are better.
You could argue that we already send a lot of data to public clouds. However, there was no economically viable way for cloud vendors to read, interpret, and reuse my data — my intellectual property and private information. With more and more companies forcing AI capabilities on us, it's often unclear who runs those models and who receives the data and what is really happening to the data.
I dispute that it was not already a problem, due to the GDPR consent popups often asking to share my browsing behaviour with more "trusted partners" than there were pupils in my secondary school.
But I agree that the aggregation of power and centralisation of data is a pertinent risk.
how much will they charge us for prioritised access to these resources
For the consumer side, you'll be the product, not the one paying in money just like before.
For the creator side, it will depend on how competition in the market sustains. Expect major regulatory capture efforts to eliminate all but a very few 'sanctioned' providers in the name of 'safety'. If only 2 or 3 remain, it might get realy expensive.
All the anti-AI people I know are in their 30s. I think there are many in this age group that got use to nothing changing and are wishing it to stay that way.
Or are they the only ones who understand that the rate of real information/(spam+disinformation+misinformation+lies) is worse than ever? And that in the past 2 years, this was thanks to AI, and people who never check what garbage AI spew out? And only they are who cares to not consume the shit? Because clearly above 50, most of them were completely fine with it for decades now. Do you say that below 30 most of the people are fine to consume garbage? I mean, seeing how many young people started to deny Holocaust, I can imagine it, but I would like some hard data, and not just some AI level guesswork.
So what are you talking about?
All these things you mention are completely minor compared to the seismic changes in 1995-2001 and 2016-2025.
I agree that iPhone was revolutionary, but it was released back in 2007, well within the timeline.
Also once again, the general populace didn’t call, and doesn’t call those smartphones. I had a P900, and exactly zero people used the word “smartphone” back then, except marketing people. You remember terribly wrong, if you think otherwise. Also smartphone penetration skyrocketed not in 2007, but between 2013 and 2015. In 2010-2011, I was still considered early adopter. In 2015, half of internet users came from a phone. So no, that change didn’t happen before 2005, no matter how you want to distort reality.
We won’t solve climate change but we will have elaborate essays why we failed.
Personally, I'm still optimistic, at least for the emissions due to primary energy, because renewables and storage are just so ridiculously cheap now.
Unfortunately, all the other emissions are still enough that prisoner's dilemma type defection still comes into play. But I'm hopeful for cement, and have good reason to expect electrolytic reduction of metal oxides (beyond just aluminium) to become viable soon, as the primary energy is made increasingly renewable and cheap.
I was searching for something on Omnissa Horizon here: https://docs.omnissa.com/
It has some kind of ChatGPT integration, and I tried it and it found the answer I was looking for straight away, after 10 minutes of googling and manual searching had failed.
Seems to be not working at the moment though :-/
If you answer no, does that make you an unwilling user of social media? It’s the most visited sites in the world after all, how could randomly injecting it into your GPS navigation system be a poor fit?
I just don't participate in discussions about Facebook marketplace links friends share, or Instagram reels my D&D groups post.
So in a sense I agree with you, forcing AI into products is similar to forcing advertising into products.
Which is to say, there's already a history of AI features failing at a number of these larger companies. The public truly is frequently rejecting them.
I'm not unwilling to use AI in places where I choose. But let's not pretend that just because people do use it in one place, they are willing to have it shoved upon them in every other place.
I mean, you would think someone very rich who invests in companies would be somewhat smart. But, I'm convinced, a lot of them would do much better if they made no decisions at all and left everything up to entropy.
Why are investors so stupid?
Some are but many aren't. The reason so many investors are pushing on AI (or other buzz-trend du jour) is that history shows 'disruptive new technologies' tend to correlate with some startups quickly growing and becoming 'unicorn' successful. The problem is that this historical correlation appears more obvious after the fact than when it's still emerging. And of course there are lots of caveats and other requirements which a given startup and implementation may or may not meet.
Since professional VCs tend to take a portfolio approach to investing their funds on behalf of their limited partner stakeholders, they generally divide their funds into several broad 'investment themes' based on what they perceive as potentially disruptive new technologies or markets. Like roughly 30% here and 20% there with 15 or 20% left for things which don't fit a theme. This approach is supposed to ensure they don't miss out on having a few bets in each big category. In 2005 social media platforms were an important theme. In 2015 it was SaaS.
Imagine having so much money that you've already invested in stocks, bonds, real estate, you own everything, you got bored going to Vegas, and have nothing else to do with your money. So you start to toss some of it into a fund with others. Not a hedge fund mind you. You've done that already too. No, you stick it into a fund to be managed by lunatics. Because have enough money and you want to see a moon shot play out for fun.
That's the kinda stupid "F you" money we're talking about. It comes from people who don't care. They literally don't care. They just want to say they invested in XYZ, because no one cares about where their money came from or what their normal investments are.
This is the kinda very rich we're talking about and they aren't all there in the head sometimes.
I wish I had that much money lol.
This is the next great upset. Everyone's hair is on fire and it's anybody's ball game.
I wouldn't even count the hyperscalers as certain to emerge victorious. The unit economics of everything and how things are bought and sold might change.
We might have agents that scrub ads from everything and keep our inboxes clean. We might find content of all forms valued at zero, and have no need for social networking and search as they exist today.
And for better or worse, there might be zero moat around any of it.
agents that scrub ads from everything
This is called an ad blocker.
keep our inboxes clean
This is called a spam filter.
The entire parent comment is just buzzword salad. In fact I am inclined to think it was written by an LLM itself.
There's really only two browsers and one search engine. It doesn't matter what you do, because the rest of society is ensnared and the economic activities that matter are held captive.
If generative models compress all of the useful activities (lowering the incumbency moat) and agents can perform actions on our behalf, then it reasons that we may have agents that act as personal assistants and have our best interests as top priority. Ads are clearly in violation of that.
It's so funny to be a contrarian on HN. I get quite a lot of predictions right, yet all I get in exchange is downvotes and claims that I'm an LLM. I'll have to write a retro one of these days if I ever find the free time.
You're not a normie and defaults matter. Most of the world doesn't even know what an ad blocker is let alone know how to install one
These problems don't need LLMs to solve; they need something that also starts with 'L', but is a lot more boring—legislation. The online world is rampant with rubbish and misinformation not because LLMs aren't yet at our beck and call like a digital French maid, but because laws in most parts of the world haven't caught up and multi-national megacorps do whatever the heck they see fit. Especially so in 'capital-friendly' countries. One sees a lot less of this in the PRC, for instance.
I would love to see a GDPR-style set of legislation straight up addressing everything from privacy defaults on social media to aggressively blocking online ad networks.
I get quite a lot of predictions right
Good for you, then.
Everyone nodding along, yup yup this all makes sense
Everybody wanted the Internet.
I don't think this is true. A lot of people had no interest until smartphones arrived. Doing anything on a smartphone is a miserable experience compared to using a desktop computer, but it's more convenient. "Worse but more convenient" is the same sales pitch as for AI, so I can only assume that AI will be accepted by the masses too.
It's bullshit.
I mean, sure: there were people who hated the Internet. There still are! They were very clearly a minority, and almost exclusively older people who didn't like change. Most of them were also unhappy about personal computers in general.
But the Internet caught on very fast, and was very, very popular. It was completely obvious how positive it was, and people were making businesses based on it left and right that didn't rely on grifting, artificial scarcity, or convincing people that replacing their own critical thinking skills with a glorified autocomplete engine was the solution to all their problems. (Yes, there were also plenty of scams and unsuccessful businesses. They did not in any way outweigh the legitimate successes.)
By contrast, generative AI, while it has a contingent of supporters that range from reasonable to rabid, is broadly disliked by the public. And a huge reason for that is how much it is being pushed on them against their will, replacing human interaction with companies and attempting to replace other things like search.
But the Internet caught on very fast, and was very, very popular. It was completely obvious how positive it was,By contrast, generative AI, while it has a contingent of supporters that range from reasonable to rabid, is broadly disliked by the public.
It is absolutely wild how people can just ignore something staring right at them, plain as day.
ChatGPT.com is the 5 most visited site on the planet and growing. It's the fastest growing software product ever, with over 500M Weekly active users and over a billion messages per day. Just ChatGPT. This is not information that requires corporate espionage. The barest minimum effort would have shown you how blatantly false you are.
What exactly is the difference between this and a LLM hallucination ?
No condescension necessary.
US Public Opinion is negative ? Really ? How do you figure that ?
If you have evidence to the contrary, it seems to me the burden of proof lies on you to show it. "People frequently visit this one site that's currently talked about a lot" is not evidence that people are in favor of AI.
It's the entire premise of the article.
Yeah, and it's wrong.
Supported by data within the article.
Really Nothing in that article supports a statement as strong as "US public opinion on AI is negative".
"People frequently visit this one site that's currently talked about a lot" is not evidence that people are in favor of AI.
ChatGPT wasn't released last week. It's nearly 2 years old and it's growth has been steady. People aren't visiting the site that much because of some 15 minutes of fame, they're visiting it because they find use of it that frequently. You don't get that many weekly active users otherwise.
And yeah, if that many people use it that frequently then you're going to need real evidence to say that they have a poor opinion on it, not tangentially related random surveys.
Oh the survey said most people wouldn't pay money for features they currently get for free ? Come on.
I agree that features you don't want shouldn't be shoved down your throat. I genuinely do. But that's about the only thing in the article I agree with.
We sat yesterday and watched a table of 4 lads drinking beer each just watch their phones. At the slightest gap in conversation, out they came.
They’re ruining human interaction. (The phone, not the beer-drinking lad.)
As text, email, other messages, websites, Facebook, etc. became available the draw became stronger and so did the addiction and the normalization of looking at your phone every 30 seconds while you were with someone.
Did SNL or anyone ever do a skit of a couple having sex and then "ding" a phone chimes and one of them picks it up and starts reading the message? And then the other one grabs their phone and starts scrolling?
If only the phone was available, and there was no stream of online content, this wouldn't be a problem. Also, if the online content was available, but no phones to look at it on-the-go, it would also not be a problem. Both of these things existed in the past, too, but only when they were hooked up together did it become the problem we see today.
I almost never take my phone with me, especially when with my wife and son, as they always have theirs with them, although with elderly parents not in the best of health I really should take it more.
But it's something I see a lot these days, in fact, the latest Vodafone ad in the uk has a bunch of lads sitting outside a pub and one is laughing at something on his phone. There's also a betting ad where the guy is making bets on his phone (presumably) while in a restaurant with others!
I find this normalized behaviour somewhat concerning for the future.
[0]https://abysspostcard.substack.com/p/party-like-it-is-1975
there's a famous video of an interviewer asking people on the street ca. 1997 whether they would want a mobile phone. So not even a smartphone, just a mobile phone. The answer was overwhelmingly negative.
So people didn't want to be walking around with a tether that allowed the whole world to call them where ever they were? Le Shock!
Now if they'd asked people if they'd like a small portable computer they could keep in touch with friends and read books, play games, play music and movies on where ever they went which also made phone calls. I suspect the answer might have been different.
Obviously saying “everyone” is hyperbole. There were luddites and skeptics about it just like with electricity and telephones. Nevertheless the dotcom boom is what every new industry hopes to be.
In 20 years AI will be pervasive and nobody will remember being one of the luddites.
Internet of things was largely BS.
Whether the opposition was massive or not, in proportion to the enthusiasm and optimism about the globally connected information superhighway, isn’t something I can quantify, so I’ll bow out of the conversation.
People don't know how to search, that's it. Even the HN population.
Every time this gets posted, I ask for one example of thing you tried to find and what keywords you used. So I'm giving you the same offer, give me for one thing you couldn't find easily on Google and the keywords you used, and I'll show you Google search is just fine.
How do you set up an encrypted file on linux that can be mounted and accessed same as a hard drive.
(note: luks, a few commands)
You will see a nonsensical ai summarization, lots of videos and junk websites being promoted then you'll likely find a few blogs with the actual commands needed. Nowhere is there a link to a manual for luks or similar.
This in the past had the no-ad straightforward blogs as first links, then some man pages, then other unrelated things for the same searches that i do now and get garbage.
try "mount luks encrypted file" or "luks file mount". too many words and any grammar at all will degrade your results. it's all about keywords
edit: after trying it myself i quickly realized the problem - luks related articles are usually about drives or partitions, not about files. this search got me what i wanted: "luks mount file -partition -filesystem" i found this article[1], which is in german (my native tongue), but contained the right information.
1: https://blog.netways.de/blog/2018/07/25/verschluesselten-fil...
It shoed 25 or so URLs as the source.
That "AI generated slop" IS Google's main response now. I posted it so that someone might have a look to see if/how correct it actually is, your response, that does not deign to even look, is less than helpful - if you want to complain about Google not being useful, how about your own response?
At the top there's a "featured snippet" from opensource.com, allegedly from 2021, that begins with: create an empty file (this turns out to mean a file of given size with no useful data in it, not a size-0 file), then make a LUKS volume using cryptsetup, etc.
First actual search result is a question on Ask Ubuntu (the Stack Exchange site dedicated to Ubuntu) headed "How do I create an encrypted filesystem inside a file?" which unless I'm confused is at least the correct question. Top answer there (from 2017) looks plausible and seems to be describing the same steps as the "featured snippet". A couple of other links to Ask Ubuntu are given below that one but they seem worse.
Next search result is a Reddit thread that describes how to do something different but possibly still of interest to someone who wants to do the thing you describe.
Next search result is a question on unix.stackexchange.com that turns out to be about something different; under it are other results from the same site, the first of which has a cryptsetup-based recipe that seems similar to the other plausible ones mentioned above.
Further search results continue to have a good density of plausible-looking answers to essentially the intended question.
This all seems fairly satisfactory assuming the specific answers don't turn out to be garbage, which doesn't look very likely; it seems like Google has done a decent job here. It doesn't specifically turn up the LUKS manual, but then that wasn't the question you actually asked.
Having done that search to find that the relevant command seems to be cryptsetup and the underlying facility is called LUKS, searches for <<cryptsetup manual>> and <<luks documentation>> (again, the first search terms that came to mind) look to me like they find the right things.
(Google isn't my first-choice search engine at present; DuckDuckGo provides similar results in all these cases.)
I am not taking any sides on the broader question of whether in general Google can give good search results if one picks the right words for it, but in this particular case it seems OK.
As I said, user issue.
Nowhere is there a link to a manual for luks or similar.
Yes, thankfully. The man page for cryptsetup isn't exactly palatable.
I still find online recipes convenient, but I don't blindly trust details like cooking time and temperature. (I mean, those things are always subject to variability, but now I don't trust the times to even be in the right ballpark.)
Happily, there are some cooks that I think deserve our trust, e.g. Chef John.
I use Kagi who returns excellent results, also when I need non AI verbatim queries.
Displaying what you searched for immediately is cannibalizing that market.
I'm guessing ads in AI results is the logical next step.
It seems here on the ground in non-tech bubble land, people use ChatGPT a ton and lean hard on AI features.
When Google judges the success of bolted on AI, they are looking at how Jane and John General Public use it, not how xleet007 uses it(or doesn't).
There is also the fact that AI is still just being bolted onto things now. The next iteration of this software will be AI native, and the revisions after that will iron out big wrinkles.
When settings menus and ribbon panels are optional because you can just tell the program what to do in plain English, that will be AI integration.
If you look at the survey results, a few things jump out.
Firstly, there's a strong age skew. The people most likely to benefit from AI features in their software are those who are judged directly on their computing productivity, i.e. the young. Around half of 18-35 year olds say they would pay extra, even . It's only amongst the old that this drops to 20%.
Secondly, when asked directly if they value a range of AI-driven features, they say yes.
The skew opens up because companies like OpenAI give AI services away for free. There's just a really strong expectation established by the tech industry that software is either free or paid for by a low and very price-stable monthly subscription. This is also true in AI: you only pay for ChatGPT if you want more features and smarter models. For the majority of things that people are doing with AI right now, the free version of ChatGPT is good enough. What remains is mostly low value stuff like better autocomplete, where indeed people are probably not that interested in paying more for it.
Unfortunately Ted Gioia tries to use this stat to imply people don't want AI at all, which is not only untrue but trivially untrue; ChatGPT is the fastest growing product in history.
I will pay people for the value they create. I won't pay for AI content, or AI integrations. They are not interesting or valuable to me.
It’s like IPV6, if it really was a huge benefit to the end user, we’d have adopted it already.
isn’t good enough that people actually want it and are willing to pay for it.
Just from current ARR announcements: 3b+ anthropic, 10b+ oai, whatever google makes, whatever ms makes, yeah people are already paying for it.
If it was any good, people would pay for it.
The data shows people are paying for it.
Aah but they don't know they're paying for it.
After seeing something like blockchain run completely afoul/used for the wrong things and embraced by the public for it, I at least agree that AI has a value perception problem.
The same issue plagues many private companies. I’ve seen employees spend days drafting documents that a free tool like Mistral could generate in seconds, leaving them 30-60 minutes to review and refine. There's a lot of resistance from the public. They're probably thinking that their job will be saved if they refuse to adopt AI tools.
I’ve seen employees spend days drafting documents that a free tool like Mistral could generate in seconds, leaving them 30-60 minutes to review and refine.
What I have seen is employees spending days asking the model again and again to actually generate the document they need, and then submit it without reviewing it, only for a problem to explode a month later because no one noticed a glaring absurdity in the middle of the AI-polished garbage.
AI is the worst kind of liar: a bullshitter.
Once upon a time, not too long ago, there was someone who would bag your groceries, and someone who would clean your window at the gas station. Now you do self-checkout. Has anyone asked for this? Your quality of life is worse, the companies are automating away humanity into something they think is more profitable for them.
In a society where you don't have government protection for such companies, there would be other companies who provide a better service whose competition would win. But when you have a fat corrupt government, lobbying makes sense, and crony-capitalism births monopolies which cannot have any competition. Then they do whatever they want to you and society at large, and they don't owe you, you owe them. Your tax dollars sponsor all of this even more than your direct payments do.
https://www.sciotoanalysis.com/news/2024/7/12/how-much-do-yo...
While government sponsored monopolies certainly exist, monopolies themselves are a natural outcome of competition.
Deregulation would break some monopolies while encouraging others to grow. The new monopolies may be far worse than the ones we had before.
Of course it’s a bubble! Most new tech like this is until it gets to a point where the market is too saturated or has been monopolised.
I bet if you go back to the printing press, telegraph, telephone, etc. you will find people saying "it's only a bubble!".
The AI community treats potential customers as invaders. If you report a problem, the entire thing turns on you trying to convince you that you're wrong, or that you reported a problem because you hate the technology.
It's pathetic. It looks like a viper's nest. Who would want to do business with such people?
Actual promising AI tech doesn't even get the center stage, it doesn't get a chance to do it.
As godawful as this brute force, "change the laws because China!", 24x7 assault of LLM hypelords has been (and I love GP's analogy about finding a helicopter useful), and its been preeeeety unpleasant, there is a silver lining.
The compute and tooling needed for all the other under-explored, nifty as hell, highly accessible ML/AI stuff is like free by comparison now: there are H100s floating around for around a dollar an hour, L40s for sometimes pennies on that dollar, and like, all of neural machine translation or WaveNet era speech to text or resnet style transfer was done on like, a thousand bucks in today's compute. Lambda Labs has a promo on GB200 where its cheaper than H100!
And there's " plenty of room at the bottom": Jetson boards and super cool autonomy stuff like that is Raspberry Pi accessible.
I'd rather they didn't feel the need to like, take over the government to get terrawatts of "just make it bigger", but given that's sort of already happened, I'm looking for what opportunities are created by such monomania.
Better still, you could do that even with a hit air baloon and late middle-age technology! There is even a SF book series about that:
Any minor comment or constructive criticism is FUD and met with "oh better go destroy a loom there, Ned Ludd".
It's pathetic and I grow tired of it.
In fact I also tried the communication part - outside of Outlook - but people don't like superficial AI polish
The top of the list has got to be that one of their testimonials presented to investors is from "DrDeflowerMe". It's also interesting to me because they list financials which position them as unbelievably tiny: 6,215 subscribing accounts, 400 average new accounts per month, which to me sounds like they have a lot of churn.
I'm in my third year of subscribing and I'm actively looking for a replacement. This "Start Engine" investment makes me even more confident that's the right decision. Over the years I've paid nearly $200/year for this and watched them fail to deliver basic functionality. They just don't have the team to deliver AI tooling. For example: 2 years ago I spoke with support about the screen that shows you your credit card numbers being nearly unreadable (very light grey numbers on a white background), which still isn't fixed. Around a year ago a bunch of my auto transfers disappeared, causing me hundreds of dollars in late fees. I contacted support and they eventually "recovered" all the missing auto-transfers, but it ended up with some of them doubled up, and support stopped responding when I asked them to fix that.
I question if they'll be able to implement the changes they want, let alone be able to support those features if they do.
I was hoping that, after going through a number of other "advanced money management" fintech banks over the years and them selling out, that going with a place that I directly paid to use would allow it to sustain independently and add features, but it seems like the other scenario I worried about became the issue: The subscription fee severely limited their membership pool.
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Please don't. I am going to read this email. Adding more text just makes me read more.
I am sure there's a common use case of people who get a ton of faintly important email from colleagues. But this is my personal account and the only people contacting me are friends. (Everyone else should not be summarized; they should be trashed. And to be fair I am very grateful for Gmail's excellent spam filtering.)
no one wants this, everyone hates this
Is not false statistics. "Nobody wanted or asked for this" is literally true.
The article is about it encroaching in the domain of human communications. Mass adoption is the only way to justify the incredible financial promises.
I think there are lots of valid arguments against llm usage, but it’s extremely tiring to here how it’s not useful when I get so much use out of it.
I don’t see the utility, all I see is slop and constant notifications in google.
You can say skill issue but that’s kind of the point; this was all dropped on me by people who don’t understand it themselves. I didn’t ask or want to built the skills to understand ai. Nor did my bosses: they are just following the latest wave. We are the blind leading the blind.
Like crypto ai will prove to be a dead end mistake that only enabled grifters
The reason your bosses are being obnoxious about making people use the internal AI tool is to push them into thinking about things like this. Perhaps at your company it’s genuinely not useful, but I’ve seen a lot of people say that who I’m pretty confident are wrong.
now every presentation I give can be much more visually engaging than they would have been previously
What about the impact on your audience? A lot of people are going to view your presentations more negatively based on their views about AI.
Before proceeding let me ask a simple question: Has there ever been a major innovation that helped society, but only 8% of the public would pay for it?
Highways.
Highways.
In my European country you have to pay a toll to use a highway. Most people opt to use them, instead of taking the old 2-lane road that existed before the highway and is still free.
I say I imagine it's annoying because I've yet to actually be annoyed much but I get the idea. I actually quite like the Google AI bit - you can always not read it if you don't want to. AI generated content on youtube is a bit of a mixed bag - it tends to be kinda bad but you can click stop and play another video. My office 2019 is gloriously out of date and does that stuff I want without the recent nonsense.
And of course there's no way to disable it without also losing calculator, unit conversions, and other useful functionality.
Also:
As per SimilarWeb data 61.05% of ChatGPT's traffic comes from YouTube, which means from all the social media platforms YouTube viewers are the largest referral source of its user base,
That's deeply suspect.
As a data point, the "Stop Killing Games" one has passed the needed 1M signatures so is in good shape:
maybe i'm doing something wrong here, but even ddg is annoying me with this.
I also feel an urge to build spaces in the internet just for humans, with some 'turrets' to protect against AI invasion and exploitation. I just don't know what content would be shared in those spaces because AI is already everywhere in content production.
Only when I went to cancel[1], suddenly they made me aware that there was a "classic" subscription that was the normal price, without CoPilot. So they basically just upsized everyone to try to force uptake.
[1]I'm in the AI business and am a user and abuser of AI daily, but I don't need it built directly into every app. I Already have AI subscriptions and local models and solutions.
Tyranny is a real thing which exists in the world and is not exemplified by “product manager adding text expansion to word processor.”
The natural state of capitalism is trying things which get voted on by money. It’s always subject to boom-bust cycles and we are in a big boom. This will eventually correct itself once the public makes its position clear and the features which truly suck will get fixed or removed.
This is how AI gets introduced to the marketplace-by force-feeding the public. And they're doing this for a very good reason.
Most people won't pay for AI voluntarily-just 8% according to a recent survey. So they need to bundle it with some other essential product.
You never get to decide.
Silicon Valley and Redmond have been operating this way for quite some time.
They have been effectively removing choice long before this "AI" push. Often accomplished through "defaults".
This "AI" nonsense may be the most bold example.
But if AI is bundled into existing businesses, Silicon Valley CEOs can pretend that AI is a moneymaker, even if the public is lukewarm or hostile.
The AI business model would collapse overnight if they needed consumer opt-in. Just pass that law, and see how quickly the bots disappear.
You don't get to choose. You're never asked. It just shows up. Now you have to deal with it.
If they gave people a choice, they would reject this tyranny masquerading as innovation.
The AI business model would collapse overnight if they needed consumer opt-in.
We never get to find out what would happen.
One comment I would like to add here.
By removing meaningful choice and creating fabricated "demand" these so-called "tech" companies (unnecessary intermediaries) when faced with antitrust allegations then try to argue something like, "Everyone is using it therefore everyone wants it." And, "This shows everyone prefers us over the alternatives."
Frank Zappa offers a possible mission statement for Microsoft back in 1976, a few months after the company is founded.
RIP.
There are open source or affordable, paid alternatives for everything the author mentioned. However, there are many places where you must use these things due to social pressure, lock-in with a service provider (health insurance co, perhaps), and yes unfortunately I see some of these things as soon or now unavoidable.
Another commenter mentioned that ChatGPT is one of the most popular websites on the internet and therefore users clearly do want this. I can easily think of two points that refute that: 1. The internet has shown us time and time again that popularity doesn’t indicate willingness to pay (which paid social networks had strong popularity…?) 2. There are many extremely popular websites that users wouldn’t want to be woven throughout the rest of their personal and professional digital lives
Software is loyal to the owner. If you don't own your software, software won't be loyal to you. It can be convenient for you, but as time passes and interest changes, if you don't own software it can turn against you. And you shouldn't blame Microsoft or it's utilities. It doesn't owe you anything just because you put effort in it and invested time in it. It'll work according to who it's loyal to, who owns it.
If it bothers you, choose software you can own. If you can't choose software you own now, change your life so you can in the future. And if you just can't, you have to accept the consequences.
A few months ago, I needed to send an email. But when I opened Microsoft Outlook, something had changed.
I cannot take OP seriously when the post started like so. If you are using Microsoft services and products in 2025, well, it serves you right.
Big companies can force Microsoft, Google and alike to don't use companies data for AI training, small companies have no chance.
Everything nowadays is cloud based, all you need is internet and a browser. But nope, people and companies still using Windows, spending millions with AV software that they wouldn't have to if a decent Linux distro was being used instead.
By decent I mean user friendly such as Linux Mint or even worse Ubuntu (Ubuntu lost its way years ago, still a solid option for basic users, not for advanced users)
Any sufficiently advanced AI technology is indistinguishable from bullshit.
- me, a few years ago.
I find the whole situation with regard to AI utterly ridiculous and boring. While those algos might have some interesting applications, they're not as earth-shattering as we are made to believe, and their utility is, to me at least, questionable.
Any sufficiently advanced AI technology is indistinguishable from bullshit
love this quote !
The whole sales-pitch for AI is predicated on FOMO - from developers being replaced by AI-enabled engineers to countries being left-behind by AI-slop. Like crypto, the idea is to get-big-fast, and become too big to fail. This worked for social-media but I find it hard to believe it can work for AI.
My hope is that: while some of the people can be fooled all the time, all the people cannot be fooled all the time.
People going to lord it over others in the pursuit of what they think is proper.
Society is over-rated, once it gets beyond a certain size.
Along the same lines, I am currently starting my morning with blocking ranges of IP addresses to get Internet service back, due to someone's current desire to SYN Flood my webserver, which being hosted in my office, affects my office Internet.
It may soon come to a point where I choose to block all IP addresses except a few to get work done.
People gonna be people.
sigh.
The thing that really chafes me about this AI, irrespective of whether it is awesome or not, is emitting all of the information to some unknown server. To go with another Zappa reference, AI becomes The Central Scrutinizer[2].
I predict an increasing use of Free Software by discerning people who want to maintain more control of their information.
[1] https://www.youtube.com/watch?v=JPFIkty4Zvk
[2] https://en.wikipedia.org/wiki/Joe%27s_Garage#Lyrical_and_sto...
But using it heavily has a corollary effect: engineers learn less as a result of their dependence on it.
Less learning all around equals enshittification. Really not looking forward to this.
People would be less upset if ai is shown to support the person. This also allows that person to curate the output and ignore it if needed before sharing it, so it’s a win/win.
But is the big money in revolution?
This stuff costs so much, they need mass adoption. ASAP. I didn't think about it before, but I wonder how quickly they need the adoption.