I generally respect this guy’s opinion (I probably use the term “enshitification” multiple times a week!), but pretending that “AI” and “Chabot” are interchangeable terms seems wildly reductive, and the subtle presumption that the process of changing is a binary changed/non-changed situation instead of a curve is just ignorant.

If the internet was “turned off” after 4 or so years not much would have changed, either.

To the average person (and this includes politicians, basically anyone not a software engineer developing the tools )who Cory generally writes to try and inform, they are the same.

Should they be? No. But that’s a whole different thing and trying to change that in the general public mind doesn’t change his current point about AI as the public thinks of it right now.

Speaking at the level of your target audience is wise, but there is a point where information is not just simplfied, but lost, and I think equating chat bots with AI in the broader sense is past that point. It would be like equating websites with the internet.

I think the comparison is acceptable with maybe a terminology clarification in the footnotes, because “AI” is the false term under which this current bullshit is being marketed.

And people who believe the core mechanism in large language models to be AI are so uneducated that they will probably neither understand the distinction to legitimate AI research, nor bother to read footnotes.

That’s exactly the thing. Those who understand such a footnote don’t need it, and those who might learn more from it are unlikely to understand it. The above commenters are simply being pedantic.

Pedantic or AI bros ;) I find this new generation of gullible tech users scary. Recently saw someone voice their disappointment with Codeberg’s anto-vibecoding stance, and the person was describing themselves as “tech optimist”, which I believe is a great way to self-out as being extremely gullible.

And people who believe the core mechanism in large language models to be AI

What does that mean? Transformers aren’t AI? What? I don’t think you understand this as much as you think you do.

Think all you want, statistical text prediction has nothing to do with AI. The “intelligent” algorithms are all in the pre- and postprocessing steps that make the output sound good enough to fool gullible people into assumong intelligence.

What’s lost? I can’t think of anything.

Words are meant to convey meaning, and I bet you would lose a lot of non-technical people if you tried to explain the intricacies of what makes large language models worse than content recommendation systems, and doing this would be redundant for technical users.

So Corey can safely use the two words interchangeably and communicate with technical and non-technical alike.

Is equating chatbots with AI any different than equating a bunch of if/else statements with AI? Is any of this AI considering none of it involves actual intelligence or informed decision making?

Sure AI is more than LLMs, but those are the face of it in the public’s eye and is what’s driving these trillion dollar corporate valuations, which then lead to every company on earth declaring that their product is “AI!”

Yep, most people would not understand Machine Learning or Big Data analysis generally being AI, so just market your product by the sub category and you don’t have to deal with the expectancy of GenAI

As a non-techie: websites are the internet.

Like, I conceptualize the internet as basically just a giant shared network, but the point of it is the websites (for me).

I apologize to anyone I may have just gravely offended.

I apologize to anyone I may have just gravely offended.

lol

I don’t know how “non-techie” you are, but a good example is online games. When you’re playing Fortnite, Roblox, World of Warcraft, etc you’re not going to a web page, but you’re on the internet.

I straight up almost called it a series of tubes, lol.

Yeah, that’s probably why I do think of it as a shared network, I just don’t play online games, so that’s not what I associate with the internet.

There are other examples but I take your point.

Cory generally writes to try and inform, they are the same.

then he has completely failed to understand the concepts for people using him an a information proxy

isnt he supposed to help inform? or is he just an echo chamber to the lowest-common-loudmouth?

Stepping up and doing it better is free.

Too busy using AI at work to make everything easier for my coworkers and earn another raise

Good luck when they stop subsidizing the tokens 👍

I also can’t imagine bragging about being so busy doing work for the hope of a raise that I didn’t have time to pursue things I felt strongly about. Recenter your priorities.

I already got two raises in the past 18 months and it brings me joy to see things happening at work more efficiently, all on 20$ claude subscription, that’s fine ill pay $100/mo for the same stuff in a year, easily worth it (my employer pays it anyway)

Sorry if it replaced your only marketable skill or something, and we absolutely should have universal basic income for these kinds of cases!

I think you’re making a strawman argument here. He isn’t arguing that it’s a binary switch that has failed to throw, he’s arguing that we don’t have compelling evidence that the ends justify the means:

In other words, corporate leadership is starting from the premise that AI has (or will) radically change the business, and they’re working backwards from that premise to find the evidence to support this article of faith.

And this is completely true! Supposedly “data informed” organizations are trying to find a yardstick that shows actual, meaningful improvement in business outcomes from AI. In my own organization we have people touting LOC yet again because “big number”, but anyone who’s ever worked in software can tell you it’s an asinine metric to use as a KPI.

It’s inherently unscientific to start with the answer and work backwards to a satisfactory question. His point that this push is coming from the least knowledgeable of real processes- and more closely resembles religious fervor than business acumen- seems to at least warrant consideration.

The world is full of people who insist that “AI is changing everything” but who – when pressed – have to admit that what they mean is that they’re pretty sure that AI will change everything.

If Donald Trump ordered Big Tech to turn off all of your country’s chatbots tomorrow, nothing would change. Every one of your country’s ministries and corporations would chug on with nary a hitch. Households, too, though perhaps a few of the younger members of those families would have to do their own homework again.

He is definitely pretending that change is either on or off.

Are CEOs jumping the gun on how quickly they adopt AI into workflows? Definitely. However, there’s a big difference between “AI isn’t at a threshold where it is disruptive” and “AI isn’t disruptive”. Or, to belabor the metaphor: CEOs are jumping the gun, but the race is about to start and they’re on the correct track.

If you watch a full podcast (he has done a ton in the last couple weeks), he clearly identifies AI as not a mere hype technology, as something interesting and potentially useful from a technology perspective, and he definitely doesn’t conflate a chatbot with all AI. That doesn’t undermine the vast problems with it, how it’s being used against regular workers, the threat the bubble poses to the economy, the criti-hype cycle, etc.

I think you’re oversimplifying “Cory Doctorow” based on the article you’re reading or specifically how he presents a more complex idea to different audiences.

Watch the Jon Stewart podcast interview if you want more nuance:

www.youtube.com/watch?v=-dAIJRjb-Bw

A key word in that sentence is “if”, it is a rhetorical example, that’s probably pretty much correct at this point. It’s not a recommended course of action, it’s a declaration that it contrary to crazed hype, it isn’t currently as core to everything as would be befitting the current hype level.

I would argue that the CEOs aren’t on the correct track, they aren’t really in a particularly specific trajectory. I just had a debate with someone on this and their stance was “well in a hundred years do you expect things to be like they are”. My response “I cannot possibly speak to that, but we need to speak to today instead of pretending we know how things will be in a hundred years and pretending they are already at that level”. The “imagine a hundred years from now” by a relative outsider to the tech is dominating CEO mindset, and that’s problematic.

Once the bubble pops, we will almost certainly see a more durable and sane adoption for these technologies. For now, the hype is a problem as it enables some of the worst possible stewards of the technology and favors grift over progress.

“AI” and “Chabot” are interchangeable

In the same way that most people if you say you’ll take them to their destination with your vehicle, they assume in your car and not on the back of your bicycle, although both are technically correct.

Pretending AI and Chatbot are interchangeable is a marketing strategy by the companies who make these advanced chat bots. “They are artificially intelligent bro”.

I don’t disagree that the term AI has shifted significantly in the last 4 years or so, but I was actually meaning it in the other direction, as in, there is more to generative AI (what people are calling “AI” these days) than just chat bots.

Oh look it’s arbitrary-narrow-definition-that-fits-the-argument-I-want-to-make man!

Oh look it’s arbitrary-narrow-definition-that-fits-the-argument-I-want-to-make man!

I’m saying there’s more to generative AI than chatbots. My definition of AI is broader. What are you going on about?

That your definition is only advantageous to the argument you want to make. Everybody else understands “common usage” by non-technical folks. I’m a “technical folk” working in the area and I understand the general use by the “regular folk”.

Corey Doctorow isn’t speaking to you, he’s speaking to the many, many people who have been fooled by the ai craze.

And often I wonder, how many people have been fooled, and how much of the enthusiasm and over the top fantasy is astroturfing by a few people incentivized to sway public opinion?

And how much of the doomerism is a campaign to keep the common man from using a revolutionary tool for, well, revolution, by alienating those most likely to do revolutionary things with it?

Once you look at the actual numbers for pollution, water usage, etc it becomes very clear that the issues are vastly overstated. So, why?

That’s one for the fooled side.

Yeah. I guess we’ll see.

EDIT: you should go on claude’s reddit and look at all of the tools people have built to keep companies and politicians accountable, to renegotiate billing, to improve their lives at the expense of capital. Or you can just go on thinking you’re right!

Fuck reddit but i have a friend who automated fooa requests with claude, sent foia requests to every county in the state and has all the tracking and everything automated. Only thing he needs to do is review the emails before they get sent. It’s amazing and he’s bring transparency to every corner of the state using ai.

Yeah I personally use it to analyze public data bases for fraud.

I keep comparing the datacenter numbers to Bitcoin - only BTC, not all of crypto, is still using more electricity than AI. That may change in the future, but JFC people, where was the outrage about the global Ponzi scheme?

I generally respect this guy’s opinion (I probably use the term “enshitification” multiple times a week

A lot of people don’t use it the way he used it though. He used it specifically for two sided markets (eg a service that has both businesses/advertisers and personal users) where the focus shifts to the business customers, but people have started using it to mean anything that used to be good but isn’t good any more.

I think it’s a worthy simplification that sums up the current state of things well. We have AI that broadly has different uses and then we have the ChatBot/ChatBot derived AI applications. The market is not going insane over the broader AI category. Executives are not tripping over themselves to say things like machine vision is going to replace all labor, or at least all white collar labor. The chatbot is the only thing in the conversation of consequence.

All the “everyone says AI is the reality, so everyone feels like they must say AI is the reality” refers to this specific category. It might be veering towards being oversimplified, but it’s trying to balance a perspective that is also oversimplified. Generally, we aren’t good at weighing simple straightforward takes against complex nuanced takes, so you have to “net it out” to have any hope of the point landing.

“Chatbot” style AI is wildly good and bad at varying kinds of tasks, and a lot of that has to do with how it has been prepared.

Some LLMs have been trained to make images - I’ve not been too impressed with them, but that’s what they’re “good” at - and better than the LLMs that have been trained to write computer code when you ask the coding LLMs to draw a picture.

The code writing LLMs have actually improved the most at reviewing code over the past 8-9 months, and that ability to review their own code makes them dramatically better at writing code as well.

I find Google Gemini to be pretty impressive at scanning laws and regulations and finding, not creative, but functional solutions to stated problems within the constraints of (often frustratingly bizarre) legal structures.

And all of them will lie to you, tell you what a great idea you have, etc. They’re not really lying, they’re mostly just taking what they read at face value without checking corroborating sources enough to find the obvious (to you) blunders. If you want the LLM to be sure, ask it to go on the RAG (Research Augmented Generation) - check everything before saying it, they can do that, especially “paid mode” engines, but it reduces their capacity for analysis of complex problems by 3-10x, because they’re spending so much context window “being sure” - you can alternatively spend 3-10x as long solving complex problems / accomplishing complex tasks if you have them do their homework, verify everything from “the best” available sources 3x and build up a local document set of “trusted information” which is used in preference to whatever it might find at random on the internet. This isn’t as sexy as “Hey Claude, code me up a database that does X Y Z” and getting the result in 30 seconds, but it is how professionals have been doing their jobs for centuries: learn reliable information first, then act on it.

If the internet was “turned off” after 4 or so years not much would have changed, either.

That’s a questionable statement that may reflect on your main argument. After all, when was the Internet “switched on”? Sounds very binary to me.

To be clear, Doctorow discussed switching off AI and proposed that there would be little to no change on the world. I was just changing the technology to show the flaw in the stance.

and thereby revealed the flaw in your own counterargument.

Can you elaborate? I’m not following what you’re saying.

I don’t think they’re all lying; most are just misinformed or using the term differently than Cory.

It’s like saying “global warming is changing everything.” Technically, not true, but the knock on effects of specific aspects of it recently hit an inflection point that causes it to affect the lives of everyone.

This has also happened in the field of artificial intelligence; LLM chatbots are only the visible mushroom fruits of the vast mycelium network that has been silently growing underground for years.

silently growing underground for years.

And not so silently, for decades. Even J. Edgar Hoover was using early forms of AI to manage his data trove.

Ooo what’s the reference for this?

Hoover maintained separate, master national blacklists—such as the Security Index—which tracked tens of thousands of citizens deemed “subversive” or political dissidents for immediate detention in the event of a national emergency. He managed his master national blacklists through a fluid, multi-tiered indexing framework that evolved over five decades. These blacklists were not mere static documents; they were part of a highly coordinated operational pipeline designed for the mass roundup and indefinite detention of American citizens during a perceived national emergency.

To add or manage a person on a master blacklist, Hoover’s Bureau followed a strict administrative lifecycle:

The Dossier Trigger: When an individual was flagged via covert programs like COINTELPRO, agents opened an investigative file.

The Index Card Core: If the person was deemed a threat, a dedicated index card was generated. These cards contained the person’s name, aliases, address, physical description, occupation, and a specific "detention rationale.

"The Geographic Apportionment: Cards were duplicated and cross-filed. One went into the master archive at FBI Headquarters in Washington, D.C., and another went into the local FBI Field Office responsible for the geographic area where the target lived.

The Arrest Portfolio: For top-tier targets, field offices maintained ready-to-go arrest portfolios. If Hoover or the President gave the command, field agents could immediately seize the individual without needing to waste time researching where they were or why they were being detained.

The Fluid Evolution of Sub-Lists

Hoover managed the master blacklist by segmenting it into constantly shifting sub-indexes based on perceived ideological threats, which allowed him to scale the operation up or down

As the lists ballooned to over 20,000 active high-priority targets (and over 10 million Americans cross-indexed in general domestic files), Hoover modernized his management using early technology. The Bureau adopted mechanical punch-card sorting systems. This allowed clerical staff to instantly filter the master blacklist by city, profession, or political affiliation, providing Hoover with rapid statistical snapshots of domestic dissent to present during congressional budget hearings or White House briefings.

On the contrary - the people who tell you AI doesn’t add any value and will disappear in a few years are either lying, dumb, or completely ignorant of what AI can already do.

I’m a software developer since the 90s, basically before the internet, we had some C books for reference and that’s it. I can tell you that I started last year to use copilot in vscode and some chatgpt on a web page, and it basically changed my world, and all my 50+ years old coworkers are amazed by what it can do really.

You are right it will not fade at all in software development.

Tech in general. I’m a sysadmin in research computing. You know how many clients at prestigious universities are using AI for mathematics, biology, etc? All of them

ive heard alot of posts subs about it, they are basically just graduating learning to use AI to bs through the whole degree and wonder why they arnt getting jobs, or they arnt learning anything, plus writing peer-reviewed papers?

I’m not even talking about undergrads. I’m talking grad, post docs, and even the old professors. I’m talking prize winning mathematicians.

Completely disagree. The difficult part of software development was never writing code OR speed of delivery. It was understanding requirements and problem solving. LLMs still can not do either of those things and there is no evidence they ever will be able to.

An example of how harmful LLMs actually are to development can succinctly be described with an issue I had a few weeks ago. I found an issue in an open source project, code was fine if a bit hard to understand. I came up with a PR to fix the problem.

In the time from me checking out the code to submitting the PR, a little less than 24 hours, the maintainer had completely rewritten the entire project with Claude. It was complete nonsense. Incredibly difficult to understand. Abstracting things that didn’t need abstracting. My PR was useless, because the entire project was new. The maintainer definitely didn’t understand the changes either. If a bug came up there’s no way AI would be able to solve it (the bug was still there even though the code was entirely new).

LLMs don’t understand the code. They just make things that look like they will work. And then a human has to maintain it (or keep paying billions of dollars for Claude to try to fix it).

the person in your scenario is just fkin stupid. When people say that llms are helpful for coding, they aren’t talking about telling an llm to rewrite an entire codebase.

You are right, but right now we are living in a very messy reality where it’s hard to know who’s being stupid and who is using it well. One person I know that, well, I was never a huge fan of his work but at least it was somewhat serviceable is now all-in on AI and his code has been rewritten in a similar manner as the parent poster comments. He’s got no idea how it works or how it should worked, the AI decided to rewrite it in an entirely different language, and it’s a buggy mess and it never fixes the bugs without making new bugs. Then he hit his token quota 3 weeks early and basically said he was going to stop working on it because he no longer could manually work the codebase. He didn’t ask for a rewrite, but AI advised him that his language choice was a poor fit and reworked things in another language, one that none of us use to that level of seriousness. It also made it largely based on super convoluted regular expressions.

The problem is that the leadership is singing the praises of these people, they were on their ‘leaderboards’ of AI adoption and much like the craze of praising “lines of codes”, we are neck deep in the most stupid non-technical evaluation of technical work you could imagine.

I’m sorry but to me your comment is a bit misguided. You raise extremely valid point and are completely right in what you say, and yet all your argument fails to prove that software development hasn’t changed.

The difficult part was understanding requirements and problem solving: absolutely true. Yet most of the time of a developer was spent in writing code. Now it’s spent refining the analysis so that the LLM stops producing slop. And many programmers are doing it, even with all its downsides, because for them the fun part is understanding the requirements and problem solving, not writing code nor delivering fast. They are delegating those tasks to a machine, even with all the risks and issues.

Your second point (and the anecdote) further proves how programming changed. Before it was unthinkable that some random person, likely with no clue about what they are doing, would refactor an entire codebase in a night.

Both are massive changes. For the best? Arguably not, but I seriously doubt there will be any going back now.

Now it’s spent refining the analysis so that the LLM stops producing slop

The thing is that actually doing this isn’t faster than writing the code, robs the practitioner of learning, and more often than not doesn’t actually happen, so you have a harder to maintain codebase with more bugs and less knowledgeable developers to maintain it.

Edit: and as a fun bonus accelerates glacier melting!

I think you’re right in general. I think juniors and those who havent yet had experience are not going to understand a goddamn thing and produce broken, insecure, unmaintainable slop.

I’ve written my share of garbage code – completely by hand! And i’m much better for it.

Once you have that experience, once you’ve written a few backends and frontends, there’s not much left to understand. Move the data from here to there. Display it, transform it, slice it up. For webdev AI is a huge force multiplier. I can make a dozen features or apps in the time it used to take me to learn one framework I was curious about. It even helps me learn faster because of how quickly I can test new patterns and ideas.

There’s certainly a right way to use it if you want to continue being edified, burning the planet down aside.

burning the planet down aside

If we only used AI for codegen, this probably wouldn’t be much of an issue. Those cat videos take more energy than building a complete app. Also, it’s all pretty new and the newest tech 40 years ago would have filled a warehouse and had the computational power of a potato, but here we are now. I expect we’ll get more efficient at it and in the ways we use it. And there’s already a huge worldwide shift in energy capture (America aside…)

I’ve written my share of garbage code – completely by hand!

Whenever I look back at old code, mine or others, the first words that usually come to mind are “what you have to understand about this is… we were on a tight schedule, we never thought this was going to be used in production, we weren’t allowed to execute the planned and contracted refactor… etc. etc. etc.”

Fully agree.

Unfortunately many people would rather spin the wheel for a chance to win magically produced functioning code, rather than doing the work themselves with sure results - even if it takes the same or more time. And the kind of current politicians there are around the world proves that most people don’t give a fuck about the ice-caps (though I would also argue that it’s not so much the random person calling an LLM that is poisoning the waters - even though it does have a non-negligible effect -, rather it’s massive sociopaths in charge of the companies creating LLMs that are perfectly fine with destroying the environment and other people’s money in a vain dream of being the owner of some kind of “new order")

This is a point that really sticks with me. Using it for the sometimes spot on cakewalk segments is a fairly productive win. By the time you stubbornly insist on driving it entirely blackbox with chat and trying to get the right results without actually touching code… Well, even when it works, it’s often more work than just doing it yourself.

Someone rebased a UI I worked on in a new version of the UI framework. As a result, there was this one odd gap in the UI in one specific place. A vibe coder spent 3 hours back and forth with the AI trying to get it to correct the gap and finally submitted their merge request. Hundreds and hundreds of lines of CSS. So I declined the merge request, open the gui, looked at the gap, hit f12, adjusted a single padding statement, and it was all good. People are struggling with defining all sorts of criteria and rigging it to let it try and try and try again and hopefully laid out every contingency, every corner case, and spent hours laying the ground work and could have done similar in a more straightforward way.

Using it for the sometimes spot on cakewalk segments is a fairly productive win.

One thing that absolutely blows my mind is how many people will say how much time it saves then with repetitive or boilerplate code. It is obvious these people have never actually tried to optimize their workflow even a little bit before. Regex replace, snippets, and keyboard macros have existed in text editors forever and are actually deterministic.

As little as possible of my work is boilerplate, but some times there will be something like “I want to take width,height, and depth as arguments from the command as variables”, and poof, all the argv tedium is done.

And even better, that can be done entirely deterministicly, and once written, free to use! Instead of fixing the root cause they’re using the ultimate hammer. It’s such a cognitive surrender and a complete moral sellout.

robs the practitioner of learning

Not at all. It gives the practitioner the option of skipping the learning.

Starting in the 1990s I started skipping the learning of assembly language, compilers got good enough that I just don’t need to know how the latest SIMD/MIMD/ whatever instructions work, I just express what I want in C and gcc or whatever handles the optimization for me.

Comparing it to an (almost) entirely complete abstraction where you (almost) never have any benefit to looking under the covers like c over assembler is completely dishonest.

entirely complete abstraction where you (almost) never have any benefit to looking under the covers like c over assembler is completely dishonest.

Is it, though? In the early 1990s I could still optimize compiler output by hand, here and there. In the 1980s it was common practice and necessary in many circumstances to make complex things happen on the constrained hardware. In the 1970s there were a lot of programmers who never touched Fortran, just practiced assembly all the time because Fortran was too inefficient for their needs.

I’ll say that LLMs, this year, are something like compilers were in the 1960s - a revolutionary improvement in accessibility of coding, being able to express what you want in “natural language” - like COBOL did starting in 1959.

LLMs have plenty of pitfalls that COBOL doesn’t today, but I’ll note that Borland Turbo C++ compiler in 1991 was too damn buggy to do anything much more complex than “Hello, World.” with.

Yes. Acting like an LLM over a programming language is anything close to a higher level over lower level programming language makes you completely full of shit.

Yet most of the time of a developer was spent in writing code.

That’s what developers told the world. Now they’re exposed, it never really took that long to write the code. (Only partly joking.)

Actually, a whole lot of time went into reading other developers’ code, getting documentation in sync with the actual implementation. And if you didn’t do all that, you tended to have a lot more bugs / vulnerabilities, etc. The LLMs are wicked fast at reviewing code, they don’t find ALL the problems, a lot of problems they do find aren’t worth fixing, but they do find more actual actionable problems per minute than most developers can find per hour in a big code base.

The LLMs are wicked fast at reviewing code

That’s because it’s not actually reviewing the code, rather just producing text that statistically looks like review comments.

So it does look vaguely useful, e.g. if code has null checks, review comments usually won’t ask for them to be added, but it’s never actually reviewing, or understanding, or reasoning, or anything else people claim.

It’s like an actor playing a doctor in a TV show, they’re not actually diagnosing patients, they’re just following a script that looks like it.

Doctors “humanity” kills a lot of people, every day, that wouldn’t have died if they did a statistical matching of their symptoms to the available research literature.

It was understanding requirements and problem solving. LLMs still can not do either of those things and there is no evidence they ever will be able to.

I don’t know… I just made a scheduling / timesheet creation app. Multi-user, overlapping clients and providers, multiple funding sources. Took 10 calendar days to make the initial app working part time, maybe 2-3 hours a day. Initially written in Python, decided at that point I’d rather have it in Go. Because the initial app had robust requirements and design docs, the translation to Go happened in less than 5 calendar days, with almost zero human involvement beyond telling the agent “continue” at each stopping point. After the Go translation was done (and debugged by the LLM to a flawless translation - only difference is that it runs faster), I was given a new timesheet to use for some of the workers, weekly instead of bi-weekly. Pay weeks start on Monday instead of Thursday. Various wrinkles about how the employees and clients and services are identified, weird sub-totals by service. All I told the LLM was: “Here’s a new timesheet that we’ll be using for some workers, design the necessary modifications and extensions to accomodate it.” It did, independently. It highlighted three shortcuts it took and I told it not to take those shortcuts, it adjusted.

That’s not quite rocket science, but it’s still impressive: to dissect the given .pdf, determine what data goes in what fields, in what formats, with what calculations, based on just reading the page, then adapt the existing app to fill it out automatically.

In other words, you’re arguing that their usage without discernment is detrimental.

Cory agrees on this very same article, and it also includes the same nuance this chain is trying to give voice to:

The other question Suresh implicitly raises is: “How can you reconcile the failure of AI in the enterprise with the individual claims of skilled technologists who insist that AI is helping them do great work?” The answer is that these AI users are “centaurs” – experienced workers who are assisted by automation on terms that they set for themselves.

Thanks to their skill and experience, these workers possess discernment, the ability to tell good code from bad, and (more importantly) good uses of code-generation tools from bad. They demonstrate the adage that worker-driven automation improves quality, while capital-driven automation improves throughput.

We wouldn’t be having this conversation if LLMs had been given the chance to grow into being the same way the web did. Whereas we would be having this same conversation with the letters swapped if corporations were the ones to spawn the WWW instead of the way it came about.

The reason is the same. There is only one war.

It’s not just “usage without discernment”, it’s that AI flattens some costs which are very obvious and very measurable (coding) in such a way that it introduces or amplifies other costs which are much more diffused and hard to measure (code and design reviewing, bug fixing, maintenance, adding new requirements), plus AI is totally incapable of doing the higher level tasks that shape what code needs to be done (technical analysis, requirements analysis and in bigger companies technical architecture).

People who are non-experts, aren’t really senior domain experts or have some kinds of unbalanced expertise (they’ve never really progressed beyond being a coder, or they don’t have full life-cycle experience with big projects or they’re in an industry or position where they just make the code, shove it out the door and it’s not their problem anymore) just look at the one thing they in their ignorance think is THE cost in programming - coding - and go “hey, this AI thing is amazing” even while AI is creating all sorts of much more time consuming problems which they don’t really understand formally (they think those things are just “bad luck”, “there’s nothing we can do about avoiding this” and “it’s just the way things are in programming”) that require the time of people with higher expertise levels (i.e. who are more costly) to solve and AI doesn’t even help with the kind of stuff which if done wrongly or not at all can condemn a software project to fail before it even starts like just half-way decent Technical and Requirements Analysis.

That’s why you get some programmers going “this AI shit is amazing” whilst the really senior software development types are just nodding their heads and thinking “these people are ignorant as fuck juniors”.

Dumb people use their tools incorrectly. That’s all you’re describing.

I gave one example. In fact that’s one of the least bad examples. If that was the only problem with LLMs it wouldn’t really be that bad. But the actual reality is so much worse. But I was arguing to the point that the person I replied to made, which was about development. The problems I have with AI are not fixable without literally every government on earth taking a stand, which just will not happen.

Using LLMs for development is fantastic as long as you know how to use it, which is how it is for every tool.

The difference with LLMs and other tools is that anyone can use them easily and get incredible results……as long as you don’t look under the covers. The developers that use them well are getting results and code that are indistinguishable from code they’d write themselves.

I’m not a kid doing some vibe coding, of course I know what I want and basically know how to implement it, the AI is just helping me writing it, auto-complete and all.

The overwhelming majority of software development occurs on a team. You understanding what you’ve asked an AI to do doesn’t mean jack when you need to make sure an entire team can understand it five years from now. And LLMs tend to write code that is incomprehensible to another person the next day.

? developer can write 50 lines per day, LLM help us for some auto-complete or some easy 5 lines routine/functions, but we are not blind and do not accept them blindly without understanding them. And we have pull request and reviews etc and prior to this we have architecture and class diagram and SDD and acceptance criteria and all kinds of things.

You do think that in big companies a developer will use LLM to generate thousands of line per day by himself?

… we had autocomplete that worked better before LLMs came along. If you’re not using an LLM to generate more code then what the fuck is the point.

I’m my experience people give tech demos, everyone is impressed, my coworkers say it makes them multiples faster, then I have more work making sure the wheels don’t fall off.

I’ve spent the last 4 months using LLMs to review / ensure that junior coders’ wheels don’t fall off.

Yep I’ve got nearly 30 years of professional dev experience and Claude code/copilot/etc are absolutely groundbreaking.

I like it for making a quick crappy version of something, so then they’ll tell me the double secret requirements, and then I write the real version.

Read the article

The echo chamber on this site about AI is really quite something. Someone confidently told me Mythos probably didn’t exist and they were upvoted for it and I was downvoted for pointing out the existence of Project Glasswing.

Like, Mythos isn’t real? Fable has been out for like two months lmao

I was talking about roof sheathing with the latest chat gpt.

It was confident the joists should go above the sheathing.

You might want a 2nd opinion, did you ask Grok?

LLMs seem to be pretty bad at home improvement sort of questions in my experience. I suspect they don’t have a team of carpenters they use to RL the models, like they do for software engineers, etc

Software is easier because you can basically ‘inspect the house’ and the ‘home inspector’ via software.

Harder to do that with a house. Also a lot of different rules based on locality.

Reminder it doesn’t know anything.

It is just mimicking text it has been programmed for.

For it to be “good” at mimicking home improvement, it’s source needed to be full of home improvement talk. The best carpenters, plumbers, whatever aren’t on reddit, or wherever talking shop. They’re working, or making OSHA jokes at the bar.

How do people work? Are there ones born knowing how to use a hacksaw?

Mostly tradies are learning by hands-on apprenticeship with a master. All that discussion is not on the internet.

How did anyone ever learn anything before chatgpt?

Personally I would ask my aunt and she would be wrong.

The better question is how will anyone ever learn after ChatGPT

I can’t be the only one who will have a hard time for me to trust any professional who became qualified after 2023.

That is because carpenters and contractors talk shit at the job site, the bar, and the gas station instead of on reddit

I work in construction and so far I must admit that I have no idea what the hype is about.

But I have decades of experience, meaning I don’t know shit about anything BUT I know how to quickly find usefull information in the codes or in pertinents books.

If I were younger I might have fallen into the trap. If so I would probably be in jail by now, because in my tests I’ve seen LLM be dangerously wrong.

Vibe construction

It has become impossible to tell managers mesmerised by artificial intelligence that the tools are not, in fact, helpful.

This is stupid. The tools are helpful. Overhyped? Sure. But to pretend they are in no way helpful is just wrong.

Nothing is helpful until you need help. The fact that you think AI is helpful for you don’t have to force me to use AI. If you pay me $100k+ per year and you force me to use your tools to do my job why you even hired me ? To force this cult on me or to do the job ?

I don’t need you to use you turn signals, but it’s helpful. I’m pretty sure I could come up with a multitude of situations where your first sentence is refuted without contest.

I thought the turn signals were required by law

In a lot of countries it isn’t.

Or it isn’t enforced, which is about the same thing.

But technically different ( ͡° ͜ʖ ͡°)

Honestly if they hired someone who refuses to use AI in 2026 it’s on them.

It’s pretty normal for an employer to dictate what tools and applications an employee uses. If you prefer gitlab but your employer requires teamcity, you don’t really get a say, right?

However, if you think you can meet your deadlines without using a code generating AI tool, then don’t use it. Ideally, if they are as useless as you imply, no one will be able to tell whether you’re using it. The conflict should only really appear if they do increase productivity, and you refuse to use them, resulting in less productivity than your peers.

The conflict should only really appear if they do increase productivity, and you refuse to use them, resulting in less productivity than your peers.

While it should be easy to point out where this helps, it very much is not in reality because AI accelerates execution on ideas, but corporations nearly always suck much more at deciding which ideas to actually implement in the first place.

I predict that the longer this goes on, the more useless, untested half-features will be stuffed into software and the more bloated “fall back” implementations full of duplicated spaghetti code will exist.

If the tools don’t stick around because it’s too cost prohibitive to keep using them, we’re going to be cleaning up after the bots for a decade.

It’s been a boon for personal slopjects for me. But that’s because I could never find time for execution in the past. Now I just have the (free tier) AI slop up my ideas.

useless half features will be stuffed into software and the more bloated “fall back” implementations full of duplicated spaghetti code will exist.

Which happened without AI, too.

Remember that AI, in whatever form or field, only has to be better than the average human in the field, to be useful to a company project.

Which happened without AI, too.

Sure, but not at this speed. The bots can churn out a mountain of non-sense that does not work faster than people can review it and the bots themselves will lie to you and say “it’s all implemented”.

For people who have clear ideas of what to implement but struggle to find the time, this is a big gift. You can work hand-in-hand with the bot and say “nope, not that, this” until it implements what you had in mind. For aimless corporations with “ideation” meetings who cannot stop coming up with terrible ideas that none of their customers want, it’ll hasten the process of them making their software worse over time.

That’s exactly what I’m seeing at my current company.

That’s a fair point, but it’s not an issue with AI; it’s an issue with corporate culture. Corporate culture can and does change, especially with advances in technology, but there will be a churn period where it’s chaotic and mistakes are made.

I agree with that as well. A lot of the problem with my current company comes straight from the executive leadership level. I’m afraid that the culture will only change when they change (or more likely, the company will eventually just go bust).

They bought the lie that AI will replace all of software engineering, whereas I see the picture more like this:

normaltech.ai/…/why-ai-hasnt-replaced-software-en…

A CEO that thinks that they’re going to develop all of the software, even with Claude code, is nuts. They simply do not have the necessary skill set to run a software project even if 100% of the hands-on programming is done by AI. They are not going to be sitting there telling a machine to move things around on a web app. They are going to believe the bot when it says that “it is implemented”.

In this, I think we more-or-less agree; I just don’t see that as a fault with AI, nor as a reason not to continue to develop/adopt AI.

And maybe your point is what Doctorow was trying to make all along, but he pushed too far in the other direction? Even now, Code Generation is a useful tool, as long as it’s used as only a tool by someone who knows what they’re doing.

And maybe your point is what Doctorow was trying to make all along, but he pushed too far in the other direction? Even now, Code Generation is a useful tool, as long as it’s used as only a tool by someone who knows what they’re doing.

In this piece — which I read via RSS feed a number of weeks ago before it was published in this medium — he is writing about what most peoples’ experience is in a company where they have decided to “do AI”, and what most CEOs think about when they think about “doing AI”.

This usually comes in the form of a company chatbot or “agentic workflow”, and in the vast majority of cases these things are very much not helpful and can actively be annoying because attention is paid to developing these things when more practical and useful features could be developed instead (see: …mataroa.blog/…/i-will-fucking-piledrive-you-if-y…).

In other pieces, he discusses — using what I would call sort of esoteric terms that he often defines inline — people in saner places that get to choose how and why to use AI to assist with their jobs. But that’s not the majority of people’s experiences and that is certainly not what CEOs are often talking about.

I think he’s being somewhat hyperbolic in this particular piece, and maybe that’s due to the amount of “AI is changing everything” or “AI has changed everything” that is appearing in LinkedIn / X posts everywhere, when it’s difficult for most to even name a single thing that the chatbot has spit out that has improved lives, and the vendored chatbot attached to an existing piece of software (which is mostly what people end up doing when they “do AI” at a company) is completely useless.

If I take myself as an example, he’s largely right that I have to play this game with internal development teams that come up with “agentic workflows” and try to get me to use them to do my job when it’s a lot easier and better to just…use Claude code myself.

AI hasn’t “changed everything”. The only thing it’s really done so far is produce a lot of chatbots and agentic workflows that aren’t very useful. It remains to be seen if: (1) it can ever be made to work more efficiently, (2) if you can ever actually run a profitable business building or running a “neocloud”, and (3) what the broader effects on software will be from mass usage of this technology.

The early signals on all of these very real concerns (not to mention others such as environmental, cultural, and economic impact) are not very positive.

Company will be able to tell if you’re using it because companies behind sota models come with monitoring. You will have meeting with hr and your manager when you will be forced to use it even if you meet quotas. They will argument it that maybe you can do more and increase your normal quota with expected ai usage increase in productivity even if you had best productivity in team without ai and your productivity stays the same. Suddenly you will be under performing because you are refusing to use ai so you will be first to fire even if you are best employee. That’s how corporations work. They are monitoring usage because it costs them money. For big corporations they pay upfront to get discounts. Company also make many trainings and meetings to encourage everyone use AI models they pay for but nobody wanted.

Part of the problem is AI isn’t a literal thing with a fixed definition. It’s more of a marketing term that translates into “I want you to buy this thing”. So some things are considered AI that are legitimately useful and others are not and then there’s the debate if wether it’s because it really is AI or if it is and AI just sucks.

In other words, corporate leadership is starting from the premise that AI has (or will) radically change the business, and they’re working backwards from that premise to find the evidence to support this article of faith.

This quote is quite funny in this context, because it’s literally what people do with AI. Half of the frontpage of lemmy is AI. The other half is people saying AI is bad with no realization of the irony, because they don’t see good AI. It’s like CGI. Everybody hates CGI, except they don’t. CGI is everywhere. They just hate bad CGI.

What is AI? Explain as if i just woke up from a coma. We don’t have flying cars, we have dumpster deloreans, but everyone is talking about AI. What is it?

AI is a vague term used colloquially to describe a variety of dissimilar and unrelated technologies each carrying a unique mixture of pros and cons.

So it doesn’t actually mean anything.

The utility of the term “AI” depends on what you’re trying to communicate.

Like most things, meaning depends on context. In the most common context of “news about AI” we’re talking about generative AI: image/video generators, LLMs, and to a lesser degree coding assistants. If you want to know more about what these technologies share in common, look up what a “perceptron” is and how it works.

More generally AI could mean anything from a neural network based approach to problem solving, to a completely deterministic, hand coded heuristic. A pure decision tree could be AI in the case of video game NPCs, for example.

So it’s a new synonym for algorithm.

Not quite. Algorithms are definite, you get the same guaranteed output if you give the same input every time. AI is generally more fuzzy logic.

If it can run on a computer, its an algorithm. Definitionally.

Great, Linux is an algorithm.

It might be accurate, but it’s also vague enough that it’s a pretty useless definition.

I mean, yes. Linux is an algorithm. The programmes called AI are also still algorithms - they’re still deterministic, just too much of a tangled mess, too complex, for a anyone too realistically break it down step by step.

There are some math problems that cannot be described algorithmically, but because of that they’re also non computable. Uh, some thermodynamics and quantum stuff from what I’ve heard.

That’s my point.

I’d say it’s more like a way to describe a certain kind of algorithm. One that adapts in some way to inputs that defy simple classification. Ultimately you’re probably just safe to replace AI in modern news and internet discussion with LLM or CNN.

I agree with that, but others don’t. And that’s the problem. It’s not a fixed definition. It’s marketing.

Since that was a lazy question I decided to be lazy and just copy paste it into Gemini. Enjoy! I have no idea what the point of this is!

Artificial Intelligence isn’t a single technology; it has been a massive umbrella term since the 1950s. Broadly, it means creating computer systems capable of performing tasks that typically require human intelligence.

If LLMs are just one tiny branch on the tree, here is what the rest of the tree looks like:

Machine Learning (ML): This is the engine driving most modern AI. Instead of a human programmer writing strict "if/then" rules, we feed the computer massive amounts of data and let it figure out the rules itself. This is what powers your Netflix recommendations, credit card fraud detection, and the algorithm deciding what you see on social media.

Computer Vision: Teaching computers to "see" and interpret the visual world. This is how self-driving cars identify stop signs versus pedestrians, how your phone unlocks when it sees your face, and how medical software spots anomalies in X-rays faster than human doctors.

Robotics: The physical application of AI. This isn't just mechanical engineering; it is the software that allows a machine to navigate the unpredictable, physical world. This covers everything from the Roomba vacuuming your floor to automated factory arms and those creepy, dog-like robots from Boston Dynamics.

Natural Language Processing (NLP): This is the branch focused on understanding and generating human language. LLMs live here, but so do older, simpler technologies like spellcheck, Google Translate, and the early versions of Siri or Alexa.

Expert Systems & Rule-Based AI: This is the older, "classic" AI. It relies on a massive database of human knowledge programmed as logical rules. When the IBM computer Deep Blue beat the world chess champion in 1997, it wasn't using an LLM; it was using raw computational power to calculate millions of possible moves and their outcomes based on strict rules.

Predictive Analytics & Optimization: The invisible math running the modern world. This is AI used by logistics companies to find the absolute most efficient routes for delivery trucks, or by hedge funds to execute high-frequency stock trades based on market micro-fluctuations.

I asked the question to demonstrate something. You didn’t want to participle in the discussion and that’s fine, but that doesn’t mean the question is “lazy”

I didn’t provide you with an answer to your question?

This response is irrelevent to my post.

LLMs are like a dishwashers. You could wash all the dishes yourself and probably get them cleaner in less time, but it’s useful to let a machine do the work, even I though it’s mandatory inspect every single dish to make sure the machine got them clean enough and re-wash a certain percentage of them. Dishwashers are useful, but the world wouldn’t end if we didn’t have them. They also have the benefit of using less water than when do them yourself, which certainly isn’t the case with LLMs.

In the same line of thinking, this is why I hand wash my car, because the automatic car wash either does a bad job (touchless) or will scratch the paint up all over (brushes). Many people don’t care very much about their cars though, so why put in the effort?

I find unreliable tools to be one of the most infuriating parts of my job, and unfortunately my entire job has effectively pivoted to spending all day babysitting an AI that frequently ignores instructions and can’t learn without a ton of expensive fine-tuning training.

I don’t let AI touch any of my personal code I care about, and with how much of the code at work is written by AI, it’s pretty demoralizing. Why put in any effort designing something if it’s just going to get blown away by a coworker’s agent the next day?

Arguing with the AI over why its code review is wrong is also a whole other part of my day now…

I’m not necessarily suggesting Doctorow is correct with this assertion, but on a macro scale what productivity gains will generate AI provide?

Yes there’s a few skills it’s good at coding, medical research, and image recognition being a few.

The genuine question is: has this improved everyone’s quality of life?

Not yet because people are still learning how to use it, but im literally ten times more organized than i was before i started using AI to automate notetaking planning calendaring all that.

I’m happy for you but isn’t really an answer.

“Not yet” isn’t an answer??

It’s a response but when provided as an answer to the question “is AI a net benefit to society” then it’s a “no”.

Ok so it’s still an answer lmao

I work in government and part of my job is writing memos or documents that nobody will ever read but that some congressional mandate requires me to have on file. I absolutely offload that to AI so I can do my real job.

This is another answer that misunderstands my question. It’s changed your job but has it improved the quality of life of the public served by your office ?

What a grotesque job this is.

I work at a sales-first (read: no accountability) software company, ran by inept nepo-babies and a hostile VC. I frequently have to completely re-do all of the sales engineering work when a new project starts, because they refuse to standardize or talk to each other.

After enough time doing this shit by hand, I took my best examples from previous projects and had an agent “fix the errors based on the conventions I established over here”. Instead of going line by line on these big serialized data formats now, I do about 10min of checking the results and editing down the change log.

It’s wack that I have to re-do someone else’s job still, but at least I can force some version of standardization without killing myself on the tedious bits.

That doesn’t really answer my question. It’s changed your job, but has it improved your customers quality of life?

It’s improved mine. I save hours (that I didn’t really have anyhow) on meaningless tedium. I thought that was pretty obviously implied though?

Maybe it helps to say that this was in no way forced on me. The stuff my employer has done to “adapt AI” is laughable and seemed to be nothing more than lip service to the execs who want to slap that sticker on the product.

The gains for me personally have been strictly using it as a tool in niche situations. I prefer to do things the hard way when it’s for me but I’m not breaking my back for a corporation.

But how do you spend the time you saved?

Either you’re arguing in bad faith by nitpicking for some reason, or you are incredibly dense. Do I need to send you my calendar dude? I just freed up some time, maybe we can hop on a call and I can spell it out for you?

I dont understand. That seems like a perfectly reasonable question.

If gen AI has saved you a heap of time, how do you now use that time that was saved?

However they like, are you their mum?

Hah. ooh good one. What a clever snarky joke you have made. An epic burn if you will. well done.

I see Doctorow’s post has summoned an army of AI dweebs, so I will leave you to your witticisms.

You’re getting direct examples of people its helped, and you keep brushing it off and asking what about everybody else. You’re obviously not going to get a single answer that affects everybody, and why do small successes not matter?

Because they’re not really successes.

No one here is saying AI has reduce their workload so now they spend half their time catching butterflies.

The answers are all saying that AI has automated some mundane task that wasn’t particularly important, and now they can spend their time doing something more interesting.

Is the changes to their role, multiplied by millions of employees, actually better for humanity or society?

In a way, this is what Doctorow is arguing: where is the data showing the productivity gains.

My follow up question is, how are these productivity gains being used, if they exist.

Aren’t you assuming productivity gains will lead to free time? Why? It never has. Most of us work a fixed amount of time and get paid for that time. Not the amount of tasks we finish if that can even be measured.

Salaries have also not increased with increased productivity long before chatbots came online. So they won’t suddenly increase now.

AI is much more just like a new laptop you get from work. The new device is faster, has more RAM and allows you to do more, potentially.

The big question is, if the purchase was worth it. Unlike a new laptop, LLMs are an ongoing cost to users and providers. Nobody except for GPU producers are actually making money right now. Hyperscalers make some of the money back they themselves invested into the companies using their services, but in the end the ROI looks pretty dim for anyone.

So even if there are real productivity gains, you won’t see people relax with their kids more. And even if these gains are significant they may not prevent a huge economic collapse so we all will have plenty of time with our kids in the end anyway.

AI has automated some mundane task that wasn’t particularly important, and now they can spend their time doing something more interesting.

That absolutely sounds like a win to me. Why is productivity gains the only thing that’s important? QoL is what matters. Unless you’re a capitalist overlord.

100%! I don’t understand how this is a hard concept to grasp. I’ve been able to make productivity gains, sure, but who gives a fuck? Even if my productivity is the same, my QoL is still much improved when I’m not buried in mundanity.

The problem here is that the technology is owned by capitalist overlords.

And their goal isn’t improving your QoL. If that was the goal we would already enjoy 4 hour work week long time ago.

Doesn’t matter what the goal is, the context of this particular discussion is that AI is benefiting at least some people by making their lives easier. And one guy who’s trying to argue ‘so what, it only matters if productivity goes up’.

With ownership being as concentrated as it is now (more concentrated than during the guilded robber baron age), naturaly, most of the benefit flows to the biggest owners, and AI seems poised to deepen the wealth inequality further.

In a scenario without the insane wealth concentration, and with a dignified and decent economic floor being guaranteed universally, I would be in favor of any AI that does not burn our planet to a crisp and stink up the neighborhood with the gas turbine exhaust.

So in our world as it is, I have to be agaisnt the AI for the foreseeable future.

The later section addresses this

The other question Suresh implicitly raises is: “How can you reconcile the failure of AI in the enterprise with the individual claims of skilled technologists who insist that AI is helping them do great work?”

OK, but the point still stands if you allow that: It has become impossible to tell managers mesmerised by artificial intelligence that the tools are often not, in fact, helpful.

Sure, although I would phrase it I think a bit more pointedly: “it has become impossible to tell managers mesmerized by artificial intelligence that the tools are not, in fact, capable of what they imagine they are capable of.”

This points, rightly, at it being a failure on the part of the managers - not artificial intelligence. And God knows that’s spot fucking on.

They have uses in certain circumstances. They also have lots of negative aspects (that are broader and more systematic, as far as I can see). I think in a net-benefit sense they are not, in fact, helpful (to an organisation, or to a society).

It is a double edged sword.

If you’re already experienced developer and you use it skillfully, validate produced code it can help.

If you aren’t (and unfortunately everyone thinks they are better than they actually are), it actually can do the opposite, it can help generate a lot of junk code.

Now when working in a team usually majority of people aren’t that great developers typically there might be one or two star developers. The problem is that the other people will still use AI and generate MRs. Reviewing those is extremely time consuming and no one wants to do it. It’s weird to say what’s exactly wrong with those MRs they seem to do things but they seem to do things in a more complicated way.

The thing is that no one wants to review those MRs, everyone is afraid to point AI slop, so the quality of code goes down and even the people who previously were good are getting lost in the code themselves.

It depends on the tool to be honest. If it’s Claude code and you’re a programmer, sure that’s helpful. But often it’s an “agentic workflow engine” that’s been developed in house that’s complete garbage, or a chatbot that doesn’t know anything telling you that it can’t answer your question that was put together by your company. These custom tools are almost always not helpful.

Doctorow knows this as well, he writes about this crap constantly and has broken it down by user type in other pieces.

I have tons of random receipts that come in every month that need to be captured, memoed, categorized and sent off to finance.

I made python to read image/pdf/whatever to text, easy.

I regex the value out, hard and fragile

I try to pull the vendors name out, just not reliable

Category… nearly impossible so i need to make some form of memory system to remember once I do it.

Python renames the file with input if it needs it and provides the meta

OR

python to text because it’s effciient

api call to Ollama and let my old 2070 work it out. (highly reliable)

python renames the file and provides the meta

It’s small, low power, hard to do with code, basically the perfect use case. This is a good use of AI

Go Rewrite FFMPEG in Rust… that’s a bad use of AI.

There’s a lot of people out there using it in ways that cost a lot more than human eyes and have horrible societal and environmental impacts.

There’s a lot of people using it for dictation, document triage and basic scripting where it has great advantages and isn’t making the world a worse place.

His article is at odds with a great deal of his book. I wonder if this isn’t just a grab to get hardcore anti-ai to buy it.

It has already changed a lot of things in my life to the worst. that I am scared what it would look like once It changes ‘Everything’

I mean they are changing everything because they are being pushed and forced to be used even if they are making jobs and things in general worse and harder.

In other words, corporate leadership is starting from the premise that AI has (or will) radically change the business, and they’re working backwards from that premise to find the evidence to support this article of faith.

Sounds familiar. The proof-of-work blockchain scheme which was the fad a while ago is also often described as a solution in search of a problem.

That’s top down thinking for you.

I feel like block chain as a public ledger has found quite a few purposeful uses, and was a solution to a handful of problems most people rarely if ever encounter. Then fake money got involved

Yes, exactly.

Block chain would have been an amazing solution to most democratic vote validation processes.

It can still be used that way. While most people talk about how corrupt politicians enrich themselves with fake currencies, more legal and legitimate uses can still use decentralized ledgers for useful things. Of course these won’t make headlines.

There are far better global ledger approaches than blockchain.

I believe you, but can you name one so we can all learn more about the alternatives out there?

SWIFT is the easy one. Fun fact they looked into blockchain at one point and went “yeah, nah”

I thought Swift is currently Block-Chain based. Do you mean this Swift?

I meant the true and proper SWIFT that is used by almost all financial institutions for transferring funds.

Aren’t votes meant to be anonymous to avoid retribution?

Isn’t the public ledger identifying information to be used by authoritarian violence on every single voter in that case?

Proposed ideas for block chain to maintain election integrity would only use a block chain ledger to verify counts, not store voter information.

Well no, because we don’t have a problem that blockchain solves (better than open software, if it must be digital), and there are loads of problems that the blockchain doesn’t solve. Like how am I supposed to vote with that thing under no coercion.

I’m not really sure what you’re getting at. The blockchain is a good mechanism to maintain a source of truth that is inspectable and verifiable by anyone. It would only serve to certify that official counts can’t be tampered with in certain ways.

But it doesn’t replace the whole democratic voting process. It doesn’t protect against coercive voting, for instance.

My point originally was that blockchain can be of use to us, but tech bros ruined its rep. Same with AI. I’ve been waiting for routers with ai-powered (really, just machine learning) firewalls that can adapt rules to environments. But instead of that, everyone is obsessed with generative stuff that isnt very good.

Yeah I hear you, but the 51% attack exists. Like if you use paper ballots, you’re safer and it consumes less energy. There is just nothing better by using a blockchain, and paper ballots (for example) can be verified by anyone, the blockchain not so much.

The core issue feels like the dead torrent problem.

Its distributed, so it needs to be hosted, by several locations. Which becomes probitive over time as.it becomes huge. Its also massively inefficient for what its trying to do.

At some point, its basically just one server farm somewhere hosting a glorified MySWL database that doubles as a space heater for the entire planet every time you need to add a row.

I was thinking that with legal backing, blockchain could be a good record of ownership for deeds. Like NFTs but for physical property.

The history of ownership and the public visibility are good features there.

Lack of reliable, authoritative attribution of changes and no rollback or compensatory transactions are all dealbreakers.

Yeah who doesn’t want a ledger you cannot change, sounds like a good idea. /s

In the case of deeds, i think it is desirable because it gives you an immutable history of ownership.

There are other problems but i don’t believe that is one.

No it doesn’t. It only works because 51% doesn’t change the ledger.

You already have this with a simple database for ownership. It already exists, and ledgers made out of paper predates that.

Which purposeful uses did you have in mind?

Nothing that a centralized server wouldn’t be able to handle better.

Or several other solutions for different use cases. I always remember this article about alternatives: gist.github.com/…/a90e21e3d06e1ad924a1bfdfe3c1690…

I’m glad you specifically pointed out proof-of-work blockchains.
They’re very inefficient (economically and ecologically) by design. In almost all cases this design isn’t warranted.
Alas there are other designs and while most of them are rubbish as well, a few ones are doing things quite right.

I hope that just like other schemes than proof-of-work were thought of regarding blockchains, there will be AI models that are way, way more efficient and ideally can be run locally - for those cases that can use AI…

Wasn’t Nano predicated on those lines? I remember reading transactions could be done locally, even off-line, validated beetween the parts and only later added to the global ledger.

Funny, that you even know about Nano!
…it’s one of the often overlooked projects because it doesn’t have a ton of fuck-off-money and instead tries to focus on a solid protocol.

Nano has a lot of interesting attributes, but I fail to see how what you describe would work in practice.
If both parties want to make sure there are no shenenigans at play, they need to know about the most recent state of the respective account chains, which essentially requires them to be online for agreeing on said transaction.
But overall Nano is very fast and efficient by design and only a failure in terms of “gainz for Lambo”.

As we’re here in a thread about AI I should remark that machine-to-machine-payments - in this case: agent-to-agent-payments - would work pretty well with Nano as currency because of the transaction finality (typically less than 1 second) and the feeless nature of transactions.
If AI agents are looking for the most viable way to transfer tiny amounts of value fast and without fees they might find Nano and use it - who knows…
…and just like Nano showed that efficient ways to create digital money are possible I’m hoping for efficient AI models that are economically and ecologically worthwhile.

What I understood then was that in person transactions could be done, as the local ledger of each user would authorize and record operations on and off the available balance and wait until network availability to syncronize with the global record.

Returning to the subject at hand: I can imagine very specialized “AI” being useful for scientifical research, where very knowledgeable people use it as a tool to facilitate processes but are nonetheless capable of reviewing whatever results it produces.

Not gigantic datacenters required for this but small, purpose made and perfected, locally run, even if on higher specifications hardware to do so, but machines built for a given task and purpose. The economic viability on it be damned; it’s a tool for research, it is not made to earn money.

To put it bluntly: if you do an offline transaction, you’re prone to fraud.
If you expect the senders account chain to have balance x (because that’s your offline record for that) and the sender has sent all funds to a different address after you synced that account chain, you receive money that isn’t there - kind of like an invalid cheque.
I have no clue how that would work in practice, because to know a random account in advance, you’d have to sync the whole amount of account chains there is (called block lattice in Nano’s case).
With a mobile device that’s hardly feasible and without a mobile device I don’t see how you’d get in contact with people to make such an offline transaction.

That kind of specialized AI is what I imagine to be a use case for locally run AI, too.
After all you don’t want to build processes on an AI, where you have zero control over what happens behind the curtains.
That includes feeding potentially sensitive data back to it as well as being unable to control the training data set, its learning, version numbers, etc.

That requires a global ledger. Blockchain is an appallingly bad way to implement one.

What I understood then was that the local ledger of a given user of the coin would authorize and register the transactions done on/off the recorded balance and, when network available, would broadcast such transactions to the global ledger.

The global ledger was the total sum of transactions, not imperatively required to authorize transaction by transaction.

Nano uses “proof of stake” instead of proof of work to decide the order of transactions, and who receives block rewards.

The main problem with PoS is that it’s essentially the same as Federal Reserve bonds: all the new money goes towards people with extra money to freeze. This is part of why inequality has spiraled out of control since the Nixon Shock. Proof of work literally burns most of the profits because of difficulty adjustment.

A more specific problem with Nano (formerly RaiBlocks) is that the entire supply was centrally issued, with a pinky promise from this private organization that they only issued coins by CAPTCHA. If they were lying, then they could have issued 51% of the supply to themselves for permanent control. The only way we’d be able to detect it is if the price kept going down for years.

It’s also notable that MBAs tend to be people who are into the networking versus the work and they will follow the trends wherever they leave because that’s what good monkeys do.

This unfortunately, MBAs are the worst, too many of them simp for the billionaire broligarchs

I work at a university and manage large teams of students. Without exception, each time I have hired a business student, they have had a fantastic interview and then contributed nothing to the team. In most cases, they stayed long enough to create a resume entry and then went on to the next “bigger” thing. I don’t discriminate against hiring from them, but I do not advertise job openings in their circles anymore.

If AI data centers drastically accelerate us towards a point of no return for climate change, then they will in fact have “changed everything”.

In the near future an ai company will turn on their ASI and ask it what to do about climate change and it will say “first off shut me down, second off eat the rich” and only then will we know we did it

Or tell us to construction satellite to collect solar energy and beam it down.

The Author’s Note section of State of Fear, Michael Crichton made numerous predictions that some of which have come true thus far.

It was published in 2004

One of which is that any climate change related legislation implemented by one party, will eventually overturned / reversed when the other party takes power.

Nothing is more inherently political than our shared physical environment, and nothing is more ill served by allegiance to a single political party. Precisely because the environment is shared it cannot be managed by one faction according to its own economic or aesthetic preferences. Sooner or later, the opposing faction will take power, and previous policies will be reversed. Stable management of the environment requires recognition that all preferences have their place: snowmobilers and fly fishermen, dirt bikers and hikers, devel-opers and preservationists. These preferences are at odds, and their incompatibility cannot be avoided. But resolving incompatible goals is a true function of politics

He also predicted that in around 2100, people will be far richer, use lots of energy than ever before, and the global population will be much smaller.

This unfortunately, I’m sick tech broligarchs and billionaire broligarchs and broligarch billionaires ruining everything

I hate to be the bearer of bad news, but we’re already past the point of no return.

The climate is changing more drastically every year. Hurricanes and typhoons are more severe and the season lasts longer than just a couple years ago. Thunderstorms are more severe, with longer and hotter periods between them and fewer regular rain events. Ocean currents are changing and stopping, meaning the flows that sea life use aren’t there. There is no longer a constant polar ice cap on the north pole.

We will not be staying under 2°C average warming, which was the target back in 2010.

Ehhhh, I’m sure it will be fine. 😎

The question isn’t where the point of no return is - we’ve been beyond those the whole time. The question is how bad we want to let it get. Ever fraction of a degree extra warming means worse impacts.

Agree that less than +2°C is unlikely (though not completely impossible). But still, better that than +2.5°C, etc.

In this way AI reminds me of when Elon was saying we should build a giant vacuum tube instead of a train in California.

They all pretend this AI god that they’re sure will surface will solve the climate crisis partially because they already know what needs to be done and don’t want to do it. They’d rather baffle you with bullshit instead of trying to fix the problem.

But it IS changing everything, and not in a good way

It’s a self-fulfilling prophecy: if all the corporate leaders believe it will change everything and, in a fit of FOMO, force it into every corner of our lives, then everything will be changed. But not so much because of what AI can do, as because of a mass delusion among the LinkedIn types.

It’s changing a lot of things, but many of them in only relatively minor ways. It’s not affecting e.g. food supply systems in a significant way - it might be screwing up the management structures of companies involved, but that will be repairable fairly quickly…

His analogy about turning off AI is bloody weird. Pick almost any technology from history. Chances are if you had turned it off a few short years after it was first introduced, it wouldn’t have had any major impact. Why would AI be any different?

Corey Doctorow is an industry plant, anyone who goes to Burning Man and writes books like he does, has some major skin in the Techno Feudalism game.

Edit:I can only offer what is revealed to me. It’s too much for me to handle. Our reality is planned out. AI is here to enslave us. It’s not just some financial scheme. Doctorow is part of what they call a “hoodwink”. I personally never use AI product, never have, but I see what’s coming. I believe the solution is to stop using smart phones completely.

This is a bit unrelated but this is the cover or DJ Shadow’s Action Adventure. It has a track called “Reflecting Pool”. It was released in 2003. If this isn’t sufficient proof I don’t know what to tell ya.

Help us understand what do you mean by that? I see him opposing technofeudalism, not for it. So I’m not sure how is he an industry plant.

maybe as a resistance grifter? but i dont see him doing that.

Here’s an exerpt from one of his public talks about the AI bubble. From his Pluralist blog.

“OK,” the young man said, “but what can we do about the crash?” He was clearly very worried.

“I don’t think there’s anything we can do about that. I think it’s already locked in. I mean, maybe if we had a different government, they’d fund a jobs guarantee to pull us out of it, but I don’t think Trump’ll do that, so –”

“But what can we do?”

We went through a few rounds of this, with this poor kid just repeating the same question in different tones of voice, like an acting coach demonstrating the five stages of grieving using nothing but inflection. It was an uncomfortable moment, and there was some decidedly nervous chuckling around the room as we pondered the coming AI (economic) apocalypse, and the fate of this kid graduating with mid-six-figure debts into an economy of ashes and rubble.

… What.

I’m running into a lot of nuts on Lemmy today. Who moved the fucking rock?!

He’s been with the EFF for decades. Do some research first.

I’m aware of his career and related, history.

However it was Doctorow himself that mentioned his Burning Man attendance on a live tech podcast I was listening / watching 5 or so years ago.

Here’s an excerpt from audience questions after a he gave on the AI Bubble (or Apocalypse as he puts it often), published on his blog Pluralist. Written by Doctorow

“OK,” the young man said, “but what can we do about the crash?” He was clearly very worried.

“I don’t think there’s anything we can do about that. I think it’s already locked in. I mean, maybe if we had a different government, they’d fund a jobs guarantee to pull us out of it, but I don’t think Trump’ll do that, so –”

“But what can we do?”

We went through a few rounds of this, with this poor kid just repeating the same question in different tones of voice, like an acting coach demonstrating the five stages of grieving using nothing but inflection. It was an uncomfortable moment, and there was some decidedly nervous chuckling around the room as we pondered the coming AI (economic) apocalypse, and the fate of this kid graduating with mid-six-figure debts into an economy of ashes and rubble.

Okay. I’m not sure how that excerpt proves he’s an industry plant. It sounds like he’s talking about the massive financial crater that will be left behind when the fact that zero companies in the AI sector that do not manufacture hardware are profitable catches up with them and the bubble pops, which I personally think he’s right about.

Oh, has he had a change of heart? A few months ago he was being dragged on mastodon for telling everyone to stop complaining about AI.

Really? I saw two of his articles and also interview and everywhere he was negative about it.

link?

he’s been skeptical of at least the finance side of it since sept 2025: …medium.com/https-pluralistic-net-2025-09-27-econ…

I see a lot of useless people creating a ton of busy work that just becomes an unbearable workload for the people under them. Working in manufacturing environments which (you aren’t gonna believe this) are all horrifically understaffed is bad enough. Now i get to watch good workers get buried under Solvace/Fabriq/etc checklists and Leading2Lean/PerformOEE/etc task lists and spreadsheets and meetings to discuss kpis and metrics.

I need a new line of work

Yeah, right. My boss gave me a bunch of risk assessments to read through and let slip that AI had ‘helped’ produce them. So I said I’d get AI to summarise them. All this vast computing power and climate damage is being used for bullshit.

That’s all an LLM is, a gas-powered bullshit firehose.

Person A gives an AI a list of bullet points and instructs it to expand the bullet points into an email.

Person B receives the email and asks AI to summarize it into bullet points.

An ocean of water could have been saved if the Person A had just emailed the bullet points directly, and the information transfer would have been far superior.

Hate to day it, but every industry has turned into that. At least in my anecdotal experience and my network’s anecdotal experience, useless people using AI to create shit that means extra work for others is happening everywhere. Not saying you should stay in manufacturing if you hate it, but just be aware that you probably won’t be leaving that issue behind.

Well I wouldn’t recommend data & analytics right now, if you treasure your sanity.
Imagine tech debt accumulated over decades absolutely skyrocketing because LLM can generate thousands of lines of SQL or Python that noone understands.

I mean, I’m sure the same applies to all tech sectors and likely worse. I do admit it also helps us fix and uncover some old and buried bugs. It’s just not used with any sort of care or long term plan.

I’m a long term contractor. Process engineer and Certified Quality Engineer with up to date ASQ certs. I can technically go anywhere but i really love manufacturing processes.

This moment in history is a lot like when power tools were first invented. The bad news is corporate leaders seem to view AI as a means to downsize people. The good news is that AI lets many people who formerly lacked the ability to do anything on their own to move on the “I wish I could do ____. but _____” ideas in their head. It could be anything. You can run a competent local AI model in LM Studio with a $1000 gaming PC or you can pay $20 a month to run a frontier-quality open weights model without the usage limits of the big names. The free AIs can help you get started on doing that. Big companies are using AI for a lot of stupid crap that they try to make sound very profound. They assume customer service and quality assurance are no longer “a thing”. This era reminds me of the 1970s when Bill Gates and Steve Jobs had their opening. Sometimes the big guys get sloppy. We’re at that point right now. Enshittification is everywhere.

A more recent (and maybe closer) analogy is the advent of ebook readers and the heralding of the Death Of The Book. I suspect we’ll be in a similar place in a few years - books stores are doing just fine, perhaps even better than pre-ebook.

never seen it helpful to anyone, except for the people wanting to do the layoffs. most of the time it gives misinformation to people looking for abstract-to-reality ideas.

I mean its use as a tool, maybe in a small business context. The individual can be the “visionary” giving their idea for a book to a de-facto ghost writer. AI’s will often give you back info that confirms your ideas, so its like any other tool. If I am not good with a hammer then I smash my fingers and if I’m not good with AI then I convince myself the earth is actually flat.

The recent Bank of England report indicates that, yes, small businesses are seeing the biggest benefit of the tool (AI agents), but they are not spending big dollars on it. Free models and/or entry accounts are more than enough per their survey.

I agree, its a tool. I have used free models for the last few weeks to make python scripts into portable exes to help streamline some onsite job activities that normally are done multiple times a month. I never would have the time to research how to code python, figure out libraries and such to put together, and then compile all that into an exe. You’ll never see me spend money on AI when the free models work well enough not needing “frontier” features.

The funny thing to me is that Lemmy users seem to be so staunchly against LLMs, that they are unable to even acknowledge the small things it truly is good for as a tool. It’s like the opposite of hackernews on here.

I don’t expect movement towards AI among small business to show up in total cloud AI revenues.

Small businesses are typically more competently run than the typical big corporation, so I doubt they’re going to do things like lay most of their quality control people off thinking AI will do it. Big companies are mostly mid-wit trendies not anything that bright. Due to the perpetual motion of mature companies once they get off the ground, its hard for their stupid decisions to lead to bankruptcy. It takes a long time. They’re essentially doing the bumper cars version of business, while small business is in the 1960s Formula 1 where any major mis-step could end their companies’ existence.

Small businesses also don’t need to spend a lot. Their needs could be met by a semi-skilled local IT guy they hire to set up LM Studio on a gaming PC to run a 26B or 35B model. Big companies will spend the same amount on their peoples’ Dell or HP laptops business laptops that cost about the same but can’t run local AI.

Open weights models like Kimi and GLM provide near-frontier quality on any low cost service that installs them. GLM 5.2 is excellent.

I don’t know about other industries, but I’m a programmer, and I firmly believe that people who cannot be bothered to put in the hours to learn to do so the old fashioned way, should not make software.

Vibe coding is how you get software designed by people who do not know how to design software and implemented by a chatbot that is infamous for writing buggy, insecure, impossible-to-maintain code.

Software developers will always be needed, at least to make sure the vibe code didn’t make a logic error that will cause the elevator to crash when someone pushes the wrong series of floor buttons, etc. However its making a lot more coding equivalent to an Excel Macro, where an end-user can do it without a pro developer being involved. Things like JSONs for AIs or basic python apps. It might even lure some of those people into learning a little about the language.

memisrized? dont you mean hypnotized, and is in a honeymoon phase.

There is no difference between hypnotism and mesmerism. Mesmer was the great populariser of hypnotism, so that’s what it was called at first.

I typed into Google “potatoes pressure cooker” to get a reminder of how long to cook them. The AI told me, and correctly said that I should use the steamer sieve, but also told me to salt the water. The water that would be under the potatoes and that wouldn’t touch them.

I asked why, and the AI said that little splashes of salt water from the boiling would land on the surface of the potatoes and salt them gently, that the salt would increase the boiling temperature, and that the vapour would be aromatic.

I pointed out that the amount of salt landing on the surface of the potatoes would be negligible, that the vapour is distilled water in gas form, and that the increase in boiling temperature from salting the water is less than 1°C. The AI said, oh yeah, you’re right on all counts. Don’t salt the water.

What it has been doing was exactly what an LLM does. It gave me the received wisdom from the internet.

That aligns with what Cory Doctorow was saying here, namely that all the AI successes were from people who already knew what they were doing and could use the AI as a tool. Not from people who had no clue and just let AI do the job.

Salting the water would increase the boiling temperature I guess, but it seems like a waste of salt because that info isn’t meaningfully changing the cooking time

Salting the water is relevant when not using a pressure cooker. It makes no sense when you’re steaming them in a pressure cooker. AI is a good tool, sometimes. But for everyday tasks, it is irrelevant and often wrong.

I do potatoes in my instant pot but leave the water in, it just gets absorbed. So the salt is effective in that case. I salt the potatoes themselves and toss them before adding the water, too, so they should get reasonably seasoned.

Also i use soup stock.

Mashed, though.

That “AI” is a completely dumbed down one with reasoning disabled. Of course it’ll make stuff up.

I use google “ai” in when thrifting for audio gear. Minimum 80% of the time it gets something GLARINGLY wrong. “Oh ho ho! You correctly pointed out that I was completely wrong on all counts! An astute observation! Let me try again but this time give you the CORRECT information.”

I mean, to be fair, that’s a dirt cheap model you’re talking to, and Google is not exactly at the forefront of any tier of model, cheap or otherwise.

Is that you being fair or is that you rushing to defend something?

As if you don’t know these “tools” don’t work.

Exactly. I use AI primarily as a natural-language search engine, when searching for things that can quickly be independently verified. One of the best uses for it is when you know a thing probably exists, but you don’t know the name for it. You can describe to the LLM the object or problem in detail, and it will give you a name. You can then take that word and do a non-AI search to instantly confirm if it got it right.

I’ll never use an AI for something I can’t at least verify without an AI.

I really like a song that has intro and outro samples and I wanted to know where they were from, so I googled. Google AI promptly informed me that the song has no samples. Guess that settles that.

My husband was recently trying to find an exact quote from Richard Pryor’s character in Lost Highway, so he Googled it.

Google’s AI overview said that Richard Pryor was not in Lost Highway and instead gave some suggestions for other films we might be thinking of.

The quote he was trying to remember was, “There’s nine people down here, and you can ask seven of them. If you can get that price from one of them, I’ll let you ask the other two.”

I use AI as a writing tool, not as a substitute for authorship.

The ideas, intent, perspective, and final judgment are mine. I decide what I want to say, what belongs in the piece, what does not, and whether the finished version accurately represents me. AI may help me organize my thoughts, clarify a sentence, improve the flow, or find wording that better expresses what I already mean. That is not fundamentally different from working with an editor, dictating to a transcriber, or revising a draft after receiving feedback.

What matters is that I review the final work, approve it, and put my name on it. By doing that, I take responsibility for every sentence. If the writing is thoughtful, accurate, and effective, I am responsible for those choices. If it is careless, misleading, generic, or full of slop, that is also my responsibility. Blaming the tool would be an attempt to avoid accountability.

AI does not decide what I believe. It does not decide what I am willing to defend. It does not decide what I publish under my name. I do.

The tool may assist with the writing process, but the authorship comes from intention, judgment, selection, revision, and responsibility. The final work is mine because I chose it, shaped it, approved it, and signed my name to it.

that’s bullshit.

Why?

“authorship is when i make a few edits and sign my name to slop”

if you have willingly handed over your primary writing tool to a machine (for which there is no real meaning, association, appreciation, or delight in words), it is difficult for me to take your commitment to writing or “authorship” seriously.

I did not say “authorship is when I make a few edits and sign my name to slop.” That is not my argument.

My argument is that authorship is grounded in the origination of the ideas and intent, control over what goes into the work, judgment over how those ideas are expressed, endorsement of the final form, and responsibility for the result. Assistance with the linguistic realization does not, by itself, transfer authorship.

You point out that the machine has no meaning, association, appreciation, or delight in the words. I agree. But I do. That is precisely the distinction I am making. The machine does not know what I mean, care whether it represents me, recognize when an analogy fails, or decide whether an argument is worth defending. I am doing those things.

You seem to be defining “writing” as personally generating the initial sequence of words. Maybe that is where we actually disagree. But that definition cannot simply be assumed and then used to prove that I am not writing. Why should authorship reside specifically in first-pass word generation rather than in the thought, intention, judgment, revision, and control that determine what the final words mean?

That is the argument I am interested in having.

i started to read your response, and then i remembered i can’t trust the degree to which you authored it.

i’m not interested.

All their comments are very clearly gpt slop.

A sneer is easier than an answer, and certainty is a lovely refuge when you have no intention of defending it.

You accuse me of surrendering thought to a machine, and then, the moment you are asked to justify that accusation, you surrender thought entirely.

How convenient.

With a wave of the hand, every difficult question disappears. You declare the words contaminated by the tool that helped shape them and thereby absolve yourself of any obligation to consider what they actually say.

No examination. No argument. No possibility of correction.

You accuse me of regurgitating the output of a machine, yet what have you offered in return?

A familiar sneer. A borrowed insult. A conclusion retrieved intact from the cupboard of approved contempt.

You recognize the hated object, produce the expected response, and call the reflex thought.

That is the irony you seem determined not to notice.

My argument from the beginning has been that a machine must be tempered by a human mind. It possesses no responsibility for what it produces. Whatever value emerges from its use depends upon the person capable of examining it, rejecting it, correcting it, shaping it, and finally standing behind it.

But a mind cannot temper anything if it refuses examination itself.

And that is where your position becomes more interesting than your insult.

A mind that refuses examination begins to resemble the very machine you claim to despise. It receives a familiar stimulus, reaches for the familiar pattern, and produces the familiar response without troubling itself over whether the response is true.

Input. Recognition. Output.

The machinery of contempt.

Ignorance itself is no shame. Every one of us is ignorant of almost everything. I have been wrong before and will be wrong again. If you can show me where, I gain something from the correction.

But there is something contemptible in making a virtue of ignorance.

In encountering an argument and proudly announcing that you will not examine it, then mistaking that refusal for discernment. In treating incuriosity as wisdom. In believing that because you have protected yourself from an argument, you have somehow defeated it.

You have not defended your position.

You have insulated it.

Those are not the same thing.

Thought requires a certain vulnerability. You must permit an idea close enough to examine it. You must risk finding weakness in your own position. You must allow the possibility that another person, even one using a tool you despise, might know something you do not.

Without that, certainty does not become strength.

It becomes brittleness.

You tried to cut me with the accusation that I had surrendered my mind to a machine. But in your eagerness to make it, you performed the very surrender you condemn.

You saw the signal.

You retrieved the response.

You refused examination.

You produced the slop.

And so the question remains exactly where I left it:

Will you defend what you believe?

Or is your conviction only strong enough to survive so long as you never allow an argument near it?

lol

Oh! I wasn’t sure you would read it, let alone reply. Writing it was a fun little excursion from my day, so I’m glad at least four lines made it through.

My offer still stands if you’d like to posit a position and defend it.

And I meant what I said about ignorance. I would genuinely love to be shown that I’m wrong. Sincerely. I would. Being wrong means there is something for me to learn, something against which to temper the mind.

What do ya say? Want to offer me something more substantial than slop?

You’re lost in the sauce. Do you know any other writers in real life? How do they feel about this?

Could you tell me which part of my argument you think is wrong?

I’m not wasting my time or energy arguing with a writer that doesn’t write.

I was hoping “a writer that doesn’t write” was the conclusion to an argument rather than the entire argument. I’m perfectly happy to be wrong. What I can’t do is respond to an argument you’re keeping to yourself.

You didn’t make an argument, you listed your justifications for using plagiaristic genAI as a shortcut instead of doing the actual work yourself.

I did make an argument. My argument is that authorship is grounded in the origination of the ideas and intent, control over what goes into the work, endorsement of the final form, and responsibility for the result. Assistance with the linguistic realization does not, by itself, transfer authorship.

I’ve thought through that position, refined it, and I’m willing to defend it. Calling AI use “plagiaristic” or a “shortcut” doesn’t actually address the argument.

So which part do you reject: that I originated the ideas, that I exercised control over the final work, that I endorsed it, or that I’m responsible for it? And why?

None of “your work” is yours in any meaningful way the second you use a tool that cannot exist without plagiarism therefore any aspect of its contribution immediately forfeits all ownership of the ideas conveyed. Because they’re not yours, they’re stolen by an inherently plagiaristic tool. Unless you’re using a local model trained exclusively on your own writing, none of your AI powered work is in any way yours. Even then it still wouldn’t be original or created by you, it would be algorithmic nonsense. You are not a writer if you use an LLM to do the actual writing. Couldn’t even write that fuckin response without your gpt slop.

I spent this morning sitting with this exchange, coffee in hand, reading back over what I had written.

I’m not especially interested in responding to the insults. And I’m going to set aside the argument about how AI systems are trained, plagiarism, consent, compensation, and so on. Those are legitimate ethical questions and I share them, but they are a different conversation.

The part that stayed with me was the simpler claim underneath all of that:

“You didn’t write this.”

And the more I thought about it, the more I realized that my original answer had blurred together several different things.

I had been treating “writing,” “authorship,” “ownership,” and “responsibility” almost as synonyms.

They aren’t.

You can originate an idea, develop an argument, direct how it is expressed, edit it, approve the final version, and take responsibility for publishing it without necessarily having composed every sentence yourself.

Nickiwest had responded to my original comment and said “That sounds more like the job of an editor than the job of a writer.” and that got me thinking about my old work as an art director for a studio.

I could come up with the concept, find references, explain what I wanted, reject drafts, change direction, combine ideas, and go through revision after revision until the finished image expressed what I had in mind.

That was real creative work.

But I still wouldn’t say I drew the picture.

The idea might have been mine. The direction might have been mine. The judgment might have been mine.

The drawing wasn’t.

I think AI creates a similar complication with language.

If I give an LLM a thought and it produces a sentence, then no, I did not necessarily compose that sentence in the ordinary sense.

If I argue with it, reject things, rearrange them, rewrite parts, refine distinctions, change the reasoning, and decide what survives, then I have obviously contributed something substantial. But calling every form of that contribution “writing” may sometimes be too simple.

Sometimes “writer” will be perfectly natural.

Sometimes “editor,” “director,” or simply “AI-assisted” may be more accurate.

What interests me more than the title is what happens during the process.

I can put a half-formed thought into language and look at it from the outside.

Sometimes I read a sentence and realize, that sounds like something I believe, but it goes further than I mean.

Sometimes two ideas I had been treating as the same turn out not to be the same at all.

Sometimes an argument looks convincing in my head and falls apart the moment I see the missing step written down.

Sometimes the model gives me wording I dislike, and figuring out why I dislike it forces me to understand my own position better.

Then I put that thought back in and try again.

The result I am looking for is not better prose, it is a better thought.

That is basically what happened to me this morning.

My original position was that the work was mine because I chose it, shaped it, approved it, put my name on it, and accepted responsibility for it.

I still think responsibility matters enormously. If I publish bad reasoning, false claims, unsupported accusations, or generic slop, I do not get to shrug and say, “The AI wrote it.”

I chose to publish it.

But I no longer think responsibility settles the question of who “wrote” something.

An editor can be responsible for publishing a piece without having written it. An art director can shape an image without having drawn it.

So now I think the more interesting questions are smaller ones.

Who originated the idea?

Who developed the argument?

Who decided what belonged?

Who noticed the mistakes?

Who revised the thinking?

Who directed the expression?

Who actually composed the prose?

Those answers may not always point to the same person, and maybe that is fine.

What I don’t think follows is that the human contribution disappears the moment a machine becomes involved.

My ideas do not disappear. My judgment does not disappear. The things I accept, reject, revise, and ultimately stand behind do not disappear.

But neither do I need to pretend I personally composed every sentence if I didn’t.

I’m comfortable with that distinction.

And I think it leaves me somewhere more interesting than where I started.

What fascinates me about A.I. is the strange process of having a thought, seeing it reflected back, noticing where it fails, and slowly discovering what I actually mean; what I believe.

This reply is a product of that process. I do not care what title we end up giving the result, I enjoyed spending my morning with it.

You didn’t write this, an LLM did.

Ah, but how much? Where does me end and the machine begin?

Lets assume you are correct and I had no part in the reply. Here is a 100%, hand to keyboard question from me.

Would you like to talk about what it said?

“I dont write anything but i take credit for everything”

I was reading the initial comment with peer-reviewed publications in mind and it all sounded like standard practice, but then I read yours and was like “oh, are we talking about books now?”

Either way, its still a bad look. Peer review is even worse than Op Eds, or fiction, or what have you… But its all equally socially degenerate

Interesting. When you want to criticize the argument, the words are mine enough to hold me responsible for them. But when I claim responsibility for those same words, suddenly they’re not mine.

“I don’t write anything but take credit for everything” is a straw man. If you disagree, address what I actually said.

What matters is that I review the final work, approve it, and put my name on it.

that’s just called writing.

That’s my point.

That sounds more like the job of an editor than the job of a writer.

When I was an editor, my job was to assign stories to writers and then to work with the editorial team to revise and refine their output into an end product that the publication could stand behind.

Your process sounds a lot like that.

I think that is a fair comparison, and it is much closer to the conversation I was hoping to have.

I used “writing” and “authorship” pretty broadly in the original comment. I was talking about expressing my thoughts in things like letters, emails, comments, essays, and so on. What has been interesting is watching people get very hung up on the labels “writer” and “author,” then react to what they think I am claiming without really engaging with the process I was describing.

Your editor comparison is interesting because I used to work as an art director, and there are similarities. I did not paint or draw. I would explain an idea in words, hunt down reference images, point to compositions or details I liked, and work with a concept artist through round after round of sketches. I would say, “this is closer,” “that misses the idea,” “combine this with that,” or sometimes realize that my original concept itself needed to change.

I did not draw the artwork, and I would never claim that I did. But I was still doing creative work, and more importantly, the process was refining my own thinking.

That is the part of using AI that interests me most. I give it a thought, see that thought reflected back at me in language, judge what works and what does not, clarify what I actually mean, reject bad reasoning, discover distinctions I had not articulated yet, and then go through the process again.

Sometimes I come out of that process with better prose. More importantly, sometimes I come out of it with a better thought. So whether we ultimately call that “writing,” “editing,” or something else is honestly less interesting to me than what is happening in the process itself. I am not just refining the words. The act of interrogating and refining the words is also refining me.

It gave me the received wisdom from the internet.

I mean… it gave you the amalgamated wisdom of the internet after a very complex game of telephone.

It’s not lying if they have no idea what they’re talking about and constantly swoon over it.

What worries me is people using it for safety-critical things without appreciating the danger, for a test some of my friends who sail for a living asked various chatbots some questions like ‘how would you approach a berth under sail when the wind is against the tide?’ and every single one but Claude got it dangerously wrong, and Claude wasn’t 100% either it just refrained from suggesting a sail plan that would lead to you crashing into the pontoon.

Will give a shout out to Mistral which got the sailing element of the question very wrong but did sensibly suggest ‘turn your engine on and perform the manoeuvre under power’ which of course is what you should do if you can.

i person i know uses it for determining something biological, where is the smell coming, what is it causeing. of course it hallucinates pictures as well as the cause. he uses it ALOT. why is he using it/reccommending it to his family member to "plan his career track around too.

Well, he hasn’t been describing our future for at least two decades. /sarcasm

What has AI changed so far?

• Hardware prices are so far up that nobody can buy good hardware anymore
• we have AI child porn now, awesome
• we have AI undressing apps, cool
• we have AI, the great cheat tool now † we have AI, the confidently wrong 20% of the time tool
• we have AI, the great misinformation tool
• we are at the brink of the largest economic collapse in human history caused again by the rich loving to play Russian roulette
• there are a limited amount of actual useful applications as well, yay

And all it cost us were millions of jobs, thousands of psychosis and deaths, hundreds of new loud polluting data centers, untold amounts of co2 when were right smack in the mids of a climate crisis…

I’m not advocating for murdering the AI CEOs, but I’m definitely a fan of at least permanent tarring and feathering

• two of my friends finally have diagnoses for their health problems they had been living with for decades, and can now get appropriate treatment

Trust me, bro!

They are real people these friends of mine, but you don’t know them, because they are from a different school. And you won’t see them this summer at all because… they are both actually models in Canada during the summer.

But totally cured and now they get boners also with THIS ONE TRICK that doctors HATE it!!!

c/nothingeverhappens

The medical field is one of the places where AI has made real, tangible improvements.

I can’t vouch for that other person’s anecdote, but it seems entirely plausible that an LLM chatbot would be able to better diagnose an issue than a search engine, or even a human doctor, especially if the underlying issue was something like an autoimmune disease, which are notoriously difficult to diagnose.

One of the big flaws in the medical profession (and, really, most professions) is that once you are out of school, nothing forces you to keep learning as the field advances. This potentially, and counterintuitively, results in gaps in knowledge that increase the longer a doctor has been practicing. LLMs, with access to the newer medical information and with the ability to receive and output natural language, do make good tools for diagnosis.

AI, as it turns out, is also way better at analyzing MRI scans and x-rays than humans.

An old-fashioned Google search can point you to more information, if you’re curious about how AI has advanced the medical field.

Funny how my experience is the American health care system is still 100% absolute shit that costs me 2k a month for the privilege of said shit. My wait times are the same, my “go fuck yourselves” are the same, my healthcare workers are still underpaid, and the systems for consumers are still shit.

“But the charts say AI is helping”

Once again it’s consumers get to wait for the piss to trickle down our backs. AI hasn’t done shit for the average Joe except make everything cost more while providing a worse service. Please, tell me how my healthcare experience has improved in the past 2 years. Go.

You are talking about a different thing than I am. The American healthcare system is garbage with or without AI, but AI is helping the medical field. Getting a more accurate analysis of an MRI scan is objectively a good thing, for example, even if the healthcare system that performed the scan is garbage.

They’re not using LLMs to analyse MRI scans. There are image processing algorithms for that, but they’re a whole other thing to what this entire thread is about.

Also, are you a fucking bot? I’ve read all your comments so far in this thread and uhh yeah. Please feel free to jog on now.

I’ve read all your comments so far

I’m flattered. Did you read the first one where I said it was reductive to equate AI with chatbots?

When people say AI they are talking about Gen AI chatbots. Pretending they aren’t in a thread discussing an article where the author is definitely talking about chatbots is being really pedantic.

Which was my earlier point. He is wrong when he equates AI with nothing more than chatbots.