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.
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
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.
But with good legal backing, all you need is a shared database and maybe basic cryptographic signatures to prove ownership in case of malicious actors. So basically, traditional banking.
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?
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.
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: https://gist.github.com/joepie91/a90e21e3d06e1ad924a1bfdfe3c16902
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.
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.
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’s top down thinking for you.
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.
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.
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.”
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 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.
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.
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.
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Could you tell me which part of my argument you think is wrong?
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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.
“I dont write anything but i take credit for everything”
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.
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
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.
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.
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
That “AI” is a completely dumbed down one with reasoning disabled. Of course it’ll make stuff up.
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 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.
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
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.
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:
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.
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.
“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.
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 “common usage” by the “regular folk”.
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 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.
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.
“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.
In my academic field of sustainability, AI still fails to explain what economic sustainability means. It’s very simple.
I lecture and explain to my students that economic sustainability is about how we assess and manage our knowledge, innovations, wealth, and other man-made capital. I stress that it is ABSOLUTELY NOT profit and revenue.
Sure enough, my students submit assignments clearly written by AI that state economic sustainability is profit and revenue.
AI is regurgitating decades of green-washing and even bad academic articles that state economic sustainability is about profits. I use that AI god mode site to test several AI’s at once and they’ve consistently gotten this wrong for 3 years.
The trick that AI pulls is just that it is really good at appearing competent, and we as humans really bad at acknowledging how little we individually know about the world around us.
The easiest example to ask AI detailed questions about something that you know you have expertise in. If you try to test its limit, no matter what your particular expertise is, I’m confident that in 5-10 minutes you’ll realize it’s no more intelligent than an average Joe who spent their lunch break Googling the subject or maybe someone who read a book about it once.
To give credit where it’s due, that does mean that these AIs have a much wider breadth of knowledge than any one person could have and that is, objectively, very cool.
On the other hand, that’s why it’s so easy to believe that AI is this all-knowing oracle that can replace all of humanity. It can give you a way better answer in an instant about things you know nothing about than you ever could off the top of your head, so it seems unbelievable. It’s instant gratification that also feels really valuable.
Despite the trick that AI plays on our psychology, you could also just read a book or two or participate in any form of genuine learning, and it really doesn’t take long for you to outgrow whatever the AI is capable of. It takes a bit longer, but now you have grown your actual intelligence and you get to keep it for a $0/month subscription and they don’t need to build another data center in your backyard.
The trick that AI pulls is just that it is really good at appearing competent
Yeah it’s almost like the AI is an unthinking machine trying to model what a person would answer to the question (prompt) provided to it.
I’m so goddamn tired of people (not you) acting like an LLM “knows” anything nevermind whether it’s right or wrong or could even tell you if it didn’t know the answer. It’s not like a person that can say, “I don’t have that knowledge so I know any answer is likely wrong” the machine is going to give you an answer every time. That answer will never be “idk” it’ll be whatever hallucination it’s confident enough is right based on the model and training.
I do think there are use cases for it and the technology is cool but the vision being sold and propping up our economy with a venture capitalist circle jerk is so far removed from reality.
that’s also how really successful human beings work. they bullshit and lie and appear confident, and people believe them and ignore the people who don’t do that, because they don’t appear as confident.
also why people basically fall in love with AI, because it spouts nothing but bullshit at them and they eat it up like candy because being fed back bullshit is a dopamine hit.
interacting with real people doesn’t supply endless dopamine and they might not tell you what you want to hear.
I love your approach. This way, you don’t even have to bust the students for using AI!
It’s often very difficult to tell the difference between a student that uses AI and a student that performs a traditional search of the topic and just copies facts blindly and uncritically. AI writing detectors are simply not reliable enough to serve as the basis for a charge of academic misconduct. What do you do if you accuse someone of using AI, and it turns out they just happen to write in a style that’s reminiscent of AI? What do you do if someone claims they just happen to write how an AI sounds? What if someone says they just used Grammerly, so now it’s ambiguous what portion of their writing is their own? Academic misconduct and AI gets very thorny very quickly.
But your approach? Brilliant. Just give them a paper whose topic you taught them correctly, but you know AI will screw up. No need to affirmatively prove the cheater used AI. Just give the cheater a failing grade on the paper, because it clearly doesn’t reflect what you taught them. They have no ability to later argue that they’re innocent or lie that they did not in fact use AI. Their paper simply wasn’t good enough to get a passing grade.
Honestly, it’s pure genius. As a fellow educator, I commend you!
You will get an email at some point asking you if you would accept some well paid contract to train an LLM to answer like you would. That’s how they simulate professional behavior on top of the probabilistic monsters that is behind.
So you teach something different from the mainstream - cool. But don’t be surprised when students or AI cite others or take an alternative position.
I’m no economist but I had plenty of professors who wanted their particular theories reflected back at them.
While students should do their own reading and writing, using ‘they write mainstream ideas’ as a litmus test for writing and AI seems parochial. And I’d go so far as to say AI is becoming a standard research and writing aid like the web word processors, and you sound like teachers who ranted against those too.
Have you engaged either group in open discussion about comparing the perspectives you’re challenging? I bet several modern models could debate your philosophy on merit, I wonder if you ever gave students the chance.
So you teach something different from the mainstream - cool. But don’t be surprised when students or AI cite others or take an alternative position.
Not every class is debate. Plenty of courses are simply taught by an expert in their field, and students are graded on how well they learn from that professor’s instruction. Is it a perfect system? No. But that’s why we have experts. Some classes exist to simply establish a baseline of knowledge among the students. Such classes cannot be continuously derailed by engaging in rigorous debate about topics that experts in the field consider unambiguous and long-settled. Students are free to form their own opinions and whatever conclusions they want, but they need to demonstrate that they have at least learned the material as taught. You can believe the Universe is 6000 years old if you want. You cannot expect your astronomy professor to spend half the class debating with you about it.
A creationist student doesn’t have to believe in evolution to get an A in an evolutionary biology class. But they do have to demonstrate mastery or at least competence in the principles of evolutionary biology as taught by their professor. If they want to view the whole course as “a silly exercise in memorizing the lies of evolutionists,” well they’re free to do so. It’s a free country. Believe whatever you want. But that doesn’t mean they can just write, “God did it” to every answer on the final and still receive a passing grade.
You’re free to believe or reject anything a teacher or professor teaches you. That doesn’t mean you get out of learning the lesson entirely.
I’m pretty sure the fucking economist you’re replying to is not “teach(ing) something different from the mainstream” that’s the whole point of their post.
The whole point is the AI tools are hallucinating* completely incorrect answers and that students trying to use them as study aids and tools to learn with are going to have a lot of fake and wrong information to sort out and good luck to them knowing what is or isn’t incorrect or wrong when they’re just trying to learn a subject they know nothing about.
The only reason you or I or anyone else knows when AI hallucinates in its output is because we know stuff from our prior knowledge and can catch it that way. If you’re ignorant to a subject and trying to learn stuff you’re not going to know off hand the AI is lying to you if it sounds convincing enough for a layperson to accept.
*which I kind of hate that term because to the AI there’s no difference between truth and hallucination - it’s just output as far as the robot is concerned
he expects to debate every professor he encounters, which brings up other quesitons about his motives.
I teach what is correct, but the incorrect mainstream understanding is what AI provides. It’s the crux of the problem I focus on in my research.
most classes arnt debate classes, they have a course specifically for those if you want to argue a position, yes public speaking and debate courses. trying to challenge a professor every class seems very counterintuitive if you are trying to get a degree , or pass classes. most people who likes to debate like yuo do often wont survive in college very long, or drop out very quickly. they are often conservative or religious nutties,.
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 ?
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.
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.
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:
https://www.normaltech.ai/p/why-ai-hasnt-replaced-software-engineers
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”.
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.
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…
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.
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.
The utility of the term “AI” depends on what you’re trying to communicate.
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.
AI is any compute model performing complex enough reasoning that the output does not always resemble the input. They are by definition non deterministic and the same ask can provide different outputs without being influenced by other inputs.
So its a lottery
No
I put in a 5 and get a triangle. I put in the same 5 again and get a transvestite. This is intended behavior.
That system isn’t a magic box. It’s a bag of variables linked together by statistics and what’s basically automated guessing.
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.
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.
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).
AI has been helpful for my job, sure, but the cost in terms of societal damage is so great it makes me wish AI were never created.
And ecological
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.
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.
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.
AI is changing everything. Its not a lie, its absolute truth.
The problem is the changes are fuckawful terrible for everyone and everything.
Honestly, I didn’t jump on the AI hate train like everyone else did. And I was wrong. I literally only use it to make profile pictures on steam to make a sekiro photo. Other than I literally can live without it.
And at this point all of the damage it is doing to our communities; I am angry at this point that people in positions of power are blatantly ignoring the people they represent who say they don’t want this. This is almost as bad as the epstein corruption. Just blatantly lying to our faces and ignoring frustration.
Not just ignoring it but trying to grift to their supporters that these ‘contrarians’ aren’t Americans and flew in. I’m so unbelievably sick of conservative influence in my home country just using lies as a real mantra. Because everyone around me values money over anything else and it seems like they genuinely are dissonant and don’t understand this is a bubble waiting to burst unlike anything we have ever seen.
Work keeps insisting we use it and I keep telling them to fuck off.
I get more done than my colleagues BECAUSE i don’t use the not-actually-AI “AI” tool.
Just use it for pointless reasons. Burn tokens the way they want you to. Money is the only language they speak, and models are getting expensive.
If AI data centers drastically accelerate us towards a point of no return for climate change, then they will in fact have “changed 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.
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.
Ehhhh, I’m sure it will be fine. 😎
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
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
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
I truly don’t want to try your app… I don’t want all my data leaked or my PC, laptop or phone bricked just because a dumb AI-slop code error.
It’s not for you, anyway.
In my professional work, I have watched AI code reviews catch 10x more dumb slop human errors than human reviews used to the same time a year earlier, consistently for about 8 months now.
To add to this: Writing code is easier than reading code.
This is why we have comments: understanding the intent helps guide the reader and makes it easier to see where the implementation does not match the intent.
This is one of the biggest reasons why we have code review: so that at least one person has read the code before it lands, to make sure that it is possible to understand it by reading it.
Reading code is harder than writing code even when that code is written by someone who is trying to make it clear what the code does and why.
LLMs make it easier to write code but harder to read because they produce statistically plausible code, which means code that looks right. Spotting bugs is harder. The code is often commented but the comments don’t reflect what it does, they direct you to misunderstand what the code does in subtle ways.
But even if they generated code that is as easy to read as a good human programmer, they would not be solving the right problem.
And, yes, LLMs can ‘explain’ code. And sometimes those explanations are correct and not subtly misleading. Often enough that they allow skills to atrophy, but not often enough that they’re trustworthy. They are a perfect example of The Paradox of Automation.
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.
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
Read the article
Here’s a link to the exact same post on Cory Doctorow’s (tracker-free) blog: https://pluralistic.net/2026/08/01/dare-snot/#i-will-fucking-piledrive-you-if-you-mention-ai-again
I can’t say AI hasn’t changed how I work, it has. Instead of going to Google, vendor docs, or the manpage for whatever code or system I am working on I will ask Claude or Gemini to take a stab at building the initial pass of the code or to look up what some cryptic error code means for me. Or even a better way to write some code that I think can be more concise.
The key I think is I never let it make the final decisions for me. The workflow is always look up information > shows it to me > ask for permission to do the thing > do the thing.
But none of that is “changing everything” in the way executives and the Sam Altman’s of the world want to profess. It is just a sometimes more efficient for me way of doing the same thing I was doing before.
A lot of people I have seen that crow about how they are using AI to change their work lives are just doing really fancy Github workflows and actions to automate something they used to still do manually for some reason. They rarely are doing anything that couldn’t be done with the time to research and test without AI.
They didn’t have the framework knowledge before to automate the thing. It’s exactly where I was before AI. I knew that I could work more efficiently, but spending months learning Python was a lot slower than the 45 minutes, every other week I save by having a script do something for me. I don’t save enough budget to actually afford the time and effort to find a programmer, tell them what I need and then pay them to write it for me. Took 20 minutes worth of tweaking with the free tier of Claude and it was done to a “good enough” standard to save the time.
Same here too busy to automate before AI and exactly this would have lead to more efficiency.
I could have told people I am sick for two weeks and then went on to learn and write scripts.
my routine was search for similar function, read/understand a piece of code. copy/paste block. modify for my context. test, move on hardly ever wrote anything from scratch because it just took longer. ai seems like an unmodified copy/paste that doesn’t quite work. like generated art for non–artists, sucks. generated code for non-coders also sucks.
