Again, they didn’t smash the machines because they hated the technology or wanted everyone to be making lace by hand.
They were arguing for basic human rights in the workplace. Things like child labour were still super common and were among the practices the Luddites wanted to abolish. Along with the workhouses they wanted to replace with protection for workers (they didn’t have the word for it but they wanted a social security system).
They smashed the machines for leverage. There was little labour law at the time. Most of it was written by the capital holders with the help of the constabulary. Things like showing up to work on time or no pay, etc. Violence, controlled violence, was the tool they used to try and get the capital holders to the table and negotiate.
It failed, as we know, and it was a bloody failure. People were executed and jailed. The movement became a pejorative for someone who is backwards and against technology and progress.
I think the vibe is generally same with AI too. If people believe AI will genuinely be used for good (like many in China seem to think, I get the feeling) then the public at large seems fine with industry focusing on it. But when people have the feeling or even expectation that it'll be used for things like increasing the wealth/wage/inequality gap, control of the masses, automated widespread surveillance and more, you get the violent attacks on data centers and such.
Maybe if these people who are basically wealthier than some countries would share their loot or just care a tiny bit about 99% of the rest of the world, people would feel differently about things. Use AI and your data centers to figure out how to quench, feed and power the entire world, and probably a bunch of people who hate AI would even get onboard.
Writing tip: you do not need to have LLMs expand your ideas into a longer form for you. Spreading out your ideas across a longer post does not make them better.
These types of posts are tediously regular. Aschenbrenner’s “Situational Awareness” in 2024 [1]. Fraudster Matt Schumer’s “Something Big is Happening” in February [2].
> Go where it’s new. Build things no one has built yet — things that other people’s workflows can come to depend on. One important rule here: don’t build what can be obtained by burning tokens. Any engineering product that someone has already built and that you can simply have AI clone — one more review bot, one more workflow tool — is not worth your energy, because its acquisition cost has already approached zero. What’s worth building is paradigm-level work: things that, once they exist, change how other people work.
But once you build something new, that people depend on, they will shortly want to move away from that dependency, lest you raise prices or disappear. Even paradigm-level work ends up as tools that can be replicated by other humans, at the cost of a few tokens. That's a difficult dragon to catch and ride successfully.
I think the author had it right when they talked about going deeper into the stack, where we are still loath to deploy AI. The only way to stay ahead of the beast is to do things the beast can't do reliably.
I've noticed that "18 months" is a very popular prediction target, across a wide variety of prognosticators. Close enough to seem urgent, and if what they predict comes to pass anytime in the next few years, they can claim "See, I was right, just a little off on the timing because <excuse>". If it doesn't happen, most everyone will have forgotten their prediction. And in the unlikely event they get called out, they have over a year of random events to find a plausible fall guy for their failure.
I have my own take on what is ours and can never be taken away by AI.
When a task is initiated, it starts from a need, from a specific context. To work it out the AI needs to continuously interact with the context, and get feedback from it. At the end gains, losses, risks and costs sink back in the context.
The context is you, the person who prompts, your team or company. It is indexical and relational. It is maximally distributed. It cannot be hoarded. You can't eat so that I feel satiated. AI is called to do the work, but it can't handle 3 things - start, middle and end of a task.
I agree with all that but the problem is what others will pay for. There might be niche artisans that survive but most of it might go the way of wallmart.
> My answer: soon. By the end of this year or next, the year after at the very latest.
Which is based on what?
> AI can process the entire world
It can process what is in it's training set. Which is a monumental gap to step over. Failing to understand this leads to all kinds of silly predictions and mindless prognostication.
I'd like to see an AI article based on data and not paragraphs of internal monologue spewed out onto the internet.
> Once AI takes everything it can take, what is left for us?
This is a good question, but is perhaps too abstract to address well. I think a better question for right now is:
Once AI generates all the wealth it can generate, who benefits from that wealth?
If the answer is a small number of humans, that is probably a dystopia worth resisting.
If the answer is some number of AI agents, but no humans at all, that is probably also a dystopia worth resisting.
I think the only good outcome is one in which humanity benefits on the whole. If that means that we have to become a post-capitalist society in order to share in the wealth, so be it.
This post is a little long winded. At times I agreed, and others I felt the author was doing a little too much hand-wringing on what-is-mine vs. what-is-the-AI's.
The opportunity is and always has been the possibility of accelerating work. Honestly, if something works (I mean genuinely, actually works) I don't care at all about what craftsmanship or insight went into its creation.
We value these things because they have become correlated with quality. We now have the opportunity to decouple these things; maybe something that took no effort will be just as good as a painstaking human labor.
The risk is if this doesn't come true. If we let our skills degrade and get ahead of our skis, embracing "slop" that superficially appears to "work", we will eventually pay the price. Financially and culturally, it seems like we are already all-in on the bet that it will work.
I hope it does, I just want to solve the problems I am working on.
But once you put something out, an app, repo, content, music or photo - it can be easily cloned with AI, changed to be different just enough to avoid accusations and customized as needed. The price of replication is dropping fast, and that means any perceived arbitration point will be competed away. I predict AI will be immensely valuable like Linux, but "nobody" (excluding shovel makers, for a while) will get rich off AI, because whatever it can clone is not a moat anymore.
If AI was paying for us all the content we create daily just by living, humans would love it. It would be a cyclical relationship where AI thrives off all the daily content all humans create daily and AI pays us for the privilege to thrive off of us (our content).
I saw Trump saying AI companies are discussing offering Americans stock in AI companies so that's one way where the narrative changes and a few days I thought of one way how AI can pay all of us for our content ..posted quick thoughts on my Substack... https://ryanspahn.substack.com/p/ai-to-pay-for-all-americans...
Overall, AI is irrelevant without us and it needs to pay us to keep it relevant I think! It can not continue to be a bloodsucker!
I've started to realize after poring over pull requests which are, frankly, slop that the devs who are the most bullish on AI are the ones who raise those PRs and don't recognize the slop.
AI for sure is giving all of them existential crises but I'm not sure most of them ever really belonged in the industry in the first place.
I give it 9-12 months before they start to realize that acknowledgement of this existential crisis is at its core, acknowledgement of of a skill issue.
I’ve tried to explain this to folks as “having taste”, but I’m always worried it comes off as subjective and snobby. It might be a fair assessment honestly, it’s hard for me to describe so I wouldn’t hold anyone to it as a standard. Give me an honest vibe check on that.
Theres a lot of codebases out there that are at odds with my own opinions about syntax/structure/purpose, but there’s evidence of “taste” that I absolutely respect. I can look at a couple modules, and have a good idea what the other modules are going to be like, because the mental model of the author is clear from the code itself. Even teams with multiple authors with taste average out to one taste-profile and in a similar way, I’ve seen LLM output shaped by someone with taste and had the same feeling: “yeah I see the direction you’re going in”.
Someone without taste using an LLM writes slop. I can’t tell what you’re doing. Any question about what you’re doing results in “sorry that was Claude”. Entirely pointless that you’re even involved.
It’s a property of the author IMO. They were kind of owed an existential crisis as cruel as that is to say.
The only problem with Microsoft is they just have no taste. They have absolutely no taste. And I don't mean that in a small way, I mean that in a big way, in the sense that they don't think of original ideas, and they don't bring much culture into their products.
Many managers are more bullish on AI and less able to recognize slop, they are unlikely to recognize quality crisis. And they are the people who decide who belong to the industry and who is not. As a result we will get an escalation of enshittification and people will start to forget that slop is not the only option.
Yeah, I keep coming back to a point that the way people talk about AI is still entirely disconnected from what it can actually do. I think of the bell curve meme a lot when I see people talking about AI. the people most bullish to perpetuate that it's going to take over are people that have vested interested, or people that are fall on the bottom half of the bell curve. I mean ... come on, by design an AI is literally a statistical averaging of all the data it's seen. AI is extremely average at nearly everything it does. If you find yourself using AI and it's doing something amazing, that speaks more to your knowledge/ability about a subject more than it speaks about AIs ability
I mean, I guess if all you do is work on implementing CRUD endpoints ... sure I guess you're cooked. but we had tech to automate this already, this isn't anything new. But oh man, if you're doing real engineering, the tools are barely usable.
I hate when people don't give examples, so I am going to throw one here. just the other day, I asked the newest and most expensive claude model to write an LRU and to have a running tally of the capacity of bytes in the cache as the threshold to evict something from the cache. It wrongly implemented the threshold checks and just tracked how many elements were in the cache. this might sound small, but scale that mistake up to a real production system. this is literally unusable. and the expectation to sit there, have it generate 1000s of lines of code for you, and then spot check that small but huge error is not worth it. you have to move so slow to spot check everything - to the point that it's literally faster to type it. This is a model that costs $100s to run per hour and is advertised as "PHD level intelligence" making High school AP computer science to freshman computer science errors - like come on.
If you're reading this, are an expert in your field, and are actually worried about your job - you got be able to have some mental fortitude and not fall for this ...
so much to unpack here and almost poetic that you say this
first is that the model will write out that it “thought” and “double checked” it’s output
Second, this was in a fresh context window of the latest model (that isn’t fable b/c we can’t use for reasons beyond this thread), and it was on it’s second highest thinking mode. I shouldn’t have to double check something that it claimed to have burned more tokens on to double check
Outside of it costing me more money to fix what it claims to do, the main point of this article is that models are implementing things nearly end to end, and if we scale it up, it will only continue to do that. I Intentionally chose the example of something that is < 70 lines to implement in TS (btw, the language with the second most amount of data available to scrape and train on) I would assume a machine that can almost implement things end to end should be able to implement something of 70 lines of code and has been documented for nearly 50 years.
My point is that time and time again on the most trivial examples, under the best of conditions, and with unlimited amounts of money, they can’t do what it claims
Outside of that, this follow up comment(s) that say, “oh you need to ask it to check its own work and be so involved in the process of it writing the code that you need to spot check it” goes against everything the article states
The best analogy I have for this is New speak in 1984, it’s just vibes dictating vibes and trying to make people claim that the vibes are right. and if you try to validate the vibes, your vibes are just wrong because you don’t get the vibes. The claims that it made have no data backing it. And if there is data, it’s cherry picked. Please use your brain and stop outsourcing your ability to think to a machine that is incorrectly thinking on your behalf
Where you see quality crisis I see job security! Honest question, when it comes to enshittification of software quality.. have you ever had to use a Meta framework? How many times have they rewritten their mobile apps to use some architect's bespoke code pipe dream? The quality crisis has always been here, now there's just more of it.
I think it's likely that what you call slop is more often than not "good enough".
One thing a lot of developers aim for in their code, beyond "it does what it is meant to", is something along the lines of elegance (that's my word for it, there may be a better one).
With AI generated code there is no time for elegance. It will happily recreate the same function in several different places for no reason. And that really doesn't matter anymore.
Said another way: AI generated code doesn't chase perfection. It just chases good enough.
But it's not good enough. We can see this all over industry where even M$ is producing software so bad even calculator is electron app. Slow, poor quality and for any engineer below acceptable
>> It will happily recreate the same function in several different places for no reason
So do many developers. I've lost count how many times a code review had to be rejected or cleaned up because of copy and pasted code and I'm going to admit, sometimes it's just quicker to duplicate a little code and leave a comment for 'next time'.. we've all done it.
.. like this one time I had a PR and the developer created on loooong linear method, couldn't figure out how to share between targets and copied and pasted the same bad code somewhere else. Somehow it got through and when asked why this was on production the answer was 'it worked'.
>> no time for elegance
This happens, your experience in is generally your quality out. But that doesn't necessarily mean there's going to be elegance. I've worked at major product driven companies where elegance took a back seat to getting release out the door.
They were arguing for basic human rights in the workplace. Things like child labour were still super common and were among the practices the Luddites wanted to abolish. Along with the workhouses they wanted to replace with protection for workers (they didn’t have the word for it but they wanted a social security system).
They smashed the machines for leverage. There was little labour law at the time. Most of it was written by the capital holders with the help of the constabulary. Things like showing up to work on time or no pay, etc. Violence, controlled violence, was the tool they used to try and get the capital holders to the table and negotiate.
It failed, as we know, and it was a bloody failure. People were executed and jailed. The movement became a pejorative for someone who is backwards and against technology and progress.
Maybe if these people who are basically wealthier than some countries would share their loot or just care a tiny bit about 99% of the rest of the world, people would feel differently about things. Use AI and your data centers to figure out how to quench, feed and power the entire world, and probably a bunch of people who hate AI would even get onboard.
That's a different question than the included Luddite example, which I take as "what do we do to prevent change?".
Related, I've been maintaining a list of anti-tech Luddite movements here: https://docs.google.com/spreadsheets/d/1M_UjOPxpbKMYes5CcWRW...
I would have written a letter, but I couldn’t be bothered.
Next viral doom post in September-ish?
[1] https://situational-awareness.ai/
[2] https://lowendbox.com/blog/ai-fraudster-matt-shumer-wrote-so...
But once you build something new, that people depend on, they will shortly want to move away from that dependency, lest you raise prices or disappear. Even paradigm-level work ends up as tools that can be replicated by other humans, at the cost of a few tokens. That's a difficult dragon to catch and ride successfully.
I think the author had it right when they talked about going deeper into the stack, where we are still loath to deploy AI. The only way to stay ahead of the beast is to do things the beast can't do reliably.
lol
Two More Weeks(TM)
When a task is initiated, it starts from a need, from a specific context. To work it out the AI needs to continuously interact with the context, and get feedback from it. At the end gains, losses, risks and costs sink back in the context.
The context is you, the person who prompts, your team or company. It is indexical and relational. It is maximally distributed. It cannot be hoarded. You can't eat so that I feel satiated. AI is called to do the work, but it can't handle 3 things - start, middle and end of a task.
Which is based on what?
> AI can process the entire world
It can process what is in it's training set. Which is a monumental gap to step over. Failing to understand this leads to all kinds of silly predictions and mindless prognostication.
I'd like to see an AI article based on data and not paragraphs of internal monologue spewed out onto the internet.
This is a good question, but is perhaps too abstract to address well. I think a better question for right now is:
Once AI generates all the wealth it can generate, who benefits from that wealth?
If the answer is a small number of humans, that is probably a dystopia worth resisting.
If the answer is some number of AI agents, but no humans at all, that is probably also a dystopia worth resisting.
I think the only good outcome is one in which humanity benefits on the whole. If that means that we have to become a post-capitalist society in order to share in the wealth, so be it.
If human "taste" changes our cursor for no reason, maybe we should just go with the AI's "taste"?
Sorry for the snark - nothing wrong with the essay - I just prefer a plain, unobtrusive style.
The opportunity is and always has been the possibility of accelerating work. Honestly, if something works (I mean genuinely, actually works) I don't care at all about what craftsmanship or insight went into its creation.
We value these things because they have become correlated with quality. We now have the opportunity to decouple these things; maybe something that took no effort will be just as good as a painstaking human labor.
The risk is if this doesn't come true. If we let our skills degrade and get ahead of our skis, embracing "slop" that superficially appears to "work", we will eventually pay the price. Financially and culturally, it seems like we are already all-in on the bet that it will work.
I hope it does, I just want to solve the problems I am working on.
That's where I'm at too. Saves you weeks of effort to boot.
I saw Trump saying AI companies are discussing offering Americans stock in AI companies so that's one way where the narrative changes and a few days I thought of one way how AI can pay all of us for our content ..posted quick thoughts on my Substack... https://ryanspahn.substack.com/p/ai-to-pay-for-all-americans...
Overall, AI is irrelevant without us and it needs to pay us to keep it relevant I think! It can not continue to be a bloodsucker!
AI for sure is giving all of them existential crises but I'm not sure most of them ever really belonged in the industry in the first place.
I give it 9-12 months before they start to realize that acknowledgement of this existential crisis is at its core, acknowledgement of of a skill issue.
Theres a lot of codebases out there that are at odds with my own opinions about syntax/structure/purpose, but there’s evidence of “taste” that I absolutely respect. I can look at a couple modules, and have a good idea what the other modules are going to be like, because the mental model of the author is clear from the code itself. Even teams with multiple authors with taste average out to one taste-profile and in a similar way, I’ve seen LLM output shaped by someone with taste and had the same feeling: “yeah I see the direction you’re going in”.
Someone without taste using an LLM writes slop. I can’t tell what you’re doing. Any question about what you’re doing results in “sorry that was Claude”. Entirely pointless that you’re even involved.
It’s a property of the author IMO. They were kind of owed an existential crisis as cruel as that is to say.
The only problem with Microsoft is they just have no taste. They have absolutely no taste. And I don't mean that in a small way, I mean that in a big way, in the sense that they don't think of original ideas, and they don't bring much culture into their products.
Steve Jobs
In that light I’m not happy about it, but the code always was just a means to an end.
I mean, I guess if all you do is work on implementing CRUD endpoints ... sure I guess you're cooked. but we had tech to automate this already, this isn't anything new. But oh man, if you're doing real engineering, the tools are barely usable.
I hate when people don't give examples, so I am going to throw one here. just the other day, I asked the newest and most expensive claude model to write an LRU and to have a running tally of the capacity of bytes in the cache as the threshold to evict something from the cache. It wrongly implemented the threshold checks and just tracked how many elements were in the cache. this might sound small, but scale that mistake up to a real production system. this is literally unusable. and the expectation to sit there, have it generate 1000s of lines of code for you, and then spot check that small but huge error is not worth it. you have to move so slow to spot check everything - to the point that it's literally faster to type it. This is a model that costs $100s to run per hour and is advertised as "PHD level intelligence" making High school AP computer science to freshman computer science errors - like come on.
If you're reading this, are an expert in your field, and are actually worried about your job - you got be able to have some mental fortitude and not fall for this ...
first is that the model will write out that it “thought” and “double checked” it’s output
Second, this was in a fresh context window of the latest model (that isn’t fable b/c we can’t use for reasons beyond this thread), and it was on it’s second highest thinking mode. I shouldn’t have to double check something that it claimed to have burned more tokens on to double check
Outside of it costing me more money to fix what it claims to do, the main point of this article is that models are implementing things nearly end to end, and if we scale it up, it will only continue to do that. I Intentionally chose the example of something that is < 70 lines to implement in TS (btw, the language with the second most amount of data available to scrape and train on) I would assume a machine that can almost implement things end to end should be able to implement something of 70 lines of code and has been documented for nearly 50 years.
My point is that time and time again on the most trivial examples, under the best of conditions, and with unlimited amounts of money, they can’t do what it claims
Outside of that, this follow up comment(s) that say, “oh you need to ask it to check its own work and be so involved in the process of it writing the code that you need to spot check it” goes against everything the article states
The best analogy I have for this is New speak in 1984, it’s just vibes dictating vibes and trying to make people claim that the vibes are right. and if you try to validate the vibes, your vibes are just wrong because you don’t get the vibes. The claims that it made have no data backing it. And if there is data, it’s cherry picked. Please use your brain and stop outsourcing your ability to think to a machine that is incorrectly thinking on your behalf
Edit: Typos
One thing a lot of developers aim for in their code, beyond "it does what it is meant to", is something along the lines of elegance (that's my word for it, there may be a better one).
With AI generated code there is no time for elegance. It will happily recreate the same function in several different places for no reason. And that really doesn't matter anymore.
Said another way: AI generated code doesn't chase perfection. It just chases good enough.
So do many developers. I've lost count how many times a code review had to be rejected or cleaned up because of copy and pasted code and I'm going to admit, sometimes it's just quicker to duplicate a little code and leave a comment for 'next time'.. we've all done it.
.. like this one time I had a PR and the developer created on loooong linear method, couldn't figure out how to share between targets and copied and pasted the same bad code somewhere else. Somehow it got through and when asked why this was on production the answer was 'it worked'.
>> no time for elegance
This happens, your experience in is generally your quality out. But that doesn't necessarily mean there's going to be elegance. I've worked at major product driven companies where elegance took a back seat to getting release out the door.
I like that. We are all machines.