I wish I could recommend this piece more, because it makes a bunch of great points, but the "normal technology" case feels misleading to me.
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For me, this is the body horror money quote from that Scientific American article:
"participants who saw the AI autocomplete prompts reported attitudes that were more in line with the AI’s position—including people who didn’t use the AI’s suggested text at all"
So maybe you can't use it "responsibly", or "safely". You can't even ignore it and choose not to use it once you've seen it.
If you can see it, the basilisk has already won.
@glyph oh man, that’s dangerously close to giving an abstinence-based push some fuel and whew does my rather hedonistic ass have some thoughts on that
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For me, this is the body horror money quote from that Scientific American article:
"participants who saw the AI autocomplete prompts reported attitudes that were more in line with the AI’s position—including people who didn’t use the AI’s suggested text at all"
So maybe you can't use it "responsibly", or "safely". You can't even ignore it and choose not to use it once you've seen it.
If you can see it, the basilisk has already won.
@glyph "Interestingly, the people in the study didn’t tend to think the AI autocomplete suggestions were biased or to notice that they had changed their own thinking on an issue in the course of the study. Warning the participants that they might be exposed to misinformation by the AI didn’t temper the persuasive effect either."
Also, being aware that it can fuck with your head does not make you less susceptible to it fucking with your head. So you can't really judge if you could use it safely.
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@glyph Wait. Does Doctorow try to soften the edge of the fact that LLMs do a bad thing to people by pointing out sometimes people do bad things to people? Isn't that just two bad things?
If his point is it's still bad but it isn't *novel*, isn't the fact of a Fortune 500 company doing it still novel?
@mcc Let me just give you the pull quote:
"""
For many programmers – including several of my acquaintance, whom I know to be both thoughtful and skilled – AI is another plugin, one they find useful enough to be modestly enthusiastic about.It is nuts to deny the experiences these people are having. They're not vibe-coding mission-critical AWS modules…
""" -
@mcc Let me just give you the pull quote:
"""
For many programmers – including several of my acquaintance, whom I know to be both thoughtful and skilled – AI is another plugin, one they find useful enough to be modestly enthusiastic about.It is nuts to deny the experiences these people are having. They're not vibe-coding mission-critical AWS modules…
"""@mcc He thinks the technology is capable of many horrors but it can also be useful for pedestrian things.
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@glyph "Interestingly, the people in the study didn’t tend to think the AI autocomplete suggestions were biased or to notice that they had changed their own thinking on an issue in the course of the study. Warning the participants that they might be exposed to misinformation by the AI didn’t temper the persuasive effect either."
Also, being aware that it can fuck with your head does not make you less susceptible to it fucking with your head. So you can't really judge if you could use it safely.
@glyph Nor can you reliably judge if it has or has not already fucked with your head.
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@mcc He thinks the technology is capable of many horrors but it can also be useful for pedestrian things.
@glyph That sounds to me like a way to get horrors but you're probably not the person to convince
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I'm open to a future where we do some research and figure out the limits of how AI influence works, and where the safety valves are, and also the extent to which it's *fine* that AI can influence our views because honestly many different kinds of stimuli can influence our views, not least of which is each other. But it sure looks right now like it has a bunch of very dangerous feedback loops built-in, and it's not clear how to know if you're touching one.
But, as Cory puts it:
"""
It is nuts to deny the experiences these people are having. They're not vibe-coding mission-critical AWS modules. They're not generating tech debt at scale.
"""I had a very visceral emotional reaction to this particular paragraph, and I find it very important to refute. Here are two points to consider:
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But, as Cory puts it:
"""
It is nuts to deny the experiences these people are having. They're not vibe-coding mission-critical AWS modules. They're not generating tech debt at scale.
"""I had a very visceral emotional reaction to this particular paragraph, and I find it very important to refute. Here are two points to consider:
1. YES THEY ARE.
They are vibe-coding mission-critical AWS modules. They are generating tech debt at scale. They don't THINK that that's what they're doing. Do you think most programmers conceive of their daily (non-LLM) activities as "putting in lots of bugs"? No, that is never what we say we're doing. Yet, we turn around, and there all the bugs are.
With LLMs, we can look at the mission-critical AWS modules and ask after the fact, were they vibe-coded? AWS says yes https://arstechnica.com/civis/threads/after-outages-amazon-to-make-senior-engineers-sign-off-on-ai-assisted-changes.1511983/
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But, as Cory puts it:
"""
It is nuts to deny the experiences these people are having. They're not vibe-coding mission-critical AWS modules. They're not generating tech debt at scale.
"""I had a very visceral emotional reaction to this particular paragraph, and I find it very important to refute. Here are two points to consider:
@glyph but they are, at scale, generating tech debt
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1. YES THEY ARE.
They are vibe-coding mission-critical AWS modules. They are generating tech debt at scale. They don't THINK that that's what they're doing. Do you think most programmers conceive of their daily (non-LLM) activities as "putting in lots of bugs"? No, that is never what we say we're doing. Yet, we turn around, and there all the bugs are.
With LLMs, we can look at the mission-critical AWS modules and ask after the fact, were they vibe-coded? AWS says yes https://arstechnica.com/civis/threads/after-outages-amazon-to-make-senior-engineers-sign-off-on-ai-assisted-changes.1511983/
2. If it is "nuts" to dismiss this experience, then it would be "nuts" to dismiss mine: I have seen many, many high profile people in tech, who I have respect for, take *absolutely unhinged* risks with LLM technology that they have never, in decades-long careers, taken with any other tool or technology. It reads like a kind of cognitive decline. It's scary. And many of these people are *leaders* who use their influence to steamroll objections to these tools because they're "obviously" so good
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2. If it is "nuts" to dismiss this experience, then it would be "nuts" to dismiss mine: I have seen many, many high profile people in tech, who I have respect for, take *absolutely unhinged* risks with LLM technology that they have never, in decades-long careers, taken with any other tool or technology. It reads like a kind of cognitive decline. It's scary. And many of these people are *leaders* who use their influence to steamroll objections to these tools because they're "obviously" so good
The very fact that things like OpenClaw and Moltbook even *exist* is an indication, to me, that people are *not* making sober, considered judgements about how and where to use LLMs. The fact that they are popular at *all*, let alone popular enough to be featured in mainstream media shows that whatever this cognitive distortion is, it's widespread.
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For me, this is the body horror money quote from that Scientific American article:
"participants who saw the AI autocomplete prompts reported attitudes that were more in line with the AI’s position—including people who didn’t use the AI’s suggested text at all"
So maybe you can't use it "responsibly", or "safely". You can't even ignore it and choose not to use it once you've seen it.
If you can see it, the basilisk has already won.
@glyph@mastodon.social I wonder if this is why I find the whole genAI thing to be so very antithetical to creative pursuits; once you've been exposed to it, it's in there, and I feel like that just isn't broadly compatible with creativity?
Like we're all influenced, for sure, and those influences can become part of our own creative output. But I think there's a difference between, "I read a particular author" vs. "that author is standing over my shoulder telling me what to write". It doesn't help that the output is literally the most average output, either. It's like if the world's most generic author was hovering over your shoulder, telling you what to write.
That seems like creative death, not like a helper. And for programming, which is creative (and I'm glad we're all saying it), I feel that same element very much at play. -
1. YES THEY ARE.
They are vibe-coding mission-critical AWS modules. They are generating tech debt at scale. They don't THINK that that's what they're doing. Do you think most programmers conceive of their daily (non-LLM) activities as "putting in lots of bugs"? No, that is never what we say we're doing. Yet, we turn around, and there all the bugs are.
With LLMs, we can look at the mission-critical AWS modules and ask after the fact, were they vibe-coded? AWS says yes https://arstechnica.com/civis/threads/after-outages-amazon-to-make-senior-engineers-sign-off-on-ai-assisted-changes.1511983/
Two statements I believe are consistently correct:
(1) Generative “AI” produces code significantly faster than humans do only when nobody takes sufficient time to understand it (not just in a narrow syntactic sense; also in the context of organizational needs, longer-term plans, interaction with other applications, etc.)
(2) Code nobody understands well is “technical debt” *by definition*, because it takes a disproportionate amount of time and brain power to change or improve.
Conclusion: unless software developers are incredibly disciplined, and have a level of time and autonomy they generally do not have in a major tech company, generative “AI” usage will *consistently* create large amounts of “tech debt”.
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The very fact that things like OpenClaw and Moltbook even *exist* is an indication, to me, that people are *not* making sober, considered judgements about how and where to use LLMs. The fact that they are popular at *all*, let alone popular enough to be featured in mainstream media shows that whatever this cognitive distortion is, it's widespread.
Furthermore, it is not "nuts" to dismiss the experience of an LLM user. In fact, you must dismiss all experiences of LLM users, even if the LLM user is yourself. Fly by instruments because the cognitive fog is too think for your eyes to see.
Because the interesting, novel thing about LLMs, the thing that makes them dangerous and interesting, is that they are, by design, epistemic disruptors.
They can produce symboloids more rapidly than any thinking mind. Repetition influences cognition.
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M mttaggart@infosec.exchange shared this topic
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Furthermore, it is not "nuts" to dismiss the experience of an LLM user. In fact, you must dismiss all experiences of LLM users, even if the LLM user is yourself. Fly by instruments because the cognitive fog is too think for your eyes to see.
Because the interesting, novel thing about LLMs, the thing that makes them dangerous and interesting, is that they are, by design, epistemic disruptors.
They can produce symboloids more rapidly than any thinking mind. Repetition influences cognition.
I have ADHD. Which means I am experienced in this process of self-denial. I have time blindness. I run an app that tells me how long I've been looking at other apps, because if I trust my subjective perception, I will think I've been looking at YouTube for 10 minutes instead of 4 hours. Every day I need to deny my subjective feelings about how using software is going, in order to function in society.
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I have ADHD. Which means I am experienced in this process of self-denial. I have time blindness. I run an app that tells me how long I've been looking at other apps, because if I trust my subjective perception, I will think I've been looking at YouTube for 10 minutes instead of 4 hours. Every day I need to deny my subjective feelings about how using software is going, in order to function in society.
This disability gives me a superpower. I'm Geordi with the visor, able to see what everybody else's regular eyes are missing. This is basically where the idea for https://blog.glyph.im/2025/08/futzing-fraction.html originally came from: since I already monitor my time use, and I noticed that my time in LLM apps was WAY out of whack, consistently in "hyperfocus" levels of time-use, without any of the subjective impression of engagement or pleasure. Just dull frustration and surprising amounts of wasted time.
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This disability gives me a superpower. I'm Geordi with the visor, able to see what everybody else's regular eyes are missing. This is basically where the idea for https://blog.glyph.im/2025/08/futzing-fraction.html originally came from: since I already monitor my time use, and I noticed that my time in LLM apps was WAY out of whack, consistently in "hyperfocus" levels of time-use, without any of the subjective impression of engagement or pleasure. Just dull frustration and surprising amounts of wasted time.
The suggestion that the article makes is all about passive monitoring of the amount of time that your LLM projects *actually* take, so you can *know* if you're circling the drain of reprompting and "reasoning". Maybe some people really *are* experiencing this big surge in productivity that just hasn't shown up on anyone's balance sheet yet! But as far as I know, nobody bothers to *check*!
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Two statements I believe are consistently correct:
(1) Generative “AI” produces code significantly faster than humans do only when nobody takes sufficient time to understand it (not just in a narrow syntactic sense; also in the context of organizational needs, longer-term plans, interaction with other applications, etc.)
(2) Code nobody understands well is “technical debt” *by definition*, because it takes a disproportionate amount of time and brain power to change or improve.
Conclusion: unless software developers are incredibly disciplined, and have a level of time and autonomy they generally do not have in a major tech company, generative “AI” usage will *consistently* create large amounts of “tech debt”.
@glyph I should add: I am being careful to say “produces”, not “writes”. It is becoming clear that even if we grant that the pre-LLM bottleneck was developer code-authoring speed, in LLM-heavy workflows, the bottleneck is now “verify that this code is ready to deploy”. This is partly because there is so much more code coming in, but even more because far fewer people have any depth of understanding of the code being PR’ed. *All* the incentives lead to people saying “LGTM, it passes tests, ship it.”
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@glyph I should add: I am being careful to say “produces”, not “writes”. It is becoming clear that even if we grant that the pre-LLM bottleneck was developer code-authoring speed, in LLM-heavy workflows, the bottleneck is now “verify that this code is ready to deploy”. This is partly because there is so much more code coming in, but even more because far fewer people have any depth of understanding of the code being PR’ed. *All* the incentives lead to people saying “LGTM, it passes tests, ship it.”
@dpnash I am, as always, open to seeing real evidence that this is not the case. However, everything I've seen and heard thus far tells me that it is.
Your point (1) could be factually disputed, although I think it would be hard to prove, but your point (2) is just… logically necessary, I think. I cannot imagine ramming the code through a human brain thoroughly enough to actually understand it.
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@dpnash I am, as always, open to seeing real evidence that this is not the case. However, everything I've seen and heard thus far tells me that it is.
Your point (1) could be factually disputed, although I think it would be hard to prove, but your point (2) is just… logically necessary, I think. I cannot imagine ramming the code through a human brain thoroughly enough to actually understand it.
@dpnash I mean, heck, the whole concept of the very popular problem of "NIH" is that code *already exists* and we *could* use it, but we don't use it *because writing it is an easier way to understand it*!
