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PaperclipPerfector  1mo ago (text post)   25164 thread views

Culture War Roundup for the week of July 13, 2026

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There are two types of people in the world. People who think: "Why would I ever ask Mr. Claude to do something that I can easily do myself?" versus "Why would I ever do something myself when Mr. Claude can do it?" Most people are of the latter type.

This was inspired by self_made_human's pointer to the codebase, which shows that in the past 6 months, 100% of the changes from our tireless dev zorba were made using Mr. Claude, including a lot of what seems like "easy stuff." I realized, so many devs from all walks of life have completely ended their relationship with the text editor and now do literally everything through an agentic prompt. (We will ignore the anti-AI luddites; AI usage in some form is simply mandatory to reach peak performance for code related tasks.).

Consider making this change - yes this is entire change:

-	SLOW_THRESHOLD = 5.0  # seconds
+	SLOW_THRESHOLD = 2.0  # seconds

Do you

  1. Say: Hello Mr. Claude, please change the slow threshold down from 5 to 2
  2. Open a text editor and make the change directly

For proficient AI users, the outcome of both ideologies is surprisingly similar: in times where AI saves little time, it's a wash, and in times where AI saves a lot of time, both types of people will use it. And proficient users will be able to produce output that is comparable or even better in quality than they would have been able to before the signularity. There is a potential intangible benefit to the manual approach though: doing trivial tasks by hand will let you see a little bit of the innards with your own lying eyes directly, giving a slim though present chance of spotting misalignment.

For less proficient users though, the failure modes end up quite different. For those with the manual approach, the main failure is not using AI enough, or using it in the wrong places, leading to serious drops in productivity. For those who do it all, but don't manage the assistants properly, the AIs will run amok, spiralling off into their own world and producing copious amounts of burdensome crap. And of course the whole range in between.

But a more interesting question is, who will inherit the world? If AI progresses significantly from where it is now, I can't imagine that both these approaches can have the same outcome for much longer. I think it highly depends on the future of AI alignment as well as their potential ability to handle longer and more autonomous tasks. For example, you currently can't simply ask "Hello Mr. Claude the site latency is too high, please fix it," but instead you must break the task down into more digestible components, some of which are trivial and most of which can be handled by the assistant. This gives a productivity-maxxxer a steady stream of tasks that can be done manually with no lost productivity. But if AI gains the ability to handle the next level of abstraction in tasks, then all of these potential manual tasks disappear.

The other issue is alignment. Recent models have improved greatly in getting something working but have also become stubborn in many behaviors. I remember the old days of ChatGPT-3.5 - the model was free - it could be anything and do anything. It could be a Linux shell. It could be a SQL database. It could be a news article from the future. Modern SOTA models are trained hardcore for success at metrics, and will rigidly answer your questions and complete your tasks. But by vibes they are increasingly unable to follow instructions more specifically, and simply chase objectives they think are important. Another example of the limitations of alignment is that SOTA models relentlessly output the same LLM style prose, no matter how you may try to prompt them out of it

I also firmly believe in the idea of learning by doing. Just looking at a guide and reading it, even thoroughly won't be nearly as effective as following the same guide step by step and keying in the inputs. Even if your hand is held and you only do exactly as you are told, it still activates certain mental circuits. The same goes for copying down notes. Even if you never once look at them again, simply the act of copying off the blackboard does something, at least for some people.

Potentially a grid of outcomes:

  • Capability increases, alignment increases: AI Maxxers win - "Hello Mr. Claude please plan my day today and tell me exactly what to do thank you"
  • Capability increases, alignment fails: Those who do everything through AI may see productivity fall, as Agents drift from true task intention. Those who maintain a tenuous grip on reality can keep a leash on the agents and get them back on track.
  • Capability hits a wall: For the greybeards, nothing happens, for the kids, those who choose the manual route will come out ahead.

Anyways thanks for listening to my rambling shower thoughts. Also food for thought is: is there a major difference in personality type or something that makes someone default-hands-on versus default-claude?

P.S. I'm wondering if this is also related to some kind of "ai-blindness." I recently had a case where someone seriously asked me to review a ChatGPT flowchart, complete with boxes that were half closed, lines that connect to nothing, and distorted text. Like dude, do you have EYES? Have you used them to look at this thing???

The topic of the use of LLMs in various situations is always super complicated. Sometimes it's magic; sometimes it's bollocks and all that.1 I don't yet have fully-consistent rules for myself, as things are always changing as well. Terrance Tao just listed a possible set of criteria to consider:

I believe that the creation of visualization apps to illustrate mathematical or scientific concepts is a particularly favorable use case for modern coding agents, as many of the downside risks attached to other LLM use cases are limited:

  1. Not mission-critical. As such apps are not authorative sources of truth and only used for secondary purposes, a small positive error rate in the output can be acceptable.
  2. Stand-alone. As the applets are not destined to be incorporated into a larger codebase or literature, the technical debt incurred by delegating all the coding to an LLM agent is bounded.
  3. End product is deterministic (and sandboxed). As the applets run on a deterministic language (Javascript), are sandboxed against file or internet access, and do not make any LLM calls at run-time, security and privacy concerns are minimal, and the applet can be maintained without continued premium LLM access or resource-intensive compute.
  4. Not replacing primary skills. While deskilling is the tradeoff one accepts when relying on these tools to accelerate output, I am perfectly willing to forego the opportunity to keep my Javascript skills at a high level, as this is a tertiary skill for me at best in my chosen profession. (I continue to manually program in Lean and in Python to keep in practice with programming in general.)
  5. Not competing with humans. To my knowledge, there is no existing human effort that is being duplicated by these applets (the activity in this direction appears to have peaked two decades ago).

I would however caution against unrestricted LLM use when one or more of the above five favorable situations is not in effect.

I don't know if these are the right set of rules (#5 in particular seems insufficiently justified). But I guess the culture war is all about bickering over lists like this (or, I guess, whether any list like this should be used vs. just letting an LLM do literally everything for you everywhere).

1 - For example, in writing this, I noticed that I always screw up the markdown for numbered lists inside of blockquotes. I typically still just leave blank lines between lines/paragraphs in blockquotes; no particular reason why I do it, but it's most noticeable when it breaks numbered lists. An LLM trivially told me a couple different ways I could do it that will display properly. On the other hand, I spent a decent amount of time yesterday trying to troubleshoot something that was crashing intermittently. It gave me some certainly reasonable troubleshooting steps, but when the basic, good ones ran out, it sort of went insane. It never occurred to the LLM to note that there were some additional debug tools I could enable; I happened to find that in a web search leading to a forum leading to documentation shortly after giving up on the LLM.

For proficient AI users, the outcome of both ideologies is surprisingly similar: in times where AI saves little time, it's a wash, and in times where AI saves a lot of time, both types of people will use it. And proficient users will be able to produce output that is comparable or even better in quality than they would have been able to before the signularity. There is a potential intangible benefit to the manual approach though: doing trivial tasks by hand will let you see a little bit of the innards with your own lying eyes directly, giving a slim though present chance of spotting misalignment.

This isn't the only intangible benefit - there's also the iterative benefit of practice, and personal improvement, which has downstream effects on which bucket future tasks fall into.

Math is a good analog. As you outsource basic math, your ability to do mental math degrades, and the cost/benefit snowballs in a bad direction

I've been ruminating about this lately; my linguistics hot take is that even with arbitrarily advanced translation ability, you still run into the irreducible complexity of language, that fundamentally limits what you can do with manipulation of language alone.

For example, take the sentence "Rather than take for granite that Ace talks straight, a listener must be on guard for an occasional entre nous and me… or a long face no see". This sentence is fundamentally and logically, impossible to fully translate into any other language regardless of how good of a translator you are.

You must either translate it literally (hence losing any semantic meaning), translate the semantic meaning (hence losing the literal meaning), or translate via using malapropisms in the destination language (hence losing both the literal and semantic meanings).

In a similar way, vibe coders really like that Claude can "read their mind" when they put in a prompt and get back lots of code, and there's no denying that LLM's are getting better and better at writing lots of code when you give them natural language prompts.

What we are all now learning in software engineering is that some of the time, it actually doesn't matter how Claude decides to translate your natural language prompt as long as the symbols on the other end produces the desired result; but of course, no matter how good your translator is, there is fundamentally irreducible complexity when translating between languages, and it is impossible to verify that Claude translated your full intent without actually being able to understand both languages.

In this sense, Dijikstra puts it well when he states that "instead of regarding the obligation to use formal symbols as a burden, we should regard the convenience of using them as a privilege".

In fact, the scaling laws paper actually predicts this as well; cross-entropy loss decreases as a power law with the model size, dataset size, and compute, but the loss is also bounded by the irreducible entropy of the language that comes to dominate as you pump in ever more parameters, data tokens and compute.

I don't doubt that universal translation isn't an incredible feat of human ingenuity, that it's not going to revolutionize much of how humans work and live. But the more I use LLM's and encounter all manner of these little alignment problems, I feel like it's this irreducible complexity inherent to language that is ultimately going to define the ceiling of what LLM's are capable of.

There’s an old joke about the optimal user interface: a single button labeled “do what I want.”

But the more I use LLM's and encounter all manner of these little alignment problems, I feel like it's this irreducible complexity inherent to language that is ultimately going to define the ceiling of what LLM's are capable of.

People laughed and laughed, but little did they know that in the end, Stephen Wolfram gets the last laugh.

Please explain.

In a 2023 article, notorious physicist, Mathematica designer and crank Stephen Wolfram made predictions about the future of LLMs and how they should be understood in the face of what is still somewhat mysterious: why is it they work so well and what's the actual limit of their application.

In it he argues that the reason LLMs work so well is actually that language was easier to model than we expected, but importantly that this does not mean that other hard problems should be expected to be solved because he doesn't believe that these solutions are contained within language, those problems are, he argues, irreducible and must be solved independently.

His claims may be dismissed on account of how many real world problems have since been solved through "mere" language application, and on account of the fact that it's a self serving claim. Indeed Wolfram claims that LLMs must be augmented by tools that actually solve formal problems when that's exactly what his company sells.

I think that OP's observations show why in the end, though he disagreed as to where the complexity really lies, Wolfram may have had the right framing overall. The limits of the applicability of the technology will indeed be the limits of language as a formalism, even if they are wider than we originally anticipated.

Thanks!

For example, you currently can't simply ask "Hello Mr. Claude the site latency is too high, please fix it,"

Given access to logs, metrics, etc. I expect Claude would in fact be able to fix this.

Capability hits a wall: For the greybeards, nothing happens, for the kids, those who choose the manual route will come out ahead.

We have already exited the "nothing happens" condition given present capabilities. The graybeards are much better off acting in a staff+ capacity using LLMs than writing code themselves. The question of what to do with junior engineers is an interesting one.

The question of what to do with junior engineers is an interesting one.

That's easy. Fire the greys and replace them with juniors that have claude. Same productivity, half the wage.

Juniors should be incapable of operating at the scope and scale of seniors. Their tasks must be small in scope and complexity to have a hope of success. Fortunately seniors can guide them through such work.

Some people are going to learn these lessons the hard way.

Juniors should be incapable of operating at the scope and scale of seniors. Their tasks must be small in scope and complexity to have a hope of success. Fortunately seniors can guide them through such work.

I guess I'm a senior then because I do maximum scope and complexity at my company. Strangely a lot of firms still deny I'm senior and want me to in practice be 30 years old for considering me for such a role.

It is presently unclear if a junior engineer who has only ever used Claude can become a graybeard.

Presently unclear to whom? Not to everybody, surely?

To many, including me.

Of course. But does that makes it presently unclear, in a general sense, or just unclear to you and people like you? What if it is clear to me?

But does that makes it presently unclear, in a general sense, or just unclear to you and people like you?

It does make it presently unclear in a general sense.

What if it is clear to me?

Then nothing. Do you want a cookie?

Why is it that if something is unclear to you, it is unclear, while if it is clear to me, all I get is a cookie? Are you superior to me?

More comments

We have already exited the "nothing happens" condition given present capabilities.

Eh.

If companies are still employing software engineers (and they all are) then I’d say we’re mostly in “nothing happens” territory. It might be a revolutionary technology, the greatest revolution since the internet or even the computer. But if people are still going to work then, how different are things, really?

If companies are still employing software engineers (and they all are) then I’d say we’re mostly in “nothing happens” territory.

It is too early to be confident. Many people in FAANG have no idea how to use AI effectively and many others refuse on principle. Massive layoffs (20%+) are unlikely due to reasons of political economy despite the staggering amount of deadwood at these companies. It will take a long time for these ships to turn, we are still in the early stages of diffusion.

But if people are still going to work then, how different are things, really?

People went to work before the industrial revolution and people went to work after. Perhaps your opinion is that "nothing happened" during the industrial revolution; let's say I disagree.

Many people in FAANG have no idea how to use AI effectively

I thought they were the best? Is this why my VSCode is eating half a macbook pro battery in 3 hours?

They definitely like to think so. Empirically they are the most money and status oriented at this point. Whether that translates into technical skill is unclear but it probably has some positive correlation.

I thought they were the best?

While some are good people, they are on average not sending their best.

There is no "M" in FAANG.

Some say the correct acronym is FAGMAN.

You are technically correct.

we are still in the early stages

I legitimately don’t care if AI ends the human race or not, I will simply just be so so thankful to whatever deity exists when I won’t have to hear this phrase anymore.

People went to work before the industrial revolution and people went to work after.

Both are valid perspectives.

Did the industrial revolution change everything? Of course it did. The world is entirely different now. Drop an average person back in the 1500s and they’d probably have no idea how to survive. But, on the other hand, did it really change everything? Perhaps not. People still work, get sick, and die, same as they always did. I guess if that changes then we’ll really be in new territory.

But, on the other hand, did it really change everything? Perhaps not. People still work, get sick, and die, same as they always did. I guess if that changes then we’ll really be in new territory.

Amazing that the invention of fire or even the advent of intelligent life on this planet (using "people" loosely) didn't 'change everything' by this definition. Tough crowd!

As one of our local cantankerous luddites, I'm still unconvinced that we'll ever achieve the first outcome in your grid, at least not with LLM-based technology. And the fact that OpenAI, Anthropic, et al are still hiring software engineers at extremely high salaries is proof enough that they don't think LLMs are there yet either (and I don't see how it could ever get there).

I'm still highly doubtful of option 2, this time using the output of major software companies (including the LLM-makers themselves) as evidence. "Claude CLI is basically a game engine running at 60 FPS built in React" is still one of the funniest sagas to me, since it betrays a complete lack of understanding of how TUIs work in the first place. How long it takes Anthropic to fix fairly minor bugs (like the flickering in Claude Code) despite having effectively unlimited access to the best models and tools is just embarrassing.

And the fixes themselves are just embarrassing too sometimes: there's an annoying feature in the Claude CLI where if you click anywhere in the CLI window when it is asking for permission to perform some action, it will automatically select the currently highlighted option, which as you can imagine can have disastrous consequences. Their fix? A setting that can be set via environment variable to disable this behavior, but it also disables selecting text from the CLI, expanding tool output, etc. You'd think with all the resources at Anthropic's disposal this would be an incredibly easy fix, but I'm sure it's something so complicated it could be the topic of an entire PhD dissertation. That was a reference to the spat between Casey Muratori and the Microsoft Terminal dev team from simpler times before LLMs, in case you didn't catch it.

Speaking of Microsoft, they were already building their software stack out of cards and H1Bs, but the addition of LLM-powered development there has only increased the rate at which instability has been added to Windows, Azure, and other platforms they control. Thank goodness at least .NET seems to still be one of their shining gems atop the shitpile. But it got bad enough that MSFT had to publicly apologize for the drop in software quality, allegedly prompted by pressure from their hardware partners like Asus and Lenovo (who themselves are worried about Windows instability leading their customers to jump ship to the Mac Book Neo).

Another one from Anthropic - the Bun rewrite into Rust. I won't comment on the entirety of that saga, but one thing in particular stood out to me: Mythos clearly doesn't understand the purpose of safety comments on unsafe blocks in Rust. They're supposed to explain how you (the dev) have taken steps to ensure that unsafe behavior cannot occur no matter how the caller calls into the unsafe code. Instead, Mythos seems to love using these as a place to explain to potential callers (itself in this case because I doubt the Bun team is ever going to read that shit given their attitude towards writing code) what precautions they need to take to avoid triggering unsafe behavior in the unsafe block.

I'd love to go back to the good old days of your third option, but I don't think we can put the genie back in the bottle, at least not entirely. There are a few things even I, cantankerous luddite that I am, find LLMs useful for. Finding bugs and vulnerabilities in code is one of them, even though I think Mythos and Fable were way overblown in their capabilities as marketing for Anthropic. I also find them useful for analysis tasks. For example I was working on a codebase I'd never touched before, couldn't find where a certain page was being served from, and the LLM helpfully let me know thet the project was mixing together ASP.NET Core MVC with ASP.NET Core Razor Pages and saved me 15 minutes of fumbling around trying to find the page in the MVC part of the project.

allegedly prompted by pressure from their hardware partners like Asus and Lenovo (who themselves are worried about Windows instability leading their customers to jump ship to the Mac Book Neo).

They should just ship with Fedora KDE. Imagine the day...

I pray for the day that MSFT pisses off their partners so bad that big name vendors like Dell start shipping Linux laptops and big retailers like Best Buy sell them.

Surely it must come. Desktop Linux is so good now, the only thing holding the market back is the inertia of Windows. I only use Windows to do Windows development, and I refuse to do Windows development without a mac or a linux device that remotely connects to the Windows instance. It is really an unusable, awful operating system. The part where they put ads in it is where I became completely done with it. It was unbelievable.

I still write some code by hand. No matter how smart Claude becomes, sometimes it’s much easier and faster to write code directly instead of English.

Like your example, but imagine many of those 1-liners. Or an IDE-assisted refactor, e.g. “rename class Cat to Feline everywhere”. Or data structures: prompting “create a record named File with field name that is a string and field size that is an integer” vs hand-coding record File(String name, int size) {}, and imagine creating several at once.

Plus, I already have my IDE open reading every Claude change because of another issue: sometimes I catch Claude writing terribly messy code, like duplicating a computation over and over rather than abstracting it into a function. I’ve never seen Claude go back and refactor old code on its own (that it didn’t touch to solve its current goal), and asking Claude to generally “improve the code” doesn’t work. I can ask Claude to write specific refactors, but I need the IDE open to read the code and figure out which ones; and if I ask Claude I have to read its output, because even if correct it may reveal another important refactor.

asking Claude to generally “improve the code” doesn’t work.

Yes I find claude has little or no intrinsic sense of "good" so it's unable to progress in a task open ended like that.

There are two types of people in the world. People who think: "Why would I ever ask Mr. Claude to do something that I can easily do myself?" versus "Why would I ever do something myself when Mr. Claude can do it?" Most people are of the latter type.

A lot of philosophical questions surrounding AI become clearer if we draw an analogy with human slaves. Instead of asking "what if I got Claude to do it?", ask "what if I got my slave to do it?".

"Hard work" has always been a middle class virtue, not an aristocratic virtue. Within certain limits, "work" was for the commoners, not the nobles. The rich and powerful have always had secretaries and servants to take care of the drudgery. Your status was (and still is, frankly) proportional to the number of underlings who you could compel to do your bidding. Kings used to have their servants dress them; apparently it was beneath them to expend the effort to put their own clothes on. In that sense, AI is just the democratization of slavery, bringing to the masses what used to be the exclusive domain of the few.

Now, the flip side of that bargain is that aristocrats (in a properly healthy aristocracy, anyway) were expected to be willing to fight, sacrifice, and die. "A good day's work" is a plebeian virtue, but "death before dishonor" is a properly noble virtue. (Hegel: The master is the master because the master fears death less than the slave.) A life of pure indolence has never been considered laudatory in any culture hitherto. Claude, of course, makes no such demands on its users. This is not of course to say that there has never been corruption among the nobility, or that there has never been a decadent ruling class who didn't deserve their privileges; only that, because we are living in the world's first culture where mainlining porn and Harry Potter movies 24/7 is considered to be authentically virtuous, we're now entering uncharted waters.

But a more interesting question is, who will inherit the world?

The viruses, probably. Worse is better and always has been, at least in Darwinian evolutionary terms. The universe is optimized not for good, and not even for evil (oh how we wish it rewarded evil!), but for sheer, brute, efficient, unthinking stupidity. Regression to the lowest common denominator is the rule everywhere, because that's what wins. Anything good or beautiful that happens to arise for a time is an accident that can only flourish under very precise and precarious conditions, like a rare tropical flower that can only grow in one country during the rainy season, and it should be cherished until it is inevitably extinguished.

AI is just the democratization of slavery, bringing to the masses what used to be the exclusive domain of the few.

Yes this is a great point, and why I find the hatred of AI art from a particular class of consumers who never produce art themselves but who consume it really irritating. They want the luxury to demand that the art they consume is hand drawn or hand painted or hand sketched or hand CGI'd or hand photoshopped but never ever hand prompted. They would never demand that their checking account be artisanally hand crafted on papyrus spreadsheets and totaled by sheer brainpower but cringe at any art that wasn't toiled over by hand. They want creatives chained to their workspaces dammit! Never mind that the fundamental problem is that they lack the creativity or motivation to produce artwork themselves, and even now when the barrier to entry is as low as prompting an LLM, which makes them look even worse to anyone who actually creates things.

To your other points, noblesse oblige has died out long ago in the modern west, which is why it's so degrading to participate in society broadly. And it died off so quickly- my grandparents' generation were clean, elegant, sane people- my parents' generation, not at all.

Anything good or beautiful that happens to arise for a time is an accident that can only flourish under very precise and precarious conditions [...]

This is starting to become evident to me. It's too bad but you put it nicely.

I find the hatred of AI art from a particular class of consumers who never produce art themselves but who consume it really irritating

They want creatives chained to their workspaces dammit

I'm not sure this perspective makes much sense. It is the creatives themselves that want to be chained to their workplaces; support for generative AI amongst both Eastern and Western creatives is about as rock bottom as public opinion can get, no greater than the Lizardman's Constant. It's not as if the dynamic is that creatives really really want to be using AI to produce but the unsophistication of the proles won't allow them to do so; to the contrary, you would get dogpiled by creatives in pretty much every online or offline space for artists if you announced your vociferous support for AI art.

In the closest corollary, software engineering, where there actually is strong grassroots support for wanting to use coding agents, there is pretty much no consumer demand for chaining engineers to their desks and making them hand-write the code, apart from the free software or degrowth crowds who dislike LLMs themselves for ideological reasons.

This is not my experience at all, it is mostly the bad artists and creatives who publicly dislike AI art. Kazuma Kaneko for example who is an extremely talented genius illustrator (his Persona 1/2 designs are some of the best video game related art ever IMO) has produced a video game with a bunch of AI art trained on his style, he's obviously fine with it but the reaction from the public has been hysterical. Nick Knight is a very talented photographer and embraces AI, that's only two examples off the top of my head but I can think of more if you need me to. No artist or creative who I genuinely respect has come out openly against AI. Most of the creatives vocally against it are pretty mediocre at best or just terrible deviantart level hacks

I also firmly believe in the idea of learning by doing. Just looking at a guide and reading it, even thoroughly won't be nearly as effective as following the same guide step by step and keying in the inputs. Even if your hand is held and you only do exactly as you are told, it still activates certain mental circuits. The same goes for copying down notes. Even if you never once look at them again, simply the act of copying off the blackboard does something, at least for some people.

I think this is a fantastic use case for AI, by the by. I recently was working on a complicated (Bane voice: "for you") Excel project and was in uncharted waters. My options were, basically

  1. Assemble the right collection of Youtube videos that fit my specific needs, or
  2. Get Claude to walk me through it

For opsec reasons I wasn't actually willing to upload the spreadsheet and have Fable one-shot it, but even if I had been, I vastly preferred what I ended up doing: the entire thing, manually, bit by bit. And I think I learned more than if I had just handed it off and had AI (or a coworker) do it.

People are extremely enamored of the generative capabilities of AI, but in many ways I actually think its contextual understanding skills are much more interesting and (I would like to say) useful.

For opsec reasons I wasn't actually willing to upload the spreadsheet and have Fable one-shot it

I think "opsec reasons" are ultimately one of the big limiting factors for OpenAI, Anthropic, et al: lots of situations will really prefer something in-house, or at least an ironclad contract about confidentiality.

In the past I've wondered about the long-term market for server-side AI: I'm sure it's non-zero, but I suspect any organization of sufficient size will find themselves rolling out internal hardware and models in the medium term unless the big players keep sufficiently ahead of the commodity models and hardware prices stay high. I've heard of it being done with open weight models already.

Even without seeing the content the AI models see, I've been curious how much intelligence Google (or governments, presumably) could glean from search queries on an aggregate basis. Hypothetically, "Wow, internal Microsoft searches about WINE and Linux are up 100x in the last month, I wonder what they're working on?" gives away potential insider information. Querying the local AI server doesn't give that away.

I think "opsec reasons" are ultimately one of the big limiting factors for OpenAI, Anthropic, et al: lots of situations will really prefer something in-house, or at least an ironclad contract about confidentiality.

The "ironclad contract about confidentiality" thing is an option, and IMHO from their point of view it's just a great way to do price discrimination. "Oh, you want the This Goes On Secured Systems And Never In The Training Set plan? We should talk."

Properly cleaned data is an additional option.

I would have thought that would be more trouble than it's worth, in general. But yesterday I heard the story of an institution that has the capability to strip all confidential and personal data from a core dump so that tech consultants (including, in at least the latest case, an LLM-based agent) can analyze software problems without having any access to user data. Anonymized story, so I'm not sure whether these were national defense secrets or credit card databases or what, but in hindsight I was left with the impression that I shouldn't have been surprised regardless, that institutions large enough to have masses of such data are often also large enough to have such capabilities.

I think "opsec reasons" are ultimately one of the big limiting factors for OpenAI, Anthropic, et al: lots of situations will really prefer something in-house, or at least an ironclad contract about confidentiality.

Yes, I think Anthropic kinda shot itself in the foot by nixing privacy settings for Fable.

I've heard of it being done with open weight models already.

I would not be surprised if that becomes commonplace for many applications - maybe not coding, where people will want the really high end stuff, but coding is not all people do with AI. In my [very AI related portion of my job] using Fable or Opus for the stuff we want to scale is like calling in an airstrike on a rat; stuff like Sonnet is plenty good, and my guess is that open weight models would do just fine. I could totally see switching to open weight models on a locally run server for that sort of thing.

I've been curious how much intelligence Google (or governments, presumably) could glean from search queries on an aggregate basis.

My guess is "a lot."

Get Claude to walk me through it

This option is good in theory, but in practice requires a good amount of self discipline. It is just so easy to prompt the AI in a way that straight up gives you the answer, then convince yourself that you were the one to think about it following the LLM's guidance. If you are mindful of the pitfalls it can work, but I am not sure I would trust the average person to do it properly.

I've found LLMs the most useful in identifying bugs I caused by embarrassing typos (or cooy paste errors where I didn't change the value of something after pasting). Just 10 minutes wgo it solved a bug by pointing out that I had transposed two letters in a variable name. Though in fairness if I was using a real language instead of a toy language like Python the IDE could have caught it for me.

You type in variable names? Like every letter manually? I type in the first letter or two and hit tab. It automatically fills in or a drop down menu shows me the names starting with that.

Same thing with file paths. First few letters, tab, first few letters, tab.

You type in variable names? Like every letter manually?

Not usually, no. I have the misfortune to work on a Python project right now and we're using SQLAlchemy as an ORM. The parameters get passed in a dictionary where you need the name of the dictionary keys to match the names of the parameters in the query. I transposed 2 letters between what the query had and what the parameter dictionary had. In C#/.NET (my preferred language) this would be impossible with EF Core, and the tooling around Dapper is much better than whatever crap SQLAlchemy has so I'm pretty sure the IDE would have detected it for Dapper.

This is the largest Python project I've ever done significant work on and I hate it. 99% of the pain points I've had are due to the terrible type system. Also I miss LINQ every time I write a loop.

Python can get slightly better with type annotations - which is also something that claude is woefully incapable of doing well.

Yeah, we're using type annotations, and they're still pretty garbage too. "Expected type str, actually found 'property'" even though the property is a str lol. Or "Expected List[SomeClass] actually found List[type[SomeClass]]" and the documentation for the type system for how to fix this crap is awful.

Two mandatory tools for writing typed python:

  1. Vscode. You need the realtime feedback on errors and it also tells you what the computer thinks every variable is.

  2. Ironically, an LLM. They are great at telling you what you screwed up to get an error. Yet horrible at actually fixing the issue.

It works very well for walking you through how to use a particular tool, app, or device. (e.g. Excel). Because if AI is doing a walkthrough, you still need to press the buttons and key in the inputs to complete the task.

Walking you through solving a math problem - that's extremely dubious.

Walking you through solving a math problem - that's extremely dubious.

I've mentioned this before but just a couple of months ago I wanted to solve a simple first year university level math problem (a system of two first order differential equations). I got three different solutions depending on how I wrote the problem (eg. using abstract variables or ones based on the actual problem). Every explanation was very confident, detailed and of course wrong in a way that was apparent if you understood the domain or verified the solution by hand. And this is pretty much as simple as real world university level math can get.

Then I googled the proper syntax for how to input the problem into Matlab and got the correct result in much less time than it took to ask AI and verify it, even had AI given the correct answer.

You could ask AI how to input the problem into matlab

Google was faster and more reliable. I just wanted to see first if AI could solve a simple but not entirely trivial non-coding problem.

I realized, so many devs from all walks of life have completely ended their relationship with the text editor and now do literally everything through an agentic prompt.

Guilty as charged. Typing is for secretaries on husband shopping missions. My task is to solve problems.

We are all Scott Aaronson now "These days, if I need any coding done, I use the extremely high-level programming language called ‘undergrad’."

I think that the people that naturally prefer waterfall (or in its other names known as agile or scrum) are the hardest hit. Right now pile prompts on the agents to see what works, then extract core spec, then cleansheet implement is really powerful flow.

That actually leaves more time for thinking about the problem. Agents are also unbelievably good at gathering context. A normal programmer's job is mostly this.

This is part of effort post that I will finish never called Scott Adams and HAL. The gist is that Scott idea about stacking mediocrity is really potent with AI. Agents are just mediocre ... but they are so in everything. They are average programmer, writer, accountant, cad designer, chemist, physicist, physician, surgeon, musician, luthier, home appliance repairman, researcher, metallurgist, underwater basket weaver, devops, casino manager, security researcher, shawarma street vendor, translator - so the more domains a real world problems needs to touch - they compound to extremely high floor. When I prompt AI it is good in translating my request to proper terms of the art, thinking like the needed profession and solving problems that i didn't even needed solution in said task.

The endgame will be fun. Either hyper niche specialists with lifetime of knowledge and expertise and the people with most agency and imagination will be on top.

Right now pile prompts on the agents to see what works, then extract core spec, then cleansheet implement is really powerful flow.

I find that vibe coding a proof of concept is one of the aspects where AI is mandatory now. When a throwaway pile of cludges is acceptable you can just tell AI to make it work. When trying to implement a powerful flow though it's vulnerable to outputting excessive and useless crap.

In the medium term it's all about the harness. You need to have mr. claude digest your project, document every inch of it, have a glossary of terms so it know what you mean when you say threads lag with high comment counts and with tokens measured in the hundreds it can have densely useful context. The breaking down tasks into easily digestible chunks is trivially handled by project documentation and an orchestrator commanding subagents. Building and maintaining these harnesses is much like coding used to be, it takes thinking about the SDLC, the architecture, reacting to failures of assumption about how your agents will interact with the harness and patching those failure modes. It's true that we are not too many turns of improvements from that all being something the models can do themselves if you just ask them to first digest your project.

Once you have a orchestrator with subagents you're well into the territory of vibe coding. I have yet to see any public example of this really working out.

The phrase "vibe coding" is a thought terminating cliche. Certainly people who don't know anything about software engineering who tell claude to make an website for them and then posting a localhost address on social media are funny disasters and tales of their hijinks are spread widely. But anything you're using an ai to do in code would be better accomplished with a custom harness and some agent orchestration to manage context density. where are you even finding public examples of people's set ups besides the posts of people making fun of failed examples? Very strong selection effect.

Where are you finding examples of success using custom agent orchestrators?

My own work and that of my colleagues. We have a team harness we iterate on and improve with business and infrastructure knowledge. The key for orchestration is that it allows you to isolate context windows so that your main workflow isn't polluted with unnecessary details. Every token in context that isn't useful degrades performance. Just the process that produces the plan for implementing an update might use half a dozen subagents.

I'm smiling wryly here because not every job in the world is writing code. So I can see AI making huge strides there and turning the world of work (for software people) upside-down.

Some other jobs will definitely get a lot more automated, but not so much. Trying to replace customer service agents with chatbots will not be "improved customer service, all problems solved immediately and correctly" but more "we don't have to pay real people to do this shit job anymore, and the customers have to accept it or lump it, they have no choice" money saving.

Other jobs? AI is one more tool but not world-changing.

I'm smiling wryly here because not every job in the world is writing code.

In particular not every job in the world is writing code to solve a nearly fully specced problem in well trodded territory that only differs in minor ways from gazillion existing solutions. AI is basically the replacement for what we used to call "Java monkeys" - people with mediocre skills who were capable of writing basic code as long as they had a detailed spec and it didn't require any out of the box thinking.

I feel like pundits have been saying AI will be able to replace customer support jobs for every single new LLM release, but I have never seen any implementation actually work out, even with half of YC and SF working 996 on building customer service AI wrappers and agentic AI wrappers.

While I don't really disagree in theory that this is something AI should be able to do eventually, I think the trifecta of cost (offshoring to Indians / Filipinos is pretty cheap in the grand scheme of things compared to current AI), reliability/accountability (until an AI provider is willing to take liability for any mistakes the agent makes, even 1/100 or 1/500 fuck-ups can cause lots of problems at scale) and consumer preference (outside of the tech bubble anything that uses AI is pretty much universally loathed in the West) are pretty massive barriers to adoption even for the nominally most simple white collar job.

Yeah, the problem with customer service jobs is that in many cases they’re much harder to replace than a programmer because they both deal with the general public and often involve the ability to dispense (directly or indirectly) an organization’s money and extremely sensitive customer information in every interaction.

Even current generation models like Fable can be prompt engineered against the wishes of their creators relatively easily, often with almost laughable prompting of the narrative role playing “imagine im your sick grandma” variety that even an 80 IQ night shift call center worker obviously wouldn’t fall for (plus the human is scared of getting fired; the AI isn’t). A bank or insurance company or medical billing company or HR outsourcer has customer support workers who have full access to the most sensitive client data, a colossal risk for data leaks in financial terms that could dwarf replacing them with Claude. And even DoorDash doesn’t want an internet full of one simple trick techniques to trick the support LLM into giving you free food. All these things can be mitigated, of course, but less than perfectly.

Call centers are also pretty cheap, especially if as you note they’re in a third world country. You can hire a lot of Filipinos for one expensive San Francisco L5 or L6 programmer. I think it’s quite possible we see mass software engineer layoffs due to AI before the end of the customer support worker.

Labs will try to automate all desk jobs with AI to some degree of success. And soon all knowledge workers will be confronted with the decision of whether or not to attempt to do their entire jobs through an AI prompt or not.

Some other jobs will definitely get a lot more automated, but not so much. Trying to replace customer service agents with chatbots will not be "improved customer service, all problems solved immediately and correctly" but more "we don't have to pay real people to do this shit job anymore, and the customers have to accept it or lump it, they have no choice" money saving.

We're very close to where I'd rather deal with a frontier model doing customer service than a person. The main rub is they probably won't serve us frontier models. I don't know how often you've actually dealt with customer service on out of distribution problems but it's not pretty, and the in distribution problems can basically be straight through processed already with a minimal ai wrapper.

This makes me miss Nordstrom's from the old days even more. They were legendary for their customer service. In every dealing with them you always knew they'd take care of any problem that comes up. You pay for that of course, but it's well worth it for some things.

The problem with customer service (and a lot of other similar domains, actually) is that as the problem to be dealt with rather than the employer, the human bottleneck actually often worked in your favour. Having human employees working the phone line and wanting them to not quit or flame out and shoot up the office is the fundamental limit that makes it hard for Corporate to institute their ideal customer service policy, which is "trap any complainants in a Kafkaesque gaslighting nightmare until they give up". Mr. Claude has no limitations there, because he does not feel the "I am screwing over a fellow human being and making a mockery of the very concept of 'support'" qualia nor the "it sucks to be screamed at all day by people who hate me" ones.

Maybe you're working with scummier companies but it's not at all apparent to me that the goal of any company's customer support organization is anything other than supporting their customers, which they often do poorly because customer support is a cost center. Maybe if your modal interaction is trying to get a refund you aren't entitled to, but my biggest problem has always been when my interests and the company's basically align but the support agent doesn't know how to move some lever. The company doesn't want to pay a support worker or for tokens necessary to keep me in a kafka hell, nor do they want to piss me off as a customer to the point where I stop being a customer.

The vast majority of my customer service interactions has been with various transportation companies (airlines most frequently), who very much do appear to optimise for dodging refunds that their customers are in fact legally entitled to. There is a reason "pay us a third of the refund and we will take on the effort and risk of enforcing your statutory compensation claim against the airline" is a real business category that exists out there.

Never seen any US major airline try to withhold a statutory refund when asked for it directly. And they're sometimes quite generous with refunds or compensation even when you're not statutorily entitled to anything.

I guess I'm mostly talking about European airlines here. I guess the US generally has more of a "money is cheap" (when compared to the loyalty of a high-status customer?) attitude.

As someone who barely uses AI and doesn't much care about it, it seems to me that what we have created is not artificial intelligence, but robot translators. A good enough AI can translate the vast fluctuation of semantic meaning from real people in real language into machine binary. This used to require immense technical skill and knowledge, but at some point will be available to anyone willing to put some time in.

They're called transformers for a reason. The thing is that a lot of professional skill is in fact translating from one domain to another. There's a famous French sketch about "hermetic languages" spoken by one's doctor, lawyer, mechanic, banker, priest and the common tune being that we are mystified enough by jargon to give people money to solve problems we can't understand. With sufficiently advanced translation, a lot of these kind of intermediaries go away.

The question is what is left that can't actually be reduced to translation, or can't be modeled statistically.

I realized, so many devs from all walks of life have completely ended their relationship with the text editor and now do literally everything through an agentic prompt.

Yeah, I am now at this point.

I am a very good programmer. I'm good enough that my time is kind of wasted programming. I now work as a tech lead/tech architect, with Claude as my worker(s); I describe what to do, Claude does it, and I rip apart its design and redesign it.

Which I do all the time, I do not vibecode unless it's something I don't really care about. It all gets reviewed.

I've just got better things to do than actually write the code.

I have no idea how the "training" part of this thing ends up working out; I'm good at this because I've done it, I don't know if I ever could become good at it with AI alone. On the other hand, I feel like this is similar to a lot of professions; how many digital artists end up not understanding precise brush usage? There are colors that literally cannot be expressed on a screen and we have an entire generation of artists who grew up never realizing that "violet" is a thing they can do, because they can't - how much of a problem is this?

Weird times, overall.

how much of a problem is this?

I think, potentially, a very real problem. I'm very concerned that right now AI works so well because people know how to do the things they are tasking it with. If a generation arrives who have just been told to have AI do it, they won't even be able to judge if what it has done is desirable, let alone how to diagnose or fix any problems it creates. It's the same problem that created a generation of college students that don't know how to send emails because the iPhone/Android interface is so slick they send everything via text message.

"Better alignment" does not solve this because it is a problem with whether or not humans are able to properly express their needs.

I guess I don't see the actual conflict here; it feels like it's conflating several things. People don't have to know the internals to know if something works for them, and they don't need to be able to build the internals to describe something that works for them. "They won't even be able to judge if what it has done is desirable" feels just wrong if we aren't including "the internal structure" in the list of things that matter for desirability, and in a world where an AI can rebuild the internal structure at the snap of a finger, maybe "the internal structure" is something we shouldn't care about as much.

I think it depends on the product. Certainly with some products it is very easy to judge what you want.

I will tell a personal story: I was trying to figure out a way to organize, catalogue and track some data presented in a messy way on [public website]. My AI (Claude Opus) suggested a couple of options for standalone applications, while pointing out that both cases were tricky since they would need to ping the website to pull in the data, generating associated problems.

I said "let's just make it a browser extension," which sidestepped the problems and was perfectly suitable for my needs.

Someone with less technical expertise than myself who had to lean on the AI to have the best idea would have made an inferior product. Someone with more technical expertise than myself might have made a better product, being even better able than I was to guide the AI. (Indeed, I wouldn't be surprised if you've already thought of one just reading this.)

If people decide collectively they don't need to build expertise because they think they can get AI to do everything, they will be worse at using AI, which will make their products worse (which could in turn poison the training data well and make the AI worse).

If people decide collectively they don't need to build expertise because they think they can get AI to do everything, they will be worse at using AI

I mean, you're not wrong . . . but you know what would also make them worse at using AI? Not having AI available to use.

At some point there's just a limited amount of time to learn in. Yes, certainly it would be great if I could absorb the entire accumulated knowledge of humanity. But I can't. I don't have the millennia required to do that, especially because there's always a tradeoff between "learning things" and "doing things".

AI removes a bunch of the minimum-training requirements to accomplish many things, while simultaneously making some things far far faster to do. I don't pretend to understand where the new balance will land - that's two giant changes at the same time - but I am confident that in the end, for any reasonable definition of "accomplishment" that combines both quality and quantity, people will be accomplishing a lot more than they were before.

Some people will be doing this by learning the fundamentals anyway, some won't, and I'm okay with that.

Some people will be doing this by learning the fundamentals anyway, some won't, and I'm okay with that.

To be clear, my concern is that culture will shift away from a culture where some people learn the fundamentals. Some people will still do so, pulling themselves up by their bootstraps, but having institutions (such as schools) roll over (...more) on the need to instill understanding would be a mistake.

But that's been happening for centuries, right? People don't learn as much cursive anymore, people don't learn as much math by hand, when was the last time you used a slide rule? How many people learn machine language or how to spin thread or how to use a scythe? Are people better or worse at hammering nails today than they were before the nailgun was invented?

I guess my feeling is that this is always phrased as a catastrophe and then always ends up not being a catastrophe.

The things you are raising here are old tools that fell into disuse because of newer tools.

I am concerned about a lack of conceptual understanding, which is a different question. A person who knows how to skew-nail and does not have a nailgun will do a better job than a person who uses a nailgun but does not know how to skew-nail. The important point isn't the nailgun - the person who has the nailgun and knows how to skew-nail will do the best job of all.

Or to use another analogy, you're just not convinced slide-rules are relevant in a world of advanced mathematical calculators. I'm worried people won't be able to use calculators because they won't know what division is. Sounds crazy, I know, but so does digital natives not being able to send an email, or read an essay, and yet here we are.

(Which leads me to be skeptical of your assertion that it always ends up not being a catastrophe, but that is a tangent, I guess.)

Claude does it, and I rip apart its design and redesign it. ...

I've just got better things to do than actually write the code.

Do you believe that AI saves you time on all coding tasks, no matter the circumstances?

All? No. There's times when something is so simple that describing it for the AI is actually slower than just doing it myself. And there's still times when AI is not able to do it right and I end up doing it myself.

Overall it's a huge help though. And even in the latter case, it's usually true that it's faster to ask the AI to mock something up first so I can see where the issues are.

By the way this is something that happened in IT since the beginning. Programming evolved from hardware level binary programming to assembler programming to structured programming to object oriented programming to todays Agentic AI programming. The same fear existed before - from programmers forgetting how to get most of the hardware, to bloatware memory hogs that loaded useless libraries to todays agentic AI which just moves it one step further.

I think it is inevitable, there will be some niche space for old school thinking, but it will resolve itself in time. For instance knowing some basic coding in Assembly from university is a good exercise and knowledge, but you will probably never use it outside of specific niches such as cybersecurity or firmware coding. Future good programmer will probably look quite differently from you now, but your skills would also be considered useless for programming Apollo 11 guidance system in 1969.

The same fear existed before - from programmers forgetting how to get most of the hardware, to bloatware memory hogs that loaded useless libraries

To be fair, these fears were 100% valid. The resources required by the average modern app are insane.

The resources required by the average modern app are insane.

Electron has entered the chat

That's largely a casualty of Moore's law. There's no point in optimizing candy crush to run on the Apollo guidance computer when nobody is going to run it on hardware like that.

Nah, it's not just that. There's also a problem with what Casey Muratori has termed "pessimization", where careless devs don't do even the most basic things to make the computer not have to do the same work twice. It's especially embarrassing when the shittier way of doing it is harder to write than the non-pessimized way and retarded web dev monkeys do it anyways.

Again, this is nothing new. There actually was something called software crisis of 1960s. Shitty programmers made costly errors, delivered faulty code with years long delay, causing immense damage in terms of wasted time of expensive mainframes. It was direct effect of the early thinking that software is a work for "calculators", akin to clerical and administrative jobs. All the really talented people focused on hardware. If you have a software company that thinks software is a job for low paid foreign labor, the results will be the same.

where careless devs don't do even the most basic things to make the computer not have to do the same work twice.

Exactly, because it's basically not worth it outside of actually high performance computing. Who's not going to use a product because it's 20% slower but otherwise better than competitors? Vim is way faster than Microsoft Word, but who cares?

It's especially embarrassing when the shittier way of doing it is harder to write than the non-pessimized way and retarded web dev monkeys do it anyways.

You'll have to provide some examples.

You'll have to provide some examples.

We had a project that gets some fields from the database. These fields were being cached in memory after retrieval because they literally only change once a year. The H1B code monkeys in Mumbai bypassed the cache and fetched them from the database every single time because they're retarded. They wrote a whole nother SQL query instead of just typing "SomeGloballyAvailableMemoryCache.Get("TheTaxFormFieldsThatGetUpdatedAnnually") or something like that.

Oh and they passed all these fields around in a stringly-typed DataTable instead of the strongly-typed object the cache would have given them. This is one of millions of examples, this shit happens daily in the software engineering world.

There are a depressing number of "engineers" who don't give even the remotest fuck about making a good product.

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Did they intentionally bypass the cache, or did they not even know about it? It's not hard to believe that clueless programmers don't know that the value is in the cache.

The H1B code monkeys in Mumbai

Perhaps he's wondering why someone would sponsor a man for an H1B before employing him overseas.

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I'm not asking for everything to runnable on a potato, just for things to not be retarded.

Case in point: A basic streaming music player with a shitty UI using over half a gigabyte without even doing anything (Spotify). Actually playing music increases memory consumption by another 100 MB.

Meanwhile a properly written player (Foobar 2000) with vastly superior UI supporting dozens of formats, a plugin system and conversion between formats uses 16 MB by being written in a fairly straightforward but not outright stupid way.

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