5 ms·

The Abstraction Rises

83 points by birdculture 9mo ago

slopusila 9mo ago
> My concerns about obsolescence have shifted toward curiosity about what remains to be built. The accidental complexity of coding is plummeting, but the essential complexity remains. The abstraction is rising again, to tame problems we haven't yet named. what if AI is better at tackling essential complexity too?
- marcus_holmes 9mo ago
  The essential complexity isn't solvable by computer systems. That was the point Fred Brooks was making. You can reduce it by process re-engineering, by changing the requirements, by managing expectations. But not by programming. If we get an LLM to manage the rest of the organisation, then conceivably we could get it to reduce the essential complexity of the programming task. But that's putting the cart before the horse - getting an LLM to rearrange the organisation processes so that it has less complexity to deal with when coding seems like a bad deal. And complexity is one of the things we're still not seeing much improvement in LLMs managing. The common experience from people using LLM coding agents is that simple systems become easy, but complex systems will still cause problems with LLM usage. LLMs are not coping well with complexity. That may change, of course, but that's the situation now.
  - selfhoster11 9mo ago
    I don't think the parent comment was saying it can be solved, only that the LLM paradigm is better at dealing with complexity. I agree that they are not great at it yet, but I've seen vast improvements in the past 3 months alone.
chrisjj 9mo ago
> LLMs ... completing tasks at the scale of full engineering teams. Ah, a work of fiction.
- bitwize 9mo ago
  StrongDM is doing it. In fact, their Attractor agentic loop, which generates, tests, and deploys code written as specs, has been released—as a spec, not code. Their installation instructions are pretty much "feed this into your LLM". They are building out not only complete applications, but test harnesses for those applications that clone popular web apps like Slack and JIRA, with no humans in the loop beyond writing the initial spec and giving final approval to deploy. We're witnessing a "horses to automobile" moment in software development. Programming, as a professional discipline, is going to be over in a year or two on the outside. We're getting the "end of software engineering in six months" before we're getting a real "year of the Linux desktop". Or GTA VI.
  - lunar_mycroft 9mo ago
    StrongDM is attempting that. The code they produced is not inspiring confidence on a relatively small scale [0], and based on what I saw with a cursory inspection I very much doubt I wouldn't find much deeper issues if I took the time to really dig into their code. Don't get me wrong, "sort of works if you squint at it" is downright miraculous by the standards of five years ago, but current models and harnesses are not sufficient to replace developers at this scale. [0] https://news.ycombinator.com/item?id=46927737 https://news.ycombinator.com/item?id=46927737
    - bitwize 9mo ago
      "Get a horse!"
      - Windchaser 9mo ago
        This doesn't really address their counterarguments. If the new-fangled "automobile" is too unreliable or slow to be useful, then yes, it might be best to stick with the horse for now. And yes, automobiles were pretty bad for a while there. It was 22 years from the first automobile to the Model T, and even the Model T was prone to breaking down a lot.
  - 9mo ago
rvz 9mo ago
> With the price of computation so high, that inefficiency was like lighting money on fire. The small group of contributors capable of producing efficient and correct code considered themselves exceedingly clever, and scoffed at the idea that they could be replaced. There will always be someone ready to drag down prices of computation low enough so that it is then democratized for all, some may disagree but that would eventually be local inference as computer hardware gets better with clever software algorithms. In this AI story, you can take a guess who are the "The Priesthood" of the 2020s are. > You still have to know what you want the computer to do, and that can be very hard. While not everyone wrote computer programs, the number of computers in the world exploded. One can say, the number of AI agents will explode and surpass humans on the internet in the next few years, and reading the code and understanding what it does when generated from an AI will be even more important than writing it. So you do not get horrific issues like this [0] since now the comments in the code are now consumed by the LLM and due to their inherent probabilistic and unpredictable nature, different LLMs produce different code and cannot guarrantee that it is correct other than a team of expert humans. We'll see if you're ready to read (and fix) an abundance of lots of AI slop and messy architectures built by vibe-coders as maintainance costs and security risks skyrocket. [0] https://sketch.dev/blog/our-first-outage-from-llm-written-code https://sketch.dev/blog/our-first-outage-from-llm-written-co...
conartist6 9mo ago
It's funny, but I think the accidental complexity is through the roof. It's skyrocketing. Nothing about cajoling a model to write what you want it to is essential complexity in software dev. In addition when you do a lot of building with no theory you tend you make lots and lots of new non-essential complexity. Devtools are no exception. There was already lots of nonessential complexity in them and in the model era is that gone? ...no don't worry it's all still there. We built all the shiny new layers right on top of all the old decaying layers, like putting lipstick on a pig.
- deleted 9mo ago
  [deleted]
- Ygg2 9mo ago
  > like putting lipstick on a pig. More like building a house in a bottomless swamp. The moment you finish it starts sinking, so you build a new floor, but it just makes it heavier, and speed up the sinking speed.
  - gessha 9mo ago
    Is there an equivalent to filling up the swamp and reaching bedrock because my optimist brain is looking for it
    - conartist6 9mo ago
      The value of AI output only decreases day to day as people do what there is to do with it. Soon if it can make something it will be making it for the 10th time instead of the 1st. So yeah, we will hit rock bottom for sure. Eventually the value of anything that is genuinely new will rocket back up because we've spent so many years imitating imitations.
    - Windchaser 9mo ago
      Much of Amsterdam and Florence is built on logs. Basically, just drive a bunch of poles in the ground until they "fill up" the swamp enough to create a stable platform. The anoxic nature of the boggy water greatly slows the decomposition of the logs. There's definitely a metaphor, here. You don't have to completely fill the swamp, just fill it enough, with enough stable pieces to create a platform you can build on.
jmclnx 9mo ago
This is well worth a read!
- measurablefunc 9mo ago
  Why? What is compelling about it?
thesz 9mo ago
Fortran is all about symbolic programming. There is no probability in the internal workings of Fortran compiler. Almost any person can learn rules and count on them. LLMs are all about probabilistic programming. While they are harnessed by a lot of symbolic processing (tokens as simple example), the core is probabilistic. No hard rules can be learned. And, for what it worth, "Real programmers don't use Pascal" [1] was not written about assembler programmers, it was written about Fortran programmers, a new Priesthood. [1] https://web.archive.org/web/20120206010243/http://www.ee.ryerson.ca/~elf/hack/realmen.html https://web.archive.org/web/20120206010243/http://www.ee.rye... Thus, what I expect is for new Priesthood to emerge - prompt writing specialists. And this is what we see, actually.
- noosphr 9mo ago
  Fortran is about numerical programming without having to deal with explicit addresses. Symbolic programming is something like lisp. Llms do not manipulate symbols according to rules, they predict tokens, or arbitrary glyphs in human parlance, based on statistical rules.
  - thesz 9mo ago
    Fortran internally transforms expressions to assembly using symbolic computation. a+0->a, a+b->b+a. LLMs are very much supported by symbolic computation. These "arbitrary glyphs" are computed by symbolic computation. The output of LLMs is constrained by program language grammar in code generation tasks. LLMs are often run with beam search, which is symbolic computation.
louiereederson 9mo ago
To the point on Jevons Paradox, the number of people/developers joining GitHub had been accelerating as of the last Octoverse report. Related: "In 2023, GitHub crossed 100 million developers after nearly three years of growth from 50 million to 100 million. But the past year alone has rewritten that curve with our fastest absolute growth yet. Today, more than 180 million developers build on GitHub." https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/ https://github.blog/news-insights/octoverse/octoverse-a-new-...
- saghm 9mo ago
  I've definitely noticed an uptick in output from "non-developers" specifically in game modding circles. It started when ChatGPT came out and people were copy-pasting code from it into files without knowing how to check if they actually were even in the proper file format for games to recognize, and but with tools like Claude the floor has risen to the point where most vibe-coded mods are at least loadable by games and most tooling people are writing will at least compile. The thing that took me by surprise is that despite a huge amount of noise, there's a non-zero number of instances where the output has ended up being quite high quality. There's someone who by their own account had no development experience before AI tooling has started doing some incredible work on mod management on Linux, and from using some of the tools they've made so far, it's way higher quality than pretty much any other native Linux tooling for managing mods I've seen so far. I ended up chatting with them a bit after I got enough use out of what they made that it seemed worth subscribing to their Patreon, and they've spent a lot of time on improving their coding skills because they can tell how much it helps them even if they're utilizing the AI tooling for a lot of it. This specific developer is probably an outlier in terms of how well they're able to identify and focus on what ends up impacting the user experience (both in terms of features and willingness to spend quite a lot of effort tracking down and fixing even small bugs), but it seems like they might have never gone down this path in the first place without AI tooling. I think the ratio of developers compared to "regular" users is still probably fairly small, so the number of people who have never had any reason to want or need a Github is still way higher than the number of people who already have accounts. I'm not trying to say that this won't cause some amount of friction in how development works or that there won't be a lot of slop out there that might make it hard to find the higher quality output, but I suspect that trying to fight against the trend is going to be a losing battle, and that "solving" it will end up requiring better ways to select which software that we want to use rather than limiting the amount of it that gets written and published. Writing software has continued to get more accessible over time; early computers weren't something you could have at home and work on stuff in your spare time on, and I'm sure that the the volume of low-quality software increased when that barrier was eliminated, but the volume of high quality software increased as well, and in the decades since then we've come up with all sorts of new ways to search for and distribute software that makes more of it available to everyone. At least right now, this is starting to look like a similar shift in how much more software is going to be produced, and although a lot of it might be bad, I'm not convinced that there won't also be some potential benefits to having a lot more people willing to try it out themselves. Trying to reap those benefits requires at least some amount of acceptance though, and I think the focus needs to be on the quality of the output independent of the process rather than on the process itself. The bar for what we consider acceptable software shouldn't be lowered, but the litmus test that it needs to be written by a "real" developer is not necessarily going to be as accurate as those of us in the old guard would like. Yes, there will likely be problems down the line for someone who only wants to vibe and not understand what's actually going on with the code itself, but I've also worked with fantastic engineers who only have vague ideas about how a compiler works or what a CPU is actually is doing under the hood because it turns out that sometimes (not always!) that's not required knowledge to be able to write software that's actually useful. What they've all had in common is that they genuinely care about the quality of what they produce, so they focused on what the actual experience and then figured out what they needed to learn to improve that rather than starting from the bottom up.
Havoc 9mo ago
Not a fan of looking at history for cases that look like the could be a step change - a new paradigm. For that it seems safer to extrapolate out from recent experiences. Normally that’s a bad idea but if you’re in uncharted territory it’s the only reference point
lsy 9mo ago
LLM coding isn't a new level of abstraction. Abstractions are (semi-)reliable ways to manage complexity by creating building blocks that represent complex behavior, that are useful for reasoning about outcomes. Because model output can vary widely from invocation to invocation, let alone model to model, prompts aren't reliable abstractions. You can't send someone all of the prompts for a vibecoded program and know they will get a binary with generally the same behavior. An effective programmer in the LLM age won't be saving mental energy by reasoning about the prompts, they will be fiddling with the prompts, crossing their fingers that it produces workable code, then going back to reasoning about the code to ensure it meets their specification. What I think the discipline is going to find after the dust settles is that traditional computer code is the "easiest" way to reason about computer behavior. It requires some learning curve, yes, but it remains the highest level of real "abstraction", with LLMs being more of a slot machine for saving the typing or some boilerplate.
bitwize 9mo ago
Going from programming language to LLM is not the same kind of abstraction as going from assembler to Fortran or Algol. With programming languages, there is a transparent homomorphism between the code you write and what the machine actually executes. Programmers use this property of programming languages to execute considerable control over the computational process they're evolving while still potentially at a high level of abstraction. With LLMs, the mapping between your input and the executable output is opaque and nondeterministic. This drives people like me batty. A while back I wrote a Lisp koan: https://www.hackersdictionary.com/html/Some-AI-Koans.html https://www.hackersdictionary.com/html/Some-AI-Koans.html Mine goes like this: "A student travelled to the East to hear Sussman's teachings. Sussman was expounding on low-level development in Lisp, when the student interrupted him. 'Master Sussman,' said he, 'is not Lisp a high-level language, indeed perhaps the highest-level of all programming languages?' Sussman replied, 'Once there was a man who was walking along the beach when he spotted an eagle. Brother eagle, said he, how impossibly distant is the sky! The eagle said nothing, and flew away.' Thus the student was enlightened." The story is in some sense true: I did meet Gerald Sussman and was in some sense enlightened by him. Another hacker was talking about working in a "low-level language" like Lisp, and I corrected him telling him that Lisp was in fact very high level. He said "Uh... I need Jerry to explain this to you. Jerry? Can you come here a minute?" "Jerry" was Gerald Sussman, who proceeded to explain to me that Lisp was a virtual machine, one which he implemented in silico for his Ph.D. thesis: https://dspace.mit.edu/handle/1721.1/5731 https://dspace.mit.edu/handle/1721.1/5731 Thus I was enlightened. If Lisp is a virtual machine, so are all programming languages. And even a buck JavaScript kiddie fresh out of boot camp, working in React, is working in machine code for the JavaScript+browser+React VM. An abstraction is a point of view, and the programmer working in a "high level" programming language is really just working in the same medium as machine code: computation itself. But with a point of view that offers more convenience. LLM work is different. LLMs are enormously complicated algorithms that give probabilistic interpretations of loose, informal human languages. So instructing a computer through the filter of an LLM is inherently probabilistic, not to mention damn near inscrutable. This is why LLMs are being met with more resistance even than compilers were. They're not the same thing. Compilers scaled the work, which remained essentially the same. LLMs are changing it.

83 points by birdculture 9mo ago

> My concerns about obsolescence have shifted toward curiosity about what remains to be built. The accidental complexity of coding is plummeting, but the essential complexity remains. The abstraction is rising again, to tame problems we haven't yet named. what if AI is better at tackling essential complexity too?

marcus_holmes 9mo ago
The essential complexity isn't solvable by computer systems. That was the point Fred Brooks was making. You can reduce it by process re-engineering, by changing the requirements, by managing expectations. But not by programming. If we get an LLM to manage the rest of the organisation, then conceivably we could get it to reduce the essential complexity of the programming task. But that's putting the cart before the horse - getting an LLM to rearrange the organisation processes so that it has less complexity to deal with when coding seems like a bad deal. And complexity is one of the things we're still not seeing much improvement in LLMs managing. The common experience from people using LLM coding agents is that simple systems become easy, but complex systems will still cause problems with LLM usage. LLMs are not coping well with complexity. That may change, of course, but that's the situation now.
- selfhoster11 9mo ago
  I don't think the parent comment was saying it can be solved, only that the LLM paradigm is better at dealing with complexity. I agree that they are not great at it yet, but I've seen vast improvements in the past 3 months alone.

chrisjj 9mo ago

> LLMs ... completing tasks at the scale of full engineering teams. Ah, a work of fiction.

bitwize 9mo ago
StrongDM is doing it. In fact, their Attractor agentic loop, which generates, tests, and deploys code written as specs, has been released—as a spec, not code. Their installation instructions are pretty much "feed this into your LLM". They are building out not only complete applications, but test harnesses for those applications that clone popular web apps like Slack and JIRA, with no humans in the loop beyond writing the initial spec and giving final approval to deploy. We're witnessing a "horses to automobile" moment in software development. Programming, as a professional discipline, is going to be over in a year or two on the outside. We're getting the "end of software engineering in six months" before we're getting a real "year of the Linux desktop". Or GTA VI.
- lunar_mycroft 9mo ago
  StrongDM is attempting that. The code they produced is not inspiring confidence on a relatively small scale [0], and based on what I saw with a cursory inspection I very much doubt I wouldn't find much deeper issues if I took the time to really dig into their code. Don't get me wrong, "sort of works if you squint at it" is downright miraculous by the standards of five years ago, but current models and harnesses are not sufficient to replace developers at this scale. [0] https://news.ycombinator.com/item?id=46927737 https://news.ycombinator.com/item?id=46927737
  - bitwize 9mo ago
    "Get a horse!"
    - Windchaser 9mo ago
      This doesn't really address their counterarguments. If the new-fangled "automobile" is too unreliable or slow to be useful, then yes, it might be best to stick with the horse for now. And yes, automobiles were pretty bad for a while there. It was 22 years from the first automobile to the Model T, and even the Model T was prone to breaking down a lot.
- 9mo ago

rvz 9mo ago

> With the price of computation so high, that inefficiency was like lighting money on fire. The small group of contributors capable of producing efficient and correct code considered themselves exceedingly clever, and scoffed at the idea that they could be replaced. There will always be someone ready to drag down prices of computation low enough so that it is then democratized for all, some may disagree but that would eventually be local inference as computer hardware gets better with clever software algorithms. In this AI story, you can take a guess who are the "The Priesthood" of the 2020s are. > You still have to know what you want the computer to do, and that can be very hard. While not everyone wrote computer programs, the number of computers in the world exploded. One can say, the number of AI agents will explode and surpass humans on the internet in the next few years, and reading the code and understanding what it does when generated from an AI will be even more important than writing it. So you do not get horrific issues like this [0] since now the comments in the code are now consumed by the LLM and due to their inherent probabilistic and unpredictable nature, different LLMs produce different code and cannot guarrantee that it is correct other than a team of expert humans. We'll see if you're ready to read (and fix) an abundance of lots of AI slop and messy architectures built by vibe-coders as maintainance costs and security risks skyrocket. [0] https://sketch.dev/blog/our-first-outage-from-llm-written-code https://sketch.dev/blog/our-first-outage-from-llm-written-co...

conartist6 9mo ago

It's funny, but I think the accidental complexity is through the roof. It's skyrocketing. Nothing about cajoling a model to write what you want it to is essential complexity in software dev. In addition when you do a lot of building with no theory you tend you make lots and lots of new non-essential complexity. Devtools are no exception. There was already lots of nonessential complexity in them and in the model era is that gone? ...no don't worry it's all still there. We built all the shiny new layers right on top of all the old decaying layers, like putting lipstick on a pig.

deleted 9mo ago
[deleted]
Ygg2 9mo ago
> like putting lipstick on a pig. More like building a house in a bottomless swamp. The moment you finish it starts sinking, so you build a new floor, but it just makes it heavier, and speed up the sinking speed.
- gessha 9mo ago
  Is there an equivalent to filling up the swamp and reaching bedrock because my optimist brain is looking for it
  - conartist6 9mo ago
    The value of AI output only decreases day to day as people do what there is to do with it. Soon if it can make something it will be making it for the 10th time instead of the 1st. So yeah, we will hit rock bottom for sure. Eventually the value of anything that is genuinely new will rocket back up because we've spent so many years imitating imitations.
  - Windchaser 9mo ago
    Much of Amsterdam and Florence is built on logs. Basically, just drive a bunch of poles in the ground until they "fill up" the swamp enough to create a stable platform. The anoxic nature of the boggy water greatly slows the decomposition of the logs. There's definitely a metaphor, here. You don't have to completely fill the swamp, just fill it enough, with enough stable pieces to create a platform you can build on.

jmclnx 9mo ago

This is well worth a read!

measurablefunc 9mo ago
Why? What is compelling about it?

thesz 9mo ago

Fortran is all about symbolic programming. There is no probability in the internal workings of Fortran compiler. Almost any person can learn rules and count on them. LLMs are all about probabilistic programming. While they are harnessed by a lot of symbolic processing (tokens as simple example), the core is probabilistic. No hard rules can be learned. And, for what it worth, "Real programmers don't use Pascal" [1] was not written about assembler programmers, it was written about Fortran programmers, a new Priesthood. [1] https://web.archive.org/web/20120206010243/http://www.ee.ryerson.ca/~elf/hack/realmen.html https://web.archive.org/web/20120206010243/http://www.ee.rye... Thus, what I expect is for new Priesthood to emerge - prompt writing specialists. And this is what we see, actually.

noosphr 9mo ago
Fortran is about numerical programming without having to deal with explicit addresses. Symbolic programming is something like lisp. Llms do not manipulate symbols according to rules, they predict tokens, or arbitrary glyphs in human parlance, based on statistical rules.
- thesz 9mo ago
  Fortran internally transforms expressions to assembly using symbolic computation. a+0->a, a+b->b+a. LLMs are very much supported by symbolic computation. These "arbitrary glyphs" are computed by symbolic computation. The output of LLMs is constrained by program language grammar in code generation tasks. LLMs are often run with beam search, which is symbolic computation.

louiereederson 9mo ago

To the point on Jevons Paradox, the number of people/developers joining GitHub had been accelerating as of the last Octoverse report. Related: "In 2023, GitHub crossed 100 million developers after nearly three years of growth from 50 million to 100 million. But the past year alone has rewritten that curve with our fastest absolute growth yet. Today, more than 180 million developers build on GitHub." https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/ https://github.blog/news-insights/octoverse/octoverse-a-new-...

saghm 9mo ago
I've definitely noticed an uptick in output from "non-developers" specifically in game modding circles. It started when ChatGPT came out and people were copy-pasting code from it into files without knowing how to check if they actually were even in the proper file format for games to recognize, and but with tools like Claude the floor has risen to the point where most vibe-coded mods are at least loadable by games and most tooling people are writing will at least compile. The thing that took me by surprise is that despite a huge amount of noise, there's a non-zero number of instances where the output has ended up being quite high quality. There's someone who by their own account had no development experience before AI tooling has started doing some incredible work on mod management on Linux, and from using some of the tools they've made so far, it's way higher quality than pretty much any other native Linux tooling for managing mods I've seen so far. I ended up chatting with them a bit after I got enough use out of what they made that it seemed worth subscribing to their Patreon, and they've spent a lot of time on improving their coding skills because they can tell how much it helps them even if they're utilizing the AI tooling for a lot of it. This specific developer is probably an outlier in terms of how well they're able to identify and focus on what ends up impacting the user experience (both in terms of features and willingness to spend quite a lot of effort tracking down and fixing even small bugs), but it seems like they might have never gone down this path in the first place without AI tooling. I think the ratio of developers compared to "regular" users is still probably fairly small, so the number of people who have never had any reason to want or need a Github is still way higher than the number of people who already have accounts. I'm not trying to say that this won't cause some amount of friction in how development works or that there won't be a lot of slop out there that might make it hard to find the higher quality output, but I suspect that trying to fight against the trend is going to be a losing battle, and that "solving" it will end up requiring better ways to select which software that we want to use rather than limiting the amount of it that gets written and published. Writing software has continued to get more accessible over time; early computers weren't something you could have at home and work on stuff in your spare time on, and I'm sure that the the volume of low-quality software increased when that barrier was eliminated, but the volume of high quality software increased as well, and in the decades since then we've come up with all sorts of new ways to search for and distribute software that makes more of it available to everyone. At least right now, this is starting to look like a similar shift in how much more software is going to be produced, and although a lot of it might be bad, I'm not convinced that there won't also be some potential benefits to having a lot more people willing to try it out themselves. Trying to reap those benefits requires at least some amount of acceptance though, and I think the focus needs to be on the quality of the output independent of the process rather than on the process itself. The bar for what we consider acceptable software shouldn't be lowered, but the litmus test that it needs to be written by a "real" developer is not necessarily going to be as accurate as those of us in the old guard would like. Yes, there will likely be problems down the line for someone who only wants to vibe and not understand what's actually going on with the code itself, but I've also worked with fantastic engineers who only have vague ideas about how a compiler works or what a CPU is actually is doing under the hood because it turns out that sometimes (not always!) that's not required knowledge to be able to write software that's actually useful. What they've all had in common is that they genuinely care about the quality of what they produce, so they focused on what the actual experience and then figured out what they needed to learn to improve that rather than starting from the bottom up.

Havoc 9mo ago

Not a fan of looking at history for cases that look like the could be a step change - a new paradigm. For that it seems safer to extrapolate out from recent experiences. Normally that’s a bad idea but if you’re in uncharted territory it’s the only reference point

lsy 9mo ago

LLM coding isn't a new level of abstraction. Abstractions are (semi-)reliable ways to manage complexity by creating building blocks that represent complex behavior, that are useful for reasoning about outcomes. Because model output can vary widely from invocation to invocation, let alone model to model, prompts aren't reliable abstractions. You can't send someone all of the prompts for a vibecoded program and know they will get a binary with generally the same behavior. An effective programmer in the LLM age won't be saving mental energy by reasoning about the prompts, they will be fiddling with the prompts, crossing their fingers that it produces workable code, then going back to reasoning about the code to ensure it meets their specification. What I think the discipline is going to find after the dust settles is that traditional computer code is the "easiest" way to reason about computer behavior. It requires some learning curve, yes, but it remains the highest level of real "abstraction", with LLMs being more of a slot machine for saving the typing or some boilerplate.

bitwize 9mo ago

Going from programming language to LLM is not the same kind of abstraction as going from assembler to Fortran or Algol. With programming languages, there is a transparent homomorphism between the code you write and what the machine actually executes. Programmers use this property of programming languages to execute considerable control over the computational process they're evolving while still potentially at a high level of abstraction. With LLMs, the mapping between your input and the executable output is opaque and nondeterministic. This drives people like me batty. A while back I wrote a Lisp koan: https://www.hackersdictionary.com/html/Some-AI-Koans.html https://www.hackersdictionary.com/html/Some-AI-Koans.html Mine goes like this: "A student travelled to the East to hear Sussman's teachings. Sussman was expounding on low-level development in Lisp, when the student interrupted him. 'Master Sussman,' said he, 'is not Lisp a high-level language, indeed perhaps the highest-level of all programming languages?' Sussman replied, 'Once there was a man who was walking along the beach when he spotted an eagle. Brother eagle, said he, how impossibly distant is the sky! The eagle said nothing, and flew away.' Thus the student was enlightened." The story is in some sense true: I did meet Gerald Sussman and was in some sense enlightened by him. Another hacker was talking about working in a "low-level language" like Lisp, and I corrected him telling him that Lisp was in fact very high level. He said "Uh... I need Jerry to explain this to you. Jerry? Can you come here a minute?" "Jerry" was Gerald Sussman, who proceeded to explain to me that Lisp was a virtual machine, one which he implemented in silico for his Ph.D. thesis: https://dspace.mit.edu/handle/1721.1/5731 https://dspace.mit.edu/handle/1721.1/5731 Thus I was enlightened. If Lisp is a virtual machine, so are all programming languages. And even a buck JavaScript kiddie fresh out of boot camp, working in React, is working in machine code for the JavaScript+browser+React VM. An abstraction is a point of view, and the programmer working in a "high level" programming language is really just working in the same medium as machine code: computation itself. But with a point of view that offers more convenience. LLM work is different. LLMs are enormously complicated algorithms that give probabilistic interpretations of loose, informal human languages. So instructing a computer through the filter of an LLM is inherently probabilistic, not to mention damn near inscrutable. This is why LLMs are being met with more resistance even than compilers were. They're not the same thing. Compilers scaled the work, which remained essentially the same. LLMs are changing it.