6 ms·

I've heard that software engineers are the automobile workers of the 50s-70s: Extremely well-compensated professionals who were ultimately thrown to the wayside

by ragnot 5y ago

I've heard that software engineers are the automobile workers of the 50s-70s: Extremely well-compensated professionals who were ultimately thrown to the wayside when technology came. I wonder if data scientists and AI will do a similar thing to software engineers.

snek_case 5y ago
In my opinion, unless we develop AGI, not at all... And if we did develop AGI, then data scientists would find themselves out of a job too. I think you'd be very hard pressed to automate systems programming, and most forms of programming for that matter. So, as long as we develop new hardware, new products, new platforms, new programming languages, then there will be lots of programming work to be done. The jobs that machine learning is replacing right now (if any?) are fairly repetitive jobs that don't really require any reasoning. Maybe machine learning is also helping automate phishing schemes, but, is there any programming job out there that's really repetitive and thoughtless, where the work is predictable and akin to factory work? Do you think a transformer model is going to be able to handle debugging?
- ragnot 5y ago
  You obviously have more knowledge than me on this subject, so take what I say with that in mind. But I look at the march of progress and can't help but draw parallels. Websites used to be made by hand. Then came frameworks and then came code-free e-commerce sites like Shopify. Github is testing the Copilot AI. How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. REST API's are already defined by their model and have code generated to match it. Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering?
  - snek_case 5y ago
    However, despite the existence of products like Shopify and Wordpress, web developers have no trouble finding jobs, as far as I know. > How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. Would that person not be, essentially, a programmer? And what would they do if they needed to interface with external hardware that's not yet supported, or some network protocol or piece of software that's not built into AI-code-generator-3000? > Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering? In my opinion, people who probably think little of software developers, keep imagining that software engineering is this primitive, simple, reducible thing, and they are simply wrong. Can there be a use for AI-generated code, and can that take over things like generating user-facing forms and website? Sure. However, what I think is likely to happen is that in 15-25 years, the world will be changing just as fast as it is now, and the software engineering discipline will just expand. Companies that hire "architects" who only know how to use AI-code-generator-3000 will be at a disadvantage compared to companies that hire more fully fledged software engineers. Imagine you run a business and you only have architects who know how to use some code generator code that runs on the Google cloud. You pay some monthly bill to Google and hefty fees to use that tool. You're locked-in to that ecosystem. If you need a new feature or access to a new API, you may need to wait for Google to add support. In contrast, your competitors who hired pesky software engineers are paying higher salaries but save on Google cloud fees, and they don't have to wait for Google to fix problems to implement new features.
    - SrslyJosh 5y ago
      > Can there be a use for AI-generated code, and can that take over things like generating user-facing forms and website? Sure, but...I'm not convinced there's any advantage over writing deterministic generators for things like that. On one hand, you have something that produces a predictable output for a given input (modulo bugs in the generator, which can be corrected over time). On the other hand, you have a black box that could misinterpret some non-obvious aspect of its input and produce completely unexpected output. Its training data will need to be carefully curated and maintained over time, similar to how a deterministic generator would need to be maintained, but, again, without the same degree of certainty as to what it's going to produce. Honestly, the black box approach just seems like a nightmare to me.
  - yuy910616 5y ago
    Progress != destruction of jobs. Farming, auto, journalism, and marketing fits your model. But medicine, finance, and tech would suggest otherwise. I've been getting advice about how there is no money in software and I should study hardware since 90s. Yes, maybe this is a freefall and we haven't made impact yet. But drawing parallel without further evidence seems to be too simple of a analysis. What cause the jobs in Detroit to be outsourced? What skills was replaced? How easy was it to train those skills? What was the potential pool of workers? What was the demand of workers? All I'm saying is that this question is a lot more nuanced. I'm a software engineer, so I'm biased. But I'm guessing that you might be biased in some ways too. The real answer, perhaps boring, is that we really don't know. Even if you're right - you could easily be off by 200 years.
    - pasquinelli 5y ago
      yeah, a lot of people are grasping at patterns they see in their understanding of the past and treating them like rules that will determine the future.
    - Clubber 5y ago
      >What cause the jobs in Detroit to be outsourced? Higher profit margins at the cost of lower quality and enabled with NAFTA. In the 90s until the mid 2000s, US cars were mostly top notch, now they're not that great. You can read what the market thinks of the quality of a vehicle by its resale price relative to its new price. Used Honda and Toyotas are pretty close to a new price. Chryslers, not so much. This isn't exclusively a US problem though. You can get a used BMW or Mercedes for cheap, but you probably shouldn't if you want to keep it for a while and not spend a lot of money. Back in the 90s, you could expect to get 10 years out of a vehicle relatively hassle free, now it's about 3. YMMV.
  - thrower123 5y ago
    These are the same arguments that people have made about outsourcing for thirty years, and we've only got more programmers who are paid vastly more money because of it.
    - pasquinelli 5y ago
      because of it?
      - thrower123 5y ago
        To some extent, yes. There's an awful lot of work that comes back to be done correctly. It's how I got my job originally.
  - webmaven 5y ago
    > Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering? Not much of a stretch at all. But we've seen this sort of transition before: lower-level languages (microcode, assembly) are now largely generated by other software yet the amount of software development work needing to be done by humans has only expanded, and I don't expect there to be much more than a bump in the road in terms of demand for software developers this time around either. Though I will say that the bump will be larger than the non-event that 4GLs turned out to be: https://en.wikipedia.org/wiki/Fourth-generation_programming_language https://en.wikipedia.org/wiki/Fourth-generation_programming_... The most disruptive scenario I can imagine will result in a slew of businesses hit with absolutely massive cloud computing bills (due to naive generated code that works, but very inefficiently, or perhaps just code running for naively specified tasks that HAVE no efficient solution as stated), leading to a standard requirement of having an expensive human software developer sign off on it before deployment like architects signing off on blueprints (which might be the thin edge of the wedge for professional licensing, but that's a different conversation). As a secondary effect, there might also be a proliferation of new (or newly popular) languages (for which insufficient public code exists for ML systems to learn from), as developers focus on domain+skill+technology combinations that haven't been automated YET.
    - pasquinelli 5y ago
      i think you're focusing too much on how many programmers will be needed in the future without considering what kind of pay they'll be able to get.
      - webmaven 5y ago
        I get that we're basically arguing over the Christensen Innovator's Dilemma, and I acknowledge that at some point ML-driven solutions will cross the 'good enough' threshold that eliminates certain swaths of work wholesale, but I don't think that an increase in the demand for software development and automating away most of today's software development related tasks spells a decrease in software development pay, any more than desktop publishing was a harbinger for fewer graphic designers or lower pay for graphic design work.
  - xmcqdpt2 5y ago
    >Github is testing the Copilot AI. How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. You should register for the Copilot beta. It's pretty eye opening. Basically it generates bog standard terrible code, because thats the majority of code. It can "Hello, world!" in most languages. Maybe it's useful for particularly boilerplate heavy languages or librairies, but for the most part it's more like StackOverflow copy-pasting except the code is worse. So quite a long way to go.
    - snek_case 5y ago
      Pretty much what should be expected by applying GPT-3 to code or whatever.
wongarsu 5y ago
We have spent the last 50 years trying to automate our job away. Many projects that 20 years ago took an entire team a year or more are now weekend projects for a single person, because of how much better our programming languages, libraries, APIs, IDEs, linters, etc have become. But demand turns out to be incredibly elastic. A 50% increase in productivity just means that there's 400% more work, because so many new things have now become cost effective to try. Of course there is bound to be an end to this, a peak-software if you so will, where productivity increases will stop being offset by demand increases. I might live to see that day, but this AI wave is unlikely to be it. Yes, it brings some new tools that make programming easier, but it stimulates more than enough demand to offset that.
- astrobe_ 5y ago
  > how much better our programming languages, libraries, APIs, IDEs, linters, etc have become It's just libraries, really. Or libraries are 80% of the gain. So it's less about automation and tools and more about code reuse (sometimes it's even just binary reuse - you surely do that everyday by calling dynamic libraries). The progress you note in the last 20-30 years is just Internet that became widely available. First at work and Unis, and then at home. We switched from sending disks by mail to downloading stuff from FTP servers. Recently WWW Git front-ends gave it another serious boost. That's what happened. > But demand turns out to be incredibly elastic. A 50% increase in productivity just means that there's 400% more work, because so many new things have now become cost effective to try. I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better.
  - hervature 5y ago
    > I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. How can you say that seriously when, for the vast majority of people, a computer vision project consists of `import cv2`?
  - 5y ago
didibus 5y ago
ML and data-science relies on software to work. Nobody is going to do data-science and ML by hand, so already you have a demand for software engineers and computer scientists to build tools and implement algorithms and information pipelines for the data-scientists and businesses who want to leverage ML. I think there might be a bit of a misconception of what existing software engineers did. Very rarely were they focused on designing inference models or performing data analysis. Most of the time the business side of things, like a business analysts, or people working on the business problem would perform analysis manually, and come up with business rules and logic. Software engineers could help them leverage more powerful techniques by building them tools that help for data analysis, like BI platforms, or data stores that can do bigger and more complex queries faster. Or they could help them with letting them know of techniques, generally known as old-school AI, such as edit distance algorithms, graph algorithms, logic rule engines, expert systems, etc. So nothing has really changed, except that there is even more demand from businesses who want software engineers to help them setup tools and pipelines for them to experiment with ML and have data scientists analyze their data. What it would take to get rid of the demand for software engineers is to be able to automate their work, and ML is far from able to do that. It could one day, but we are talking about a computer that can reprogram itself successfully, and that can also understand the requests of the user in how it should reprogram itself. And we're talking not just reprogramming, but it should also be able to replicate itself to other machines and all that, since most use case today involve distributed system, as no one computer has the compute needed for the scale we operate at. Things like GPT-3 show some promise, but as it stands today, you'd need a software engineer to inspect and review everything it generates, which would take almost as much time if not more. I think what is far more likely is that the offer increases, that is, that there eventually is just a lot more people with the ability to perform the work of software engineers. This will probably happen as a combination of more people joining the field, and the barrier to entry becoming smaller. The latter could be helped a little bit by ML, if it can deliver better auto-complete for example, but I think it largely is just driven by frameworks, PAAS, SAAS and IAAS, library, and all other type of code reuse we've been building for years now.
Aerroon 5y ago
>I wonder if data scientists and AI will do a similar thing to software engineers. I don't really see how. Software development is about translating arbitrary requirements into a rigorous form without ambiguity (ie the developer has to fill in the blanks). Unless we invent AGI we would need the 'clients' of software development to be able to unambiguously list their requirements (without there being any blanks that need to be filled in). Effectively, they'd have to become devs themselves. It might not look like the software development of today of writing text, but they'd still be doing a similar type of job.
jokoon 5y ago
It depends, because you still need people even in car factories. Also, automation really changes the nature of a product, and the quality is often poor.
ArtWomb 5y ago
I just got my email invite to the Github Copilot tech preview. So I'm definitely thinking of "Code as ML" ;) I think we begin to see software construction design oriented towards "agents". Analogous to previous abstractions like "daemons" or "running programs". They have permission to "self-assemble" intelligently.
Rury 5y ago
Depends. If you mean people who's work involves developing software by typing instructions into a computer via a keyboard interface... and this is what entirely defines a software engineer. Then possibly. If you mean people who's work involves developing software by instructing computers in various other ways (e.g. via talking, feeding it pictures, videos, audio, or other data)... and this is what defines a soft-... er I mean data scientist. Then I'd argue data scientists are just a newer software engineer. If you mean engineers in the true and general sense? Never. Devising new technology & solving problems are skills forever employable.

by ragnot 5y ago

snek_case 5y ago

In my opinion, unless we develop AGI, not at all... And if we did develop AGI, then data scientists would find themselves out of a job too. I think you'd be very hard pressed to automate systems programming, and most forms of programming for that matter. So, as long as we develop new hardware, new products, new platforms, new programming languages, then there will be lots of programming work to be done. The jobs that machine learning is replacing right now (if any?) are fairly repetitive jobs that don't really require any reasoning. Maybe machine learning is also helping automate phishing schemes, but, is there any programming job out there that's really repetitive and thoughtless, where the work is predictable and akin to factory work? Do you think a transformer model is going to be able to handle debugging?

ragnot 5y ago
You obviously have more knowledge than me on this subject, so take what I say with that in mind. But I look at the march of progress and can't help but draw parallels. Websites used to be made by hand. Then came frameworks and then came code-free e-commerce sites like Shopify. Github is testing the Copilot AI. How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. REST API's are already defined by their model and have code generated to match it. Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering?
- snek_case 5y ago
  However, despite the existence of products like Shopify and Wordpress, web developers have no trouble finding jobs, as far as I know. > How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. Would that person not be, essentially, a programmer? And what would they do if they needed to interface with external hardware that's not yet supported, or some network protocol or piece of software that's not built into AI-code-generator-3000? > Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering? In my opinion, people who probably think little of software developers, keep imagining that software engineering is this primitive, simple, reducible thing, and they are simply wrong. Can there be a use for AI-generated code, and can that take over things like generating user-facing forms and website? Sure. However, what I think is likely to happen is that in 15-25 years, the world will be changing just as fast as it is now, and the software engineering discipline will just expand. Companies that hire "architects" who only know how to use AI-code-generator-3000 will be at a disadvantage compared to companies that hire more fully fledged software engineers. Imagine you run a business and you only have architects who know how to use some code generator code that runs on the Google cloud. You pay some monthly bill to Google and hefty fees to use that tool. You're locked-in to that ecosystem. If you need a new feature or access to a new API, you may need to wait for Google to add support. In contrast, your competitors who hired pesky software engineers are paying higher salaries but save on Google cloud fees, and they don't have to wait for Google to fix problems to implement new features.
  - SrslyJosh 5y ago
    > Can there be a use for AI-generated code, and can that take over things like generating user-facing forms and website? Sure, but...I'm not convinced there's any advantage over writing deterministic generators for things like that. On one hand, you have something that produces a predictable output for a given input (modulo bugs in the generator, which can be corrected over time). On the other hand, you have a black box that could misinterpret some non-obvious aspect of its input and produce completely unexpected output. Its training data will need to be carefully curated and maintained over time, similar to how a deterministic generator would need to be maintained, but, again, without the same degree of certainty as to what it's going to produce. Honestly, the black box approach just seems like a nightmare to me.
- yuy910616 5y ago
  Progress != destruction of jobs. Farming, auto, journalism, and marketing fits your model. But medicine, finance, and tech would suggest otherwise. I've been getting advice about how there is no money in software and I should study hardware since 90s. Yes, maybe this is a freefall and we haven't made impact yet. But drawing parallel without further evidence seems to be too simple of a analysis. What cause the jobs in Detroit to be outsourced? What skills was replaced? How easy was it to train those skills? What was the potential pool of workers? What was the demand of workers? All I'm saying is that this question is a lot more nuanced. I'm a software engineer, so I'm biased. But I'm guessing that you might be biased in some ways too. The real answer, perhaps boring, is that we really don't know. Even if you're right - you could easily be off by 200 years.
  - pasquinelli 5y ago
    yeah, a lot of people are grasping at patterns they see in their understanding of the past and treating them like rules that will determine the future.
  - Clubber 5y ago
    >What cause the jobs in Detroit to be outsourced? Higher profit margins at the cost of lower quality and enabled with NAFTA. In the 90s until the mid 2000s, US cars were mostly top notch, now they're not that great. You can read what the market thinks of the quality of a vehicle by its resale price relative to its new price. Used Honda and Toyotas are pretty close to a new price. Chryslers, not so much. This isn't exclusively a US problem though. You can get a used BMW or Mercedes for cheap, but you probably shouldn't if you want to keep it for a while and not spend a lot of money. Back in the 90s, you could expect to get 10 years out of a vehicle relatively hassle free, now it's about 3. YMMV.
- thrower123 5y ago
  These are the same arguments that people have made about outsourcing for thirty years, and we've only got more programmers who are paid vastly more money because of it.
  - pasquinelli 5y ago
    because of it?
    - thrower123 5y ago
      To some extent, yes. There's an awful lot of work that comes back to be done correctly. It's how I got my job originally.
- webmaven 5y ago
  > Is it that much a stretch to say that in the near future (15-25 years), AI might take over a significant part of software engineering? Not much of a stretch at all. But we've seen this sort of transition before: lower-level languages (microcode, assembly) are now largely generated by other software yet the amount of software development work needing to be done by humans has only expanded, and I don't expect there to be much more than a bump in the road in terms of demand for software developers this time around either. Though I will say that the bump will be larger than the non-event that 4GLs turned out to be: https://en.wikipedia.org/wiki/Fourth-generation_programming_language https://en.wikipedia.org/wiki/Fourth-generation_programming_... The most disruptive scenario I can imagine will result in a slew of businesses hit with absolutely massive cloud computing bills (due to naive generated code that works, but very inefficiently, or perhaps just code running for naively specified tasks that HAVE no efficient solution as stated), leading to a standard requirement of having an expensive human software developer sign off on it before deployment like architects signing off on blueprints (which might be the thin edge of the wedge for professional licensing, but that's a different conversation). As a secondary effect, there might also be a proliferation of new (or newly popular) languages (for which insufficient public code exists for ML systems to learn from), as developers focus on domain+skill+technology combinations that haven't been automated YET.
  - pasquinelli 5y ago
    i think you're focusing too much on how many programmers will be needed in the future without considering what kind of pay they'll be able to get.
    - webmaven 5y ago
      I get that we're basically arguing over the Christensen Innovator's Dilemma, and I acknowledge that at some point ML-driven solutions will cross the 'good enough' threshold that eliminates certain swaths of work wholesale, but I don't think that an increase in the demand for software development and automating away most of today's software development related tasks spells a decrease in software development pay, any more than desktop publishing was a harbinger for fewer graphic designers or lower pay for graphic design work.
- xmcqdpt2 5y ago
  >Github is testing the Copilot AI. How much longer before some architect tells an AI to stitch together code that fits some sort of functional spec. You should register for the Copilot beta. It's pretty eye opening. Basically it generates bog standard terrible code, because thats the majority of code. It can "Hello, world!" in most languages. Maybe it's useful for particularly boilerplate heavy languages or librairies, but for the most part it's more like StackOverflow copy-pasting except the code is worse. So quite a long way to go.
  - snek_case 5y ago
    Pretty much what should be expected by applying GPT-3 to code or whatever.

wongarsu 5y ago

We have spent the last 50 years trying to automate our job away. Many projects that 20 years ago took an entire team a year or more are now weekend projects for a single person, because of how much better our programming languages, libraries, APIs, IDEs, linters, etc have become. But demand turns out to be incredibly elastic. A 50% increase in productivity just means that there's 400% more work, because so many new things have now become cost effective to try. Of course there is bound to be an end to this, a peak-software if you so will, where productivity increases will stop being offset by demand increases. I might live to see that day, but this AI wave is unlikely to be it. Yes, it brings some new tools that make programming easier, but it stimulates more than enough demand to offset that.

astrobe_ 5y ago
> how much better our programming languages, libraries, APIs, IDEs, linters, etc have become It's just libraries, really. Or libraries are 80% of the gain. So it's less about automation and tools and more about code reuse (sometimes it's even just binary reuse - you surely do that everyday by calling dynamic libraries). The progress you note in the last 20-30 years is just Internet that became widely available. First at work and Unis, and then at home. We switched from sending disks by mail to downloading stuff from FTP servers. Recently WWW Git front-ends gave it another serious boost. That's what happened. > But demand turns out to be incredibly elastic. A 50% increase in productivity just means that there's 400% more work, because so many new things have now become cost effective to try. I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better.
- hervature 5y ago
  > I would credit Moore's law for that instead. Really, if one can do e.g. affordable, real-time machine vision stuff today, it has more to do with CPUs and GPUs gaining power every year, than software getting better. How can you say that seriously when, for the vast majority of people, a computer vision project consists of `import cv2`?
- 5y ago

didibus 5y ago

ML and data-science relies on software to work. Nobody is going to do data-science and ML by hand, so already you have a demand for software engineers and computer scientists to build tools and implement algorithms and information pipelines for the data-scientists and businesses who want to leverage ML. I think there might be a bit of a misconception of what existing software engineers did. Very rarely were they focused on designing inference models or performing data analysis. Most of the time the business side of things, like a business analysts, or people working on the business problem would perform analysis manually, and come up with business rules and logic. Software engineers could help them leverage more powerful techniques by building them tools that help for data analysis, like BI platforms, or data stores that can do bigger and more complex queries faster. Or they could help them with letting them know of techniques, generally known as old-school AI, such as edit distance algorithms, graph algorithms, logic rule engines, expert systems, etc. So nothing has really changed, except that there is even more demand from businesses who want software engineers to help them setup tools and pipelines for them to experiment with ML and have data scientists analyze their data. What it would take to get rid of the demand for software engineers is to be able to automate their work, and ML is far from able to do that. It could one day, but we are talking about a computer that can reprogram itself successfully, and that can also understand the requests of the user in how it should reprogram itself. And we're talking not just reprogramming, but it should also be able to replicate itself to other machines and all that, since most use case today involve distributed system, as no one computer has the compute needed for the scale we operate at. Things like GPT-3 show some promise, but as it stands today, you'd need a software engineer to inspect and review everything it generates, which would take almost as much time if not more. I think what is far more likely is that the offer increases, that is, that there eventually is just a lot more people with the ability to perform the work of software engineers. This will probably happen as a combination of more people joining the field, and the barrier to entry becoming smaller. The latter could be helped a little bit by ML, if it can deliver better auto-complete for example, but I think it largely is just driven by frameworks, PAAS, SAAS and IAAS, library, and all other type of code reuse we've been building for years now.

Aerroon 5y ago

>I wonder if data scientists and AI will do a similar thing to software engineers. I don't really see how. Software development is about translating arbitrary requirements into a rigorous form without ambiguity (ie the developer has to fill in the blanks). Unless we invent AGI we would need the 'clients' of software development to be able to unambiguously list their requirements (without there being any blanks that need to be filled in). Effectively, they'd have to become devs themselves. It might not look like the software development of today of writing text, but they'd still be doing a similar type of job.

jokoon 5y ago

It depends, because you still need people even in car factories. Also, automation really changes the nature of a product, and the quality is often poor.

ArtWomb 5y ago

I just got my email invite to the Github Copilot tech preview. So I'm definitely thinking of "Code as ML" ;) I think we begin to see software construction design oriented towards "agents". Analogous to previous abstractions like "daemons" or "running programs". They have permission to "self-assemble" intelligently.

Rury 5y ago

Depends. If you mean people who's work involves developing software by typing instructions into a computer via a keyboard interface... and this is what entirely defines a software engineer. Then possibly. If you mean people who's work involves developing software by instructing computers in various other ways (e.g. via talking, feeding it pictures, videos, audio, or other data)... and this is what defines a soft-... er I mean data scientist. Then I'd argue data scientists are just a newer software engineer. If you mean engineers in the true and general sense? Never. Devising new technology & solving problems are skills forever employable.