5 ms·

Yeah, that's a good way of looking at it. We gradually remove technical constraints and move to a higher level of abstraction, much closer to the level of the u

Yeah, that's a good way of looking at it. We gradually remove technical constraints and move to a higher level of abstraction, much closer to the level of the user and the business rather than the individual machine. But what's the endpoint of this? There will probably always be a need for expert-level troubleshooters and optimizers who understand all the layers, but for the rest of us, I'm wondering if the job wouldn't generally become more product management than engineering.

rented_mule 3y ago
I'm not sure that there is an endpoint, only a continuation of the transitions we've always been making. What we've seen as we transitioned to higher and higher level languages (e.g., machine code → macro assembly → C → Java → Python) on unimaginably more powerful machines (and clusters of machines) is that we took on more complex applications and got much more work done faster. The complexity we manage shifts from the language and optimizing for machine constraints (speed, memory, etc.) to the application domain and optimizing for broader constraints (profit, user happiness, etc.). I think LLMs also revive hope that natural languages (e.g., English) are the future of software development (COBOL's dream finally be realized!). But a core problem with that has always been that natural languages are too ambiguous. To the extent we're just writing prompts and the models are the implementers, I suspect we'll come up with more precise "prompt languages". At that point, it's just the next generation of even higher level languages. So, I think you're right that we'll spend more of our time thinking like product managers. But also more of our time thinking about higher level, hard, technical problems (e.g., how do we use math to build a system that dynamically optimizes itself for whatever metric we care about?). I don't think these are new trends, but continuing (maybe accelerating?) ones.
- nopinsight 3y ago
  > But also more of our time thinking about higher level, hard, technical problems (e.g., how do we use math to build a system that dynamically optimizes itself for whatever metric we care about?). It’s likely that a near-future AI system can suggest suitable math and implement it in an algorithm for the problem the user wants solved. An expert who understands it might be able to critique and ask for a better solution, but many users could be satisfied with it. Professionals who can deliver added value are those who understand the user better than the user themselves.
  - rented_mule 3y ago
    This kind of optimization is what I did for the last few years of my career, so I might be biased / limited in my thinking about what AI is capable of. But a lot of this area is still being figured out by humans, and there are a lot of tradeoffs between the math/software/business sides that limits what we can do. I'm not sure many business decision makers would give free rein to AI (they don't give it to engineers today). And I don't think we're close to AI ensuring a principled approach to the application of mathematical concepts. When these optimization systems (I'm referring to mathematical optimization here) are unleashed, they will crush many metrics that are not a part of their objective function and/or constraints. Want to optimize this quarter's revenue and don't have time to put in a constraint around user happiness? Revenue might be awesome this quarter, but gone in a year because the users are gone. The system I worked on kept our company in business through the pandemic by automatically adapting to frequently changing market conditions. But we had to quickly add constraints (within hours of the first US stay-at-home orders) to prevent gouging our customers. We had gouging prevention in before, but it suddenly changed in both shape and magnitude - increasing prices significantly in certain areas and making them free in others. AI is trained on the past, but there was no precedent for such a system in a pandemic. Or in this decade's wars, or under new regulations, etc. What we call AI today does not use reason. So it's left to humans to figure out how to adapt in new situations. But if AI is creating a black-box optimization system, the human operators will not know what to do or how to do it. And if the system isn't constructed in a mathematically sound way, it won't even be possible to constrain it without significant negative implications. Gains from such systems are also heavily resistant to measurement, which we need to do if we want to know if they are breaking our business. This is because such systems typically involve feedback loops that invalidate the assumption of independence between cohorts in A/B tests. That means advanced experiment designs must be found that are often custom for every use case. So, maybe in addition to thinking more like product managers, engineers will need to be thinking more like data scientists. This is all just in the area where I have some expertise. I imagine there are many other such areas. Some of which we haven't even found yet because we've been stuck doing the drudgery that AI can actually help with. [cue the song Code Monkey]
    - SgtBastard 3y ago
- ptx 3y ago
  I don't think COBOL's dream was to generate enormous amounts of assembly code that users would then have to maintain (in assembly!) and producing differently wrong results every time you ran it.
  - bitwize 3y ago
    It may not have been the dream, but the reality is many COBOL systems have been binary-patched to fix issues so many times that the original source may not be a useful guide to how the thing actually works.
    - tra3 3y ago
      Can you share any more info on this?
- diego_sandoval 3y ago
  > machine code → macro assembly → C → Java → Python The increase in productivity, we can all agree on, but a non-negligible portion of HN users would say that each one of those new languages made programming progressively less fun.
  - flohofwoe 3y ago
    I think where people will disagree is how much productivity those steps brought. For instance I think the step from machine code to macro assembler is bigger than the step from a macro assembler to C (although still substantial), but the step from C to anything higher level is essentially negligible compared to the massive jump from machine code to a 'low level high level' language like C.
    - actionfromafar 3y ago
      So many other things happened at the same too, so it's sometimes hard to untangle what is what. For instance, say that C had namespaces, and a solid package system with a global repo of packages like Python, C# and Java have. Then you'd be able to throw together things pretty easily. Things easily cobbled together with Python often aren't attributable to Python the language per se, but rather Python, the language and its neat packages.
  - deterministic 3y ago
    Python is a step backwards in productivity for me compared with typed languages. So no I don't think we all agree on this. You might be more productive in Python but that's you not me.
- deterministic 3y ago
  > machine code → macro assembly → C → Java → Python This made me laugh out loud. Python is not a step up from Java in my opinion. Python is more of a step up from BASIC. It's a different evolutionary path. Like LISP.
vczf 3y ago
The endpoint is that being a programmer becomes as obsolete as being a human "calculator" for a career. Millions, perhaps billions of times more lines of code will be written, and automated programming will be taken for granted as just how computers work. Painstakingly writing static source code will be seen the same way as we see doing hundreds of pages of tedious calculations using paper, pencil, and a slide rule. Why would you do that, when the computer can design and develop such a program hundreds of times in the blink of an eye to arrive at the optimal human interface for your particular needs at the moment? It'll be a tremendous boon in every other technical field, such as science and engineering. It'll also make computers so much more useful and accessible for regular people. However, programming as we know it will fade into irrelevance. This change might take 50 years, but that's where I believe we're headed.
- withinboredom 3y ago
  Yet, we still have programmers writing assembly code and hand-optimizing it. I believe that for most software engineers, this will be the future. However, experts and hobbyists will still experiment with different ways of doing things, just like people experiment with different ways of creating chairs. An AI can only do what it is taught to do. Sure, it can offer unique insights from time to time, but I doubt it will get to the point where it can craft entirely new paradigms and ways of building software.
  - vczf 3y ago
    You might be underestimating the potential of an automated evolutionary programming system at discovering novel and surprising ways to do computation—ways that no human would ever invent. Humans may have a better distribution of entropy generation (i.e. life experience as an embodied human being), but compared to the rate at which a computer can iterate, I don't think that advantage will be maintained. (Humans will still have to set the goals and objectives, unless we unleash an ASI and render even that moot.)
    - beacon294 3y ago
      ASI?
financypants 3y ago
Is a more generic version of this argument be that there will always be a need for smart/experienced people?
thfuran 3y ago
>There will probably always be a need for expert-level troubleshooters and optimizers who understand all the layers There's already so many layers that essentially no one knows them all at even a basic level, let alone expert. A few more layers and no one in the field will even know of all the layers.

rented_mule 3y ago

I'm not sure that there is an endpoint, only a continuation of the transitions we've always been making. What we've seen as we transitioned to higher and higher level languages (e.g., machine code → macro assembly → C → Java → Python) on unimaginably more powerful machines (and clusters of machines) is that we took on more complex applications and got much more work done faster. The complexity we manage shifts from the language and optimizing for machine constraints (speed, memory, etc.) to the application domain and optimizing for broader constraints (profit, user happiness, etc.). I think LLMs also revive hope that natural languages (e.g., English) are the future of software development (COBOL's dream finally be realized!). But a core problem with that has always been that natural languages are too ambiguous. To the extent we're just writing prompts and the models are the implementers, I suspect we'll come up with more precise "prompt languages". At that point, it's just the next generation of even higher level languages. So, I think you're right that we'll spend more of our time thinking like product managers. But also more of our time thinking about higher level, hard, technical problems (e.g., how do we use math to build a system that dynamically optimizes itself for whatever metric we care about?). I don't think these are new trends, but continuing (maybe accelerating?) ones.

nopinsight 3y ago
> But also more of our time thinking about higher level, hard, technical problems (e.g., how do we use math to build a system that dynamically optimizes itself for whatever metric we care about?). It’s likely that a near-future AI system can suggest suitable math and implement it in an algorithm for the problem the user wants solved. An expert who understands it might be able to critique and ask for a better solution, but many users could be satisfied with it. Professionals who can deliver added value are those who understand the user better than the user themselves.
- rented_mule 3y ago
  This kind of optimization is what I did for the last few years of my career, so I might be biased / limited in my thinking about what AI is capable of. But a lot of this area is still being figured out by humans, and there are a lot of tradeoffs between the math/software/business sides that limits what we can do. I'm not sure many business decision makers would give free rein to AI (they don't give it to engineers today). And I don't think we're close to AI ensuring a principled approach to the application of mathematical concepts. When these optimization systems (I'm referring to mathematical optimization here) are unleashed, they will crush many metrics that are not a part of their objective function and/or constraints. Want to optimize this quarter's revenue and don't have time to put in a constraint around user happiness? Revenue might be awesome this quarter, but gone in a year because the users are gone. The system I worked on kept our company in business through the pandemic by automatically adapting to frequently changing market conditions. But we had to quickly add constraints (within hours of the first US stay-at-home orders) to prevent gouging our customers. We had gouging prevention in before, but it suddenly changed in both shape and magnitude - increasing prices significantly in certain areas and making them free in others. AI is trained on the past, but there was no precedent for such a system in a pandemic. Or in this decade's wars, or under new regulations, etc. What we call AI today does not use reason. So it's left to humans to figure out how to adapt in new situations. But if AI is creating a black-box optimization system, the human operators will not know what to do or how to do it. And if the system isn't constructed in a mathematically sound way, it won't even be possible to constrain it without significant negative implications. Gains from such systems are also heavily resistant to measurement, which we need to do if we want to know if they are breaking our business. This is because such systems typically involve feedback loops that invalidate the assumption of independence between cohorts in A/B tests. That means advanced experiment designs must be found that are often custom for every use case. So, maybe in addition to thinking more like product managers, engineers will need to be thinking more like data scientists. This is all just in the area where I have some expertise. I imagine there are many other such areas. Some of which we haven't even found yet because we've been stuck doing the drudgery that AI can actually help with. [cue the song Code Monkey]
  - SgtBastard 3y ago
ptx 3y ago
I don't think COBOL's dream was to generate enormous amounts of assembly code that users would then have to maintain (in assembly!) and producing differently wrong results every time you ran it.
- bitwize 3y ago
  It may not have been the dream, but the reality is many COBOL systems have been binary-patched to fix issues so many times that the original source may not be a useful guide to how the thing actually works.
  - tra3 3y ago
    Can you share any more info on this?
diego_sandoval 3y ago
> machine code → macro assembly → C → Java → Python The increase in productivity, we can all agree on, but a non-negligible portion of HN users would say that each one of those new languages made programming progressively less fun.
- flohofwoe 3y ago
  I think where people will disagree is how much productivity those steps brought. For instance I think the step from machine code to macro assembler is bigger than the step from a macro assembler to C (although still substantial), but the step from C to anything higher level is essentially negligible compared to the massive jump from machine code to a 'low level high level' language like C.
  - actionfromafar 3y ago
    So many other things happened at the same too, so it's sometimes hard to untangle what is what. For instance, say that C had namespaces, and a solid package system with a global repo of packages like Python, C# and Java have. Then you'd be able to throw together things pretty easily. Things easily cobbled together with Python often aren't attributable to Python the language per se, but rather Python, the language and its neat packages.
- deterministic 3y ago
  Python is a step backwards in productivity for me compared with typed languages. So no I don't think we all agree on this. You might be more productive in Python but that's you not me.
deterministic 3y ago
> machine code → macro assembly → C → Java → Python This made me laugh out loud. Python is not a step up from Java in my opinion. Python is more of a step up from BASIC. It's a different evolutionary path. Like LISP.

vczf 3y ago

The endpoint is that being a programmer becomes as obsolete as being a human "calculator" for a career. Millions, perhaps billions of times more lines of code will be written, and automated programming will be taken for granted as just how computers work. Painstakingly writing static source code will be seen the same way as we see doing hundreds of pages of tedious calculations using paper, pencil, and a slide rule. Why would you do that, when the computer can design and develop such a program hundreds of times in the blink of an eye to arrive at the optimal human interface for your particular needs at the moment? It'll be a tremendous boon in every other technical field, such as science and engineering. It'll also make computers so much more useful and accessible for regular people. However, programming as we know it will fade into irrelevance. This change might take 50 years, but that's where I believe we're headed.

withinboredom 3y ago
Yet, we still have programmers writing assembly code and hand-optimizing it. I believe that for most software engineers, this will be the future. However, experts and hobbyists will still experiment with different ways of doing things, just like people experiment with different ways of creating chairs. An AI can only do what it is taught to do. Sure, it can offer unique insights from time to time, but I doubt it will get to the point where it can craft entirely new paradigms and ways of building software.
- vczf 3y ago
  You might be underestimating the potential of an automated evolutionary programming system at discovering novel and surprising ways to do computation—ways that no human would ever invent. Humans may have a better distribution of entropy generation (i.e. life experience as an embodied human being), but compared to the rate at which a computer can iterate, I don't think that advantage will be maintained. (Humans will still have to set the goals and objectives, unless we unleash an ASI and render even that moot.)
  - beacon294 3y ago
    ASI?

financypants 3y ago

Is a more generic version of this argument be that there will always be a need for smart/experienced people?

thfuran 3y ago

>There will probably always be a need for expert-level troubleshooters and optimizers who understand all the layers There's already so many layers that essentially no one knows them all at even a basic level, let alone expert. A few more layers and no one in the field will even know of all the layers.