4 ms·

AI maybe eats software when people learn to fully specify requirements, which if past performance is any prediction is, um, never. Sure, have ChatGPT generate

AI maybe eats software when people learn to fully specify requirements, which if past performance is any prediction is, um, never. Sure, have ChatGPT generate your app for you. Do you plan to independently generate tests for all the functionality or are you just going to YOLO and hope everything's correct? It's only a matter of time before someone uses ChatGPT to generate an app that loses them millions of dollars because of some misgenerated functionality. Good times...

rzza 3y ago
I agree in the current state but think that's too focused on the outcomes right now. It seems more likely that a bunch of companies would perfect all those kinks because there's enough economic incentive to do so. It's similar to any disruptive technology at the beginning of an S-curve if you've read Innovator's Dilemma. At first, it just handles small niche use cases and the incumbent (software engineers) move upmarket to customers with higher demands that the disruptive technology can't satisfy (maybe the Autonomous driving software) but then the disruptive technology gets good enough as companies pour resources into developing it until it can satisfy even the higher demand customers.
- BandButcher 3y ago
  As much as I'd like to disagree, seeing big tech companies pour billions into ai chips/gpus... "Chips for America" i think was the news article headline i read last week about upcoming chip manufacturing subsidies. even our government is spending big $$$ on it
- palata 3y ago
  > It seems more likely that a bunch of companies would perfect all those kinks because there's enough economic incentive to do so. Not every problem can be solved by throwing money at it (or at all, even). Nobody knows how far AI can go. Those who invest a ton into it just hope that it will become profitable, but really they don't have a clue. It's just a big bet.
  - rzza 3y ago
    We're only 1 year into this
    - palata 3y ago
      Into machine learning? I studied machine learning, and I can assure you that it was a long time ago. Waaaaaay longer than 1 year.
      - rzza 3y ago
        Into LLM code generation
      - rzza 3y ago
        Why you don't believe we'll get to state of AI being the predominant creator of complex and usable software? It seems like you're more focused on the fact that it can't handle the most complex use cases today.
        palata 3y ago
        Because AI doesn't "understand" natural languages. Writing software is about converting some understanding of the world ("I want to be able to solve this human problem") into unambiguous specifications (usually that's a programming language). Sure, it can auto-complete such that the developer does not have to remember anything (which is arguably making developers worse at doing their job), but it cannot understand the reality of the person who will be using the program. It is already a problem with human developers who write programs that they don't use themselves, solving problems that they don't understand themselves.
palata 3y ago
> AI maybe eats software when people learn to fully specify requirements The thing is: natural languages are for humans, and they are ambiguous by construction. Programming languages are for machines, and must be unambiguous in order to work. The role of a software developer is to translate the "human requirements" into unambiguous code. Now say you manage to specify a program in a natural language in an unambiguous manner, then you have just invented a new programming language. And people will have to learn it, because it won't be a natural language anymore. It will just be a programming language that somehow looks like a natural language. IMHO, that is fundamental: there will always be a need to translate between the natural language and the programming language, and that is the role of a human who actually understands the natural language (as opposed to an IA that interpolates between data points it was trained on).

rzza 3y ago

I agree in the current state but think that's too focused on the outcomes right now. It seems more likely that a bunch of companies would perfect all those kinks because there's enough economic incentive to do so. It's similar to any disruptive technology at the beginning of an S-curve if you've read Innovator's Dilemma. At first, it just handles small niche use cases and the incumbent (software engineers) move upmarket to customers with higher demands that the disruptive technology can't satisfy (maybe the Autonomous driving software) but then the disruptive technology gets good enough as companies pour resources into developing it until it can satisfy even the higher demand customers.

BandButcher 3y ago
As much as I'd like to disagree, seeing big tech companies pour billions into ai chips/gpus... "Chips for America" i think was the news article headline i read last week about upcoming chip manufacturing subsidies. even our government is spending big $$$ on it
palata 3y ago
> It seems more likely that a bunch of companies would perfect all those kinks because there's enough economic incentive to do so. Not every problem can be solved by throwing money at it (or at all, even). Nobody knows how far AI can go. Those who invest a ton into it just hope that it will become profitable, but really they don't have a clue. It's just a big bet.
- rzza 3y ago
  We're only 1 year into this
  - palata 3y ago
    Into machine learning? I studied machine learning, and I can assure you that it was a long time ago. Waaaaaay longer than 1 year.
    - rzza 3y ago
      Into LLM code generation
    - rzza 3y ago
      Why you don't believe we'll get to state of AI being the predominant creator of complex and usable software? It seems like you're more focused on the fact that it can't handle the most complex use cases today.
      - palata 3y ago
        Because AI doesn't "understand" natural languages. Writing software is about converting some understanding of the world ("I want to be able to solve this human problem") into unambiguous specifications (usually that's a programming language). Sure, it can auto-complete such that the developer does not have to remember anything (which is arguably making developers worse at doing their job), but it cannot understand the reality of the person who will be using the program. It is already a problem with human developers who write programs that they don't use themselves, solving problems that they don't understand themselves.

palata 3y ago

> AI maybe eats software when people learn to fully specify requirements The thing is: natural languages are for humans, and they are ambiguous by construction. Programming languages are for machines, and must be unambiguous in order to work. The role of a software developer is to translate the "human requirements" into unambiguous code. Now say you manage to specify a program in a natural language in an unambiguous manner, then you have just invented a new programming language. And people will have to learn it, because it won't be a natural language anymore. It will just be a programming language that somehow looks like a natural language. IMHO, that is fundamental: there will always be a need to translate between the natural language and the programming language, and that is the role of a human who actually understands the natural language (as opposed to an IA that interpolates between data points it was trained on).