The reason that people need to gather precise requirements that are precise is because the specifications -> product loop is long. Imprecision results in lots of wasted effort.

If that loop is shortened drastically, then trying, checking and tweaking is suddenly a much more viable design method. That doesn’t require a precise set of requirements.

> That doesn’t require a precise set of requirements.

this exactly.

If the AI could make something that semi works, and you check the output, and repeat until you find the output satisfying, then it will be one of the biggest improvements to software development. Sure, you wouldn't use it to write mission critical software such as aviation, etc. But you'd use it to automate the sorting of your email, or write a quick auto-reply and auto mail merge, or bang out a quick site.

Then people will use it in production and hit edge case after edge case. Software engineers have spent their careers learning to spot these in advance and deal with them, while AI will just have to guess what to do, or just let the program crash.

Let me tell you, with some of the tickets I've had to deal with I do not think most people could actually describe the problem accurately enough to an AI to actually fix the issue.

Job postings in 10 years are gonna torture themselves not to name some random model as the only way to churn out bug fixes for some no longer supported model and it's frozen in time language of choice.

> If that loop is shortened drastically, then trying, checking and tweaking is suddenly a much more viable design method.

No, you still need the skill of gathering precise requirements, otherwise you end up in endless churn of implementing the wrong requirements and then implementing the wrong corrections when you get bad corrections.

(Maybe we didn’t know before the general adoption of notionally-Agile development methods, which didn’t have this as there premise but were focused on other benefits of a shortened spec->product loop, we certainly know it after the widespread adoption of those methods.)

Shortened development loop does mean that you are more likely to have the whole market/domain shift under you between the time the requirements are defined and when the system is implemented, though, a frequently-realized risk with big upfront design that renders even precise and accurate (at the time gathered) requirements incorrect when implemented.

A better tool to improve production would be an expert system that would gather requirements. But the people who want software to do their job either can't specify what they do or don't want to invest any of their time in what they see as someone else's job.

You're missing the point: Natural language can be much a higher layer of abstraction than the programming languages we currently have. It's much faster to say "Add a button to download the output as a PDF" than write JS directly.

You'd be surprised by what regular people can build when you give them the power to create software. Here are a bunch of apps created using my tool/GPT-4: https://showcase.picoapps.xyz Most of our users have never coded before, and are able to build small tools to make their and their customers' lives better.

Sure, they can "build" it, but can they maintain it? Can they only add more layers of mud? How do you refactor the statement "Add a button to download the output as a PDF"?

That's not a replacement for software engineering.

I thought about this for a while now and I think for the class of problems GP is referring to, no maintenance will actually be needed, the tools will just be discarded re-written again -- at least in one-man projects where no other person than the author is needed to create the tool. Maybe in the future some kind of pseudo-code between real code and natural language will be established. This kind of pseudo-code could be as expressive as code but more concise than natural language. I am not certain if it will ever replace actual software engineers for scopes beyond on-man projects.

That makes sense, but never underestimate how easy it is for a throwaway script or tool to become production-ized and a dependency for other production processes.

We've all seen that obscene production workflow built on a Google Sheet or Jupyter Notebook that now needs to support this or that new feature or integration... Add AI-generated tools to the pile.

What's hard about refactoring it? I sorta get the point you're trying to make, but codegen models generate code, but they also understand and explain code very well in plain language. I believe domain experts are smart enough to solve the problems that crop up and maintain their code with help from AI.

Hence, the usefulness of tools like UI.Vision RPA

Additional idea for why this won't be used as much as they hope: Creating the software is just one part of the entire process of utilizing software. Small scripts actually could be possible to be written by AI, but actually using them could turn up challenging for normal users.

Anyone who has setup a coding project knows that actually creating the project structure, setting up dependencies, build scripts, making the code compile/be interpreted are all problems that can have extremely obscure, frustrating errors, and they happen before you even start coding.

Then, not to mention, deploying the software. Even if you give someone code, they won't immediately know how to run it. End users get worried at the idea of opening a terminal and running a command in it, no matter how easy it is. Not to mention setting up the software to do so. (Is the right Python version even installed?)

As such, even if an AI could write a perfect script in code from standard text to, say, lowercase all of the words in a document, it would still be hard for non-developers to use because of the surrounding knowledge barrier, outside of the code itself. Although, yeah, it would be easier.

> These tools are all annoying AF. Developers don't need half-baked hints to write basic statements and regular people don't have the skills to hobble together whatever permutations these things spit out. Which rather begs the question: who the hell is the audience for this?

On the contrary: developers are exactly the people capable of handling those complex requirements you speak of. As a developer, getting a computer to handle basic statements is great and frees you to handle the big stuff.

Being able to write “// Retrieves second value from path” and have the computer spit out some string parsing method is great. All those little helper methods that showily fill up projects are great candidates for an AI. Especially if it helps you break up code into smaller, more composable (and disposable) chunks. If an AI writes the code, and can easily do it again, maybe people would be more willing to delete stuff that isn’t needed.

This is already happening.

It’s true that they won’t know how to exactly specify their needs. But they can share input and output examples and iterate on the solution.

I know folks without any programming background using ChatGPT to write code for them.

The code doesn’t work right off the bat but by iterating with the agent they can either get a solution or solve a portion of the problem.

if you mean things like "write me Python code to read a file and count all occurrences of a word" then all cool. Good luck with something like "write me some Java code for Android to send a message to an app user"

Agreed. The space of “read a file and X” problems is huge though and it’s been awesome seeing folks solve these themselves.

It’s about giving the domain experts who understand what the requirements should be a way to build something without having to have the domain knowledge of code.

I think the problem with that is the same reason why "no code" platforms struggle to succeed. Writing software without understanding control flow or libraries or APIs is practically impossible. Instead of being a liberating experience like it should be, it becomes a confining and frustrating one where you don't understand what is and isn't possible.

LLMs will work really well when developers know what they want and how to ask for it, same with many no-code platforms. If you don't understand programming though, you can't even know if your request is possible.

I've worked a fair amount with a client that was using a no-code platform (Bubble) and I agree. These platforms make it easy to get started, but quickly their users get stuck at problems that would take me 2 lines of code to solve, but they just get stuck at the glass walls of the no-code platform.

Now, is it still better that no-code platforms exists and give non-technical people the chance to get started? Yes, probably. But the transition path is not clear to me, since no-code platforms don't want their users to move on. So, naturally, they evolve to do more and more complex stuff, which in turn makes their whole platform more complicated and scares off their very target audience.

So now, you need to hire agencies and "no-code developers" to work on your no-code app. Back at square one.

I can see the same story playing out with AI-based coding. If you don't know coding, AI-based coding is just a layer on top of a no-code platform.

That is true. I have seen this first hand with bpmn tools such as camunda, where the analysts simply cannot wrap their head around it.

However, over time you will need to describe less and less of the code for a large majority of use cases. I expect Generative AI will be able to take more generic prompts based on a specific vendor and really generate more with less prompting given context of whatever you are targeting. Ie azure , camunda, etc

> However, over time you will need to describe less and less of the code for a large majority of use cases

Over the same time, the sophistication demanded of software will expand to more than offset this.

Source: this process of advancing tooling and advancing demands has been going on the whole time software has existed, with a consistent pattern.

Yes, the problem that had been “solved” a couple times a decade since the 1970s, at least, and every time the new tools that do that end up mostly used by professional software developers, not domain experts. Honestly, the only thing that has come close to actually even minimally addressing this is spreadsheet software.

It hasn't been solved any better this time, either.

Sorry, but in my experience fish think about water more than domain experts think about requirements.

I agree the claim isn't going to happen.

> Which rather begs the question: who the hell is the audience for this?

In my opinion audience for code-generation AI are developers, not the general public. It's immensely useful to be assisted by AI to autocomplete and suggest my code. Whether that is because I'm not familiar with a language syntax or just don't have all the language API in my head.

The general public isn't going to have a clue how to put things together, and until AI can generate reliable and fully functioning code I doubt this is ever going to be for the general public. AI right now is essentially the combination of Google+StackOverflow for me but in a much faster pace. Instead of browsing through tens of SO questions and Google links to get to the exact situation I'm in I can just prompt the AI with all the details and get one response that has the answer to my problem, usually!

Shit, even if you give someone all the Lego blocks in the world and a infinity accurate picture of the minimally complex final product, less than 1% would figure it out.

I bootstrapped dev learning by collecting all the necessary pieces of code but at the end of the data I feel like I'm just writing a huge semitechnical novel and the problems I encounter have nothing to do with the basic building blocks, it's entirely about code flow, data flow, entry points, race conditions and things you encounter after you hit 99% of test cases.

This stuff seems like new age "low code" environments.

The audience for this is management. They'll spend lots of their budget on it. They'll use this to put something together that does something, just not what they want. Then they'll show it to you and tell you to make it work the way they think they want. After all, if a manager can "do it" in 90 minutes with no training a developer should be able to make it perfect in a few days. And they'll make you use the new tool so you learn it and so they can justify the expense.

The point is the nuance of writing code is now no longer an elitist act that strikes the egos of those who understand the intricacies, it’s now democratized and in the hands of anyone. It’s not “good code” but it can be. It’s a kin to hiring your nephew that says he can code but can’t really other than stdio stuff but at least has the right attitude and asks the right questions.

I do believe there will be a day where we communicate what we need and software is written on the fly to facilitate that need. For better or worse.

> The point is the nuance of writing code is now no longer an elitist act that strikes the egos of those who understand the intricacies, it’s now democratized and in the hands of anyone.

Insofar as anything like that was ever true, it still is.

Not that writing code has ever been the hard part of software development.

Assuming that the requirement quality is a constant . And humans as system have the ability to compile this high level instruction to low level code. now imagine there exists systems which A. Augments human ability making it more efficient. or B. replace humans completely. The only reason this is valuable is that it might POTENTIALLY reduces the $/hr cost of the system.

Copium

More people will be able to express themselves, it doesn’t matter that your uncle won’t