AI does not help programmers

Agreed that ChatGPT can be a bit of a hit or miss when you ask it to produce code. But, I'd say a good 50% of the code I recently wrote was actually created by AI and I'm a lot more productive thanks to it.

3 big use cases for me so far:

- Github Copilot is just great. Often it helps fill in the code I was just about to write (or Google search). It just saves time, period. Plus, there's been at least a few times when I was ready to throw in the towel at the end of the day. But carried through as Copilot suggested the next line or implementation of a method I wanted to just create a placeholder for.

- ChatGPT for a project in an unknown domain. A few weeks ago I wanted to create a Chrome extension. I've never done it before and didn't know where to start. I asked ChatGPT and it delivered a great interactive tutorial with just the right code to get me started (I had to fix a few bugs that ChatGPT helped debug). Am I an expert in Chrome extensions thanks to it? Hell no! Have I created a working Chrome extension in a very short amount of time? 100% yes!

- ChatGPT for debugging. When I search for an error and get not-so-relevant Google search results, often ChatGPT can suggest rather relevant things to look into given an error message.

Of course, your mileage may vary, but saying AI does not help programmers based on a quick test of it not implementing things perfectly seems a bit surprisingly shortsighted I'd say.

As others have said, this article is written as if GitHub's Copilot + VSCode integration doesn't exist, and I think the author should get that going before concluding, "AI Does Not Help Programmers."

It saves me a decent amount of rote implementation. I read up on the APIs, I understand the pattern I'm supposed to implement, but then Copilot actually does it for me. It's not a world-changer, but it does save time, it does "help".

And not for nothing, at least once I've pasted a script into GPT4 and asked "What's wrong here?" and it correctly identified I was using `[` instead of `[[` for a Bash conditional. This was code that had been working for years (AWS must have updated what version of Bash it was running CodeDeploy through), I never would have thought to check something like that. Realistically saved me an entire day hunting that down.

There's a class of programming problems that are "keyboard-hard", in that they're a pain to type out but fairly easy to verify correctness. For this set of problems I've found coding AI assistants are sooo useful.

I think people assume CoPilot is built to be your pair-programming coworker. Maybe that's Microsoft's fault for allowing that hype to go unchecked.

Honestly, the correct way to understand this is, it's just Microsoft's IntelliSense v2.0 rebranded with an extra machine learning system

Instead of autocompleting lines of code from an index, it's autocompleting entire complete working functions based on what it learned from GitHub.

IF you learn how to use it, it can save you hours of writing repeatable code. It should almost never be used to generate completely new code out of thin air. That code is quite dangerous because it looks logically right, and at quick glance looks exactly like what you need. However, it's completely wrong and could cause catastrophic system failures if left untouched. Then you waste the time you previously saved trying to figure out what the CoPilot spit out.

It doesn't produce correct code, we all know that, but ChatGPT4 gave me incorrect answers that showed me where I should look and that unstucked me several times working with LLVM 15. It provided me much more value than the 20$ I paid for.

Like the author, I want an assistant that can reason about the problem, not just crank out plausible-looking code. ChatGPT et al. can produce code that seems plausible, and may actually work. But that happens because it got trained on a vast corpus of code, and most programmers write code that already got written thousands of times before by other people. It doesn't happen because ChatGPT has unique insight into the problem to solve, or into how to write code.

In his long and excellent explanation of LLMs, Stephen Wolfram observed:

> So how is it, then, that something like ChatGPT can get as far as it does with language? The basic answer, I think, is that language is at a fundamental level somehow simpler than it seems. [1]

I think that most programming is, at a fundamental level, simpler than it seems to most programmers. As a programmer's skill and experience increase tools like ChatGPT offer less utility. Junior programmers struggle with even simple things. But delegating those things to an LLM prevents the programmer learning the most important skill: problem solving. Similarly having someone do your homework and write your papers gets you a good grade, but you haven't accomplished anything -- you just ran in place by delegating the appearance of work and failed to actually learn anything.

As other people noted in the comments in this thread, if you get value/save time by having ChatGPT generate boilerplate and unit tests and so on, ask why you do that in the first place. I have the same opinion about ChatGPT/LLMs writing memos, emails, term papers, articles -- those artifacts cannot have much real importance if they get delegated to a tool that can only produce plausible text. The actual value of writing and communicating gets removed, only the artifact and the appearance of work remains.

[1] https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...

>> I have tried to understand how I could use ChatGPT for programming and, unlike Welsh, found almost nothing.

Errr..... how can this guy say all this in the face of thousands of developers talking about how AI helps them, about AI copilots being built that demonstrably assist programming.

My personal experience being that AI has turned programming into a completely different process.

What does this say about the author, that they can argue a point so obviously wrong?

> The title of this article is rather assertive. Take it as a proposition to be debated [...] The main immediate goal is [...] to spur a fruitful discussion to advance our understanding beyond the "Wow!" effect.

OK, I already know where this is going so I'll just skim the rest.

Hmmm... as I suspected, I can't see how this article is any different to:

"AI Does Not Help [xyz]" with cherry-picked examples to prove a point.

In fact, I'm pretty sure I've been reading similar articles several times a month since ChatGPT was released.

Here is my "proposition to be debated":

"Articles about how AI does not help [xyz] do not in fact help anyone"

ChatGPT v3.5 does have this problem where it constantly apologizes, when you as a question about the answer it provided.

It feels like it made an error by not having answered the question as expected, and then apologizes for it. This is a back-and-forth, constantly issuing apologies:

---

user -- Why does the new device need an IP in the VLAN's subnet?

ChatGPT -- I apologize for the confusion in my previous response. If the new device is only acting as a bridge or switch between the two computers on the different VLANs [...]

user -- These 3 steps you mention are required for the new device to act as a bridge?

ChatGPT -- Apologies for any confusion caused. You are correct, the three steps I mentioned earlier are not specifically required for the new device to act as a bridge. [...]

user -- Now you are confusing me. Let's start anew, but keep the history in mind. [...]

ChatGPT -- Apologies for any confusion caused earlier. In the scenario you described, where computer A and B are on different subnets

---

Luckily GPT-4 doesn't do that, but in 3.5 this also occurs when asking certain programming questions.

Well I just learned functional programming, specifically Ramda using nodejs over the weekend, thanks to ChatGPT. Didn’t even know I needed it, or what it was, but I’m working on a series of data transformations that help me clean data and oh man, Ramda makes everything so much cleaner and understandable.

ChatGPT introduced me to it, then helped me debug and write a bunch of code as I had trouble wrapping my head around functional programming.

But sure, it’s all in my head and ChatGPT wasn’t helpful at all?

Compared to what or whom? Normal people don’t have brilliant mentors, and don’t understand complicated docs at a glance.

Authors like this miss the point because they’re already brilliant. ChatGPT is for stupid people like me, not for brilliant people like him.

I'm not sure I agree with the thesis of this post. It seems to me asking if something is right vs when compiling to test, and iterated to work, does seem like it helps programmers.

I think that for the immediate future, AI will be more of a help to non-programmers who need to do simple programming tasks than to actual programmers.

I say this as a PM who is regularly using ChatGPT to prototype things and to write scripts to automate things in my side business.

Folks have been talking about no-code platform for as long as I can remember, but they've always been too minimally-featured to be useful, or they actually required significant programming knowledge. ChatGPT, in my experience, bridges the gap, at least for simple, standalone tools. I expect it will get better and better as a tool for non-developers to build simple things.

I think with GitHub Copilot I save 10-20 minutes a day and those moments it feels Like a superpower. For me personally I know what I'm doing and if the AI gets me sometimes faster there thats a personal benefit I appreciate.

I just canceled Copilot and I'm more productive now that I'm not hesitating and waiting to see if Copilot does something.

COPILOT PROS

- Good at boilerplate

- Good at unit tests

- Fairly good at pasting from Stack Overflow

CONS

- Most of the time it does nothing

- Large code blocks pop in and out while I'm typing, which is distracting

- Sometimes it correctly guesses what I want, but the code is completely wrong with non-existent variable and method names

- Visual Studio has 3 or 4 other "helpers" and they can seemingly all active at once, making hard to even see what I'm doing (Intellisense, Intellicode, CodeLens, ???)

EDIT: I include writing prompts and reviewing Copilot output as hesitating/breaking flow.

There's an interesting parallel between this critique and bad interviewing practices - the kind where the interviewer has a toy problem with a particular "correct" answer in their head and asks the interviewee to figure it out. A lot of the prominent critiques of AI seem to be taking the form "LLM are based in statistics, so they fail to produce strictly-defined solutions, therefore they have little value", which is as close to a straw-man argument as you can make.

Instead, I tend to value critiques that check whether LLMs are useful for the huge number of challenges that we have as programmers where we have to infer 'best' solutions from under-defined problems, or where we have a large set of reference data but need to infer patterns/correctness - those challenges are just as hard (if not harder) than implementing algorithms precisely, so

  - I accept that LLMs aren't good at implementing specific algorithms
  - Can they help write exhaustive unit tests based on code and/or a written spec?
  - Can they help identify potential errors in your best attempt at a solution, even if they can't 'fix' the errors?
  - I can think of a hundred ways to get use out of "a cocky graduate student, smart and widely read, also polite and quick to apologize, but thoroughly, invariably, sloppy and unreliable" - that's a skill that programmers now need to develop, but there's a huge amount of potential in such people, and an analogous potential in LLMs.

I guess you could classify myself as "not a programmer" since I don't do it daily, and I mostly make scripts and small web APIs in python for tinkering around with IoT stuff.

ChatGPT has made it incredibly easy for me to program quick solutions to problems.

"Write a simple Sanic framework app that has a POST request with route '/logtemp' that accepts two parameters - a datetime and a value between from -40 to +40. - the route will then call a function that inserts these values into a Sqlite database with method signature logTemperature(datetime, temperature)."

I have to do some additional coaxing obviously (and prompt it for edge case recovery, etc. etc.), but it takes the mundane-ness out of learning the framework for something I'm not planning on selling or scaling up - not considering any other problems that can go wrong blindly inserting data into a database, etc. etc.

Maybe for full-time programmers who need to maintain complex codebases with many different source files, etc. - I could see that AI might not be able to help with that, but given enough time I'm sure we'll see AI software that will absorb your code, run/scrutinize it, and come up with recommendations, etc. you can make to it to make it better. (maybe automatically!)

Using AI to reduce cognitive load is both a gift and a curse.

We can use ChatGPT to write essays for us. But the reason why we write essays is because the act of writing exercises our critical thinking and communication skills. Offloading that work to AI defeats the purpose. It's like going to the gym and then using a robot to lift the weights for you.

The same argument applies to programming. I think the most important skill in programming is the ability to "think in code", i.e. to be able to write, understand, and debug code without any tools other than a plain text editor and a terminal. The only way you build this mental muscle is by doing the reps. The risk of having tools do the work for you is that they ultimately become a crutch.

Of course, there are cases where using tools to increase productivity is a net benefit. An expert essayist can potentially use ChatGPT to synthesize information without compromising their thinking process. An expert programmer can potentially use Copilot to automate certain rote programming tasks. I just think we should be careful not to use tools in a way that compromises our basic mastery and foundational knowledge in a given field.

Bertrand Meyer is always worth reading.

The real question:

> What use do I have for a sloppy assistant? I can be sloppy just by myself, thanks, and an assistant who is even more sloppy than I is not welcome. The basic quality that I would expect from a supposedly intelligent assistant—any other is insignificant in comparison —is to be right.

> It is also the only quality that the ChatGPT class of automated assistants cannot promise.

This thread is filled with anecdata so I'll provide mine.. I didn't enjoy using copilot that much. Or gpt. But it could just be due to ingrained coding habits. I find that when I code I'm thinking mostly about design problems. By the time I know how I want to code something the effort to actually do the typing in is the easy part and rather enjoyable. With copilot it was rather good at filling in sections of code but that changed the experience of composing code to having it autocomplete stuff, I would check its logic. Sometimes I would still realize I needed to redesign something so I would have to axe or modify what copilot had wrote. I just didn't find that flow to be enjoyable. I tend to believe it's a matter of practice to get the most out of these AI systems. Just like someone on a scooter needs to learn to kick off with their feet, glide and steer. However for me I still need to feel the ground beneath my feet

ChatGPT is awesome. More often than not, I ask it to do something and it produces something that works in some way on the first go. Then I debug it, but I debug it using ChatGPT and, in almost all cases, get to working code faster than I could have if I were simply writing it from scratch. It's also much more enjoyable and I can do other things while it types its answer.

Perhaps I find it so useful because I'm actually using it for real world tasks instead of trying to contrive an example that demonstrates its usefulness or lack thereof.

I've definitely experienced that ChatGPT workflow here where you just kind of constantly fighting it to get the right answer and then give up disappointed.

However, my most recent use of ChatGPT was to create a very large SQL MERGE statement. I could have coded it by hand but it was long and the MERGE syntax is sufficiently unfriendly so I just pasted in the table structure and what I wanted and it generated the several hundred lines of code I needed. Boring tedious stuff.

This is probably an unreasonably hard problem, but I asked ChatGPT to write me a boot sequence for an AWS machine that would write "hello, world" to the serial port then terminate the instance.

What I got was some fairly generic stuff about bootloaders, and some hallucinated links to non-existent AWS Nitro whitepapers.

I was probably expecting far too much, but it was disappointing given reports of it approximately solving problems out of whole cloth.

If you are a programmer who finds no use in AI, try asking it things like this:

  “how do I….”
  “how does this work”
  “explain this code”
  “convert this function to typescript”
  “what’s the maximum decimal of uint16”
  “difference between this and that”
  “give me an example of”
  “what are the arguments to this function/method”
  “what causes this error”
  “explain this code”
  “given this data structure, how do I get the third row, first item”
  “how do I centre a div”
  “create a C header file for these functions”
  “what’s the command to compile this code”
  “what’s the regex to get lines containing foo”
  “ffmpeg command to convert X to y”
  “given this sql table, write a postgres query to get the first to 100th row and return the data as json of this form”
  “make 1000 rows of sample json data with fields name address phone”
  “write a sed command to search and replace X with y”
  “create a cmakelists to compile this”
  “write a python program to compare two directories A and B, if a file is in both A and B, then ensure that in B it is the same subdirectory as A, create the directory if needed”

etc etc and critically important:

  “that didn’t work”
  “another approach”
  “any other ideas?”

If a programmer sees no value in any of these sorts of AI interactions then honestly they're not worth being on your team - they're wasting productivity.

The point is not that a developer does not know how to do a given task, the point is that certain programming tasks are just hack work- it's a waste of time to be hand coding them - let the AI write a first pass for you and debug it and move on to the next thing. And when you are doing hard things, breaking new ground, learning new stuff, then the AI gives quick answers to the multitude of questions that will be coming up in your head. Sure, sometimes the answers are wrong but that's a small price to pay for the number of times the answers are right, or point you in the right direction.

Also, certain technologies are incredibly well suited to programming via English language - for example regular expressions - grep/awk/sed - I've always dreaded writing regexes because I know what I want but it's such a chore to hack through finding the right syntax. You're burning your employers time it you don't use AI to drive such tools. What could be better than saying "with grep scan all python files recursively and case insensitive for the word 'import' and return the file name and the relevant line and also the immediately previous and after line".

AI programming is programming via pseudo code. How can a developer find no value in that?

I use copilot daily. I consider it a big help when it fills in unit tests for me. It removes work that adds no value. In other words, copilot adds value. It has also been a big help when I do something that is fairly common but is my first time. I have predicted hours to work up a new feature before and was surprised to have it done in minutes after typing...a character.

I stopped using ChatGPT after it was adapted to bing. Very little, if not zero, help now. Copilot seems to assess and track context and knows what you want to do as you move across files and domains. Solid tool.

Discussion of his previous thoughts:

https://news.ycombinator.com/item?id=34473783

Okay. So, really basic things like unit tests, forecast models, or VS Code Extensions Not useful? Hard sell.

The reason why ChatGPT isn't "good at code" is because it just needs more training data and feedback. That's it. And it appears that this is a decision from above to not give GPT-4 that kind of training data (for now). One can guess as to why (...job market).

The "right" answer, as with everything, is probably a balance between full AI generation, AI copilot, and full human coding. One has to just be humble and try and adapt and do new things to reach that balance.

I get what the author is saying here, but I have to respectfully disagree.

> I would love to have an assistant who keeps me in check, alerting me to pitfalls and correcting me when I err. A effective pair-programmer. But that is not what I get. Instead, I have the equivalent of a cocky graduate student, smart and widely read, also polite and quick to apologize, but thoroughly, invariably, sloppy and unreliable. I have little use for such supposed help.

> Fascinating as they are, AI assistants are not works of logic; they are works of words.

> They have become incredibly good at producing text that looks right. For many applications that is enough. Not for programming.

Meyer may be right in that currently LLMs are illogical therefore unsuited to programming complex software, but programmers can still find use from LLMs by delegating to it simple programming tasks like glue scripts.

People should use it for small automations, especially those which aren't mission critical so it's okay for it to make a mistake. The kind of would you would delegate to that hypothetical cocky grad student.

Meanwhile we have famous programmers like Simon Willison, co-creator Django saying this[0]:

> If you're just starting to learn software engineering right now but you're considering dropping it because you think the field might be made obsolete by AI, I have an alternative approach to suggest for you:

> Start learning now, and use AI tools to learn FASTER

> I wrote about my experiences using ChatGPT and Copilot to help learn Rust back in December

> I've since started to get more ambitious - I'm using it for all sorts of other languages, like AppleScript and zsh and jq

[0]: https://twitter.com/simonw/status/1639692312585572352

He would be wrong. My sample size is only n=1, but I've already used ChatGPT / Copilot for the following tasks in a professional work environment:

- writing docker container and docker compose scripts

- Generating cloud formation deployment for dynamo database, elastic search, and lambdas

- assisting in CI CD deployment scripts for GitHub actions/workflow

- Rubber ducking best approaches for doing vector type similarities, database search problems, image convolution, etc.

- mocking interfaces and typedef decorations in typescript

I'm starting to think that the ability to leverage AI tools in an effective manner is apparently a skill that some people inherently lack.

ChatGPT is also good for giving its opinion on your design. Even if it doesn't give you the final answer, it can often make you think.

Troll headline doesn't help article writers

What the article actually says:

> Caveat 2: I am using ChatGPT (version 4). Other tools may perform better.

ai sure can't program for us yet (at least not in any appreciable level; speaking as a senior engineer). But it is quite nifty for sketching things or quickly getting some syntax or boiler palate down. It hasn't helped me solve any non-trivial problem but it has saved me a fair bit of trivial work every few days

Does anyone remember the Slashdot Spam Solutions Checklist?[1]

Here's my effort to try to build an LLM is Bad at <TASK> Checklist (:

---

    This critique of LLMs for writing software is

    (x) ill-informed ( ) outdated ( ) biased ( ) shortsighted

    and fails to work because of the following reasons. (One or more of the following may apply to your particular critique, and it may have other flaws that are specific to different LLMs or development contexts.)

    (x) Relies on ChatGPT rather than exploring alternative, better-suited LLMs
    (x) Misrepresents LLM capabilities by using single-shot examples
    (x) Neglects the benefits of prompt iteration and refinement
    (x) Disregards the value of experimenting with diverse prompt styles
    (x) Overlooks LLMs as code review or debugging assistants
    (x) Ignores the potential of ReAct, chain of thought, or other prompt-enhancing techniques
    (x) Omits crucial experiment details, hindering reproducibility
    (x) Unfairly demands second-order explanations for first-order errors
    (x) Fails to recognize that LLMs can benefit from standard software development practices (testing, specification, etc.)
    (x) Underestimates LLMs' potential for generating pseudocode or high-level design outlines
    (x) Dismisses LLMs as valuable documentation or tutorial creators
    (x) Overlooks LLMs' potential in brainstorming, creative problem-solving, or idea generation
    ( ) Underappreciates the continuous improvement of LLMs with more training data and fine-tuning
    (x) Ignores the value of LLMs for assisting novices or non-programmers in understanding code or creating simple scripts
    (x) Obsesses over LLM-generated executable code, ignoring broader software development tasks where LLMs could shine

    Additionally, your critique may face these philosophical objections:

    (x) Similar critiques are easy to make, yet none have conclusively refuted the value of LLMs for software development
    (x) Focusing on current limitations ignores the rapid advancements in LLM capabilities
    (x) LLMs should be seen as complementary tools, not as replacements for human developers
    (x) Collaboration between LLMs and humans can lead to innovative solutions and improved software quality
    (x) LLMs can democratize access to programming knowledge and resources for a wider audience

    Finally, this is what I think about your critique:

    (x) Sorry, but your critique doesn't hold up against the potential benefits of LLMs in software development.
    ( ) This is a misguided critique, and you're missing the bigger picture.
    ( ) Nice try, but you're not going to derail the progress of LLMs in revolutionizing software development!

---

  [1]: Example https://tech.slashdot.org/comments.pl?sid=10377045&cid=54065831