Comment by gitremote

> Software development jobs must be very diverse if even this anti-vibe-coding guy thinks AI coding definitely makes developers more productive.

As a Professor of English who teaches programming to humanities students, the writer has had an extremely interesting and unusual academic career [1]. He sounds awesome, but I think it's fair to suggest he may not have much experience of large scale commercial software development or be particularly well placed to predict what will or will not work in that environment. (Not that he necessarily claims to, but it's implicit in strong predictions about what the "future of programming" will be.)

[1] https://stephenramsay.net/about/

I have done both strict back-end, strict front-end, full stack, QA automation and some devops as well, I worked in an all Linux shop where we were encouraged by great senior devs to always strive for better software all around. I think you're right, it mostly depends on your mindset and how much you expose yourself to the craft. I can tackle obscure front-end things sometimes better than back-end issues despite hating front-end but knowing enough to be dangerous. (My first job in tech really had me doing everything imaginable)

I find the LLMs boost my productivity because I've always had a sort of architectural mindset, I love looking up projects that solve specific problems and keeping them on the back of my mind, turns out I was building myself up for instructing LLMs on how to build me software, and it takes several months worth of effort and spits it out in a few hours.

Speaking of vibe coding in archaic languages, I'm using LLMs to understand old Shockwave Lingo to translate it to a more modern language, so I can rebuild a legacy game in a modern language. Maybe once I spin up my blog again I'll start documenting that fun journey.

The thing is that some imagined AI that can reliably produce reliable software will also likely be able to be smart enough to come up with the requirements on its own. If vibe coding is that capable, then even vibe coding itself is redundant. In other words, vibe coding cannot possibly be "the future", because the moment vibe coding can do all that, vibe coding doesn't need to exist.

The converse is that if vibe coding is the future, that means we assume there are things the AI cannot do well (such as come up with requirements), at which point it's also likely it cannot actually vibe code that well.

The general problem is that once we start talking about imagined AI capabilities, both the capabilities and the constraints become arbitrary. If we imagine an AI that does X but not Y, we could just as easily imagine an AI that does both X and Y.

Yup. I would never be able to give my Jira tickets to an LLM because they're too damn vague or incomplete. Getting the requirements first needs 4 rounds of lobbying with all stakeholders.

>In my work, the bigger bottleneck to productivity is that very few people can correctly articulate requirements.

Agreed.

In addition, on the other side of the pipeline, code reviews are another bottleneck. We could have more MRs in review thanks to AI, but we can't really move at the speed of LLM's outputs unless we blindly trust it (or trust another AI to do the reviews, at which point what are we doing here at all...)

This means your difficulty is not programming per se, but that you are working on a very suboptimal industry / company / system. With all due respect, you use programming at work, but true programming is the act of creating a system that you or your team designed and want to make alive. Confusing the reality of writing code for a living in some company with what Programming with capitalized P is, produces a lot of misunderstanding.

To be honest I've never worked in an environment that seemed too complex. On my side my primary blocker is writing code. I have an unending list of features, protocols, experiments, etc. to implement, and so far the main limit was the time necessary to actually write the damn code.

Unfortunately a lot of it is also because of illiteracy.

Lots of people hide the fact that they struggle with reading and a lot of people hide or try to hide the fact they don’t understand something.

I don’t mind the coding, it’s the requirements gathering and status meetings I want AI to automate away. Those are the parts I don’t like and where we’d see the biggest productivity gains. They are also the hardest to solve for, because so much of it is subjective. It also often involves decisions from leadership which can come with a lot of personal bias and occasionally some ego.

I don't think the author would disagree with you. Ad you point out coding is just one part of software development. I understand his point to be that the coding portion of the job is going to be very different going forward. A skilled developer is still going to need to understand frameworks and tradeoffs so that they can turn requirements into a potential solution. It just they might not be coding up the implementation.

I constantly run into issues where features are planned and broken down outside-in, and it always makes perfect sense if you consider it in terms of the pure user interface and behaviour. It completely breaks down when you consider the API, or the backend, is a cross-cutting concern across many of those tidy looking tasks and cannot map to them 1:1 without creating an absolute mess.

Trying to insert myself, or the right backend people, into the process, is more challenging now than it used to be, and a bad API can make or break the user experience as the UI gets tangled in the web of spaghetti.

It hobbles the effectiveness of whatever you could get an LLM to do because you’re already starting on the backfoot, requirements-wise.

> very few people can correctly articulate requirements

This is the new programming. Programming and requirements are both a form of semantics. One conveys meaning to a computer at a lower level, the other conveys it to a human at a higher level. Well now we need to convey it at a higher level to an LLM so it can take care of the lower-level translation.

I wonder if the LLM will eventually skip the programming part and just start moving bits around in response to requirements?

I like my requirements articulated so clearly and unambiguously that an extremely dumb electronic logic machine can follow every aspect of the requirements and implement them "perfectly" (limited only by the physical reliability of the machine).

Yeah, the hardest part is understanding the requirements. But it then still takes hours and hours and hours to actually build the damn thing.

Except that now it still takes me the same time to understand the requirements ... and then the coding takes 1/2 or 1/3 of the time. The coding also always takes 1/3 of the effort so I leave my job less burned out.

Context: web app development agency.

I really don't understand this "if it does not replace me 100% it's not making me more productive" mentality. Yeah, it's not a perfect replacement for a senior developer ... but it is like putting the senior developer on a bike and pretending that it's not making them go any faster because they are still using their legs.

The solo projects I do are 10x, the team projects I do maybe 2-3x in productivity. I think in big companies it's much much less.

Highest gains are def in full stack frameworks (like nextjs), with Database ORM, and building large features in one go, not having to go back & forth with stakeholders or collegues.

This feels like addressing a point TFA did not make. TFA talks mostly about vibe-coding speeding up coding, whereas your comment is about software development as a whole. As you point out, coding is just one aspect of engineering and we must be clear about what "productivity" we are talking about.

Sure, there are the overhypers who talk about software engineers getting entirely replaced, but I get the sense those are not people who've ever done software development in their lives. And I have not seen any credible person claiming that engineering as whole can be done by AI.

On the other hand, the most grounded comments about AI-assisted programming everywhere are about the code, and maybe some architecture and design aspects. I personally, along with many other commenters here and actual large-scale studies, have found that AI does significantly boost coding productivity.

So yes, actual software engineering is much more than coding. But note that even if coding is, say, only 25% of engineering (there are actually studies about this), putting a significant dent in that is still a huge boost to overall productivity.

Also that requirements engineering in general isn't being done correctly.

I'm the last guy to be enthused about any "ritualistic" seeming businessy processes. Just let me code...

However, some things do need actually well defined adhered to processes where all parties are aware of and agreeing with the protocol.

This is like saying the typewriter won’t make a newspaper company more productive because the biggest bottlenecks are the research and review processes rather than the typing. It’s absolutely true, but it was still worth it to go up to typewriters, and the fact that people were spending less effort and time on the handwriting part helps all aspects of energy levels etc across their job.

Convince your PMs to use an LLM to help "breadboard" their requirements. It's a really good use case. They can ask their dumb questions they are afraid to and an LLM will do a decent job of parsing their ideas, asking questions, and putting together a halfway decent set of requirements.

"the bigger bottleneck to productivity is that very few people can correctly articulate requirements."

I've found the same way. I just published an AI AUP for my company and most of it is teaching folks HOW to use AI.

You can vibe ask the requirements. Not even kidding.

AI is making coding so cheap, you can now program a few versions of the API and choose what works better.

I write a library which is used by customers to implement integrations with our platform. The #1 thing I think about is not

> How do I express this code in Typescript?

it's

> What is the best way to express this idea in a way that won't confuse or anger our users? Where in the library should I put this new idea? Upstream of X? Downstream of Y? How do I make it flexible so they can choose how to integrate this? Or maybe I don't want to make it flexible - maybe I want to force them to use this new format?

> Plus making sure that whatever changes I make are non-breaking, which means that if I update some function with new parameters, they need to be made optional, so now I need to remember, downstream, that this particular argument may or may not be `undefined` because I don't want to break implementations from customers who just upgraded the most recent minor or patch version

The majority of the problems I solve are philosophical, not linguistic

This is one reason I think spec driven development is never really going to work the way people claim it should. It's MUCH harder to write a truly correct, comprehensive, and useful spec than the code in many cases.

Sounds like you work with inexperienced PMs that are not doing their job, did you try having a serious conversation about this pattern with them? I'm pretty sure some communication would go a long way towards getting you on a better collaboration groove.

and in reality, all the separate roles should be deprecated

we vibe requirements to our ticket tracker with an api key, vibe code ticket effort, and manage the state of the tickets via our commits and pull requests and deployments

just teach the guy the product manager is shielding you from not to micromanage and all the frictions are gone

in this same year I've worked at an organization that didn't allow AI use at all, and by Q2, Co-Pilot was somehow solving their data security concerns (gigglesnort)

in a different organization none of those restrictions are there and the productivity boost is through an order of magnitude greater

If AI doesn't make you more productive you're using it wrong, end of story.

Even if you don't let it author or write a single line of code, from collecting information, inspecting code, reviewing requirements, reviewing PRs, finding bugs, hell even researching information online, there's so many things it does well and fast that if you're not leveraging it, you're either in denial or have ai skill issues period.