This is going to catch some heat, but what if the most important professional “developer skill” to learn or improve is how to effectively use coding agents?

I saw something similar in ML when neural nets came around. The whole “stack moar layerz” thing is a meme, but it was a real sentiment about newer entrants into the field not learning anything about ML theory or best practices. As it turns out, neural nets “won” and using them effectively required development and acquisition of some new domain knowledge and best practices. And the kids are ok. The people who scoffed at neural nets and never got up to speed not so much.

Edit: as an aside, I have learned plenty from reviewing coding agent generated implementations of various algorithms or methods.

> what if the most important professional “developer skill” to learn or improve is how to effectively use coding agents?

Well, it's not. There's a small moat around that right now because the UX is still being ironed out, but in a short while able to use coding agents will be the new able to use Excel.

What will remain are the things that already differentiate a good developer from a bad one:

- Able to review the output of coding agents

- Able to guide the architecture of an application

- Able to guide the architecture of a system

- Able to minimize vulnerabilities

- Able to ensure test quality

- Able to interpret business needs

- Able to communicate with stakeholders

The endgame in programming is reducing complexity before the codebase becomes impossible to reason about. This is not a solved problem, and most codebases the LLMs were trained on are either just before that phase transition or well past it.

Complexity is not just a matter of reducing the complexity of the code, it's also a matter of reducing the complexity of the problem. A programmer can do the former alone with the code, but the latter can only be done during a frank discussion with stakeholders.

A vibe coder using an LLM to generate complexity will not be able to tell which complexity to get rid of, and we don't have enough training data of well-curated complexity for LLMs to figure it out yet.

> This is going to catch some heat, but what if the most important professional “developer skill” to learn or improve is how to effectively use coding agents?

If it does go as far that way as many seem to expect (or, indeed, want), then most people will be able to do it, there will be a dearth of jobs and many people wanting them so it'll be a race to the bottom for all but the lucky few: development will become a minimum wage job or so close to that it'll make no odds. If I'm earning minimum wage it isn't going to be sat on my own doing someone else's prompting, I'll find a job that involves not sitting along in front of a screen and reclaim programming for hobby time (or just stop doing it at all, I have other hobbies to divide my time between). I dislike (effectively) being a remote worker already, but put up with it for the salary, if the salary goes because “AI” turns it into a race-to-the-bottom job then I'm off.

Conversely: if that doesn't happen then I can continue to do what I want, which is program and not instruct someone else (be it a person I manage or an artificial construct) to program. I'm happy to accept the aid of tools for automation and such, I've written a few of my own, but there is a line past which my interest will just vanish.

I also remember a similar wave around 10-15 years ago regarding ML tooling and libraries becoming more accessible, more open source releases etc. People whose value add was knowing MATLAB toolboxes and keeping their code private got very afraid when Python numpy, scikit learn and Theano etc came to the forefront. And people started releasing the code with research papers on github. Anyone could just get that working code and start tweaking the equations put different tools and techniques together even if you didn't work in one of those few companies or didn't do an internship at a lab who were in the know.

Or other people who just kept their research dataset private and milked it for years training incrementally better ML models on the same data. Then similar datasets appeared openly and they threw a hissy fit.

Usually there are a million little tricks and oral culture around how to use various datasets, configurations, hyperparameters etc and papers often only gave the high level ideas and math away. But when the code started to become open it freaked out many who felt they won't be able to keep up and just wanted to keep on until retirement by simply guarding their knowledge and skill from getting too known. Many of them were convinced it's going to go away. "Python is just a silly, free language. Serious engineers use Matlab, after all, that's a serious paid product. All the kiddies stacking layers in Theano will just go away, it's just a fad and we will all go back to SVM which has real math backing it up from VC theory." (The Vapnik-Chervonenkis kind, not the venture capital kind.)

I don't want to be too dismissive though. People build up an identity, like the blacksmith of the village back in the day, and just want to keep doing it and build a life on a skill they learn in their youth and then just do it 9 to 5 and focus on family etc. I get it. But wishing it won't make it so.

Talented, skilled people with good intuition and judgements will be needed for a long time but that will still require adapting to changing tools and workflows. But the bulk of the workforce is not that.

Using a coding agent seems quite low skill to me. It’s hard to see it becoming a differentiator. Just look at the number of people who couldn’t code before and are suddenly churning out work to confirm that.

It absolutely is, but the fundamental misunderstanding around this seems to be that "effectively using coding agents" is a superset of the 2023-era general understanding of "Senior Software Engineer".

At least when you're talking about shipping software customers pay for, or debugging it, etc. Research, narrow specializations, etc may be a different category and some will indeed be obsoleted.

I think your argument is predicated on LLM coding tools providing significant benefit when used effectively. Personally I still think the answer is "not really" if you're doing any kind of interesting work that's not mostly boilerplate code writing all day.

Many of those skills have temporary value before they're incorporated into the models/harnesses

There was a moment we thought JS had won. And then crypto. I personally believed low-level development was done.

Not sure why this would catch heat rationally speaking. It is quite clear in a professional setting effective use of coding agents is the most important skill to develop as an individual developer.

It’s also the most important capability engineering orgs can be working on developing right now.

Software Engineering itself is being disrupted.

I'd offer an edit that the most important skill may be knowing when the agent is wrong.

There's so much hand wringing about people not understanding how LLMs work and not nearly enough hand wringing about people not understanding how computer systems work.

> This is going to catch some heat, but what if the most important professional “developer skill” to learn or improve is how to effectively use coding agents?

Doing so will effectively force a (potentially unwanted) career change for many people and will lead to the end of software engineering (and software as a career), assuming AI continues to improve.

"Effectively" using agents means that you're writing specs and reading code (in batches through change diffs) instead of writing code directly. This requires the ability to write well (or well enough to get what you want from the agent) and clearly communicate intent (in your language of choice, not code; very different IMO).

The way that you read code is different with agents as well. Agents can produce a smattering of tests alongside implementation in a single turn. This is usually a lot of code. Thus, instead of red-green-refactor'ing a single change that you can cumulatively map in your head, you're prompt-build-executing entire features all at once and focusing on the result.

Code itself loses its importance as a result. See also: projects that are moving towards agentic-first development using agents for maintenance and PR review. Some maintainers don't even read their codebases anymore. They have no idea what the software is actually doing. Need security? Have an agent that does nothing but security look at it. DevOps? Use a DevOps agent.

This isn't too far off from what I was doing as a business analyst a little over 20 years ago (and what some technical product managers do now for spikes/prototypes). I wrote FRDs [^0] describing what the software should do. Architects would create TRDs [^1] from those FRDs. These got sent off to developers to get developed, then to QA to get bugs hammered out, then back to my team for UAT.

If agents existed back then, there would've been way fewer developers/QA in the middle. Architects would probably do a lot of what they would've done. I foresee that this is the direction we're heading in, but with agents powered by staff engineers/Enterprise Architects in the middle.

> Edit: as an aside, I have learned plenty from reviewing coding agent generated implementations of various algorithms or methods.

People learn differently. I (and others) learn from doing. Typing code from Stack Overflow/Expertsexchange/etc instead of pasting it, then modifying it is how I learned to code. Some can learn from reading alone.

[^0]: https://www.modernanalyst.com/Resources/Articles/tabid/115/I...

I don’t think it could be the most important skill to have. The most common, and the most standardized one for sure, but if coding agents are doing fundamental R&D or running ops then nobody needs skills anyway.

> As it turns out, neural nets “won”

> The people who scoffed at neural nets and never got up to speed not so much.

I get the feeling you don’t know what you’re talking about. LLMs are impressive but what have they “won” exactly? They require millions of dollars of infrastructure to run coming around a decade after their debut, and we’re really having trouble using them for anything all that serious. Now I’m sure in a few decades’ time this comment will read like a silly cynic but I bet that will only be after those old school machine learning losers come back around and start making improvements again.

I'd say viewing it as most important is pretty unprofessional. But isn't it the point of this extreme AI push? To replace professional skills with dummy parrots.