[ben30]

This echoes my experience with Claude Code. The bottleneck isn't the code generation itself—it's two critical judgment tasks:

1. Problem decomposition: Taking a vague idea and breaking it down into well-defined, context-bounded issues that I can effectively communicate to the AI

2. Code review: Carefully evaluating the generated code to ensure it meets quality standards and integrates properly

Both of these require deep understanding of the domain, the codebase, and good software engineering principles. Ironically, while I can use AI to help with these tasks too, they remain fundamentally human judgment problems that sit squarely on the critical path to quality software.

The technical skill of writing code has been largely commoditized, but the judgment to know what to build and how to validate it remains as important as ever.

[bcrosby95]

This would be at least the third time in history we've tried to shunt writing code to low paid labor. We'll see if it's successful this time.

The problem tends to be that small details affect large details which affect small details. If you aren't good at both you're usually shit at both.

[mlinhares]

The problem wasn't low paid labor, it was just incompetent labor. You can find competent developers in all these countries offering lower pay, India, Brazil, Romania, Poland, China, Pakistan, its just that they would already be hired by other higher paying companies and what is left for the ones that are looking for the lowest paid possible workers are the incompetent ones.

[wvoch235]

IMO attempts to make it low paid work will fail, just like almost every STEM profession. But... the number of engineers that we need who operate as "power multipliers" on team will continue to decrease. Many startup and corporate teams already aren't needing junior/mid level engineers any longer.

They just need "drivers", senior/lead/staff engineers that can run independent tracks. AI becomes the "power multiplier" in the teams who amplify the effects of the "driver".

Many people pretend that 10x engineers don't exist. But anyone who has worked on an adequately high performing team at a large (or small) company knows that skill, and quite frankly intelligence, operate on power laws.

The bottom 3 quartiles will be virtually unemployable. Talent in the top quartile will be impossible to find because they're all employed. Not all that unlike today, though which quartile you fall into is largely going to depend on how "great" of an engineer you are AND how effectively you use AI.

As this happens, the tap of new engineers who are learning how to make it into the top quartile, will cutoff for everyone except for those who are passionate/sadistic enough to programming without AI, then learn to program WITH AI.

Meanwhile the number of startups disrupting corporate monopolies will increase as the cost of labor goes down due to lower headcount requirements. Lower head counts will lead to better team communication and in general business efficiency.

At some point the upper quartile will get automated too. And with that, corporate moats evaporate to solo-entrepreneurs and startups. The ship is sinking, but the ocean is about to boil too. When economic formulas start dividing by zero, we can be pretty sure that we can't predict the impact.

[tom_m]

Someone told me AI was like having a bunch of junior coders. You have to be very explicit in telling it what to do and have to go through several iterations to get it right. Though it was cheaper.

[gherkinnn]

That matches my experience.

Decomposing a problem so that it is solvable with ease is what I enjoy most about programming and I am fine with no longer having to write as much code myself, but resent having to review so much more.

Now, how do we solve the problem of people blindly accepting what an LLM spat out based on a bad prompt. This applies universally [0] and is not a technological problem.

0 - https://www.theverge.com/policy/677373/lawyers-chatgpt-hallu...

[ben30]

Agreed on the review burden being frustrating. Two strategies I've found helpful for managing the cognitive load:

1. Tight issue scoping: Making sure each issue is narrowly defined so the resulting PRs are small and focused. Easier to reason about a 50-line change than a 500-line one.

2. Parallel PR workflow: Using git worktrees to have multiple small PRs open simultaneously against the same repo. This lets me break work into digestible chunks while maintaining momentum across different features.

The key insight is that smaller, well-bounded changes are exponentially easier to review thoroughly. When each PR has a single, clear purpose, it's much easier to catch issues and verify correctness.

Im finding these workflow practices help because they force me to engage meaningfully with each small piece rather than rubber-stamping large, complex changes.

[steveBK123]

So really the same two skills that a senior engineer needs to delegate tasks to juniors & review the results..

[skydhash]

Nope, dealing with juniors is way less frustrating because they learn. So overtime, you can increase the complexity of their tasks until they're no longer junior.

[AndrewKemendo]

This is exactly how to use it and exactly why it’s a huge deal

In my experience so far, the people that aren’t getting value out of LLM code assistants, fundamentally like the process of writing code and using the tooling

All of my senior, staff, principals love it because we can make something faster than having to deal with a junior because it’s trivial to write the spec/requirement for Claude etc…

[prmph]

What the heck, the code generation _is_ absolutely still a bottle-neck.

I dare anyone who making these arguments that LLMs have removed the need for actual programming skill, for example, to share in a virtual pair programming session with me, and I will demonstrate their basic inability to do _any_ moderately complex coding in short order. Yes, I think that's the only way to resolve this controversy. If they have some magic sauce for prompting, they should post a session or chat that can be verified by other (even if not exactly repeatable).

Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet. Having to hand hold it every step of the way, continually keep correcting basic type and logic errors (even ones I had corrected previously in the same session), and in the end it just could solve the challenge I gave it.

I have to be cynical and believe those shouting about LLMs taking over technical skill must have lots of stock in the AI companies.

[coffeefirst]

Indeed.

All this “productivity” has not resulted in one meaningful open source PR or one interesting indie app launch, and I can’t square my own experience with the hype machine.

If it’s not all hat and no cattle, someone should be able to show me some cows.

[sgarland]

> Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet

I have been extremely cynical about LLMs up until Claude 4. For the specific project I've been using it on, it's done spectacularly well at specific asks - namely, performance and memory optimization in C code used as a Python library.

[whatarethembits]

Honestly, its mind boggling. Am I the worst prompter ever?

I have three python files (~4k LOC total) that I wanted to refactor with help from Claude 4 (Opus and Sonnet) and I followed Reed Harper's LLM workflow...the results are shockingly bad. It produces an okay plan, albeit full of errors, but usable with heavy editing. In the next step though, most of the code it produced was pretty much unusable. It would've been far quicker for me to just do it myself. I've been trying to get LLMs on various tasks to help me be faster but I'm just not seeing it! There is definitely value in it in helping to straighten out ideas in my head and using it as StackOverflow on roids but that's where the utility starts to hit a wall for me.

Who are these people who are "blown away" by the results and declaring an end to programming as we know it? What are they making? Surely there ought to be more detailed demos of a technology that's purported to be this revolutionary!?

I'm going to write a blog post with what I started with, every prompt I wrote to get a task done and responses from LLMs. Its been challenging to find a detailed writeup of implementing a realistic programming project; all I'm finding is small one off scripts (Simon Willison's blog) and CRUD scaffolding so far.

[sokoloff]

I don’t think AI marks the end of software engineers, but it absolutely can grind out code for well specified, well scoped problem statements in quarter-minutes that would take a human an hour or so.

To me, this makes my exploration workflow vastly different. Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”

I think that gets to a better outcome faster in perhaps 10-25% of software engineering work. That’s huge and today is the least capable these AI assistants will ever be.

Even just the human/social/mind-meld aspects will be meaningful. If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out. (This might even turn out to be half the benefit of productivity enhancing tools.)

[nyarlathotep_]

> I have to be cynical and believe those shouting about LLMs taking over technical skill must have lots of stock in the AI companies.

I'm far from being a "vibe" LLM supporter/advocate (if anything I'm the opposite, despite using Copilot on a regular basis).

But, have you seen this? Seems to be the only example of someone actually putting their "proompts" where their mouth is, in a manner of speaking. https://news.ycombinator.com/item?id=44159166

[ofjcihen]

It’s interesting that your point about wasting time makes a second point in your favor as well.

If you don’t have the knowledge that begets the skills to do this work then you would never have known you were wasting your time or at least how to stop wasting time.

LLM fanboys don’t want to hear this but you can’t successfully use these tools without also having the skills.

[prmph]

Edit for the parent comment:

> in the end it just could NOT solve the challenge I gave it.

[numpad0]

Last week I was like, I might as well vibe code with free Gemini and steal his credit than researching something destined to be horrible as Android Camera2 API, and found out that at least me using this version of Gemini do better if I prompt it in a... casual language.

"ok now i want xyz for pqr using stu can you make code that do" rather than "I'm wondering if...", with lowercase I and zero softening languages. So as far as my experience goes, tiny details in prompting matter and said details can be unexpected ones.

I mean, please someone just downvote and tell me it's MY skill issue.

[dgb23]

I want to add something to this which is rarely discussed.

I personally value focus and flow extremely highly when I'm programming. Code assistance often breaks and prevents that in subtle ways. Which is why I've been turning it off much more frequently.

In an ironic way, using assistance more regularly helped me realize little inefficiencies, distractions and bad habits and potential improvements while programming:

I mean that in a very broad sense, including mindset, tooling, taking notes, operationalizing, code navigation, recognizing when to switch from thinking/design to programming/prototyping, code organization... There are many little things that I could improve, practice and streamline.

So I disagree with this statement at a fundamental level:

> The technical skill of writing code has been largely commoditized (...)

In some cases, I find setting yourself up to get into a flow or just high focus state and then writing code very effective, because there's a stronger connection with the program, my inner mental model of how it works in a more intricate manner.

To me there are two important things to learn at the moment: Recognizing what type of approach I should be using when and setting myself up to use each of them more effectively.

[thrwthsnw]

Just move up an abstraction level and put that flow into planning the features and decomposing them into well defined tasks that can be assigned to agents. Could also write really polished example code to communicate the style and architectural patterns and add full test coverage for it.

I do notice the same lack of flow when using an agent since you have to wait for it to finish but as others have suggested if you set up a few worktrees and have a really good implementation plan you can use that time to get another agent started or review the code of a separate run and that might lend itself to a type of flow where you’re keeping the whole design of the project in your head and rapidly iterating on it.

[eweise]

"these require deep understanding of the domain, the codebase, and good software engineering principles" Most of this AI can figure out eventually, except maybe the domain. But essentially software engineering will look a lot like product management in a few years.

[virgilp]

As a (very good I would say) product manager once told me - the product vision and strategy depends very much on the ability to execute. The market doesn't stand still, and what you _can_ do defines very much what you _should_ do.

What I mean to say here is that not even product management is reduced to just "understand the domain" - so it kinda' feels that your entire prediction leans on overly-simplified assumptions.

[TaupeRanger]

That's a narrow view of the issue described in the blog post. You're coming at this from the perspective of a software engineer, which is understandable given the website we're posting on, but the post is really focusing on something higher level - the ability to decide whether the problems you're decomposing and the code you're reviewing is for something "good" or "worthwhile" in the first place. Claude could "decompose problems" and "review code" 10x better than it currently does, but if the thing it's making is useless, awkward, or otherwise bad (because of prompts given by people without the qualities in the blog post), it won't matter.

[esafak]

You still need to be able to code to recognize when it's done poorly, and to write the technical specification.