The rise of judgement over technical skill

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.

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.

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.

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.

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.

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…

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.

> 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.

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.

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.)

> 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

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.

Edit for the parent comment:

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

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.

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.

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.

>For the first couple years you make stuff, it’s just not that good.

Sadly, that's why I don't start a lot of things that would interest me. You need to get into things when you're a kid and don't realize how junk your work is, because as an adult you just don't have time to dedicate to producing a lot of junk to get good at something. The are shortcuts and more directed learning you can do in a lot of areas to reduce some of the undirected learning you do as a child, but it's till time consuming when time is a rare commodity.

I like to do a good job on small stuff.

It works nicely for me, but doesn't really bring accolades (but a hell of a lot of folks actually rely on stuff I authored; they just don't know it, or care -which is just fine).

>>> But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you.

This is why it's so hard for good classical musicians to learn jazz improvisation, even if they love jazz.

High-energy physics?

> You cant learn on nothing, something will be used for it, that something forms the hard knowledge eventually. Typically broad set but shallow hard knowledge.

As a counterpoint though, the way things have gone in the U.K. is to go deep on niche topics without building up appreciation of the broad strokes. To give an example, there’s a GCSE History course for 14-16 year olds where the syllabus is effectively “medicine through time” and “the American West” without ever going near the British Empire, colonialism, the Tudor or Elizabethan periods, the reformation, the Industrial Revolution, Irish home rule and independence, etc. etc. any one of which gives much more insight into the formation of the state and cultural affairs as it stands today.

To my mind it’s too narrow a focus at too young an age when teaching a subject that a lot of children take. It also means there are constantly “we don’t even teach that at school” debates.

> In software, we promote good engineers to management

i've not seen this. Infact its the opposite.

It does show an interesting second order downside of publicly using an LLM for anything: it raises everyone else’s suspicion that the rest of your work is LLM generated.

A certain amount of redundancy can be helpful. If you explain the same thing two different ways, one way might make more sense than the other to a particular reader. The judgement comes in because you should not just be doing this by rote or because you were "lead to believe" it was necessary, but in consideration of who you are writing for.

That is the goal post moving, its done by AI optimists that thinks "we just need something that can solve X and it will be as smart as a human expert".

Wasn't true for chess, wasn't true for Go, we will see when its true, but they are constantly moving the goalposts and then arguing its others who are moving it.