I've come to the same conclusion: If you just want a huge volume of code written as fast as possible, and don't care about 1. how big it is, 2. how fast it runs, 3. how buggy it is, 4. how maintainable or understandable it is, or 5. the overall craftsmanship and artistry of it, then you're probably seeing huge productivity gains! And this is fine for a lot of people and for a lot of companies: Quality really doesn't matter. They just care about shitting out mediocre code as fast as possible.

If you do care about these things, it will take you overall longer to write the code with an LLM than it would by hand-crafting it. I started playing around with Claude on my hobby projects, and found it requires an enormous amount of exhausting handholding and post-processing to get the code to the point where I am really happy with it as a consistent, complete, expressive work of art that I would be willing to sign my name to.

> Quality really doesn't matter.

It does matter, but it's one requirement among many. Engineers think quality metrics as you listed are the most important requirements, but that's not typically true.

>shitting out mediocre code as fast as possible.

This really is what businesses want and always have wanted. I've seen countless broken systems spitting out wrong info that was actively used by the businesses in my career, before AI. They literally did not want it fixed when I brought it up because dealing with errors was part of the process now in pretty much all cases. I don't even try anymore unless I'm specifically brought on to fix a legacy system.

>that I would be willing to sign my name to.

This right here is what mgmt thinks is the big "problem" that AI solves. They have always wanted us to magically know what parts are "good enough" and what parts can slide but for us to bear the burden of blame. The real problem is same as always bad spec. AI won't solve that but it will in their eyes remove a layer in their poor communication. Obviously no SWE is going to build a system that spit out wrong info and just say "hire people to always double check the work" or add it to so-so's job duties to check, but that really is the solution most places seem to go with by lack of decision.

Perhaps there is some sort of failure of SWE's to understand that businesses don't care. Accounting will catch the expensive errors anyway. Then Execs will bull whip middle managers and it will go away.

The adversarial tension was all that ever made any of it work.

The "Perfectionist Engineer" without a "Pragmatic Executive" to press them into delivering something good enough would of course still been in their workshop, tinkering away, when the market had already closed.

But the "Pragmatic Executive" without the "Perfectionist Engineer" around to temper their naive optimism would just as soon find themselves chased from the market for selling gilded junk.

You're right that there do seem to be some execs, in the naive optimism that defines them, eager to see if this technology finally lets them bring their vision to market without the engineer to balance them.

We'll see how it goes, I guess.

That's a nice balanced wholesome take, only the problem is that the "Pragmatic Executive" is more like "Career-driven frenzied 'ship it today at all costs' psychopath executive".

You are describing a push-and-pull / tug-of-war balanced relationship. In reality that's absolutely exactly never balanced. The engineer has 1% say, the other 99% go to the executive.

I so wish your take was universally applicable. In my 24 years of career, it was not.

> Perhaps there is some sort of failure of SWE's to understand that businesses don't care

I think it's an engineer's nature to want to improve things and make them better, but then we naively assume that everybody else also wants to improve things.

I know I personally went through a pretty rough disillusionment phase where I realised most of the work I was asked to do wasn't actually to make anything better, but rather to achieve some very specific metrics that actually made everything but that metric worse.

Thanks to the human tendency to fixate on narratives, we can (for a while) trick ourselves into believing a nice story about what we're doing even if it's complete bunk. I think that false narrative is at the core of mission statements and why they intuitively feel fake (mission statement is often more gaslighting than guideline - it's the identity a company wants to present, not the reality it does present).

AI is eager to please and doesn't have to deal with that cognitive dissonance, so it's a metric chaser's dream.

<< They have always wanted us to magically know what parts are "good enough" and what parts can slide but for us to bear the burden of blame.

Well, that part is bound to add a level of tension to the process. Our leadership has AI training, where the user is responsible for checking its output, but the same leadership also outright stated it now sees individual user of AI as having 7 employees under them ( so should be 7x more productive ). Honestly, its maddening. None of it is how it works at all.

> This really is what businesses want and always have wanted.

There's a difference between what they really want and executives knowing what they want. You make it sound like every business makes optimal decisions to get optimal earnings.

> They literally did not want it fixed when I brought it up because

Because they thought they knew what earns them profits. The key here they thought they knew.

The real problem behind the scenes is a lot of management is short term. Of course they don't care. They roll out their shiny features, get their promotions and leave. The issues after that are not theirs. It is THE business' problem.

Senior Software Engineer. The system is a niche business software software for a specific industry. It doesn't do any fancy math, all straightforward business logic.

> Trying to get clean code erases all/most of my productivity gains, and doesn't spark joy. I find having a back-end-forth with an agent exhausting, probably because I have to build and discard multiple mental models of the proposed solution, since the approach can vary wildly between prompts

You probably work on something that requires very unique and creative solutions. I work on dumb business software. Claude Code is generally good at following existing code patterns. As far as back-and-forth with Claude Code being exhausting, I have few tips how how to minimize number or shots required to get good solution from CC: 1. Start by exploring relevant code by asking CC questions. 2. Then use Plan Mode for anything more than trivial change. Using Plan Mode is essential. You need to make sure you and CC are on the same page BEFORE it starts writing code 3. If you see CC making same mistake over and over, add instructions to your CLAUDE.md to avoid it in the future. This way your CC setup improves over time, like a coworker who learns over time.

Thank you for the actionable ideas. I'll experiment with closer supervision during the planning stage, hopefully finer-grained implementation details will reduce unnecessarily large refactors during review.

Claims about agentic workflows are the new version of "works on my machine" and should be treated with skepticism if they cannot be committed to a repository and used by other people.

Maybe parent is a galaxy-brained genius, or.. maybe they are just leaving work early and creating a huge mess for coworkers who now must stay late. Hard to say. But someone who isn't interested in automating/encoding processes for their idiosyncratic workflows is a bad engineer, right? And someone who isn't interested in sharing productivity gains with coworkers is basically engaged in sabotage.

> And someone who isn't interested in sharing productivity gains with coworkers is basically engaged in sabotage.

Who says they aren't interested in sharing? To give a less emotionally charged example: I think my specific use pattern of Git makes me (a bit) more productive. And I'm happy to chew anyone's ear off about it who's willing to listen.

But the willingness and ability of my coworkers to engage in git-related lectures, while greater than zero, is very definitely finite.

Something that is advertised as 10x improvement in productivity isn't like your personal preferences for git or a few dinky bash aliases or whatever. It's more like a secret personal project test-suite, or a whole data pipeline you're keeping private while everyone else is laboriously doing things manually.

Assuming 10x is real, then again the question: why would anyone do that? The only answers I can come up with are that they cannot share it (incompetence) or that they don't want to (sabotage). You're saying the third option is.. people just like working 8 hours while this guy works 1? Seems unlikely. Even if that's not sabotaging coworkers it's still sabotaging the business

The reason is because we are a Microsoft shop and our company doesn't have Claude account. I'm using my personal Claude Max account. My manager does know that I use Claude Code and I requested the person responsible for AI tooling in our company to use Claude Code but he just said that management already decided to go with GitHub copilot. He thinks that using Claude model in Copilot is same as using Claude Code. Another issue is that we are a Microsoft shop and I use Claude Code through WSL but I'm the only person on our team with Linux skills.

Every time I hear “we are a Microsoft shop” makes me remember the scene with Jimmy O Yang and Windows auto updating in Space Force

https://youtu.be/xDLvUqhwHZc

There are methods of connecting the claude code cli tools to copilot’s api — look at litellm or something along those lines, it’s a pip pkg and translates the calls code makes

Business and Enterprise plans have a no-training-on-your-data clause.

I’m not sure personal Claude has that. My account has the typical bullshit verbiage with opt-outs where nobody can really know whether they’re enforceable.

Using a personal account is akin to sharing the company code and could get one in serious trouble IMO.

You can opt-out of having your code being trained on. When Claude Code first came out Anthropic wasn't using CC sessions for training. They started training on it starting from Claude Code 2 that came out with Sonnet 4.5. User is asked on first use whether to opt-in or out of training.

> You're saying the third option is.. people just like working 8 hours while this guy works 1?

Nope, I don't say that at all.

I am saying that certain accommodations might feel like 10x to the person making them, but that doesn't mean they are portable.

Another personal example: I can claim with a straight face that using a standing desk and a Dvorak keyboard make me 10x more productive than otherwise. But that doesn't necessarily mean that other people will benefit from copying me, even if I'm happy to explain to anyone how to buy a standing desk from Ikea (or how to work company procurement to get one, in case you are working not-from-home).

In any case, the original commenter replied with a better explanation than our speculations here.

> And someone who isn't interested in sharing productivity gains with coworkers is basically engaged in sabotage.

I'll have to vigorously dissent on this notion: we sell our labor to employers - not our souls. Our individual labor, contracts and remuneration are personalized. Our labor. Not some promise to maximize productivity - that's a job for middle and upper management.

Your employer sure as hell won't directly share 8x productivity gains with employees. The best they can offer is a once-off, 3-15% annual bonus (based on your subjective performance, not the aggregate), alternatively, if you have RSU/options, gains on your miniscule ownership fraction.

I'm teaching a course in how to do this to one of my clients this week.

Also, I used this same process to address a bug that is many years old in a very popular library this week. Admittedly, the first solution was a little wordy and required some back and forth, but I was able to get to a clean tested solution with little pain.

It seems to me that the devs that managed to become sergeants of a small platoon of LLM agents to a crushing success deem their setup a competitive advantage and as such will never share it.

But them being humans, they do want to brag about it.

This has been my experience too. At the end of each session, i’m left very exhausted mentally without full understanding of what I just did, so I have to review it again.

Coding this way requires an effort that is equal to both designing, coding, and reviewing except the code i review isnt mine. Strange situation.

Well for me, all of my actual implementation work has been green field from “git init” and mostly coding around the AWS SDK in the target language and infrastructure as code since AI coding has gotten decent.

I haven’t had to write a line of code in a year. First ChatGPT and more recently Claude Code.

I don’t do “agentic coding”. I keep my hands on the steering wheel and build my abstractions and modules up step by step. I make sure every line of code looks like something I would write.

I’m a staff consultant (cloud + app dev) and always lead projects, discovery and design and depending on the size of the project, do all of the actual hands on work myself.

I would have had to staff at least one maybe two less senior consultants to do the actual hands on work before. It’s actually easier for me to do the work then having to have really detailed requirements and coordinating work (the whole “Mythical Man Month” thing).

FWIW: before the pearl clutching starts, I started coding in assembly in 1986 on an Apple //e and have been delivering production level code since 1996.

I have tech adjacent people on my team vibing out internal tools that are super useful, and take a load off of engineering. Most internal software is rehashing existing software with different/specific requirements.

Exactly what I experience. I don't need AI to generate complex algorithm, I need e.g. a lot of code for a UI library that is clean and maintainable - but it's can't ever generate such code and it can't be prompted, because training data has much less excellent code than good and ok code. Therefore I can't use AI for high-level design task, ony low-level code, which I then have to check and clean line by line, and that isn't an enjoyable work.

I don't need LLMs, I need some kind mind reading device :D

Not the OP but we use LLMs to build a restaurant pos system with reservations, loyalty, webshop etc. Almost at feature parity with bigwigs like lightspeed/toast.

> I find having a back-end-forth with an agent exhausting, probably because I have to build and discard multiple mental models of the proposed solution, since the approach can vary wildly between prompts

Just right now I had it improve QR payments on POS. This is standard stuff, and I have done it multiple time but i'm happy I didn't have to spend the mental energy to implement it and just had to review the code and test it.

```

Perfect! I've successfully implemented comprehensive network recovery strategies for the OnlinePaymentModal.tsx file. Here's a summary of what was added:

  Implemented Network Recovery Strategies

  1. Exponential Backoff for Polling (lines 187-191)
  2. Network Status Detection (lines 223-246, 248-251)
  3. Transaction Timeout Handling (lines 110-119)
  4. Retry Logic for Initial Transaction (lines 44-105)
  5. AbortController for Request Cancellation (lines 134-139, 216-220)
  6. Better Error Messaging (lines 85-102, 193-196)
  7. Circuit Breaker Pattern (lines 126-132)
  All strategies work together to provide a robust, user-friendly payment
  experience that gracefully handles network issues and automatically
  recovers when connectivity is restored.

```

> An agent can easily switch between using Newton-Raphson and bisection when asked to refactor unrelated arguments, which a human colleague wouldn't do after a code review.

Can you share what domain your work is in? Is it deeptech. Maybe coding agents right now work better for transactional/ecommerce systems?

I don't know if that example is real, but if it is, that's exactly the reason I find AI tools irritating. You do not need six different ways to handle the connection being down, and if you do, you should really factor that out into a connection management layer.

One of my big issues with LLM coding assistants is that they make it easy to write lots & lots of code. Meanwhile, code is a liability, and you should want less of it.

These aren't 6 different way.

You are talking about something like network layers in graphql. That's on our roadmap for other reasons(switching api endpoints to digital ocean when our main cloudflare worker is having an outage), however even with that you'll need some custom logic since this is doing at least two api calls in succession, and that's not easy to abstract via a transaction abstraction in a network layer(you'll have handle it durably in the network layer like how temporal does).

Despite the obvious downsides we actually moved it from durable workflow(cf's take of temporal) server side to client since on workflows it had horrible and variable latencies(sometimes 9s v/s consistent < 3s with this approach). It's not ideal, but it makes more sense business wise. I think many a times people miss that completely.

I think it just boils down to what you are aiming. AI is great for shipping bugfixes and features fast. At a company level I think it also shows in product velocity. However I'm sure very soon our competitors will catch up when AI skepticism flatters.