In companies like the oil industry, doubling productivity would mean reducing the expected life span. Costs tend to catch up with profits, especially due to taxes. Layoffs happen inevitably.

Is it the same with tech? Facebook has 3 billion monthly active users. No amount of tech will bring that up to 6 billion. If you were to double the amount of time someone spends on Facebook, or double the ads they see or double the click through rate, what does that really mean?

"ugh, what a hard day at work, I had to prompt the LLM for 8 hours!!"

Yeah its great but as the OP said you have to watch every P.R. like a hawk

Because it's working for a lot of people. There are people getting value from these products right now. I'm getting value myself and I know several other folks at work who are getting value.

I'm not sure what your circumstances are but even if it's not true for you, it's true for many other people.

Consider it this way as a reasoning step: We've invented a cross compiler that can handle the natural languages too. That's definitely useful; but it's still GIGO so you still need your brain.

You can launch a new product in one month instead of 12 months. I think this works best for startups where the risk tolerance is high but works less than ideal for companies such Amazon where system failure has high costs

I see more value on the business side than the tech side. Ask the AI to transcribe images, write an email, parse some excel data, create a prototype, etc. Some of which you might have hired a tech resource to write a script for.

On the tech side I see it saving some time with stuff like mock data creation, writing boiler plate, etc. You still have to review it like it's a junior. You still have to think about the requirements and design to provide a detailed understanding to them (AI or junior).

I don't think either of these will provide 90% productivity gains. Maybe 25-50% depending on the job.

I agree, but absence of evidence is not evidence of absence, and we currently have a lot of developers who feel very productive right now.

We are very much in need of an actual way to measure real economic impact of AI-assisted coding, over both shorter and longer time horizons.

There's been an absolute rash of vibecoded startups. Are we seeing better success rates or sales across the industry?

It works. You’re just not doing it right if it doesn’t work for you. It’s hard to convince me otherwise at this point.

Person that can’t use a hammer “hasn’t seen any proof” that hammers work.

> Why do people keep ralking about AI as it actually worked?

Because it does.

> I still don't see ANY proof that it doesn't generate a total unmaintainable unsecure mess, that since you didn't develop, you don't know how to fix.

I wouldn't know since it's been years since I've tried but I'd imagine that Claude Code would indeed generate a half-baked Next.js monstrosity if one-shot and left to its own devices. Being the learned software engineer I am, however, I provide it plenty of context about architecture and conventions in a bootstrapped codebase and it (mostly) obeys them. It still makes mistakes frequently but it's not an exaggeration to say that I can give it a list of fields with validation rules and query patterns and it'll build me CRUD pages in a fraction of the time it'd take me to do so.

I can also give it a list of sundry small improvements to make and it'll do the same, e.g. I can iterate on domain stuff while it fixes a bunch of tiny UX bugs. It's great.

I’ve been using it to develop firmware in c++. Typically around 10-20 KLOC. Current projects use Sensors, wire protocols, RF systems , swarm networks, that kind of stuff integrated into the firmware.

If you use it correctly, you can get better quality, more maintainable code than 75% of devs will turn in on a PR. The “one weird trick” seems to be to specify, specify, specify. First you use the LLM to help you write a spec (document, if it’s pre existing). Make sure the spec is correct and matches the user story and edge cases. The LLM is good at helping here too. Then break down separations of concerns, APIs, and interfaces. Have it build a dependency graph. After each step, have it reevaluate the entire stack to make sure it is clear, clean, and self consistent.

Every step of this is basically the AI doing the whole thing, just with guidance and feedback.

Once you’ve got the documentation needed to build an actual plan for implementation, have it do that. Each step, you go back as far as relevant to reevaluate. Compare the spec to the implementation plan, close the circle. Then have it write the bones, all the files and interfaces, without actual implementations. Then have it reevaluate the dependency graph and the plan and the file structure together. Then start implementing the plan, building testing jigs along the way.

You just build software the way you used to, but you use the LLM to do most of the work along the way. Every so often, you’ll run into something that doesn’t pass the smell test and you’ll give it a nudge in the right direction.

Think of it as a junior dev that graduated top of every class ever, and types 1000wpm.

Even after all of that, I’m turning out better code, better documentation, and better products, and doing what used to take 2 devs a month, in 3 or 4 days on my own.

On the app development side of our business, the productivity gain also strong. I can’t really speak to code quality there, but I can say we get updates in hours instead of days, and there are less bugs in the implementations. They say the code is better documented and easier to follow , because they’re not under pressure to ship hacky prototype code as if it were production.

On the current project, our team size is 1/2 the size it would have been last year, and we are moving about 4x as fast. What doesn’t seem to scale for us is size. If we doubled our team size I think the gains would be very small compared to the costs. Velocity seems to be throttled more by external factors.

I really don’t understand where people are coming from saying it doesn’t work. I’m not sure if it’s because they haven’t tried a real workflow, or maybe tried it at all, or they are definitely “holding it wrong.” It works. But you still need seasoned engineers to manage it and catch the occasional bad judgment or deviation from the intention.

If you just let it, it will definitely go off the rails and you’ll end up with a twisted mess that no one can debug. But use a system of writing the code incrementally through a specification - evaluation loop as you descend the abstraction from idea to implementation you’ll end up winning.

As a side note, and this is a little strange and I might be wrong because it’s hard to quantify and all vibes, but:

I have the AI keep a journal about its observations and general impressions, sort of the “meta” without the technical details. I frame this to it as a continuation of “awareness “ for new sessions.

I have a short set of “onboarding“ documents that describe the vision, ethos, and goals of the project. I have it read the journal and the onboarding docs at the beginning of each session.

I frame my work with the AI as working with it as a “collaborator” rather than a tool. At the end of the day, I remind it to update its journal of reflections about the days work. It’s total anthropomorphism, obviously, but it seems to inspire “trust” in the relationship, and it really seems to up-level the effort that the AI puts in. It kinda makes sense, LLMs being modelled on human activity.

FWIW, I’m not asserting anything here about the nature of machine intelligence, I’m targeting what seems to create the best result. Eventually we will have to grapple with this I imagine, but that’s not today.

When I have forgotten to warm-start the session, I find that I am rejecting much more of the work. I think this would be worth someone doing an actual study to see if it is real or some kind of irresistible cognitive bias.

I find that the work produced is much less prone to going off the rails or taking shortcuts when I have this in the context, and by reading the journal I get ideas on where and how to do a better job of steering and nudging to get better results. It’s like a review system for my prompting. The onboarding docs seem to help keep the model working towards the big picture? Idk.

This “system” with the journal and onboarding only seems to work with some models. GPT5 for example doesn’t seem to benefit from the journal and sometimes gets into a very creepy vibe. I think it might be optimized for creating some kind of “relationship” with the user.

For me, the main thing is to never have it write anything based on the goal (what the end result should look like and how it should behave). And only on the implementation details (and coding practices that I like).

Sure it is not as fast to understand as code I wrote. But at least I mostly need to confirm it followed how it implemented what I asked. Not figuring out WHAT it even decided to implement in the first place.

And in my org, people move around projects quite a bit. Hasn’t been uncommon for me to jump in projects with 50k+ lines of code a few times a year to help implement a tricky feature, or help optimize things when it runs too slow. Lots of code to understand then. Depending on who wrote it, sometimes it is simple: one or two files to understand, clean code. Sometimes it is an interconnected mess and imho often way less organized that Ai generated code.

And same thing for the review process, lots of having to understand new code. At least with AI you are fed the changes a a slower pace.

I see a lot of people talk about 'insecure code' and while I don't doubt that's true, there's a lot of software development where security isn't actually a concern because there's no need for the software to be 'secure'. Maintainability is important I'll grant you.

What's so sad to see is people excited about making something selling away the time they would have with their ideas. They're paying money to not use their brain to contemplate the thing they should be the foremost expert in the world on, and they're excited to be paying more and more for each modicum of ignorance and mediocrity dispensed.

>> then the software industry will enter into a self-serving productivity craze building all sorts of software tooling, frameworks

>> Smart organizations will not just deliver better products but likely start products [...]

This is not the 90s anymore when low hanging fruit was everywhere ready to be picked. We have everything under the sun now and more.

The problem with bullshit apps is not that it took you 5 months to build. What you build now in 5 minutes it's still bullshit. Most of the remaining work is bullshit jobs. Spinning useless "features" and frameworks that nobody needs and shove them down the throat of customers that never asked for them. Now it's possible to dig holes and fill them back (do pointless work) at much improved pace thanks to AI.

> with the exception of firing those that don't adopt the technology.

This is a crazy take. Even if said people are matching or exceeding the outcome of those using the technology?

I’m not in this group. But the closest analog to what you are saying is firing people for not using a specific IDE.

couldn't have said it better. all of the people clamoring on about eliminating the boilerplate they've been writing + enabling refactoring have had their heads in the sand for the past two decades. so yeah, i'm sure it does seem revolutionary to them!

And for a lot of AI transformation tasks, for a long time I've been using even clever regex search/replace, and with a few minutes of small adjustment afterward I have a 100% deterministic (or 95% deterministic and 5% manually human reviewed and edited) process for transforming code. Although of course I haven't tried that cross-language, etc.

And of course, we didn't see a massive layoff after the introduction of say, StackOverflow, or DreamWeaver, or jQuery vs raw JS, Twitter Bootstrap, etc.

This is the most insightful comment in the thread.

Don't break the gravy train! These discussions here are beautiful echo chambers.

I feel like most folks commenting uncritically here about second coming of Jesus must work in some code sweatshops, churning eshops or whatever is in vogue today quickly and moving on, never looking back, never maintaining and working on their codebases for decade+. Where I existed my whole career, speed of delivery was never the primary concern, quality of delivery (which everybody unanimously complaints one way or another with llms) was much more critical and thats where the money went. Maybe I just picked up right businesses, but then again I worked for energy company, insurance, telco, government, army, municipality, 2 banks and so on across 3 European states. Also, full working code delivery to production is a rather small part of any serious project, even 5x speed increase would move the needle just a tiny bit overall.

If I would be more junior, I would feel massive FOMO from reading all this (since I use it so far just as a quicker google/stackoverflow and some simpler refactoring, but oh boy is is sometimes hilariously and dangerously wrong). I am not, thus I couldn't care less. Node.js craze, 10x stronger, not seeing forest for the trees.

It's disappointing that this is clearly being downvoted due to disagreement - it's a valid perspective. We have very little evidence of the overall impact of aggressively generating code "in the wild" and plenty of bad examples. No one knows what this ends up looking like as it continues to meet reality but plenty are taking a large productivity improvement as a given.

This is fascinating to me. I completely believe you and I will not bother you with all the common "but did you try to tell it this or that" responses, but this is such a different experience from mine. I did the exact same task with claude in the Julia language last week, and everything worked perfectly. I am now in the habit of adding "keep it simple, use only public interfaces, do not use internals, be elegant and extremely minimal in your changes" to all my requests or SKILL.md or AGENTS.md files (because of the occasional failure like the one you described). But generally speaking, such complete failures have been so very rare for me, that it is amazing to see that others have had such a completely different experience.

I’m honestly baffled by this. I don’t want to tell you “you’re holding it wrong” but if this is your normal experience there’s something weird happening.

Friday afternoon I made a new directory and told Claude Code I wanted to make a Go proxy so I could have a request/callback HTTP API for a 3rd party service whose official API is only persistent websocket connections. I had it read the service’s API docs, engage in some back and forth to establish the architecture and library choices, and save out a phased implementation plan in plan mode. It implemented it in four phases with passing tests for each, then did live tests against the service in which it debugged its protocol mistakes using curl. Finally I had it do two rounds of code review with fresh context, and it fixed a race condition and made a few things cleaner. Total time, two hours.

I have noticed some people I work with have more trouble, and my vague intuition is it happens when they give Claude too much autonomy. It works better when you tell it what to do, rather than letting it decide. That can be at a pretty high level, though. Basically reduce the problem to a set of well-established subproblems that it’s familiar with. Same as you’d do with a junior developer, really.

I have similar experiences. It has worked about half the time, but the code has to be pretty simple. I’ve many experiences, where we work on something complicated for an hour, and it is good compiling code, but then an edge case comes to mind that I ask about and Claude tells me the whole approach is doomed and will never work for that case. It has even apologized a few times for misleading me :) I feel like it’s this weird mix of brilliant moron. But yeah, ask for a simple HTML page with a few fields and it rocks.

Did you use the best model available to you (Opus 4.6)? There is a world of difference between using the highest model vs the fast one. The fast ones are basically useless and it's a shame that all these tools default to it.

I don't know why but I find performance on c#/.net be several generations behind. Sometimes right ofc but my general experience is if you pull the generation slot machine in just about any other language it will work better. I regularly do python, typescript, ruby and rust with a better experience. It's even hard to find benchmarks where csharp is included.

If Claude ends up grabbing my C# TOML library, in my defense, I wrote it when the TOML format first came out over a dozen years ago, and never did anything more with it Sorry.

    var model = TomlSerializer.Deserialize<TomlTable>(toml)!;

Same here. While LLMs sometimes work surprisingly well, I also encounter edge cases where they fail surprisingly badly multiple times per day. My guess is that other people maybe just don't bother to check what the AI says which would cause them to not notify omission errors.

Like when I was trying to find a physical store again with ChatGPT Pro 5.4 and asked it to prepare a list of candidates, but the shop just wasn't in the list, despite GPT claiming it to be exhaustive. When I then found it manually and asked GPT for advice on how I could improve my prompting in the future, it went full "aggressively agreeable" on me with "Excellent question! Now I can see exactly why my searches missed XY - this is a perfect learning opportunity. Here's what went wrong and what was missing: ..." and then 4 sections with 4 subsections each.

It's great to see the AI reflect on how it failed. But it's also kind of painful if you know that it'll forget all of this the moment the text is sent to me and that it will never ever learn from this mistake and do better in the future.

The best experiences I have are those where I can describe what I want done with details. Rather than asking it add toml parsing, I would tell it to exactly which library to use ahead of time and reduce the number of decisions available to the model to make. Some of the most effective use-cases are when you have a reference to give it, e.g. "add x feature the same way as in this other project that is also in the workspace", or "make the changes I made to the contents of directory X in git commit <sha here>, but applied to directory Y instead". In both cases it's a lot of copy/paste then tweaking an obvious value (like replacing "dev" with "QA" everywhere).

I try to give the model as little freedom as possible. That usually means it's not being used for novel work.

> This morning I asked Claude to use a library to load a toml file in .net and print a value.

Legit this morning Claude was essentially unusable for me

I could explicitly state things it should adjust and it wouldn't do it.

Not even after specifying again, reverting everything eventually and reprompt from the beginning etc. Even super trivial frontend things like "extract [code] into a separate component"

After 30 minutes of that I relented and went on to read a book. After lunch I tried again and it's intelligence was back to normal

It's so uncanny to experience how much ita performance changes - I strongly suspect anthropic is doing something whenever it's intelligence drops so much, esp. Because it's always temporary - but repeatable across sessions if occuring... Until it's normal again

But ultimately just speculation, I'm just a user after all

Did you give claude access to run the compile step?

I remember having to write code on paper for my CS exams, and they expected it to compile! It was hard but I mostly got there. definitely made a few small mistakes though

My friend, with all due respect, I don't think this is a problem with the AI.

I don't know anything about DotNet, but I just fired up Claude Code in an empty directory and asked it to create an example dotnet program using the Tomlyn library, it chugged away and ~5 minutes later I did a "grep Deserialize *" in the project and it came up with exactly the line you wanted (in your comment here) it to produce: var model = TomlSerializer.Deserialize<TomlTable>(tomlContent)!;

The full results of what it produced are at https://github.com/linsomniac/tomlynexample

That includes the prompt I used, which is:

Please create a dotnet sample program that uses the library at https://github.com/xoofx/Tomlyn to parse the TOML file given on the command line. Please only use the Tomlyn library for parsing the TOML file. I don't have any dotnet tooling installed on my system, please let me know what is needed to compile this example when we get there. Please use an agent team consisting of a dotnet expert, a qa expert, a TOML expert a devils advocate and a dotnet on Linux expert.

I can't really comment on the code it produced (as I said, I don't use dotnet, I had to install dotnet on my system to try this), so I can't comment on the approach. 346 lines in Program.cs seems like a lot for an example TOML program, but I know Claude Code tends to do full error checking, etc, and it seems to have a lot of "pretty printing" code.

This is so perplexing to me. I've definitely hit these kinds of issues (which usually result in me cursing at the agent in all caps while telling it to get its shit together!), but it's almost always a long ways into a session where I know context rot is an issue, and the assumption it's making is a dumb one but it's also in the middle of a complex task...I just haven't had anything remotely like the situation you're describing, where Opus 4.6 can't make a simple change and verify that it compiles, can't look up docs, can't follow your instructions, etc. Bizarre.

There definitely isn't enough _specialised_ software. If you look at engineering tools the open source/free stuff is just not good, and the professional software packages run in the tens of thousands per user. I'm eagerly awaiting the day we get competitive open source alternatives to simulink, HFSS, autocad, solidworks, LT Spice, etc.

Unfortunately this kind of software needs specialised domain knowledge to produce that AI doesn't have yet, but when (if) it arrives I hope we see strides forwards in hardware engineering productivity.

>There's an ongoing maintenance and complexity cost.

My company has 20 years of accumulated tech debt, and the LLMs have been pretty amazing at helping us dig out from under a lot of that.

You make valid points, but I'm just going to chime in with adding code is not the only thing that these tools are good at.