A lot of people down on AI in this thread, but I'm watching the industry slip over the line of trust with these latest frontier models. GPT 5.5 is the first model good enough for me to just let rip.
Every jira ticket I see now has acceptance criteria, reproduction steps, and detailed information about why the ticket exists.
Every commit message now matches the repo style, and has detailed information about what's contained in the commit.
Every MR now has detailed information about what's being merged.
Every code base in the teams around me now has 70 to 90%+ code coverage.
Every line of code now comes with best practices baked in, helpful comments, and optimized hot paths.
I regularly ship four features at a time now across multiple projects.
The MCP has now automated away all of the drudgery of programming, from summarizing emails, to generating confluence documentation, to generating slide decks.
People keep screaming that tech debt is going to pile up, but I think it's going to be exactly the opposite. Software is going to pile up because developing it is now cheap.
Most code before llms sucked. Most projects I on-boarded to were a massive ball of undocumented spaghetti, written by humans. The floor has been raised significantly as to what bad code can even look like, and fixing issues is now basically free if your company is willing to shell out for tokens.
> Software is going to pile up because developing it is now cheap.
Software to do what, though ?!
Coding, maybe 10% of a developers job (Brooks "Silver Bullet" estimates 1/6), was never the bottleneck, and even if you automated that away entirely then you've only reduced development time by 10% (assuming you are not doing human code review etc).
I would also argue that software development as a whole (not just the coding part) was also typically never the bottleneck to companies shipping product faster, maybe also not for automating their business faster (internal IT systems), since the rest of the company is not moving that fast, business needs are not changing that fast, and external factors that might drive change are not moving that fast either.
I think that when the dust settles we'll find that LLM-assisted coding has had far less impact than those trying to sell it to us are forecasting. There will be exceptions of course, especially in terms of what a lone developer can do, or how fast a software startup can get going, but in terms of impact to larger established companies I expect not so much.
+1 for any mention of Fred Brooks. I like your point about software as a whole not being a bottleneck. In the 1970s the hardware was co-evolving with business uses (it still is, but constraints were much more severe) leading to large headcounts on software projects that _absolutely_ had to work and _absolutely_ required uncommon expertise. Most people had no concept of a computer's capabilities, computer science was not as widely distributed.
One thing that I would point to today to show that the landscape is different - the average programmer/engineer/developer today has no actual admin staff. Fred Brooks' example team setup of "The Surgical Team" has more support staff than programmers. Anyone who responds to the questions like "who manages the calendar" and "who manages the documentation" will state that the engineers doing it themselves offer the best results. Same goes for designing test cases, performing rollbacks, etc.
The fact of the matter is that any self respecting engineer today works in an environment where pro-activity and self-sufficiency are prerequisites. Managing your calendar and workload, communicating to leadership and users, these are all common tasks that would have been another person a generation ago.
So when discussing writing code more efficiently and aiding in software development, what I am essentially seeing is more people trying everything they can to offload work that used to be another person's job anyway. If you care about communication - you offload coding standards. If you care about security - you offload feature refactors, and so on.
In my opinion, I think that at some point we'll either realize that we need highly competent people _and also_ regular people to help us ensure the work gets done to a good standard. Or, we will each eventually survive by working alone in a room with a suite of AI tools, and wonder why we're still making software in the first place.
That's kind of the point in GP... everything around the code has improved... the workflows, definitions, documentation, process. I'd say that all of those things are improving and expanding at a rate faster than the improvements in code output, which are also happening at a faster turn around than actual people.
I've said several times that when I use an Agent, I'm getting about 2-4x the value and about 10x the output... the "value" is features landing in code and the difference to the 10x is documentation and testing. While a lot of that may not get reviewed by every person that touches a product, it helps with further ai based feature development.
I'm not a big fan of running many agents or outright vibe coding slop... but you can definitely leverage the coding agents and get a lot of improved output.
I'm not talking about what the developer is doing - I'm talking about what the company is doing in terms of initiating new development work. Again, startups and one-man shops are different because you control your own pace, but in many large corporations you may sit around just minding shop until the next big product development comes along (I would use this time to start my own initiatives to build tools and libraries to help the team), and that company pace is not being determined by how long development takes.
This is especially true if like most developers you are not working at a company where software is the product, but rather where software is part of the product, or where you are part of IT working on internal systems, not part of product development at all.
Then is it a real 10x increase in output if the output is in supporting areas and not the code/feature delivery?
It seems like you’re saying that you are now able to maintain documentation at a faster rate and increase testing but not the actual development speed of the feature itself.
I said 2-4x on value.. which would be development of the feature itself in terms of direct output... not even considering setting up test harnesses and doing more adventurous changes that would take me a lot longer to do.
I was referring to the sentence:
I'm getting about 2-4x the value and about 10x the output... the "value" is features landing in code and the difference to the 10x is documentation and testing.
What does the 10x imply?
And are you saying that you are outputting 2-4x as in:
value * value * value * value in the case of 4x? That seems rather high.
As I recall, “No Silver Bullet” fundamentally rested on the assumption that the subroutine was the last word in abstractions to make programming more efficient, which probably wasn’t even defensible at the time because Lisp had already been invented, and is even less defensible after the past several decades of programming language research. Brooks was still onto something when it came to irreducible complexity, but offloading complexity an LLM can tackle to the LLM still saves time.
One of the lesser discussed Brooks essays is actually the best description of AI-first development: the “surgical team”. It just turns out the surgeon is the only human, and like many modern surgeries, the surgeon is controlling a robot instead of operating by hand.
It would be interesting to reread The Mythical Man-Month and see how each essay applies to AI-first development.
The sibling comment to yours (by rafterydj) makes a good point that the surgical team is necessary, but we have eliminated the positions and put the roles on the same person. It’s like the writer being the subject expert, the reviewer, the editor,… which we all knows leads to mediocre work.
We can all agree that very big portion of the time needed during product engineering is.. syncing progress, requirements, plans, etc etc. And we have to do it over and over due to how big teams are.
Fast forward, fire half of those ppl, for sure fire all middle managers, scrum masters, coaches, wooden-architects.
Suddenly you save up so much time on syncing, you can ship twice as fast.
And NO, quality and impact doesnt go down. It actually goes up.
This is probably something you did not want to hear :)
Few competent ppl with AI are much much much better than dozens of medicore teams.
We need now „Product Builders” and „Product maintainers”. All of the other roles lost value.
you can't continue shipping code the same way pre-LLM vs post-LLM and expecting a huge speed gain. the trick is abandoning the old models and bottlenecks and embracing the new possibilities enabled by LLMs. requires a high trust environment
So, get rid of the marketing department, get rid of business owners, agile, budgets, and everything that is stopping developers from creating their self-conceived vibe-coded creations as fast as they can ?
there are workplaces that trust their employees to do the right thing to higher and lesser level. places that empower the employees the most will see the highest gains from LLMs
The ticket has subtle errors in its description that are only caught by someone experienced with the codebase.
The code hides an exception behind an if-then-else that defaults to the most common state, which isn't caught until it breaks things for the 1% of users who don't have that state.
The new feature quietly breaks a feature not covered by the acceptance tests.
The documentation is four times as long and nobody who relies on it can read it.
And I'm stuck spending my time going over tickets with a fine-toothed comb, reviewing PRs, and mentoring contributors to prevent all of this garbage from ending up in the live code.
1, 2 and 3 happened a ton in the good old times before AI. If anything, we can make the code be more tested than before, but that requires a lot more engineering, that is made easier by LLMs.
People noted similar issues ever since LLMs came out, but the rate at which they have been rapidly improving on all of these is significant. Documentation being 4x too long could probably be fixed with a rule instructing the agent to keep it concise and no longer than 2-3 paragraphs.
They seem to be converging toward an asymptotic accuracy level that is not particularly close to 100%. That is not good enough when you're trying to instruct engineers, particularly junior ones.
Conciseness is variable. Sometines a paragraph is enough, some other tome you need multiple chapters and a glossary to get the point accross. 4x too long may be 4 paragraphs in the first case and a 400 pages book in the second case.
What you are describing is a the role of a manager, not a software engineer. Software engineering has very little to do with writing code, but more on architecting at the higher level on what needs to be done. The code is just the executional part. LLMs can code? Ok good. Without a clear architectural pathway / direction, that code is just useless. It's not tech debt. It's just a bunch of random strings. You can argue that Claude code and others do create a plan of attack - but still, it's not at the architectural level, but rather executional level.
To me, architecture starts all the way from the top - even before you write a single line of code, you do the DDD (Domain-Driven Design) and then create a set of rulesets (eg. use the domain name as table prefix) and contexts and then define the functionality w.r.t to that architecture. LLMs can do all this - only if you ask them to explicitly. So, they are pretty useful to brainstorm with, but not autonomously design reliably and push it to production with your eyes closed and support a 100,000 user base. It's a far cry from that.
But sure, you can upsell to management about the vanity metrics like lines of code and get that promotion with LLM. But, it's still not software engineering.
That's just not accurate. I haven't studied SWE Bench Pro in detail, so I can't tell you exactly what the flaw is, but SOTA models routinely make bad architectural choices I have to intervene to fix.
TL;DR its very effective as it directly tests model on REAL codebases: "The benchmark is constructed from GPL-style copyleft repositories and private proprietary codebases". The use case is very real.
It doesn't sound to me like this benchmark is attempting to measure architecture design. As far as I see in the paper, they do not evaluate the architectural quality of a task completion, only whether the model is capable of completing it at all.
It's "not software engineering" but neither was what most people writing code did before LLMs.
> Without a clear architectural pathway / direction, that code is just useless. It's not tech debt. It's just a bunch of random strings
This is pretty clearly false. It's a bunch of random strings that you can compile and run to do what you want. It's more akin to a black box. A compiled closed source dependency.
Agreed. I never considered myself an "engineer". Honestly just a regular code monkey. Software Engineer was just my job title. Folks higher up the ladder did engineer software. You know what? It sucked. Was always broken, we were always patching, we never saw around corners. But hey - they software engineered it.
For people who like to tick boxes, which is essentially most of the above, AI is welcome. That includes managers.
It still has nothing to do with software engineering. All good code was written by humans. AI took it, plagiarizes it, launders it and repackages it in a bloated form.
Whenever I look deeply at an AI plagiarized mess, it looks like it is 90% there but in reality it is only 50%. Fixing the mess takes longer than writing it oneself.
"Writing code" as a task of its own is called cowboy coding. It's neat that AI can do this now, but that has nothing to do with proper software engineering which always starts from a careful, human-led design.
You call someone an author when they use a ghostwriter. They're giving inputs that are core to the output, even though they aren't doing all the writing. Same thing.
>You think telling humans with specialized crafts that they don't matter is good politics? Good grief.
Yeah, of course not. There are lot's of historical examples of this. That being said, those historical examples don't play out well for the craftsmen, either.
Look, I'm a SWE myself. I see my job drastically changing right in front of my eyes. I know there's nuance to it, too, that's hard to articulate in these comment threads.
But I think a lot of people here are biased against thinking that they are irreplaceable - I've definitely been in that camp. I don't think that it's wise, however.
Or even more appropriate: a movie director is almost never on-screen but the actors aren't the ones determining the shots to use or writing the script.
Yes and every AI-first development workflow worth its salt does exactly this, and it does it much more thoroughly than I’ve ever seen a team of meatbags do it.
My workflow, at a high level, is:
1. I write a high level spec. Not as high level as a single-sentence prompt, but high level enough to capture my top requirements.
2. I prompt the AI to interview me about the spec to clear up any ambiguity or open questions, then when I’m satisfied, the AI writes a longer spec, which I then review.
3. Then I prompt the AI to write an implementation plan based on the spec. I might just skim this, and by this point I might be asking the LLM more questions than it’s asking me.
4. Now I hand it off to the implementer agent.
This isn’t cowboy coding, it’s not even agile. It’s waterfall. The problem with doing waterfall was that it’s too slow, especially with the deserialization/serialization cost of routing all of this documentation through meatbrains. The LLM is doing just as much work, true, but faster.
The thing I found surprising was that, while LLM’s are still pretty awful at writing as an art form, they are better technical writers than I have the time to be, especially when writing for an audience of other LLM’s.
The hard part of software engineering is turning a vague problem description into a set of box-ticking exercises. If ticking boxes became genuinely easier, the software engineering part is now a lot more valuable.
You’re reminding me a lot of those old assembly hackers who thought compilers were bullshit because they could hand-write better assembly. And I don’t mean that as an insult; those guys were probably right about their assembly code, just like an Amish craftsman will make better furniture than a factory in China. The problem is that the world needs more furniture and more software than skilled craftsmen can produce, and the skill gap between the craftsman and the mass production process is diminishing fast.
We’re still going to have handwritten software, just like we still have handwritten assembly. It just won’t be the norm.
> Your linter should identify all issues - including architectural
If a linter could deterministically identify bad architecture, you wouldn't need an LLM, your linters could just write your code for you. The vibe coding takes are just getting more and more empty-headed...
Your custom linters don't check architectural design?
linters statically check code and provide deterministic recommendations. LLMs are used to make judgement. I specifically write my linters for my project to make recommendations for LLMs.
This is how you save on token usage, so your LLMS aren't wasting tokens on static analysis that a linter could do for free.
Incredibly impressive how, the moment AI becomes the topic of conversation, trivial things such as speaking in relative terms become incredibly difficult for the more addled of the prompting users.
It can't happen without someone, but certainly it can happen with a lot less people, which is what's going to happen to the industry. Some days I'm shook to my core about how much I did relative to how long it would have taken just a year ago.
All software engineers will become product managers as the agents take over doing the bulk of the work.
Companies will either do the same with less or more with the same.
My opinion is that any company whose business model is selling software is going to go out of business.
Software is going to pile up because developing it is now cheap.
It won't, because right now we're busy exhausting the vein of good-ideas-we-wanted-to-build, and that's the source of all the good stuff you listed. When that runs out you'll see teams building any old crap because building is cheap, and learning that experimenting by putting any old crap in front of users is a fast way to burn goodwill and brand loyalty.
You still need good ideas and the taste to choose which to put out there over the bad ideas that people actively dislike.
> I regularly ship four features at a time now across multiple projects.
Many people are missing the fact that LLMs allow ICs to start operating like managers.
You can manage 4 streams now. Within a couple years, you may be able to manage 10 streams like a typical manager does today.
IME, LLMs don't speed you up that much if 1) you're already an expert at what you're doing (inherently not scalable), 2) you're only working on one thing (doesn't make sense when you can manage multiple streams), or 3) doing something LLMs are particularly bad it (not many remaining coding tasks, but definitely still some).
A manager doesn't have to look at the code that's being shipped. An IC will still need to do that, and this will eventually take up much of their work. It can be addressed by moving up the stack to higher level and more strictly checked languages, where there's overall less stuff to review manually.
People typically think it's not a new person's fault if they come in to a team and bring down production.
That's a failure of the existing infrastructure to allow someone to do this.
LLM coding will work like this.
If you're letting LLMs go wild with no system in place to automatically know they're moving in the right direction and "shipping" things up to your standards, the failure is you, not the LLM.
I didn't say shipping a day. I said shipping at the same time.
The review comes at the end, though I truly believe this will go away as well. Agents will also get better at review until they're good enough that no one will want to do it anyways. Good enough is good enough.
Claude absolutely improves code review quality, but it still misses a lot. It's a second pair of eyes, it doesn't replace/remove the work you have to put in to fully review the code yourself.
It's like saying that you code reviewed faster just because someone else also reviewed the code, that's not how it works.
Agree, and with CC my volume and quality of PR review has substantially increased since 4.5. Without CC for review we would have a ridiculous bottleneck in our dev/qa pipeline.
I'm faster, sure, but more thorough, no. The same, because I was already very careful. But it's not a massive win either; 4.7 misses too much still because it would need to read too much of the context each time to understand the architectural problems I'm catching.
Its nice to not have to care about nits and other things that we don't have lints for though, so that's useful.
Just like a manager, you don't need to look at the code. You need to set up quality systems to provide evidence the code does what it is supposed to do, just like a manager.
I’ve never met a manager that have setup “quality” systems to ensure that the job is done correctly. Their actions are always retroactive. And not pertaining to code at all. The overarching contract is “You do a bad job, you will be fired”.
Code review has a number of important purposes beyond merely verifying functionality. It's true that some managers don't recognize this, fail to allocate time for anything but feature work, and then wonder a few years later why the software is so buggy and new feature development is so hard.
Software engineers were always creating, maintaining and updating automated business processes. In olden days we would have computers, that is rows of people computing things. That room of people is replaced with code in von Neumann machines.
The economic tension has always been a resistance to grant programmers status and class of management. Instead management wants to treat programmers like labor.
> and fixing issues is now basically free if your company is willing to shell out for tokens.
Does "basically free" to you mean for you just that someone else is paying the cost? That's a mentality that has only made the world worse when applied to a wider range of things. Be hesitant in that line of thinking, I suggest, and consider the future.
I agree with most of this, I just have sort of turned a blind eye to what the code actually probably looks like. Reviews are rapid, and I’ll admit I do feel like I’m betraying my inner programmer by just optimizing directly against the claims of token bot. But the way I see it, as long as the numbers don’t lie I’m okay with the process.
I'm seeing the exact opposite with LLMs. So much unmaintainable brittle code is being generated since devs are not even looking at the code and LLMs are dumb like 75% of the time
Agreed on the floor being raised. The part I'd push back on is "fixing issues is now basically free." That's true for the issues that surface in code review or a failing test. The new class of issue is good-looking code that does something unexpected at runtime, usually through chains of tool calls that each looked fine in isolation. Those don't fail your tests. They fail in prod, sometimes quietly.
Sometimes I wonder if people praising AI work on the same type of code as I do.
Just now, I was working on a bug report. I had Claude write the code. Perfect, CI is green, new tests, everything seems fine. Took me 5 minutes. Then looking closer, I can see that there may be a performance regression and that the code seems pretty verbose. I iterate on the prompt "of course, you're right, let me fix this". New code is even more verbose, lots of comments that shouldn't be there, the code is more intricate, it takes me some time to understand what's going on. Plus new test cases to review.
After a day of asynchronous iterations on this, I finally sit down to look at this problem. There was a one line fix that Claude couldn't find on its own.
I lost time, reviewer lost time, and if this had been shipped as is, the system would have been worse. I could go on and on because this happens daily. And the worst part is teammates submitting slop.
This better stated as:
Use of agents has forced teams to adopt best practices and guide style guides.
Which is my experience. Once you get into the actual development process, the code itself produced by the agents is not good enough. Still needs editing and rewriting.
Everyone talks about productivity as if that is the only metric that matters in the business.
The MCP has now automated away all of the drudgery of programming, from summarizing emails, to generating confluence documentation, to generating slide decks.
I wonder about the hallucination. Reading someone's writing doesn't take all that long.
Is programming supposed to suck all the time? Am I doing it wrong? I mean yeah, sure, it sucks sometimes, but overcoming that "suck" is where I feel progress and growth. If we decide to optimise that away...What the fuck am I doing here? No offence to managers, but if everybody is a manager, is anybody?
Feels kind of like the problem of everybody wanting to be an entrepreneur in the 2010s. Just led to people basically trying to get paid to be middleman companies skimming from others that don’t really need them, or worse, selling supplements and life coaching or whatever on social media and other grifts.
I think numerically this is the exception - and it's a fantastic exception! But in practice what I've seen is things getting worse because people still just aren't very good at thinking, so the great-looking Jira ticket actually turns out to be nonsensical in some subtle way, whereas before it was just lacking in some obvious way that could immediately be called out and had an obvious solution.
I.e. it's making good output better, but it's making mediocre output (which is most output) worse by adding volume and the appearance of quality, creating a new layer of FUD, stress, tedium, and unhappiness on top of the previously more-manageable problems that come with mediocre output.
I'm still seeing this even with the newest models, because the problem is the user, not the model - the model just empowers them to be even worse, in a new and different way.
I was an LLM naysayer for a very long time. I continue to have serious reservations about the ethics of LLM use and the likely economic effects (these tools are likely to empower the owners of capital and disempower labor). On the other hand, I had a rather striking experience the other day that convinced me that the future in which these tools write software may not be so bad:
I had an idea to improve performance in one of the slowest but also one of the most critical parts of the codebase I own, so I asked Claude to re-write it. I gave it exact instructions. It got most things right but key things wrong. I caught the bugs and then asked it for some optimizations, and it came up with a number that were quite good. As I read the code, I saw more and more opportunities for improvement. To make a long story short, code that used to require upwards of 30 seconds in a particularly heinously ugly stress test now finishes in about 8ms.
My original code was terrible. That's indisputable. Maybe the bar for improvement was low. Still, the algorithms and optimizations that I was able to devise while using Claude Opus 4.6 surprised me. I don't often feel pleased with the cleverness of my work, but in this case the work really is stellar -- or at least enough of an improvement that it feels stellar.
Could I have written it without Claude? Yes, definitely. But I was able to produce the code in a few days while having a fever of 100-102, which I definitely couldn't have done on my own.
Moreover, it was plainly apparent to me, while I worked, that I was better able to think about high-level architecture and design because I wasn't stuck on the details of actually writing the code. The code itself, line by line, isn't difficult if you have familiarity with bitwise operations, but there's enough of it, with enough branches, that it's difficult as a whole and the work of writing it would have consumed much of my attention and energy.
Claude missed a huge amount. I improved performance by more than 95% after it told me there were no other opportunities for major optimizations.
Using the tool freed me, I found, to think more clearly, more deeply, and more effectively. Does the result create tech debt? I don't think so. I've pored over it and can't find anything lacking in style, design, or architecture. It's very well documented. Claude wrote tests, as I requested, for everything, including all the bugs that Claude missed and I caught. Test coverage is probably 100%, but, much more importantly, tests exhaustively cover cases, including edge cases, that would have, again, been difficult to enumerate and write by myself.
I doubt Claude could have done all this as well if the codebase and tests weren't already as mature as they are. I really wonder about the feasibility and advisability of greenfield software development with these tools. And a junior developer absolutely couldn't have accomplished what I did. The tool would have produced far worse work in the hands of someone who doesn't know what they were doing.
So I agree with you and disagree: I'm turning a corner on these tools, but I absolutely could not just let rip and trust it to do anything correctly. Moreover, I could not be less impressed by the MCPs written by people in my company. The bare tool by itself is better, though maybe that says more about my company, and my regards for the people I work with, than the tools.
I think this is the way. Human-machine co-design worked great for me so far. Hell, even the test writing alone is great, because I can have more confidence in my code. And test writing was mostly drudgery. On the other hand, you _must_ have a good mental model of the thing in your head else this will not work. And it's much easier to believe you have it and not really have it if you don't engage with the codebase.
> Could I have written it without Claude? Yes, definitely. But I was able to produce the code in a few days while having a fever of 100-102, which I definitely couldn't have done on my own.
While I admire your strength in attempting it, this just adds one more brick to the wall of precedents that "what's stopping you from just sending one prompt, it'll just take 30 seconds and you can do it in bed!"
You could sum it up into a simple equation as
Features Shipped = Features/Hour * Developer Hours
Developer hours has remained a constant, and F/H has gone up. I am of the opinion that the ideal is the inverse.
That's an excellent point. To be fair, I allowed myself to work on this while I was sick only because it was fun. This was a bit like scratching an itch, because I'd had the germ of an idea for a while and wanted to get it out of my head. You're absolutely right that it sets a dangerous precedent: the easier it becomes to do work, the easier it is to demand work of the people doing it. The boundaries need to be firm.
On the other hand, this was also a case where Claude really did help me finish something more quickly than I could have without it. So in thi scase I do think it lowered the number of developer hours per feature.
> GPT 5.5 is the first model good enough for me to just let rip.
You know this is the exact same thing said during Opus 4.6, right?
That makes it hard to believe because it's the same "last week's model was so much behind you can't even comprehend" meme that's been going on throughout last year.
More info dumped into tickets and projects is great for understanding for both people and LLM. But hopefully not LLM generated.
It's just cope. I'm so close to just never coming back to HN because the quality of thought has just gone through the floor. Anything whatsoever to hedge one's way to fellating a phallusless chatbot
There wasn't any personal mention in my post. A snark remark at the fact that this cycle keeps continuing and every new release is game changer except in the banchmarks where there is mostly a slight couple percent change, generally.
You're missing the point that it's (conceivably, and probably) different people making the comments. Each model release has a few new converts, which is expected if the models are in fact getting better at agentic coding.
You're implying it's a hype train when in fact it's an adoption curve.
> which is expected if the models are in fact getting better at agentic coding
Is it? Or is it also explainable that the models are not getting better but people are still adopting it.
If the models were getting we’d be seeing mobile apps with new features at 10x the rate previously, or websites with 4 times the number of features. But we’re not.
> fixing issues is now basically free if your company is willing to shell out for tokens.
Yeah, about that: I looked into Cursor's usage stats and daily I'm going through the equivalent of a bacon sandwich in my cantina, so not much, but this is at today's prices and very light usage of Sonnet.
I was for a time using Opus 4.6 for a heavier task and even then I think the cost was well into the double digit percentages of my salary.
Opus 4.7 reportedly uses more tokens overall and while they reportedly kept rates stable, that is not a given.
Just wait until, with increasing costs, the first company figures that they'll offer this as a benefit and then maybe scrap it altogether in the name of cost cutting.
Watch token budget be included as part of employee TC figures - I feel this is an eventuality due to rising costs and "true pricing" slowly creeping in.
Current ventures feel moreso like a pilot program (you bought a private jet, now get a couple of your pilots to actually fly it) versus having an entire fleet of jets, and having to pay salaries to all those pilots, plus account for their fuel charges.
Right now all expenses are relatively "someone else's {problem,money,infra}".
Yes, the software that piles up literally is the tech debt. Every automation and tool that was vibe-coded has to be maintained as well. If software is 100x easier to write and you write 100x as much of it, then taking into account network effects, your tech debt is now 100x worse. Congrats!
The gap between the ai haves and have-nots is starting to appear. 6 months ago a developer with copilot was about on par with one without. The AI code required a lot of review, about the same amount of time as writing the code manually.
Now.. the AI first engineer might still have to deal with hallucinated things. But.. they can also use the newfound cheapness of code to improve their workflow. Instead of just testing on localhost and manually deploying to prod, you can have a full dev, staging, prod pipeline for free. Tech debt can be one command from being refactored. The open source package that doesn’t quite do what you need it to do? Fork it and write a patch. The ai will be able to maintain the patch. Oh.. you need that bespoke feature for management? Np, done in a 1hr ai session.
Each of these things might be arguably insignificant on their own but net over a projects lifetime they really build up.
If writing code was the only part of the job, and it was easy, these jobs wouldn't pay so well.
Engineering is hard. It's always going to be hard. I'm glad that AI makes some parts of it easier, and we (software engineers) can focus on engineering, that's nice.
Code is NEVER cheap. Just because, at current completely unrealistic AI pricing, using agents is cheaper than hiring juniors, does not make code cheap. It makes producing code cheap, which has always been low-cost. Every line of code is a cost, is a maintenance burden, is complexity. An AI, even with somehow infinite context window, will cost more money the more code you have.
Could you replace a whole team of engineers with AI? Probably, yeah. Could you simply fire everyone at your company and close it down, without much of a problem? Also probably yes, for most companies.
AIs can help with debugging, can help with writing code, with drafting designs, they can help with almost every step. The second you let OpenAI, or Anthropic, take full code ownership over your products, and you fire the last engineer, is the time when the AI pricing can go up to match what engineers make today. You've just reinvented the highly paid consultant.
Or you could take the middle-ground and hire good engineers, make sure they maintain an understanding of the codebase, and let them use whatever tools they use to get the job done, and done well. This is the way that I've seen competent companies handle it.
I don't understand people dismissing the massive decrease in both cost of producing code and the speed of producing code.
Before AI, people running businesses had similar issued as people have with AI now, but the costs were much greater.
They could hire someone to write them a prototype for their idea, but it would cost them on the order of 1000s of dollars, and it would take weeks at the minimum!
Now it could cost them 20$ and be done in a few days. The feedback loop is the bottleneck.
I'm not dismissing it, I'm saying it's never been the bottleneck. Like you said, the feedback loop is A bottleneck, so is figuring out all the nasty things that nobody on HN seems to have heard of before, like "what are the requirements", "which tradeoffs can we make to get this done in time", "what is the architecture for this", and building it in such a way that you can guarantee that it won't fall apart in 20 years when your requirements change.
I understood it in the spirit of “code is a liability not an asset.” Code still needs to be maintained, changed, etc whether that is by a human or LLM.
In other words, just because more code can be produced quickly does not mean that it is cheap.
edit: I’m maybe hearing your point is that LLMs may change that POV but I think that is TBD.
I don't really think that's accurate either, from a business POV.
Software has been such a gold mine, exactly because the maintenance are minimal when you scale, compared to the revenue. The upfront costs are expensive, but once you have software built, in most cases it's relatively cheap to maintain
Things can provide value to a business while still acting as a liability -- that is to say, by virtue of their existence they cost you money, even if, when properly leveraged, they can be used as a tool to make more.
Consider a canonical loan: you get a pile of cash, but in exchange you need to make regular interest payments (plus, at some point pay back the principal, but this isn't strictly necessary -- e.g. governments used to issue perpetual bonds, which paid their coupons indefinitely). The analogy I would make with software is that the product has value, but the code you use to create it is a liability, in that it demands continuous maintenance and upkeep. Just as when you go shopping for a car loan, you look for one with the smallest interest rate -- if the debt itself were an asset, you would want more of it, which doesn't make sense. In the same way, it behooves a business to try and achieve its goals with a relatively small amount of code, not to create as much software as possible.
Certain types of code are cheap. Proof of concept is cheap. Adding small features that fit within the existing architecture is cheap. Otherwise, I'm not so sure. Coding agents are fantastic at minutiae, but have no taste. They'll turn a code base into a ball of mud very quickly, given the opportunity.
While I agree with you that agentic coding still has quite a way to go and is not always producing the quality that I would want from it, I can say quite confidently that its baseline is way above some of the production code in many applications many people use today. It really isn’t that code before agents was primarily written with taste and beautiful structure in mind. Your average code base is a messy hell full of quick fixes that turned into all kinds of debt over the years.
I took the previous post, with its mention of the ball of mud, to be about complexity.
“Taste”, is used in many cases, I suspect, to give a name the collection of practices and strategies developers use to keep their code and projects at a manageable level of complexity.
LLMs don’t seem to manage complexity. They’ll just blow right past manageable and keep on going. That’s a problem. The human has to stay in the loop because LLMs only build what we tell them to build (so far).
BTW, the essay that introduced the big ball of mud pattern to me didn’t hold it up as something entirely bad to be avoided. It pointed out how many projects — successful or at least on-going projects — use it, and how its passive flexibility might actually be an advantage. Big ball of mud might just be the steady state where progress can be made while leaving complexity manageable.
I think there are at least two factors behind ye olde ball of mud that LLMs should be able to help with:
1. Lack of knowledge of existing conventions, usually caused by churn of developers working on a project. LLMs read very quickly.
2. Cost of refactoring existing code to meet current best practices / current conception of architecture. LLMs are ideal for this kind of mostly mechanical refactoring.
Currently, though, they don't see to be much help. I'm not sure if this is a limitation in their ability to use their context window, or simply that they've been trained to reproduce code as seen in the wild with all its flaws.
Keeping complexity down is always a conscious act. Because you need to go past the scope of the current problem and start to think about how it affects the whole project. It’s not a matter of convention, nor refactoring. It’s mostly prescience (due to experience) that a solution, even if correct and easy to implement, will be harmful in the long term.
Architecture practices is how to avoid such harmful consequences. But they’re costly and often harmful themselves. So you need to know which to pick and when to start applying them. LLM won’t help you there.
I agree. I do wonder if what I'm seeing is a limitation of the reasoning power of LLMs or if it's just replicating the patterns (or lack thereof) in the training data.
Preproduction code was always cheap or even free. Sales people have been selling software that didn't do what was on the tin since the dawn of time. Those features cost 0 dollars to write!
Production code. Especially production code with bugs is expensive. It can cost you customers, you can even get negative money for it in the form of law suits.
Coding agents are great for preproduction and one offs. For production I really wouldn't chance it at any scale above normal human output.
Except here's the thing, that's the sort of code that was extremely expensive before, in large part because of our day jobs (which still to this day require mindfulness and can't just be vibe-coded).
However, an extra script here or there to make your life easier, adding extra UI features based on some datapoint to your internal dashboard, ect, these were things that could've taken a few days you didn't have before to get exactly right and now they can be done with only a few minutes of attention.
I think back to my past jobs how there were people who'd work weekends on random (not to technically difficult to implement) efforts that probably got them promoted, but we would never make time for during the regular work week that now would take next to no time to implement with AI.
Anyone with any small amount of creativity for this sort of thing could really make a big difference on improving the productivity of all sorts of team wide investigations as a running background task they have during their regular work.
I came here exactly to point out what I'm glad to see is 10. "Free as in puppies" is a wonderful way to put it.
Every time I open linkedin I'm scared of how many big heads have taken the wrong lesson that coding almost free == free engineering. So many bait posts asking engineers why they would need to pay them any longer, or being glad they're generating millions of lines a month....this is going to end badly.
I had a business owner tell me that they don't need to hire juniors anymore because claude can do all of that work for them. This was not a software shop so it's not even about writing code but I also thought that was something that will bite in the near future. A business that is not investing in juniors is a business that is not investing in the future.
The role of AI in non-software shops is going to be interesting. To a great extent it's not competing with devs, it's competing with Excel. However bad a system your AI can produce, it can't compare to the workflows that a group of non-techies armed only with Office can produce.
On the other hand, like giving a supercar to a teenager, this just enables them to get into trouble faster.
(the "my vibe coded app deleted prod!" stories are funny schadenfreude when they happen to SV startups, whose whole business is pretending to know better. When this happens to a small business who've suddenly lost all their finanacials and now maybe will lose their house, it's a tragedy. And this can happen on a much larger, not AI-related scale, like Jaguar Land Rover: https://www.bbc.co.uk/news/articles/cy9pdld4y81o )
> The role of AI in non-software shops is going to be interesting
I have friend in west Texas who does industrial electrical gear sales (like those giant spools of cable you see on tractor trailers). He’s 110% good old boy Texan but has adopted and loves AI. He says it’s been a huge help pulling quotes together and other tasks. Coincidentally he lives in Abilene where one of the stargate campuses are going. Btw, the scale of what’s being built in Abilene is like nothing I’ve ever seen.
> 10. Code is cheap, but maintenance, support, and security aren’t.
I also keep circling around this point. So many software repositories in the AI space seem to follow a publish and forget pattern. If you simply can show that you have the patience to maintain a project, ideally with manual intervention instead of a fully autonomous AI, then you already have an outstanding project.
Agreed, but a worrying amount of managers and leaders spend time there for reasons I never fully understood, so it offers a glimpse into their worldview.
The issue is that when you gaze long into an abyss, the abyss also gazes into you.
I am in India, junior developer hiring is all down. Ai has reduced offshoring to India and eliminated the need for janitor work (often offloaded to juniors).
Many people are finding it difficult to even land internships.
The most affected areas are sysadmin, devops, and frontend. Where you'll have very hard time getting any offer.
Companies like BrowserStack are withdrawing campus placement offers.
Meanwhile, I am writing apps for my own use and have reached 10,000+ monthly active users already, even though I am making zero money from doing all this, but it's fun.
Looking at the entire market in Europe it is also down but that is not due to "AI" but because they are easiest to fire with least consequences. There is a global recession looming, despite Wall Street saying otherwise.
>Sysadmins, Devops engineers will the be the last ones replaced by AI.
Most setups aren't properly documented which makes the discovery and exploitability part the major bottleneck when this is facilitated by AI, the sysadmin/devops team is downsized.
Guy works for the Overture Map Foundation, with Amazon, Microsoft etc. being sponsors. He has been boosting AI all over the Internet. I'm sure Microslop and Amazon are very happy with these efforts.
I'm glad that "10 ways to do X" submissions are allowed as long as they boost AI.
Are you suggesting that Microsoft and Amazon's sponsorship of Overture comes with an understanding that people who work on Overture will spend their time writing articles that "boost AI"?
Does "boosting AI" include opening an article with "Frontier models are really good at coding these days, much better than they are at other tasks"?
Can't speak for the former, but the latter question: yes.
"Product is really good at X, much better than at Y" does not imply that it's bad at Y, and even if it did, if you're targeting an audience that only cares about X, who gives a shit about Y? Might as well throw Y under the bus to boost the perceived effectiveness of product at X even more in comparison.
It’ll be priced slightly higher than the cost to actually run. But it’s still not clear what the real cost of the big models is. They seem very subsidised, but by how much?
It remains an unproven hypothesis. The revenue of the top 2-3 labs is still growing nearly exponentially, which is the ultimate piece of data that settles the question empirically for now. Benchmark scores aren't really proof. Benchmaxxing is possible, for example. Only revenue numbers (and gross margins) count.
The ultimate piece is not revenue but profit. At some point these enormous investments will have to be earned back. Good luck with that when open weight models are also continuously improving, have cheap providers and for many are already very usable.
The other point to make is that companies are starting to worry about the risks of externally hosted models.
This is at multiple levels if you have a remote API call as a key part of your workflow/software system.
1. Price risk - might be affordable today - but what about tomorrow?
2. Geopolitical risk - your access might be a victim of geopolitics ( seems much more likely that it used to be ).
3. Model stability/change management - you've got something working at the API get's 'upgraded' and your thing no longer works.
If you are running on open weight models - you are potentially fully in control - ( even if you pay somebody to host - you'd expected there to be multiple hosting options - with the ultimate fallback of being able to host yourself ).
You cutoff a generation of juniors from employment and learning , the seniors are gone and it's all harnesses and AI systems.
I'm not all gloom and doom but the treatment of junior engineers is something I think we will either regret or rejoice. Either will have a spur of creative people doing their own independent thing or we'll have lost a generation of great engineers.
The problem of "instant legacy" systems: something that's vibe coded and reached unmaintainable by either the AI or humans, but is also now indispensable because users are relying on it.
Some of that is already there .. but the users generally have nowhere else to go and ineffective pushback. "Enterprise software" has been awful for decades, things like Lotus Notes and SAP. Everyone hates Windows; everyone continues to use Windows.
Users don't currently trust software. Look at what we've done to them - can you blame them?
The consumer space is about extracting every ounce of personal data possible.
The b2b space is about "maximizing customer value" - that is, not maximizing the value of your product to the customer, but maximizing the value of the customer to your business. Lock them in and lock them down, make your product "sticky" so they can't leave without immense cost.
There will always be competition. For every company negatively impacting customer experience and their own ability to compete, there will be others happy to step in and take advantage of that.
If you fire all your SWEs they won't sit around twiddling their thumbs waiting for an AI collapse, they'll career shift. Maybe to an unemployment line and/or homelessness, maybe to something else productive, but either way they'll lose SWE skills.
If you close down all the SWE junior positions you'll strongly discourage young people training in the field. They'll do something else.
Then if you want to go back, who will you hire for it?
They are large language models. Not automated development machines. They hallucinate.
The goal post has not shifted since 2023 or so. Make an LLM that doesn't blatantly disregard knowledge it has, instructions it has been giving, over and over, and you win. If trillions of USD of investment can't do it, I'd be curious to see what can.
There are definitely automated dev systems, of which an LLM is a part. The remaining part may be called a 'harness' or whatever. The quality of the generated software is another matter.
If the AI is not good enough, then don't fire the devs. If/when the devs are no longer needed, I don't see why the need would return later, that was my point.
A harness like Claude Code does not turn an LLM into a software developer.
If that was the case companies could just have their project managers managing Claude Code instead of developers, and they would immediately realize that using Claude Code to develop software is just as complex and geeky as it ever was - nothing changed in that regard.
A harness and a bunch of skills is just the new "think step by step" prompting technique. Don't just let the LLM rip and write a bunch of code, but try to get it to think before coding, avoid things like churning the code base for no reason, and generally try to prompt it to behave more like a developer not an LLM. Except it still is an LLM.
A coding agent is really not much different to a chat "agent" in this regard. You've got the base LLM then a system prompt trying to steer it to behave in a certain way, always suggest "next step", keep to a consistent persona, etc. None of this actually makes the LLM any smarter or turns it into a brilliant conversationalist, anymore than the coding agent giving the LLM a system prompt magically turns it into a software developer.
You can easily develop with models like GLM 5.1 and Kimi k2.6 at a fraction of the cost of GPT 5.5 or Opus 4.7. Requests often cost just a few cents.
Open-source models have caught up tremendously recently. Those who can’t or don’t want to invest a lot of money can already develop with Kimi and GLM without any problems. We don’t have to wait another year for that.
Tried deepseek 4 w/ CC yesterday, and was watch my usage eke up by only 0.01 at a time while doing plenty of high-token-count tasks. I understand it's currently at a discount, but even after that expires the same-quality output will be available at a fraction of the cost of the expensive models.
From experience, the same level of usage would have left me stranded on my CC 5 hr limit within an hour.
There were some difficulties with tool calls, in particular with replacing tab-indented strings - but taking no steps to mitigate that (which meant the model had to figure it out every time I cleared context) only cost relatively few extra tokens -- and it still came in well under 4.6, nevermind 4.7. And of course, I can add instructions to prevent churning on those issues.
I have no reason to go back to anthropic models with these results.
Sure, but there will always be some monstrosities like Mythos that'll pwn all software written by local models in 0.01 seconds, thus forcing people/companies to use the most advanced paid models to keep up and stay unpwned for 1 second longer.
Short-short version, code will still be accruing value in proportion to how much of the real world it has encountered. The bottleneck on building valuable code will be how much real world there is to go around. As is so often the case, what may initially seem to kill SaaS will actually make them stronger as they end up with more exposure to the real world than some random guy's random AI code.
This is such a weird argument, beside obvious #10 which will bite back with a vengeance, because... code can't be cheaper than free!
Since at least the early 80s a LOT of very important code wasn't cheap, it was free. Both free of cost (you could "just" download it and run it) but also free as freedom-respecting software.
I just don't get the argument that cheap is new. Cheap is MORE expensive than free!
The list in the article looks like verification practices. Document intent, develop taste, find the hard stuff, etc. It assumes that when code is cheap the bottleneck shifts to knowing whether what you generated is actually right.
e2e tests can do a lot, but in my experience it's not enough. By the time the test fails you've already burned a generation cycle on an artifact that came from a flawed spec or design. I've gotten more mileage from having checks at stage boundaries (standard SDLC: plan, design, code, test). We all know the earlier you catch the mistake, the cheaper the fix.
The "implement to learn" is the same idea: you need to know enough about both where you want to go AND the path to get there to guide the agents to a proper implementation. You have contact with the world, both the users and the operational considerations that come from running software. Agents do not. We do the same thing with spikes, but now our spikes become far more sophisticated.
Code being cheap doesn't remove verification, it moves it earlier.
It's not a question of cheap or expensive; it's when to pay what costs.
With LLM's, it's arguably easier to avoid exporting costs to the future, or to export them.
Whether because of LLM's or frameworks, process consistency typically creates a forcing function to continuously improve quality (i.e., avoid exporting costs); for each problem, create step in the process to surface and address it - hopefully in an automated way.
Having spent lifetimes trying to get teams to up their game, I'm hopeful this may help, if it gets baked in not just to code generation, but to process.
I used to work as a VP and a part of my responsibilities was to chop up tasks to self-contained work units that can be easily assigned to random devs. This was both morally problematic for humans (i.e. EVPs forced treating human = CPU) and very optimistic when it came to individual dev capabilities and domain knowledge. However, this style is precisely what works well with agentic AI coding and I have no qualms to use it.
Good list, I have intuitively realized all of these except for (3). I agree with the author: cheap experiments to figure out a grand architectural plan, then executing fast without needing to wait for input is the right way to go. "Automate everything that's easy" is also important, but I'd extend it and say you probably should try to automate things that are hard too: it's worth spending a great deal of time on it simply due to how crazy valuable the returns are. But I guess I should look more into end-to-end testing.
"We" should not do anything. The LLM industry should go and find solutions for the problems they created, themselves. Not offload it to others through sneaky influencer posts. And we should hold them responsible, should they not be able to address the problems they are creating.
Free from friction, constrained by pricing and a massive ladder that can be pulled up by companies that are in no way benevolent.
If you're one of the lucky few who were able to already have hardware that can run some of the open weight models you can be a beneficiary for now - but that won't last forever.
#10 needs more emphasis than it receives. Cheaper code doesn't automatically lead to good product decisions.
Instead of focusing on whether you can build it, the scarcer resource becomes whether you should build it. And most teams lack a clear process for addressing this latter question. Requirements are collected in all sorts of places without ever being prioritized in an organized fashion. This is exacerbated by cheaper code. With cheaper code, you can release five times what you used to be able to release in a given period of time, but only if you knew which five products you needed.
For most teams, whether or not you can say no to building something is ambiguous at best, at least if you wish to stay on that team and at that company. It's definitely one of the things that has made me vote with my feet in the past. With agentic coding, the ability to say no is pretty much gone because the perception is that it's just one more parallel thing we can throw an agent at.
The thing I see from agentic adoption that I find lamentable as a software engineer is that timeline expectations have collapsed to absurdity. You can plan a project to do a major migration, do all the estimations on how long something will take, and if you give an answer that says weeks and cite the evidence, product and leadership will now claim it should take days, citing their ai's design.
It's exhausting. Even if you are an expert, you now have lost the implicit trust that came from years of building political capital, shipping efficiently, and delivering value for multiple companies, because a different prompt with different context from the one you provide gave a different answer than what you did.
During delivery, if you read your code produced line-by-line and review for correctness, and put in additional guardrail automations that slow the automated build, and ship 4 times a day with a defect rate of 5.4% with agentic coding, you are compared unfavorably to teams with a change defect rate of 15.7% that ship 13 times per day, because you are too slow.
And you are individually compared with whole team outputs. Even if you deliver at a rate ten times greater than the worst contributor at your company, if you are not outputting code at the rate of an entire team of 5, you are not meeting the expectations of product and leadership anymore.
All of this is to say, yes, people are looking at software engineers as both the bottleneck and unnecessary, even at high technology companies, right now. They are looking at them that way because they have their own agents that are biased to think that the engineering claims are wrong and agents are sycophantic.
That's right, and even more alarming: no one is pushing back, not only the engineers. The leadership team has AI estimates. The product team has AI estimates. Even the engineering team has AI estimates. Everybody says "Ship." So we ship.
What the company lacked was never engineering deliverables; what it really lacked was a prioritization owner who could draw the line. Bad code certainly wasn't the cause of this problem.
Not all code is cheap. Some code remains very expensive.
But the idea that some code is cheap and some code is expensive is not new.
The only new thing is there are some adjustments on how to asses the value of the code you’re presently, or about to, work on.
AI has absolutely expanded the set of code that is cheap and if you can make a thing easily with AI then so can someone else. That project is unlikely to result in valuable code. Which is not to say it doesn’t have utility. Just its monetary value is low.
Would add my biggest tip to that: TDD. Most people omit it.
There is a difference between:
- write code, write tests
And
- write tests, write code
Had another agentic (vibe) coding experience, which confirmed that for me. Creating an SDK for a $500 light so I can control it from my Steam Deck instead of my phone (no SDK existed before yesterday). For anyone interested, I'm teaching my vibe coding (I meant agentic) tutorial at pycon next week. The 3-hour-long version should be posted to YouTube soon thereafter.
The pure "coder" role, per that paper, died out almost immediately. Nowadays it's done by compilers (a deterministic automation). The distinction between analyst and programmer held out a bit longer - ten years ago I was working somewhere that had "business analysts", essentially requirements-wranglers. It's possible that the "programmer" job of converting a well-defined specification into a program is also going to start disappearing.
.. but that still leaves the specification as the difficult bit! It remains like the old stories with genies: the genie can give you what you ask for. But you need to be very sure what you want, very clear about it, and aware that it may come with unasked-for downsides if you're not.
I think you can boil down most of the list to: Understand what you want to do.
I’m not convinced about rebuilding repeatedly as a learning tool though. As relatively quick as it is, it over emphasizes the front line problems you face early. Those tend to be simpler, more straightforward issues that can be more quickly solved by a few minutes of thought (and more cheaply too).
The danger is treating code like disposable output when production systems treat it like a long-term liability. Someone still has to debug it at 2 a.m.
Make usable software. Cheap code means that you can create a lot more prototypes to then perform usability tests by finding a user and sitting next to them. I mostly worked on internal apps lately, so perhaps it's much easier for me to do than it is for some others.
Code is a liability - the more there is, the higher potential for bugs and poor performance. I'd recommend treating cheap code like cheap toxic waste, and try to minimize how much is generated.
Code might be cheaper but it's still a liability. In that regard anything that's not been properly designed and documented is going to be an even bigger issue.
Stick to patterns which were painful before. For example, I recently refactored a project written in TS to use better-result instead of throwing errors. Without Claude writing out all of that boilerplate I could not have imagined transitioning to this. Right now the cost of "doing it right" is decreased so much there is no reason to ship slop / poorly thought out code.
I've found the get-shit-done tool[1] to be quite useful for forcing me to properly plan the implementation and ensuring the context remains small and relevant at all times.
It is slower than when I was just using Claude directly though.
I've tried this, it's honestly not worth the amount of time (and additional context) for the results. I've had more success prompting Claude with manageable and testable iterations.
Planning is good but get-shit-done just added too much planning in my opinion.
People should do what has always been needed, rather than focus on how hard it is to build something, or easy, find what is needed, what right is, what good is, what quality is that actually solves problems and do those things.
A lot of people down on AI in this thread, but I'm watching the industry slip over the line of trust with these latest frontier models. GPT 5.5 is the first model good enough for me to just let rip.
Every jira ticket I see now has acceptance criteria, reproduction steps, and detailed information about why the ticket exists.
Every commit message now matches the repo style, and has detailed information about what's contained in the commit.
Every MR now has detailed information about what's being merged.
Every code base in the teams around me now has 70 to 90%+ code coverage.
Every line of code now comes with best practices baked in, helpful comments, and optimized hot paths.
I regularly ship four features at a time now across multiple projects.
The MCP has now automated away all of the drudgery of programming, from summarizing emails, to generating confluence documentation, to generating slide decks.
People keep screaming that tech debt is going to pile up, but I think it's going to be exactly the opposite. Software is going to pile up because developing it is now cheap.
Most code before llms sucked. Most projects I on-boarded to were a massive ball of undocumented spaghetti, written by humans. The floor has been raised significantly as to what bad code can even look like, and fixing issues is now basically free if your company is willing to shell out for tokens.
> Software is going to pile up because developing it is now cheap.
Software to do what, though ?!
Coding, maybe 10% of a developers job (Brooks "Silver Bullet" estimates 1/6), was never the bottleneck, and even if you automated that away entirely then you've only reduced development time by 10% (assuming you are not doing human code review etc).
I would also argue that software development as a whole (not just the coding part) was also typically never the bottleneck to companies shipping product faster, maybe also not for automating their business faster (internal IT systems), since the rest of the company is not moving that fast, business needs are not changing that fast, and external factors that might drive change are not moving that fast either.
I think that when the dust settles we'll find that LLM-assisted coding has had far less impact than those trying to sell it to us are forecasting. There will be exceptions of course, especially in terms of what a lone developer can do, or how fast a software startup can get going, but in terms of impact to larger established companies I expect not so much.
+1 for any mention of Fred Brooks. I like your point about software as a whole not being a bottleneck. In the 1970s the hardware was co-evolving with business uses (it still is, but constraints were much more severe) leading to large headcounts on software projects that _absolutely_ had to work and _absolutely_ required uncommon expertise. Most people had no concept of a computer's capabilities, computer science was not as widely distributed.
One thing that I would point to today to show that the landscape is different - the average programmer/engineer/developer today has no actual admin staff. Fred Brooks' example team setup of "The Surgical Team" has more support staff than programmers. Anyone who responds to the questions like "who manages the calendar" and "who manages the documentation" will state that the engineers doing it themselves offer the best results. Same goes for designing test cases, performing rollbacks, etc.
The fact of the matter is that any self respecting engineer today works in an environment where pro-activity and self-sufficiency are prerequisites. Managing your calendar and workload, communicating to leadership and users, these are all common tasks that would have been another person a generation ago.
So when discussing writing code more efficiently and aiding in software development, what I am essentially seeing is more people trying everything they can to offload work that used to be another person's job anyway. If you care about communication - you offload coding standards. If you care about security - you offload feature refactors, and so on.
In my opinion, I think that at some point we'll either realize that we need highly competent people _and also_ regular people to help us ensure the work gets done to a good standard. Or, we will each eventually survive by working alone in a room with a suite of AI tools, and wonder why we're still making software in the first place.
> Most people had no concept of a computer's capabilities, computer science was not as widely distributed.
I am not sure that has changed....
> Software to do what, though ?!
Replace all Oracle Applications in the Enterprise, for example. That will keep Corporate IT/Dev teams busy for quite a while.
Of course, this does not involve Oracle infrastructure, such as Database.
That's kind of the point in GP... everything around the code has improved... the workflows, definitions, documentation, process. I'd say that all of those things are improving and expanding at a rate faster than the improvements in code output, which are also happening at a faster turn around than actual people.
I've said several times that when I use an Agent, I'm getting about 2-4x the value and about 10x the output... the "value" is features landing in code and the difference to the 10x is documentation and testing. While a lot of that may not get reviewed by every person that touches a product, it helps with further ai based feature development.
I'm not a big fan of running many agents or outright vibe coding slop... but you can definitely leverage the coding agents and get a lot of improved output.
I'm not talking about what the developer is doing - I'm talking about what the company is doing in terms of initiating new development work. Again, startups and one-man shops are different because you control your own pace, but in many large corporations you may sit around just minding shop until the next big product development comes along (I would use this time to start my own initiatives to build tools and libraries to help the team), and that company pace is not being determined by how long development takes.
This is especially true if like most developers you are not working at a company where software is the product, but rather where software is part of the product, or where you are part of IT working on internal systems, not part of product development at all.
Then is it a real 10x increase in output if the output is in supporting areas and not the code/feature delivery? It seems like you’re saying that you are now able to maintain documentation at a faster rate and increase testing but not the actual development speed of the feature itself.
I said 2-4x on value.. which would be development of the feature itself in terms of direct output... not even considering setting up test harnesses and doing more adventurous changes that would take me a lot longer to do.
I was referring to the sentence: I'm getting about 2-4x the value and about 10x the output... the "value" is features landing in code and the difference to the 10x is documentation and testing.
What does the 10x imply?
And are you saying that you are outputting 2-4x as in: value * value * value * value in the case of 4x? That seems rather high.
As I recall, “No Silver Bullet” fundamentally rested on the assumption that the subroutine was the last word in abstractions to make programming more efficient, which probably wasn’t even defensible at the time because Lisp had already been invented, and is even less defensible after the past several decades of programming language research. Brooks was still onto something when it came to irreducible complexity, but offloading complexity an LLM can tackle to the LLM still saves time.
One of the lesser discussed Brooks essays is actually the best description of AI-first development: the “surgical team”. It just turns out the surgeon is the only human, and like many modern surgeries, the surgeon is controlling a robot instead of operating by hand.
It would be interesting to reread The Mythical Man-Month and see how each essay applies to AI-first development.
The sibling comment to yours (by rafterydj) makes a good point that the surgical team is necessary, but we have eliminated the positions and put the roles on the same person. It’s like the writer being the subject expert, the reviewer, the editor,… which we all knows leads to mediocre work.
Everybody’s cooking, nobody’s eating.
Pivot to be a user and demand requirements
We can all agree that very big portion of the time needed during product engineering is.. syncing progress, requirements, plans, etc etc. And we have to do it over and over due to how big teams are.
Fast forward, fire half of those ppl, for sure fire all middle managers, scrum masters, coaches, wooden-architects.
Suddenly you save up so much time on syncing, you can ship twice as fast.
And NO, quality and impact doesnt go down. It actually goes up.
This is probably something you did not want to hear :)
Few competent ppl with AI are much much much better than dozens of medicore teams.
We need now „Product Builders” and „Product maintainers”. All of the other roles lost value.
you can't continue shipping code the same way pre-LLM vs post-LLM and expecting a huge speed gain. the trick is abandoning the old models and bottlenecks and embracing the new possibilities enabled by LLMs. requires a high trust environment
So, get rid of the marketing department, get rid of business owners, agile, budgets, and everything that is stopping developers from creating their self-conceived vibe-coded creations as fast as they can ?
A hard dependency like that doesn't seem flexible. There's no such thing as a high-trust environment, abstractly.
there are workplaces that trust their employees to do the right thing to higher and lesser level. places that empower the employees the most will see the highest gains from LLMs
After having a job, I do not trust my coworkers. I might trust a few selected ones, but I certainly do not trust all of them.
High... Ugh, trust... In the... LLM? Hah!
in the employees...
The ticket has subtle errors in its description that are only caught by someone experienced with the codebase.
The code hides an exception behind an if-then-else that defaults to the most common state, which isn't caught until it breaks things for the 1% of users who don't have that state.
The new feature quietly breaks a feature not covered by the acceptance tests.
The documentation is four times as long and nobody who relies on it can read it.
And I'm stuck spending my time going over tickets with a fine-toothed comb, reviewing PRs, and mentoring contributors to prevent all of this garbage from ending up in the live code.
I will give you 4.
1, 2 and 3 happened a ton in the good old times before AI. If anything, we can make the code be more tested than before, but that requires a lot more engineering, that is made easier by LLMs.
It's just we haven't adapted to do them.
People noted similar issues ever since LLMs came out, but the rate at which they have been rapidly improving on all of these is significant. Documentation being 4x too long could probably be fixed with a rule instructing the agent to keep it concise and no longer than 2-3 paragraphs.
They seem to be converging toward an asymptotic accuracy level that is not particularly close to 100%. That is not good enough when you're trying to instruct engineers, particularly junior ones.
Conciseness is variable. Sometines a paragraph is enough, some other tome you need multiple chapters and a glossary to get the point accross. 4x too long may be 4 paragraphs in the first case and a 400 pages book in the second case.
Adding a rule like yours is not the solution.
Definitely, but the first 3 issues are also created by human co-workers.
Do you use Microsoft Copilot ?
What you are describing is a the role of a manager, not a software engineer. Software engineering has very little to do with writing code, but more on architecting at the higher level on what needs to be done. The code is just the executional part. LLMs can code? Ok good. Without a clear architectural pathway / direction, that code is just useless. It's not tech debt. It's just a bunch of random strings. You can argue that Claude code and others do create a plan of attack - but still, it's not at the architectural level, but rather executional level.
To me, architecture starts all the way from the top - even before you write a single line of code, you do the DDD (Domain-Driven Design) and then create a set of rulesets (eg. use the domain name as table prefix) and contexts and then define the functionality w.r.t to that architecture. LLMs can do all this - only if you ask them to explicitly. So, they are pretty useful to brainstorm with, but not autonomously design reliably and push it to production with your eyes closed and support a 100,000 user base. It's a far cry from that.
But sure, you can upsell to management about the vanity metrics like lines of code and get that promotion with LLM. But, it's still not software engineering.
That is why we have SWE bench pro, they test architecture design too, turns out 1000 dollars of tokens outperform 10k dollars of labor in meta design.
That's just not accurate. I haven't studied SWE Bench Pro in detail, so I can't tell you exactly what the flaw is, but SOTA models routinely make bad architectural choices I have to intervene to fix.
You can read the paper here: https://labs.scale.com/papers/swe_bench_pro
TL;DR its very effective as it directly tests model on REAL codebases: "The benchmark is constructed from GPL-style copyleft repositories and private proprietary codebases". The use case is very real.
It doesn't sound to me like this benchmark is attempting to measure architecture design. As far as I see in the paper, they do not evaluate the architectural quality of a task completion, only whether the model is capable of completing it at all.
1000 dollars of subsidized tokens.
Eh.
It's "not software engineering" but neither was what most people writing code did before LLMs.
> Without a clear architectural pathway / direction, that code is just useless. It's not tech debt. It's just a bunch of random strings
This is pretty clearly false. It's a bunch of random strings that you can compile and run to do what you want. It's more akin to a black box. A compiled closed source dependency.
Agreed. I never considered myself an "engineer". Honestly just a regular code monkey. Software Engineer was just my job title. Folks higher up the ladder did engineer software. You know what? It sucked. Was always broken, we were always patching, we never saw around corners. But hey - they software engineered it.
For people who like to tick boxes, which is essentially most of the above, AI is welcome. That includes managers.
It still has nothing to do with software engineering. All good code was written by humans. AI took it, plagiarizes it, launders it and repackages it in a bloated form.
Whenever I look deeply at an AI plagiarized mess, it looks like it is 90% there but in reality it is only 50%. Fixing the mess takes longer than writing it oneself.
How can you say it has "nothing to do with software engineering" with a straight face?
I think you might be in serious denial.
Of course writing code isn't the only task of a software engineer, but it's an important one.
There wouldn't be so much controversy if it wasn't the case
"Writing code" as a task of its own is called cowboy coding. It's neat that AI can do this now, but that has nothing to do with proper software engineering which always starts from a careful, human-led design.
"has nothing to do with proper software engineering"
So you're saying software engineers don't write code? Just because there are other things that SWEs do, does not mean it has nothing to do with it.
It's arguably a pretty important part. Would you really hire a software engineer who can't code?
Writing code and copying the output of an LLM is absolutely not the same.
You wouldn't call someone an author that takes LLM outputs and shoves it in a book. IDK why this distinction doesn't apply to devs too.
You call someone an author when they use a ghostwriter. They're giving inputs that are core to the output, even though they aren't doing all the writing. Same thing.
I can assure you a sizable amount of people in the writing community look down on "authors" that only use ghostwriters.
Why do tech workers act shock that people hate this junk being force fed to them that they are now resorting to violence to reject said junk?
You think telling humans with specialized crafts that they don't matter is good politics? Good grief.
Of course.
I'm not surprised at all that devs are upset.
>You think telling humans with specialized crafts that they don't matter is good politics? Good grief.
Yeah, of course not. There are lot's of historical examples of this. That being said, those historical examples don't play out well for the craftsmen, either.
Look, I'm a SWE myself. I see my job drastically changing right in front of my eyes. I know there's nuance to it, too, that's hard to articulate in these comment threads.
But I think a lot of people here are biased against thinking that they are irreplaceable - I've definitely been in that camp. I don't think that it's wise, however.
>You call someone an author when they use a ghostwriter.
i don't know about you, but i absolutely don't. either you write the book yourself or you are not the author.
as kendrick lamar wrote:
I can dig rappin', but a rapper with a ghostwriter?
What the fuck happened? (Oh no)
Or even more appropriate: a movie director is almost never on-screen but the actors aren't the ones determining the shots to use or writing the script.
What's a good example of human-led design?
Yes and every AI-first development workflow worth its salt does exactly this, and it does it much more thoroughly than I’ve ever seen a team of meatbags do it.
My workflow, at a high level, is:
1. I write a high level spec. Not as high level as a single-sentence prompt, but high level enough to capture my top requirements.
2. I prompt the AI to interview me about the spec to clear up any ambiguity or open questions, then when I’m satisfied, the AI writes a longer spec, which I then review.
3. Then I prompt the AI to write an implementation plan based on the spec. I might just skim this, and by this point I might be asking the LLM more questions than it’s asking me.
4. Now I hand it off to the implementer agent.
This isn’t cowboy coding, it’s not even agile. It’s waterfall. The problem with doing waterfall was that it’s too slow, especially with the deserialization/serialization cost of routing all of this documentation through meatbrains. The LLM is doing just as much work, true, but faster.
The thing I found surprising was that, while LLM’s are still pretty awful at writing as an art form, they are better technical writers than I have the time to be, especially when writing for an audience of other LLM’s.
Is this project in production and for how long? How many users?
The hard part of software engineering is turning a vague problem description into a set of box-ticking exercises. If ticking boxes became genuinely easier, the software engineering part is now a lot more valuable.
You’re reminding me a lot of those old assembly hackers who thought compilers were bullshit because they could hand-write better assembly. And I don’t mean that as an insult; those guys were probably right about their assembly code, just like an Amish craftsman will make better furniture than a factory in China. The problem is that the world needs more furniture and more software than skilled craftsmen can produce, and the skill gap between the craftsman and the mass production process is diminishing fast.
We’re still going to have handwritten software, just like we still have handwritten assembly. It just won’t be the norm.
No fixing the mess definitely does not take longer than writing it oneself.
Your linter should identify all issues - including architectural and stylistic choices - and the AI agents will immediately repair them.
It's about 1000x faster than a human code at repairing its own mess.
> Your linter should identify all issues - including architectural
If a linter could deterministically identify bad architecture, you wouldn't need an LLM, your linters could just write your code for you. The vibe coding takes are just getting more and more empty-headed...
Your custom linters don't check architectural design?
linters statically check code and provide deterministic recommendations. LLMs are used to make judgement. I specifically write my linters for my project to make recommendations for LLMs.
This is how you save on token usage, so your LLMS aren't wasting tokens on static analysis that a linter could do for free.
That's at least how I make my linters.
> If a linter could deterministically identify bad architecture, you wouldn't need an LLM,
a) that's not what a linter is built for, its a tool with very specific role
b) You must've never seen LLM expose secrets in plain text or use the most convoluted scenarios you can think of.
I think you missed the point of the person you are replying to.
>I regularly ship four features at a time now across multiple projects.
Well, this explains why so much software nowadays is so slow, buggy, and chaotic.
Unlike 3 years ago, when nobody complained about software being slow, buggy and chaotic
Incredibly impressive how, the moment AI becomes the topic of conversation, trivial things such as speaking in relative terms become incredibly difficult for the more addled of the prompting users.
Hell has no true bottom.
> I regularly ship four features at a time now across multiple projects.
Can that happen without you? I would assume this is the next step. I don't find it either good or bad, but I'm genuinely curious where this all goes.
It can't happen without someone, but certainly it can happen with a lot less people, which is what's going to happen to the industry. Some days I'm shook to my core about how much I did relative to how long it would have taken just a year ago.
All software engineers will become product managers as the agents take over doing the bulk of the work.
Companies will either do the same with less or more with the same.
My opinion is that any company whose business model is selling software is going to go out of business.
> I'm genuinely curious where this all goes
Maybe toward autonomous/sovereign capital with no humans in the loop, not even at the level of (asset) ownership.
Software is going to pile up because developing it is now cheap.
It won't, because right now we're busy exhausting the vein of good-ideas-we-wanted-to-build, and that's the source of all the good stuff you listed. When that runs out you'll see teams building any old crap because building is cheap, and learning that experimenting by putting any old crap in front of users is a fast way to burn goodwill and brand loyalty.
You still need good ideas and the taste to choose which to put out there over the bad ideas that people actively dislike.
> I regularly ship four features at a time now across multiple projects.
Many people are missing the fact that LLMs allow ICs to start operating like managers.
You can manage 4 streams now. Within a couple years, you may be able to manage 10 streams like a typical manager does today.
IME, LLMs don't speed you up that much if 1) you're already an expert at what you're doing (inherently not scalable), 2) you're only working on one thing (doesn't make sense when you can manage multiple streams), or 3) doing something LLMs are particularly bad it (not many remaining coding tasks, but definitely still some).
A manager doesn't have to look at the code that's being shipped. An IC will still need to do that, and this will eventually take up much of their work. It can be addressed by moving up the stack to higher level and more strictly checked languages, where there's overall less stuff to review manually.
People typically think it's not a new person's fault if they come in to a team and bring down production.
That's a failure of the existing infrastructure to allow someone to do this.
LLM coding will work like this.
If you're letting LLMs go wild with no system in place to automatically know they're moving in the right direction and "shipping" things up to your standards, the failure is you, not the LLM.
The dirty secret is all the people talking about shipping 4 features a day etc are just lying about reviewing anything. They don’t review it at all.
I didn't say shipping a day. I said shipping at the same time.
The review comes at the end, though I truly believe this will go away as well. Agents will also get better at review until they're good enough that no one will want to do it anyways. Good enough is good enough.
I review more thoroughly and faster with Claude than without.
Claude absolutely improves code review quality, but it still misses a lot. It's a second pair of eyes, it doesn't replace/remove the work you have to put in to fully review the code yourself.
It's like saying that you code reviewed faster just because someone else also reviewed the code, that's not how it works.
Agree, and with CC my volume and quality of PR review has substantially increased since 4.5. Without CC for review we would have a ridiculous bottleneck in our dev/qa pipeline.
I'm faster, sure, but more thorough, no. The same, because I was already very careful. But it's not a massive win either; 4.7 misses too much still because it would need to read too much of the context each time to understand the architectural problems I'm catching.
Its nice to not have to care about nits and other things that we don't have lints for though, so that's useful.
Spot on. When will the cretins understand, it's not about how much code you can generate.
Just like a manager, you don't need to look at the code. You need to set up quality systems to provide evidence the code does what it is supposed to do, just like a manager.
I’ve never met a manager that have setup “quality” systems to ensure that the job is done correctly. Their actions are always retroactive. And not pertaining to code at all. The overarching contract is “You do a bad job, you will be fired”.
Code review has a number of important purposes beyond merely verifying functionality. It's true that some managers don't recognize this, fail to allocate time for anything but feature work, and then wonder a few years later why the software is so buggy and new feature development is so hard.
A software engineer was always a manager.
Software engineers were always creating, maintaining and updating automated business processes. In olden days we would have computers, that is rows of people computing things. That room of people is replaced with code in von Neumann machines.
The economic tension has always been a resistance to grant programmers status and class of management. Instead management wants to treat programmers like labor.
> and fixing issues is now basically free if your company is willing to shell out for tokens.
Does "basically free" to you mean for you just that someone else is paying the cost? That's a mentality that has only made the world worse when applied to a wider range of things. Be hesitant in that line of thinking, I suggest, and consider the future.
I agree with most of this, I just have sort of turned a blind eye to what the code actually probably looks like. Reviews are rapid, and I’ll admit I do feel like I’m betraying my inner programmer by just optimizing directly against the claims of token bot. But the way I see it, as long as the numbers don’t lie I’m okay with the process.
I'm seeing the exact opposite with LLMs. So much unmaintainable brittle code is being generated since devs are not even looking at the code and LLMs are dumb like 75% of the time
Agreed on the floor being raised. The part I'd push back on is "fixing issues is now basically free." That's true for the issues that surface in code review or a failing test. The new class of issue is good-looking code that does something unexpected at runtime, usually through chains of tool calls that each looked fine in isolation. Those don't fail your tests. They fail in prod, sometimes quietly.
Sometimes I wonder if people praising AI work on the same type of code as I do.
Just now, I was working on a bug report. I had Claude write the code. Perfect, CI is green, new tests, everything seems fine. Took me 5 minutes. Then looking closer, I can see that there may be a performance regression and that the code seems pretty verbose. I iterate on the prompt "of course, you're right, let me fix this". New code is even more verbose, lots of comments that shouldn't be there, the code is more intricate, it takes me some time to understand what's going on. Plus new test cases to review.
After a day of asynchronous iterations on this, I finally sit down to look at this problem. There was a one line fix that Claude couldn't find on its own.
I lost time, reviewer lost time, and if this had been shipped as is, the system would have been worse. I could go on and on because this happens daily. And the worst part is teammates submitting slop.
This better stated as: Use of agents has forced teams to adopt best practices and guide style guides.
Which is my experience. Once you get into the actual development process, the code itself produced by the agents is not good enough. Still needs editing and rewriting.
Everyone talks about productivity as if that is the only metric that matters in the business.
The MCP has now automated away all of the drudgery of programming, from summarizing emails, to generating confluence documentation, to generating slide decks.
I wonder about the hallucination. Reading someone's writing doesn't take all that long.
> the drudgery of programming
Is programming supposed to suck all the time? Am I doing it wrong? I mean yeah, sure, it sucks sometimes, but overcoming that "suck" is where I feel progress and growth. If we decide to optimise that away...What the fuck am I doing here? No offence to managers, but if everybody is a manager, is anybody?
I'm referring to things like spending a whole day pruning a jira backlog and cleaning up stale git branches.
Classic drudgery that were part of the day in the life that we're not directly writing code.
Feels kind of like the problem of everybody wanting to be an entrepreneur in the 2010s. Just led to people basically trying to get paid to be middleman companies skimming from others that don’t really need them, or worse, selling supplements and life coaching or whatever on social media and other grifts.
Bingo! Nobody wants to build actual stuff. They all want to be intermediaries.
I think numerically this is the exception - and it's a fantastic exception! But in practice what I've seen is things getting worse because people still just aren't very good at thinking, so the great-looking Jira ticket actually turns out to be nonsensical in some subtle way, whereas before it was just lacking in some obvious way that could immediately be called out and had an obvious solution.
I.e. it's making good output better, but it's making mediocre output (which is most output) worse by adding volume and the appearance of quality, creating a new layer of FUD, stress, tedium, and unhappiness on top of the previously more-manageable problems that come with mediocre output.
I'm still seeing this even with the newest models, because the problem is the user, not the model - the model just empowers them to be even worse, in a new and different way.
I was an LLM naysayer for a very long time. I continue to have serious reservations about the ethics of LLM use and the likely economic effects (these tools are likely to empower the owners of capital and disempower labor). On the other hand, I had a rather striking experience the other day that convinced me that the future in which these tools write software may not be so bad:
I had an idea to improve performance in one of the slowest but also one of the most critical parts of the codebase I own, so I asked Claude to re-write it. I gave it exact instructions. It got most things right but key things wrong. I caught the bugs and then asked it for some optimizations, and it came up with a number that were quite good. As I read the code, I saw more and more opportunities for improvement. To make a long story short, code that used to require upwards of 30 seconds in a particularly heinously ugly stress test now finishes in about 8ms.
My original code was terrible. That's indisputable. Maybe the bar for improvement was low. Still, the algorithms and optimizations that I was able to devise while using Claude Opus 4.6 surprised me. I don't often feel pleased with the cleverness of my work, but in this case the work really is stellar -- or at least enough of an improvement that it feels stellar.
Could I have written it without Claude? Yes, definitely. But I was able to produce the code in a few days while having a fever of 100-102, which I definitely couldn't have done on my own.
Moreover, it was plainly apparent to me, while I worked, that I was better able to think about high-level architecture and design because I wasn't stuck on the details of actually writing the code. The code itself, line by line, isn't difficult if you have familiarity with bitwise operations, but there's enough of it, with enough branches, that it's difficult as a whole and the work of writing it would have consumed much of my attention and energy.
Claude missed a huge amount. I improved performance by more than 95% after it told me there were no other opportunities for major optimizations.
Using the tool freed me, I found, to think more clearly, more deeply, and more effectively. Does the result create tech debt? I don't think so. I've pored over it and can't find anything lacking in style, design, or architecture. It's very well documented. Claude wrote tests, as I requested, for everything, including all the bugs that Claude missed and I caught. Test coverage is probably 100%, but, much more importantly, tests exhaustively cover cases, including edge cases, that would have, again, been difficult to enumerate and write by myself.
I doubt Claude could have done all this as well if the codebase and tests weren't already as mature as they are. I really wonder about the feasibility and advisability of greenfield software development with these tools. And a junior developer absolutely couldn't have accomplished what I did. The tool would have produced far worse work in the hands of someone who doesn't know what they were doing.
So I agree with you and disagree: I'm turning a corner on these tools, but I absolutely could not just let rip and trust it to do anything correctly. Moreover, I could not be less impressed by the MCPs written by people in my company. The bare tool by itself is better, though maybe that says more about my company, and my regards for the people I work with, than the tools.
I think this is the way. Human-machine co-design worked great for me so far. Hell, even the test writing alone is great, because I can have more confidence in my code. And test writing was mostly drudgery. On the other hand, you _must_ have a good mental model of the thing in your head else this will not work. And it's much easier to believe you have it and not really have it if you don't engage with the codebase.
> Could I have written it without Claude? Yes, definitely. But I was able to produce the code in a few days while having a fever of 100-102, which I definitely couldn't have done on my own.
While I admire your strength in attempting it, this just adds one more brick to the wall of precedents that "what's stopping you from just sending one prompt, it'll just take 30 seconds and you can do it in bed!"
You could sum it up into a simple equation as Features Shipped = Features/Hour * Developer Hours
Developer hours has remained a constant, and F/H has gone up. I am of the opinion that the ideal is the inverse.
That's an excellent point. To be fair, I allowed myself to work on this while I was sick only because it was fun. This was a bit like scratching an itch, because I'd had the germ of an idea for a while and wanted to get it out of my head. You're absolutely right that it sets a dangerous precedent: the easier it becomes to do work, the easier it is to demand work of the people doing it. The boundaries need to be firm.
On the other hand, this was also a case where Claude really did help me finish something more quickly than I could have without it. So in thi scase I do think it lowered the number of developer hours per feature.
> GPT 5.5 is the first model good enough for me to just let rip.
You know this is the exact same thing said during Opus 4.6, right?
That makes it hard to believe because it's the same "last week's model was so much behind you can't even comprehend" meme that's been going on throughout last year.
More info dumped into tickets and projects is great for understanding for both people and LLM. But hopefully not LLM generated.
> You know this is the exact same thing said during Opus 4.6, right?
Yeah, and for Sonnet 3.5 or even GPT4o. Because it was true for many. Different people have different timing to reach acceptance stage.
It's just cope. I'm so close to just never coming back to HN because the quality of thought has just gone through the floor. Anything whatsoever to hedge one's way to fellating a phallusless chatbot
>You know this is the exact same thing said during Opus 4.6, right?
spicyusername said this exact same thing about Opus 4.6?
or is there more than one person on HN, and perhaps they have different opinions?
There wasn't any personal mention in my post. A snark remark at the fact that this cycle keeps continuing and every new release is game changer except in the banchmarks where there is mostly a slight couple percent change, generally.
You're missing the point that it's (conceivably, and probably) different people making the comments. Each model release has a few new converts, which is expected if the models are in fact getting better at agentic coding.
You're implying it's a hype train when in fact it's an adoption curve.
> which is expected if the models are in fact getting better at agentic coding
Is it? Or is it also explainable that the models are not getting better but people are still adopting it.
If the models were getting we’d be seeing mobile apps with new features at 10x the rate previously, or websites with 4 times the number of features. But we’re not.
> fixing issues is now basically free if your company is willing to shell out for tokens.
Yeah, about that: I looked into Cursor's usage stats and daily I'm going through the equivalent of a bacon sandwich in my cantina, so not much, but this is at today's prices and very light usage of Sonnet.
I was for a time using Opus 4.6 for a heavier task and even then I think the cost was well into the double digit percentages of my salary.
Opus 4.7 reportedly uses more tokens overall and while they reportedly kept rates stable, that is not a given.
Just wait until, with increasing costs, the first company figures that they'll offer this as a benefit and then maybe scrap it altogether in the name of cost cutting.
Watch token budget be included as part of employee TC figures - I feel this is an eventuality due to rising costs and "true pricing" slowly creeping in.
Current ventures feel moreso like a pilot program (you bought a private jet, now get a couple of your pilots to actually fly it) versus having an entire fleet of jets, and having to pay salaries to all those pilots, plus account for their fuel charges.
Right now all expenses are relatively "someone else's {problem,money,infra}".
> Software is going to pile up because developing it is now cheap.
https://somehowmanage.com/2020/10/17/code-is-a-liability-not...
Kind of like credit card.
Every american learns how to live with debt :)
I don't feel so good, Mr. Stark:
https://www.federalreserve.gov/releases/z1/dataviz/z1/nonfin...
Yes, the software that piles up literally is the tech debt. Every automation and tool that was vibe-coded has to be maintained as well. If software is 100x easier to write and you write 100x as much of it, then taking into account network effects, your tech debt is now 100x worse. Congrats!
> The floor has been raised significantly as to what bad code can even look like
It's hard for me to disagree with this take more wow. LLM slop code is TERRIBLE and verbose.
The gap between the ai haves and have-nots is starting to appear. 6 months ago a developer with copilot was about on par with one without. The AI code required a lot of review, about the same amount of time as writing the code manually.
Now.. the AI first engineer might still have to deal with hallucinated things. But.. they can also use the newfound cheapness of code to improve their workflow. Instead of just testing on localhost and manually deploying to prod, you can have a full dev, staging, prod pipeline for free. Tech debt can be one command from being refactored. The open source package that doesn’t quite do what you need it to do? Fork it and write a patch. The ai will be able to maintain the patch. Oh.. you need that bespoke feature for management? Np, done in a 1hr ai session.
Each of these things might be arguably insignificant on their own but net over a projects lifetime they really build up.
This is what people were telling me when opus 4.6 was released 3 months ago, that this time it’s different.
If writing code was the only part of the job, and it was easy, these jobs wouldn't pay so well.
Engineering is hard. It's always going to be hard. I'm glad that AI makes some parts of it easier, and we (software engineers) can focus on engineering, that's nice.
Code is NEVER cheap. Just because, at current completely unrealistic AI pricing, using agents is cheaper than hiring juniors, does not make code cheap. It makes producing code cheap, which has always been low-cost. Every line of code is a cost, is a maintenance burden, is complexity. An AI, even with somehow infinite context window, will cost more money the more code you have.
Could you replace a whole team of engineers with AI? Probably, yeah. Could you simply fire everyone at your company and close it down, without much of a problem? Also probably yes, for most companies.
AIs can help with debugging, can help with writing code, with drafting designs, they can help with almost every step. The second you let OpenAI, or Anthropic, take full code ownership over your products, and you fire the last engineer, is the time when the AI pricing can go up to match what engineers make today. You've just reinvented the highly paid consultant.
Or you could take the middle-ground and hire good engineers, make sure they maintain an understanding of the codebase, and let them use whatever tools they use to get the job done, and done well. This is the way that I've seen competent companies handle it.
"Producing code has always been low cost"
Relative to what?
I don't understand people dismissing the massive decrease in both cost of producing code and the speed of producing code.
Before AI, people running businesses had similar issued as people have with AI now, but the costs were much greater.
They could hire someone to write them a prototype for their idea, but it would cost them on the order of 1000s of dollars, and it would take weeks at the minimum!
Now it could cost them 20$ and be done in a few days. The feedback loop is the bottleneck.
I'm not dismissing it, I'm saying it's never been the bottleneck. Like you said, the feedback loop is A bottleneck, so is figuring out all the nasty things that nobody on HN seems to have heard of before, like "what are the requirements", "which tradeoffs can we make to get this done in time", "what is the architecture for this", and building it in such a way that you can guarantee that it won't fall apart in 20 years when your requirements change.
I understood it in the spirit of “code is a liability not an asset.” Code still needs to be maintained, changed, etc whether that is by a human or LLM.
In other words, just because more code can be produced quickly does not mean that it is cheap.
edit: I’m maybe hearing your point is that LLMs may change that POV but I think that is TBD.
I don't really think that's accurate either, from a business POV.
Software has been such a gold mine, exactly because the maintenance are minimal when you scale, compared to the revenue. The upfront costs are expensive, but once you have software built, in most cases it's relatively cheap to maintain
Things can provide value to a business while still acting as a liability -- that is to say, by virtue of their existence they cost you money, even if, when properly leveraged, they can be used as a tool to make more.
Consider a canonical loan: you get a pile of cash, but in exchange you need to make regular interest payments (plus, at some point pay back the principal, but this isn't strictly necessary -- e.g. governments used to issue perpetual bonds, which paid their coupons indefinitely). The analogy I would make with software is that the product has value, but the code you use to create it is a liability, in that it demands continuous maintenance and upkeep. Just as when you go shopping for a car loan, you look for one with the smallest interest rate -- if the debt itself were an asset, you would want more of it, which doesn't make sense. In the same way, it behooves a business to try and achieve its goals with a relatively small amount of code, not to create as much software as possible.
Certain types of code are cheap. Proof of concept is cheap. Adding small features that fit within the existing architecture is cheap. Otherwise, I'm not so sure. Coding agents are fantastic at minutiae, but have no taste. They'll turn a code base into a ball of mud very quickly, given the opportunity.
While I agree with you that agentic coding still has quite a way to go and is not always producing the quality that I would want from it, I can say quite confidently that its baseline is way above some of the production code in many applications many people use today. It really isn’t that code before agents was primarily written with taste and beautiful structure in mind. Your average code base is a messy hell full of quick fixes that turned into all kinds of debt over the years.
I took the previous post, with its mention of the ball of mud, to be about complexity.
“Taste”, is used in many cases, I suspect, to give a name the collection of practices and strategies developers use to keep their code and projects at a manageable level of complexity.
LLMs don’t seem to manage complexity. They’ll just blow right past manageable and keep on going. That’s a problem. The human has to stay in the loop because LLMs only build what we tell them to build (so far).
BTW, the essay that introduced the big ball of mud pattern to me didn’t hold it up as something entirely bad to be avoided. It pointed out how many projects — successful or at least on-going projects — use it, and how its passive flexibility might actually be an advantage. Big ball of mud might just be the steady state where progress can be made while leaving complexity manageable.
I think there are at least two factors behind ye olde ball of mud that LLMs should be able to help with:
1. Lack of knowledge of existing conventions, usually caused by churn of developers working on a project. LLMs read very quickly.
2. Cost of refactoring existing code to meet current best practices / current conception of architecture. LLMs are ideal for this kind of mostly mechanical refactoring.
Currently, though, they don't see to be much help. I'm not sure if this is a limitation in their ability to use their context window, or simply that they've been trained to reproduce code as seen in the wild with all its flaws.
Keeping complexity down is always a conscious act. Because you need to go past the scope of the current problem and start to think about how it affects the whole project. It’s not a matter of convention, nor refactoring. It’s mostly prescience (due to experience) that a solution, even if correct and easy to implement, will be harmful in the long term.
Architecture practices is how to avoid such harmful consequences. But they’re costly and often harmful themselves. So you need to know which to pick and when to start applying them. LLM won’t help you there.
I agree. I do wonder if what I'm seeing is a limitation of the reasoning power of LLMs or if it's just replicating the patterns (or lack thereof) in the training data.
Indeed. On github I wonder what the proportion is between well engineered big systems versus throw away/student projects.
Even for the well engineered stuff I suspect there is a strong bias towards standalone projects versus larger multi-component systems.
Preproduction code was always cheap or even free. Sales people have been selling software that didn't do what was on the tin since the dawn of time. Those features cost 0 dollars to write!
Production code. Especially production code with bugs is expensive. It can cost you customers, you can even get negative money for it in the form of law suits.
Coding agents are great for preproduction and one offs. For production I really wouldn't chance it at any scale above normal human output.
Except here's the thing, that's the sort of code that was extremely expensive before, in large part because of our day jobs (which still to this day require mindfulness and can't just be vibe-coded).
However, an extra script here or there to make your life easier, adding extra UI features based on some datapoint to your internal dashboard, ect, these were things that could've taken a few days you didn't have before to get exactly right and now they can be done with only a few minutes of attention.
Yea, the amount of dev tools I'm creating per project is astounding. Usually tools that help me to debug certain things better.
I think back to my past jobs how there were people who'd work weekends on random (not to technically difficult to implement) efforts that probably got them promoted, but we would never make time for during the regular work week that now would take next to no time to implement with AI.
Anyone with any small amount of creativity for this sort of thing could really make a big difference on improving the productivity of all sorts of team wide investigations as a running background task they have during their regular work.
I came here exactly to point out what I'm glad to see is 10. "Free as in puppies" is a wonderful way to put it.
Every time I open linkedin I'm scared of how many big heads have taken the wrong lesson that coding almost free == free engineering. So many bait posts asking engineers why they would need to pay them any longer, or being glad they're generating millions of lines a month....this is going to end badly.
I had a business owner tell me that they don't need to hire juniors anymore because claude can do all of that work for them. This was not a software shop so it's not even about writing code but I also thought that was something that will bite in the near future. A business that is not investing in juniors is a business that is not investing in the future.
The role of AI in non-software shops is going to be interesting. To a great extent it's not competing with devs, it's competing with Excel. However bad a system your AI can produce, it can't compare to the workflows that a group of non-techies armed only with Office can produce.
On the other hand, like giving a supercar to a teenager, this just enables them to get into trouble faster.
(the "my vibe coded app deleted prod!" stories are funny schadenfreude when they happen to SV startups, whose whole business is pretending to know better. When this happens to a small business who've suddenly lost all their finanacials and now maybe will lose their house, it's a tragedy. And this can happen on a much larger, not AI-related scale, like Jaguar Land Rover: https://www.bbc.co.uk/news/articles/cy9pdld4y81o )
> The role of AI in non-software shops is going to be interesting
I have friend in west Texas who does industrial electrical gear sales (like those giant spools of cable you see on tractor trailers). He’s 110% good old boy Texan but has adopted and loves AI. He says it’s been a huge help pulling quotes together and other tasks. Coincidentally he lives in Abilene where one of the stargate campuses are going. Btw, the scale of what’s being built in Abilene is like nothing I’ve ever seen.
> 10. Code is cheap, but maintenance, support, and security aren’t.
I also keep circling around this point. So many software repositories in the AI space seem to follow a publish and forget pattern. If you simply can show that you have the patience to maintain a project, ideally with manual intervention instead of a fully autonomous AI, then you already have an outstanding project.
yeah, if a project is purely vibe coded, it tend to need to be rewritten at some point of time
LinkedIn is a circle of hell even dante couldn't imagine.
Agreed, but a worrying amount of managers and leaders spend time there for reasons I never fully understood, so it offers a glimpse into their worldview.
The issue is that when you gaze long into an abyss, the abyss also gazes into you.
This is a repeat of paying devs by SLC(source line of code).
I am in India, junior developer hiring is all down. Ai has reduced offshoring to India and eliminated the need for janitor work (often offloaded to juniors).
Many people are finding it difficult to even land internships.
The most affected areas are sysadmin, devops, and frontend. Where you'll have very hard time getting any offer.
Companies like BrowserStack are withdrawing campus placement offers.
Meanwhile, I am writing apps for my own use and have reached 10,000+ monthly active users already, even though I am making zero money from doing all this, but it's fun.
Looking at the entire market in Europe it is also down but that is not due to "AI" but because they are easiest to fire with least consequences. There is a global recession looming, despite Wall Street saying otherwise.
Europe is easiest to fire? I would've thought the opposite.
depends on the country, i would guess.
but firing because "ai makes us more productive" is basically impossible in most eu countries.
I'm surprised sys-admin hires are down, is AI doing a lot of that as well?
Yah, that comment is odd.
Sysadmins, Devops engineers will the be the last ones replaced by AI. The context window for their problems are huge.
Unless you define Sysadmins and Devops as fiddling with YAML all day, which might be the case here.
>Sysadmins, Devops engineers will the be the last ones replaced by AI.
Most setups aren't properly documented which makes the discovery and exploitability part the major bottleneck when this is facilitated by AI, the sysadmin/devops team is downsized.
Guy works for the Overture Map Foundation, with Amazon, Microsoft etc. being sponsors. He has been boosting AI all over the Internet. I'm sure Microslop and Amazon are very happy with these efforts.
I'm glad that "10 ways to do X" submissions are allowed as long as they boost AI.
Are you suggesting that Microsoft and Amazon's sponsorship of Overture comes with an understanding that people who work on Overture will spend their time writing articles that "boost AI"?
Does "boosting AI" include opening an article with "Frontier models are really good at coding these days, much better than they are at other tasks"?
Can't speak for the former, but the latter question: yes.
"Product is really good at X, much better than at Y" does not imply that it's bad at Y, and even if it did, if you're targeting an audience that only cares about X, who gives a shit about Y? Might as well throw Y under the bus to boost the perceived effectiveness of product at X even more in comparison.
How is that not boosting AI? It's certainly not denigrating it
I read it as mild denigration, because the AI companies would very much like you to believe that agentic AI works great for everything beyond code.
Realize it's going to be 10-100x more expensive once you have no way back?
There is no moat. https://newsletter.semianalysis.com/p/google-we-have-no-moat...
It’ll be priced slightly higher than the cost to actually run. But it’s still not clear what the real cost of the big models is. They seem very subsidised, but by how much?
The article is from 2023, I’m wondering if things mentioned still stand true today, can someone pls let me know.
It's much truer today. You can say that article is extremely insightful, as it predicted today's open weighted models scenario 2 years earlier.
It remains an unproven hypothesis. The revenue of the top 2-3 labs is still growing nearly exponentially, which is the ultimate piece of data that settles the question empirically for now. Benchmark scores aren't really proof. Benchmaxxing is possible, for example. Only revenue numbers (and gross margins) count.
The ultimate piece is not revenue but profit. At some point these enormous investments will have to be earned back. Good luck with that when open weight models are also continuously improving, have cheap providers and for many are already very usable.
The other point to make is that companies are starting to worry about the risks of externally hosted models.
This is at multiple levels if you have a remote API call as a key part of your workflow/software system.
1. Price risk - might be affordable today - but what about tomorrow?
2. Geopolitical risk - your access might be a victim of geopolitics ( seems much more likely that it used to be ).
3. Model stability/change management - you've got something working at the API get's 'upgraded' and your thing no longer works.
If you are running on open weight models - you are potentially fully in control - ( even if you pay somebody to host - you'd expected there to be multiple hosting options - with the ultimate fallback of being able to host yourself ).
What will close the way back?
You cutoff a generation of juniors from employment and learning , the seniors are gone and it's all harnesses and AI systems.
I'm not all gloom and doom but the treatment of junior engineers is something I think we will either regret or rejoice. Either will have a spur of creative people doing their own independent thing or we'll have lost a generation of great engineers.
This is not happening at least for 25 years, is what seniors I trust tell me.
I'd say closer to 10-15 but... I'm not sure the point you're making. Is it okay because it's 25 years in the future?
If we try hard enough, we can destroy the planet before we get there, I guess? 25 years is not a long time.
Today junior assembly language programmer are all gone, too.
Yes and that’s why I can charge premium rates for debugging. Most people cannot read a stack trace anymore.
And that’s going to cause serious issues when people like Linus die and nobody knows how to make operating systems anymore.
We’ve been coasting along on a single generation who have ruled with iron fists.
The problem of "instant legacy" systems: something that's vibe coded and reached unmaintainable by either the AI or humans, but is also now indispensable because users are relying on it.
I'm curious if this will cause a drop in quality that will lead users to generally lose trust in software.
Some of that is already there .. but the users generally have nowhere else to go and ineffective pushback. "Enterprise software" has been awful for decades, things like Lotus Notes and SAP. Everyone hates Windows; everyone continues to use Windows.
Users don't currently trust software. Look at what we've done to them - can you blame them?
The consumer space is about extracting every ounce of personal data possible.
The b2b space is about "maximizing customer value" - that is, not maximizing the value of your product to the customer, but maximizing the value of the customer to your business. Lock them in and lock them down, make your product "sticky" so they can't leave without immense cost.
There will always be competition. For every company negatively impacting customer experience and their own ability to compete, there will be others happy to step in and take advantage of that.
See Windows 11
Hey you can just rewrite (or should we say regenerate) it. Second system has never been cheaper!
Brain drain.
If you fire all your SWEs they won't sit around twiddling their thumbs waiting for an AI collapse, they'll career shift. Maybe to an unemployment line and/or homelessness, maybe to something else productive, but either way they'll lose SWE skills.
If you close down all the SWE junior positions you'll strongly discourage young people training in the field. They'll do something else.
Then if you want to go back, who will you hire for it?
Why would anyone want to go back? It seems likely that the automated dev systems will just keep improving and get faster, cheaper, stronger.
> automated dev systems
They are large language models. Not automated development machines. They hallucinate.
The goal post has not shifted since 2023 or so. Make an LLM that doesn't blatantly disregard knowledge it has, instructions it has been giving, over and over, and you win. If trillions of USD of investment can't do it, I'd be curious to see what can.
There are definitely automated dev systems, of which an LLM is a part. The remaining part may be called a 'harness' or whatever. The quality of the generated software is another matter.
If the AI is not good enough, then don't fire the devs. If/when the devs are no longer needed, I don't see why the need would return later, that was my point.
A harness like Claude Code does not turn an LLM into a software developer.
If that was the case companies could just have their project managers managing Claude Code instead of developers, and they would immediately realize that using Claude Code to develop software is just as complex and geeky as it ever was - nothing changed in that regard.
A harness and a bunch of skills is just the new "think step by step" prompting technique. Don't just let the LLM rip and write a bunch of code, but try to get it to think before coding, avoid things like churning the code base for no reason, and generally try to prompt it to behave more like a developer not an LLM. Except it still is an LLM.
A coding agent is really not much different to a chat "agent" in this regard. You've got the base LLM then a system prompt trying to steer it to behave in a certain way, always suggest "next step", keep to a consistent persona, etc. None of this actually makes the LLM any smarter or turns it into a brilliant conversationalist, anymore than the coding agent giving the LLM a system prompt magically turns it into a software developer.
I agree with you, but it's a case of the tradegy of the commons. One single company cannot make a meaningful dent even with your insight.
Lack of developers, if juniors don't get hired they will move onto other industries.
Company brain drain, knowledge leaves with your seniors if you decide to get rid of them, or they just leave due to the conditions AI creates.
I don't know if the above comes to fruition, there's a lot of questions that only time will answer. But those are my first thoughts.
Time. In a few years there might be no old-school way to develop anymore. Everything will be built around AI.
And blockchain, don't forget blockchain.
Even the programming languages will be made for AI.
All code that could be written by humans, has been written. Henceforth, the rest will be generated.
How do you reconcile these ideas with the fact that cheap open weight models are only slightly behind the state of the art?
If anything, I would bet that next year you could get today’s flagship performance for significantly cheaper via an open-weights model.
You can easily develop with models like GLM 5.1 and Kimi k2.6 at a fraction of the cost of GPT 5.5 or Opus 4.7. Requests often cost just a few cents.
Open-source models have caught up tremendously recently. Those who can’t or don’t want to invest a lot of money can already develop with Kimi and GLM without any problems. We don’t have to wait another year for that.
Tried deepseek 4 w/ CC yesterday, and was watch my usage eke up by only 0.01 at a time while doing plenty of high-token-count tasks. I understand it's currently at a discount, but even after that expires the same-quality output will be available at a fraction of the cost of the expensive models.
From experience, the same level of usage would have left me stranded on my CC 5 hr limit within an hour.
There were some difficulties with tool calls, in particular with replacing tab-indented strings - but taking no steps to mitigate that (which meant the model had to figure it out every time I cleared context) only cost relatively few extra tokens -- and it still came in well under 4.6, nevermind 4.7. And of course, I can add instructions to prevent churning on those issues.
I have no reason to go back to anthropic models with these results.
"No moat" indeed.
By that time, the hypebeasts will be explaining how worthless the models of today always were.
And there’s some truth to it.
I expect tomorrow’s models will be so much more capable that we will happily pay more.
But if not, we will still likely get today’s capabilities or more for cheap.
I don’t see a realistic scenario in which the AI genie is going back into the bottle because of affordability.
It seems like wishful thinking by people who dislike the new paradigm in software engineering.
Sure, but there will always be some monstrosities like Mythos that'll pwn all software written by local models in 0.01 seconds, thus forcing people/companies to use the most advanced paid models to keep up and stay unpwned for 1 second longer.
(Timeframes are hyperbolical).
We change how we value code: https://jerf.org/iri/post/2026/what_value_code_in_ai_era/
Short-short version, code will still be accruing value in proportion to how much of the real world it has encountered. The bottleneck on building valuable code will be how much real world there is to go around. As is so often the case, what may initially seem to kill SaaS will actually make them stronger as they end up with more exposure to the real world than some random guy's random AI code.
This is such a weird argument, beside obvious #10 which will bite back with a vengeance, because... code can't be cheaper than free!
Since at least the early 80s a LOT of very important code wasn't cheap, it was free. Both free of cost (you could "just" download it and run it) but also free as freedom-respecting software.
I just don't get the argument that cheap is new. Cheap is MORE expensive than free!
Re: personalized software via vibe coding
Free but you're responsible for maintaining it means it's not free. It's the same issue as maintaining your own fork. It's just an ongoing cost.
(Though as AI becomes autonomous enough to be the maintainer, that cost kind of goes away. Then it's just the cost of managing the "dev".)
Related recent discussion https://news.ycombinator.com/item?id=48005809 including distinction between your/our software
The list in the article looks like verification practices. Document intent, develop taste, find the hard stuff, etc. It assumes that when code is cheap the bottleneck shifts to knowing whether what you generated is actually right.
e2e tests can do a lot, but in my experience it's not enough. By the time the test fails you've already burned a generation cycle on an artifact that came from a flawed spec or design. I've gotten more mileage from having checks at stage boundaries (standard SDLC: plan, design, code, test). We all know the earlier you catch the mistake, the cheaper the fix.
The "implement to learn" is the same idea: you need to know enough about both where you want to go AND the path to get there to guide the agents to a proper implementation. You have contact with the world, both the users and the operational considerations that come from running software. Agents do not. We do the same thing with spikes, but now our spikes become far more sophisticated.
Code being cheap doesn't remove verification, it moves it earlier.
It's not a question of cheap or expensive; it's when to pay what costs.
With LLM's, it's arguably easier to avoid exporting costs to the future, or to export them.
Whether because of LLM's or frameworks, process consistency typically creates a forcing function to continuously improve quality (i.e., avoid exporting costs); for each problem, create step in the process to surface and address it - hopefully in an automated way.
Having spent lifetimes trying to get teams to up their game, I'm hopeful this may help, if it gets baked in not just to code generation, but to process.
I used to work as a VP and a part of my responsibilities was to chop up tasks to self-contained work units that can be easily assigned to random devs. This was both morally problematic for humans (i.e. EVPs forced treating human = CPU) and very optimistic when it came to individual dev capabilities and domain knowledge. However, this style is precisely what works well with agentic AI coding and I have no qualms to use it.
Good list, I have intuitively realized all of these except for (3). I agree with the author: cheap experiments to figure out a grand architectural plan, then executing fast without needing to wait for input is the right way to go. "Automate everything that's easy" is also important, but I'd extend it and say you probably should try to automate things that are hard too: it's worth spending a great deal of time on it simply due to how crazy valuable the returns are. But I guess I should look more into end-to-end testing.
"We" should not do anything. The LLM industry should go and find solutions for the problems they created, themselves. Not offload it to others through sneaky influencer posts. And we should hold them responsible, should they not be able to address the problems they are creating.
Stop spending time on design. Try all of the permutations and stress test them.
Stop caring about extendable modular code. Just rewrite from scratch when you have new requirements.
Build a demo for every idea you have.
Build 100 visualizations and metrics for any algorithm you develop.
We are finally free of friction. Bad news is we have no brakes. Brace for impact.
> We are finally free of friction
Free from friction, constrained by pricing and a massive ladder that can be pulled up by companies that are in no way benevolent.
If you're one of the lucky few who were able to already have hardware that can run some of the open weight models you can be a beneficiary for now - but that won't last forever.
#10 needs more emphasis than it receives. Cheaper code doesn't automatically lead to good product decisions.
Instead of focusing on whether you can build it, the scarcer resource becomes whether you should build it. And most teams lack a clear process for addressing this latter question. Requirements are collected in all sorts of places without ever being prioritized in an organized fashion. This is exacerbated by cheaper code. With cheaper code, you can release five times what you used to be able to release in a given period of time, but only if you knew which five products you needed.
For most teams, whether or not you can say no to building something is ambiguous at best, at least if you wish to stay on that team and at that company. It's definitely one of the things that has made me vote with my feet in the past. With agentic coding, the ability to say no is pretty much gone because the perception is that it's just one more parallel thing we can throw an agent at.
The thing I see from agentic adoption that I find lamentable as a software engineer is that timeline expectations have collapsed to absurdity. You can plan a project to do a major migration, do all the estimations on how long something will take, and if you give an answer that says weeks and cite the evidence, product and leadership will now claim it should take days, citing their ai's design.
It's exhausting. Even if you are an expert, you now have lost the implicit trust that came from years of building political capital, shipping efficiently, and delivering value for multiple companies, because a different prompt with different context from the one you provide gave a different answer than what you did.
During delivery, if you read your code produced line-by-line and review for correctness, and put in additional guardrail automations that slow the automated build, and ship 4 times a day with a defect rate of 5.4% with agentic coding, you are compared unfavorably to teams with a change defect rate of 15.7% that ship 13 times per day, because you are too slow.
And you are individually compared with whole team outputs. Even if you deliver at a rate ten times greater than the worst contributor at your company, if you are not outputting code at the rate of an entire team of 5, you are not meeting the expectations of product and leadership anymore.
All of this is to say, yes, people are looking at software engineers as both the bottleneck and unnecessary, even at high technology companies, right now. They are looking at them that way because they have their own agents that are biased to think that the engineering claims are wrong and agents are sycophantic.
That's right, and even more alarming: no one is pushing back, not only the engineers. The leadership team has AI estimates. The product team has AI estimates. Even the engineering team has AI estimates. Everybody says "Ship." So we ship.
What the company lacked was never engineering deliverables; what it really lacked was a prioritization owner who could draw the line. Bad code certainly wasn't the cause of this problem.
Not all code is cheap. Some code remains very expensive.
But the idea that some code is cheap and some code is expensive is not new.
The only new thing is there are some adjustments on how to asses the value of the code you’re presently, or about to, work on.
AI has absolutely expanded the set of code that is cheap and if you can make a thing easily with AI then so can someone else. That project is unlikely to result in valuable code. Which is not to say it doesn’t have utility. Just its monetary value is low.
Would add my biggest tip to that: TDD. Most people omit it.
There is a difference between:
- write code, write tests
And
- write tests, write code
Had another agentic (vibe) coding experience, which confirmed that for me. Creating an SDK for a $500 light so I can control it from my Steam Deck instead of my phone (no SDK existed before yesterday). For anyone interested, I'm teaching my vibe coding (I meant agentic) tutorial at pycon next week. The 3-hour-long version should be posted to YouTube soon thereafter.
Apart from (2), the first seven lessons are exactly identical to good project management practices with humans. Which are also the difficult bits.
Once upon a time, highly bureaucratic organizations tried to make a distinction between "analyst", "programmer" and "coder": https://cacm.acm.org/opinion/the-myth-of-the-coder/
The pure "coder" role, per that paper, died out almost immediately. Nowadays it's done by compilers (a deterministic automation). The distinction between analyst and programmer held out a bit longer - ten years ago I was working somewhere that had "business analysts", essentially requirements-wranglers. It's possible that the "programmer" job of converting a well-defined specification into a program is also going to start disappearing.
.. but that still leaves the specification as the difficult bit! It remains like the old stories with genies: the genie can give you what you ask for. But you need to be very sure what you want, very clear about it, and aware that it may come with unasked-for downsides if you're not.
I think there is a massive divide in the market between large enterprises and smaller org.
The large enterprises are their typically laggard self. Prioritizing governance over innovation.
Everyone else, at least in tech or tech adjacent, is building. Build vs buy is the conversation.
Wrong answers: $1
Plausible answers: $10
Good, useful answers: $100
Validation and Verification is what separate "should work" from "is shown to work".
Anyone can write non-functional code.
It's functional useful code that takes more work.
I think you can boil down most of the list to: Understand what you want to do.
I’m not convinced about rebuilding repeatedly as a learning tool though. As relatively quick as it is, it over emphasizes the front line problems you face early. Those tend to be simpler, more straightforward issues that can be more quickly solved by a few minutes of thought (and more cheaply too).
The danger is treating code like disposable output when production systems treat it like a long-term liability. Someone still has to debug it at 2 a.m.
> What should we do when code is cheap?
Make usable software. Cheap code means that you can create a lot more prototypes to then perform usability tests by finding a user and sitting next to them. I mostly worked on internal apps lately, so perhaps it's much easier for me to do than it is for some others.
Code is a liability - the more there is, the higher potential for bugs and poor performance. I'd recommend treating cheap code like cheap toxic waste, and try to minimize how much is generated.
Is it cheap though? Do we actually know how much it costs? Isn't it possible that TODO app you vibe coded in 15 minutes actually costs investors $50k?
Code might be cheaper but it's still a liability. In that regard anything that's not been properly designed and documented is going to be an even bigger issue.
It's cheap to change code, it doesn't mean you have to add more of it...
the best code is the code you never have to write.
this means code also written by 'A.i'.
seems as an industry we're hellbent on optimizing for the wrong-thing.
Stick to patterns which were painful before. For example, I recently refactored a project written in TS to use better-result instead of throwing errors. Without Claude writing out all of that boilerplate I could not have imagined transitioning to this. Right now the cost of "doing it right" is decreased so much there is no reason to ship slop / poorly thought out code.
Lessons for agentic swimming: What should we do when lead weights are cheap?
Also:
> It’s invigorating, rewarding, and deeply weird.
It's mind-numbing, infanialising, and deeply dehumanizing. Which, I suppose, is a valid description of the future silicon valley is building.
The remainder of the post is as vapid as the AI generating it.
Learn to throw code away
I've found the get-shit-done tool[1] to be quite useful for forcing me to properly plan the implementation and ensuring the context remains small and relevant at all times.
It is slower than when I was just using Claude directly though.
[1] https://github.com/gsd-build/get-shit-done
I've tried this, it's honestly not worth the amount of time (and additional context) for the results. I've had more success prompting Claude with manageable and testable iterations.
Planning is good but get-shit-done just added too much planning in my opinion.
It seems there is a new version [1] - I'll try it out and see if it is better.
[1] https://github.com/gsd-build/gsd-2
Every thread on this topic has so much authoritative pontification. In the end, it doesn't really matter what some $random internet person thinks.
Finish unmaintained Open Source OSes?
> Automate everything thats easy.
Hold on, I better write this down, this is good stuff..
People should do what has always been needed, rather than focus on how hard it is to build something, or easy, find what is needed, what right is, what good is, what quality is that actually solves problems and do those things.
Disappointing - not a very insightful list
>What should we do when code is cheap?
Buy in bulk and resell. /s