Right now I see the former as being hugely risky. Hallucinated bugs, coaxed into dead-end architectures, security concerns, not being familiar with the code when a bug shows up in production, less sense of ownership, less hands-on learning, etc. This is true both at the personal level and at the business level. (And astounding that CEOs haven't made that connection yet).
The latter, you may be less productive than optimal, but might the hands-on training and fundamental understanding of the codebase make up for it in the long run?
Additionally, I personally find my best ideas often happen when knee deep in some codebase, hitting some weird edge case that doesn't fit, that would probably never come up if I was just reviewing an already-completed PR.
In the early days, the interfaces were so complex and technical, that only engineers could use them.
Some of these early musicians were truly amazing individuals; real renaissance people. They understood the theory, and had true artistic vision. The knew how to ride the tiger, and could develop great music, fairly efficiently.
A lot of others, not so much. They twiddled knobs at random, and spent a lot of effort, panning for gold dust. Sometimes, they would have a hit, but they wasted a lot of energy on dead ends.
Once the UI improved (like the release of the Korg M1 sampler), then real artists could enter the fray, and that’s when the hockey stick bent.
Not exactly sure what AI’s Korg M1 will be, but I don’t think we’re there, yet.
It's like saying if you don't learn to use a smartphone you'll be left behind. Even babies can use it now.
I think if you orient your experimentation right you can think of some good tactics that are helpful even when you're not using AI assistance. "Making this easier for the robot" can often align with "making this easier for the humans" as well. It's a decent forcing function
Though I agree with the sentiment. People who have been doing this for less than a year convinced that they have some permanent lead over everyone.
I think a lot about my years being self taught programming. Years spent spinning my wheels. I know people who after 3 months of a coding bootcamp were much further than me after like ... 6 years of me struggling through material.
I don't have that skill; I find that if I'm using AI, I'm strongly drawn toward the lazy approach. At the moment, the only way for me to actually understand the code I'm producing is to write it all myself. (That puts my brain into an active coding/puzzle solving state, rather than a passive energy-saving state.)
If I could have the best of both worlds, that would be a genuine win, and I don't think it's impossible. It won't save as much time as pure vibe coding promises to, of course.
I can confidently say that being able to prompt and train LoRAs for Stable Diffusion makes zero difference for your ability to prompt Nano Banana.
There's a dissonance I see where people talk about using AI tools leading to an atrophy of their abilities to work with code, but then expecting that they need no mastery to be able to use the AI tooling.
Will the AI tooling become so much better that you need little to no mastery to use it? Maybe. Will those who have a lot of fundamentals developed over years of using the tooling still be better with that tooling than the "newbs"? Maybe.
Until then, I keep up and add my voice to the growing number who oppose this clear threat on worker rights. And when the bubble pops or when work mandates it, I can catch up in a week or two easy peasy. This shit is not hard, it is literally designed to be easy. In fact, everything I learn the old way between now and then will only add to the things I can leverage when I find myself using these things in the future.
People write long prompts primarily to convince themselves that they're casting some advanced spell. As long as the system prompt is good you should start very simply and only expand if results are unsatisfactory.
It is strange because the tech now moves much faster than the development of human expertise. Nobody on earth achieved Sonnet 3.5 mastery, in the 10k hours sense, because the model didn't exist long enough.
Prior intuitions about skill development, and indeed prior scientifically based best practices, do not cleanly apply.
Oooh, let me dive in with an analogy:
Screwdriver.
Metal screws needed inventing first - they augment or replace dowels, nails, glue, "joints" (think tenon/dovetail etc), nuts and bolts and many more fixings. Early screws were simply slotted. PH (Philips cross head) and PZ (Pozidrive) came rather later.
All of these require quite a lot of wrist effort. If you have ever screwed a few 100 screws in a session then you know it is quite an effort.
Drill driver.
I'm not talking about one of those electric screw driver thingies but say a De W or Maq or whatever jobbies. They will have a Li-ion battery and have a chuck capable of holding something like a 10mm shank, round or hex. It'll have around 15 torque settings, two or three speed settings, drill and hammer drill settings. Usually you have two - one to drill and one to drive. I have one that will seriously wrench your wrist if you allow it to. You need to know how to use your legs or whatever to block the handle from spinning when the torque gets a bit much.
...
You can use a modern drill driver to deploy a small screw (PZ1, 2.5mm) to a PZ3 20+cm effort. It can also drill with a long auger bit or hammer drill up to around 20mm and 400mm deep. All jolly exciting.
I still use an "old school" screwdriver or twenty. There are times when you need to feel the screw (without deploying an inadvertent double entendre).
I do find the new search engines very useful. I will always put up with some mild hallucinations to avoid social.microsoft and nerd.linux.bollocks and the like.
This framing is exactly how lots of people in the industry are thinking about AI right now, but I think it's wrong.
The way to adopt new science, new technology, new anything really, has always been that you validate it for small use cases, then expand usage from there. Test on mice, test in clinical trials, then go to market. There's no need to speculate about "too much" or "too little" usage. The right amount of usage is knowable - it's the amount which you've validated will actually work for your use case, in your industry, for your product and business.
The fact that AI discourse has devolved into a Pascal's Wager is saddening to see. And when people frame it this way in earnest, 100% of the time they're trying to sell me something.
My theory is that executives must be so focused on the future that they develop a (hopefully) rational FOMO. After all, missing some industry shaking phenomenon could mean death. If that FOMO is justified then they've saved the company. If it's not, then maybe the budget suffers but the company survives. Unless of course they bet too hard on a fad, and the company may go down in flames or be eclipsed by competitors.
Ideally there is a healthy tension between future looking bets and on-the-ground performance of new tools, techniques, etc.
Testing medical drugs is doing science. They test on mice because it's dangerous to test on humans, not to restrict scope to small increments. In doing science, you don't always want to be extremely cautious and incremental.
Trying to build a browser with 100 parallel agents is, in my view, doing science, more than adopting science. If they figure out that it can be done, then people will adopt it.
Trying to become a more productive engineer is adopting science, and your advice seems pretty solid here.
This is fair. And what I've been doing it. I still mostly code the way I've always coded. The AI stuff is mostly for fun. I haven't seen it transformatively speed me up or improve things.
So I make that assessment, cool. But then my CEO lightly insists every engineer should be doing AI coding because it's the future and manual coding is a dead end towards obsolescence. Uh oh now I gotta AI-signal for the big guy up top!
You're neglecting the cost of testing and validation. This is the part that's quite famous for being extremely expensive and a major barrier to developing new therapies.
I notice that I get into this automatically during AI-assisted coding sessions if I don't lower my standards for the code. Eventually, I need to interact very closely with both the AI and the code, which feels similar to what you describe when coding manually.
I also notice I'm fresher because I'm not using many brainscycles to do legwork- so maybe I'm actually getting into more situations where I'm getting good ideas because I'm tackling hard problems.
So maybe the key to using AI and staying sharp is to refuse to sacrifice your good taste.
That said, maybe it's not a big deal. Kind of like way back when I moved from C++ to GC code, I remember I missed memory leaks, because having it all automatically taken care of for free felt like giving up control and encouraging of lazy practices and loose ends. Turns out it wasn't really a big deal at all.
When people talk about this stuff they usually mean very different techniques. And last months way of doing it goes away in favor of a new technique.
I think the best you can do now is try lots of different new ways of working keep an open mind
Note, if staying on the bleeding edge is what excites you, by all means do. I'm just saying for people who don't feel that urge, there's probably no harm just waiting for stuff to standardize and slow down. Either approach is fine so long as you're pragmatic about it.
Using LLMs to generate documentation for the code that I write, explaining data sheets to me, and writing boilerplate code does save me a lot of time, though.
Another good alike wager I remember is: “What if climate change is a hoax, and we invested in all this clean energy infrastructure for nothing”.
There is zero evidence that LLM's improve software developer productivity.
Any data-driven attempts to measure this give ambivalent results at best.
It's both. It's using the AI too much to code, and too little to write detailed plans of what you're going to code. The planning stage is by far the easiest to fix if the AI goes off track (it's just writing some notes in plain English) so there is a slot-machine-like intermittent reinforcement to it ("will it get everything right with one shot?") but it's quite benign by comparison with trying to audit and fix slop code.
How's that? If it ever gets good, it seems rather implausible that today's tool-of-the-month will turn out to be the winner.
that's nowhere near guaranteed
You think it's going to get harder to use as time goes on?
I don't think these are exclusive. Almost a year ago, I wrote a blog post about this [0]. I spent the time since then both learning better software design and learning to vibe code. I've worked through Domain-Driven Design Distilled, Domain-Driven Design, Implementing Domain-Driven Design, Design Patterns, The Art of Agile Software Development, 2nd Edition, Clean Architecture, Smalltalk Best Practice Patterns, and Tidy First?. I'm a far better software engineer than I was in 2024. I've also vibe coded [1] a whole lot of software [2], some good and some bad [3].
You can choose to grow in both areas.
[0]: https://kerrick.blog/articles/2025/kerricks-wager/
[1]: As defined in Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge, wherein you still take responsibility for the code you deliver.
I think there are a ton of people just pulling the lever over and over, instead of stepping back and considering how they should pull the lever. When you step back and consider this, you are for sure going to end up falling deeper into the engineering, architecture realm. Ensuring that continually pulling the lever doesn't result in potential future headaches.
I think a ton of people in this community are struggling with the lose of flow state, and attempting to still somehow enter it through prompting. The game in my view has just changed, its more about just generating the code, and being thoughtful about what comes next, its rapid usage of a junior to design your system, and if you overdue the rapidness the junior will give you headaches.
No, it's different from other skills in several ways.
For one, the difficulty of this skill is largely overstated. All it requires is basic natural language reading and writing, the ability to organize work and issue clear instructions, and some relatively simple technical knowledge about managing context effectively, knowing which tool to use for which task, and other minor details. This pales in comparison with the difficulty of learning a programming language and classical programming. After all, the entire point of these tools is to lower the required skill ceiling of tasks that were previously inaccessible to many people. The fact that millions of people are now using them, with varying degrees of success for various reasons, is a testament of this.
I would argue that the results depend far more on the user's familiarity with the domain than their skill level. Domain experts know how to ask the right questions, provide useful guidance, and can tell when the output is of poor quality or inaccurate. No amount of technical expertise will help you make these judgments if you're not familiar with the domain to begin with, which can only lead to poor results.
> might be useful now or in the future
How will this skill be useful in the future? Isn't the goal of the companies producing these tools to make them accessible to as many people as possible? If the technology continues to improve, won't it become easier to use, and be able to produce better output with less guidance?
It's amusing to me that people think this technology is another layer of abstraction, and that they can focus on "important" things while the machine works on the tedious details. Don't you see that this is simply a transition period, and that whatever work you're doing now, could eventually be done better/faster/cheaper by the same technology? The goal is to replace all cognitive work. Just because this is not entirely possible today, doesn't mean that it won't be tomorrow.
I'm of the opinion that this goal is unachievable with the current tech generation, and that the bubble will burst soon unless another breakthrough is reached. In the meantime, your own skills will continue to atrophy the more you rely on this tech, instead of on your own intellect.
You'll probably be forming some counter-arguments in your head.
Skip them, throw the DDD books in the bin, and do your co-workers a favour.
But it should be a philosophy, not a directive. There are always tradeoffs to be made, and DDD may be the one to be sacrificed in order to get things done.
And it seemed pretty clear to me that they would have to do with the sort of evergreen, software engineering and architecture concepts that you still need a human to design and think through carefully today, because LLMs don't have the judgment and a high-level view for that, not the specific API surface area or syntax, etc., of particular frameworks, libraries, or languages, which LLMs, IDE completion, and online documentation mostly handle.
Especially since well-designed software systems, with deep and narrow module interface, maintainable and scalable architectures, well chosen underlying technologies, clear data flow, and so on, are all things that can vastly increase the effectiveness of an AI coding agent, because they mean that it needs less context to understand things, can reason more locally, etc.
To be clear, this is not about not understanding the paradigms, capabilities, or affordances of the tech stack you choose, either! The next books I plan to get are things like Modern Operating Systems, Data-Oriented Design, Communicating Sequential Processes, and The Go Programming Language, because low level concepts, too, are things you can direct an LLM to optimize, if you give it the algorithm, but which they won't do themselves very well, and are generally also evergreen and not subsumed in the "platform minutea" described above.
Likewise, stretching your brain with new paradigms — actor oriented, Smalltalk OOP, Haskell FP, Clojure FP, Lisp, etc — gives you new ways to conceptualize and express your algorithms and architectures, and to judge and refine the code your LLM produces, and ideas like BDD, PBT, lightweight formal methods (like model checking), etc, all provide direct tools for modeling your domain, specifying behavior, and testing it far better, which then allow you to use agentic coding tools with more safety or confidence (and a better feedback loop for them) — at the limit almost creating a way to program declaratively in executible specifications, and then convert those to code via LLM, and then test the latter against the former!
https://www.amazon.com/Learning-Domain-Driven-Design-Alignin...
It presents the main concepts like a good lecture and a more modern take than the blue book. Then you can read the blue book.
But DDD should be taken as a philosophy rather than a pattern. Trying to follow it religiously tends to results in good software, but it’s very hard to nail the domain well. If refactoring is no longer an option, you will be stuck with a non optimal system. It’s more something you want to converge to in the long term rather than getting it right early. Always start with a simpler design.
It’s not. It’s either 33% slower than perceived or perception overestimates speed by 50%. I don’t know how to trust the author if stuff like this is wrong.
She's not wrong.
A good way to do this calculation is with the log-ratio, a centered measure of proportional difference. It's symmetric, and widely used in economics and statistics for exactly this reason. I.e:
ln(1.2/0.81) = ln(1.2)-ln(0.81) ≈ 0.393
That's nearly 40%, as the post says.
It’s more obvious if you take more extreme numbers, say: they estimated to take 99% less time with AI, but it took 99% more time - the difference is not 198%, but 19900%. Suddenly you’re off by two orders of magnitude.
Still an interesting observation. It was also on brown field open source projects which imo explains a bit why people building new stuff have vastly different experiences than this.
https://fortune.com/2026/01/29/100-percent-of-code-at-anthro...
Of course you can choose to believe that this is a lie and that Anthropic is hyping their own models, but it's impossible to deny the enormous revenue that the company is generating via the products they are now giving almost entirely to coding agents.
If you had midas touch would you rent it out?
https://sequoiacap.com/podcast/training-data-openai-imo/
The thing however is the labs are all in competition with each other. Even if OpenAI had some special model that could give them the ability to make their own Saas and products, it is more worth it for them to sell access to the API and use the profit to scale, because otherwise their competitors will pocket that money and scale faster.
This holds as long as the money from API access to the models is worth more than the comparative advantage a lab retains from not sharing it. Because there are multiple competing labs, the comparative advantage is small (if OpenAI kept GPT-5.X to themselves, people would just use Claude and Anthropic would become bigger, same with Google).
This however may not hold forever, it is just a phenomena of labs focusing more on heavily on their models with marginal product efforts.
Of course at a certain point, you have to wonder if it would be faster to just type it than to type the prompt.
Anyways, if this was true in the sense they are trying to imply, why does Boris still have a job? If the agents are already doing 100% of the work, just have the product manager run the agents. Why are they actively hiring software developers??
And people claiming it's a lie are in for a rough awakening. I'm sure we will see a lot of posters on HN simply being too embarrassed to ever post again when they realize how off they were.
I would have thought sanity checking the output to be the most elementary next step.
A lot of the time the issue isn't actually the code itself but larger architectural patterns. But realizing this takes a lot of mental work. Checking out and just accepting what exists, is a lot easier but misses subtleties that are important.
So vibers may be assuming the AI is as reliable, or at least can be with enough specs and attempts.
"Fixing defects down the road during testing costs 15x as much as fixing them during design, according to research from the IBM System Science Institute."
idk what ya'll are doing with AI, and i dont really care. i can finally - fiiinally - stay focused on the problem im trying to solve for more than 5 minutes.
Like I don’t remember syntax or linting or typos being a problem since I was in high school doing Turbo Pascal or Visual Basic.
I've never had problems with any of those things after I learned what a code editor was.
The differences are subtle but those of us who are fully bought in (like myself) are working and thinking in a new way to develop effectively with LLMs. Is it perfect? Of course not - but is it dramatically more efficient than the previous era? 1000%. Some of the things I’ve done in the past month I really didn’t think were possible. I was skeptical but I think a new era is upon us and everyone should be hustling to adapt.
My favorite analogy at the moment is that for awhile now we’ve been bowling and been responsible for knocking down the pins ourselves. In this new world we are no longer the bowlers, rather we are the builders of bumper rails that keep the new bowlers from landing in the gutter.
Going straight into dev mode with an LLM pretty much always goes wrong - a lot more time is spent in planning and in setting up constraints for how an agent can operate before letting it loose so that once you set it free it can run.
If you keep some for yourself, there’s a possibility that you might not churn out as much code as quickly as someone delegating all programming to AI. But maybe shipping 45,000 lines a day instead of 50,000 isn’t that bad.
The people on the start of the curve are the ones who swear against LLMs for engineering, and are the loudest in the comments.
The people on the end of the curve are the ones who spam about only vibing, not looking at code and are attempting to build this new expectation for the new interaction layer for software to be LLM exclusively. These ones are the loudest on posts/blogs.
The ones in the middle are people who accept using LLMs as a tool, and like with all tools they exercise restraint and caution. Because waiting 5 to 10 seconds each time for an LLM to change the color of your font, and getting it wrong is slower than just changing it yourself. You might as well just go in and do these tiny adjustments yourself.
It's the engineers at both ends that have made me lose my will to live.
Back in 2020, GPT-3 could code functional HTML from a text description, however it's only around now that AI can one-shot functional websites. Likewise, AI can one-shot a functional demo of a saas product, but they are far from being able to one-shot the entire engineering effort of a company like slack.
However, I don't see why the rate of improvement will not continue as it has. The current generation of LLM's haven't been event trained yet on NVidia's latest Blackwell chips.
I do agree that vibe-coding is like gambling, however that is besides the point that AI coding models are getting smarter at a rate that is not slowing down. Many people believe they will hit a sigmoid somewhere before they reach human intelligence, but there is no reason to believe that besides wishful thinking.
That's the nature of all tech, it keeps not being good enough, until it is, and then everything changes.
Like if the only possible issues were property damage, I kind of think it would be here. You just insure the edge cases.
The initial speed is exactly what the article describes, a Loss Disguised as a Win.
Thank you for not using an LLM.
It helps formalise your plan, then creates some code, you review, talk about what you believe to be wrong or asking it why it took that approach, or even telling it to take the approach that you want.
The end result a world of difference, and I feel I have a better grasp of what is going on in the whole application.
I use AI for the mundane parts, for brainstorming bugs. It is actually more consistent than me in covering corner cases, making sure guard conditions exist etc. So I now focus more on design/architecture and what to build and not minutea.
People seem to think that just because it produces a bunch of code you therefore don’t need to read it or be responsible for the output. Sure you can do that, but then you are also justifying throwing away all the process and thinking that has gone into productive and safe software engineering over the last 50 years.
Have tests, do code reviews, get better at spec’ing so the agent doesn’t wing it, verify the output, actively curate your guardrails. Do this and your leverage will multiply.
Not everyone who plays slot machines is worse off — some people hit the jackpot, and it changes their life. Also, the people who make the slot machines benefit greatly.
Sure. But the converse is true as well: consider the case where you don't learn the AI tooling and AI does improve apace.
That is also gambling your career. Are you ready for pointed questions being asked about why you spent 2 days working on something that AI can do in 15 minutes, so be prepared with some answers for that.
What is there to learn, honestly? People act like it's learning to write a Linux driver.
The maximum knowledge you need how to write a plan or text file. Maybe throw in a "Plz no mistakes"
There's no specific model, a better one comes out every month, everything is stochastic.
With all due respect, that answer shows that you don't know enough about agentic coding to form an opinion on this.
Things to learn:
- What agent are you going to use?
- What skills are you going to use?
- What MCPs are you going to use?
- What artifacts are you going to provide beyond the prompt?
- How are you going to structure it so the tooling can succeed without human interaction?
- Are you going to use agent orchestration and if so which?
- Are you going to have it "ultrathink" or not?
- Are you going to use a PRD or a checklist or the toolings own planning?
- Which model or combination of models are you going to use today? (Yes, that changes)
- Do you have the basic English (or whatever) skills to communicate with the model, or do you need to develop them? (I'm seeing some correlations between people with poor communication skills and those struggling with AI)
The current Claude Code setup with Opus 4.6 and their Max subscription (the 100 USD one was enough for me, don't need the 200 USD one) was enough for me to do large scale refactoring across 3 codebases in parallel. Maybe not the most innovative or complex tasks in absolute terms, but it successfully did in one day what would have taken regular developers somewhere between 1 and 2 weeks in total.
I hate to be the anecdote guy, but with the current state of things, I have to call bullshit on the METR study, there is no world in which I work slower with AI than without. Maybe with the Cerebras Code subscription where it fucked some code up and I had to go back to it and fix it twice, but that's also because Vue had some component wrapping and SFC/TypeScript bullshit going on which was honestly disgusting to work on, but that's because you really need the SOTA models. The current ones are good enough for me even if they never improved further.
I never want to go back to soul sucking boilerplate or manual refactoring. It works better than I can alone. It works better than my colleagues can. I think I might just suck, maybe I'm cooked because at this point I mostly just guide and check it and sometimes do small code examples for what I want and explore problems instead of writing all of it myself, but honestly a lot of work was done in JetBrains IDEs previously where there's also lots of helpful snippets, autocomplete, code inspections and so on, so who knows - maybe it doesn't matter that I write everything line by line myself.
But personally: yes and to an immense degree. The excuse “we don’t have the time for this” has pretty much evaporated when it comes to me. I do more than colleagues do and have gotten enough automation working that the AI will be made to iterate and fix its code to my desires before I ever see a line of it. I’ve added tests to entire systems thanks to it, fixed bugs across the codebase, added a bunch of additional quality control scripts and tools, improved CI, built and shipped not only entire features but systems. I can now work on about 3 projects in parallel, even if it can be super tiring.
But hey, I’m also working more on side projects outside of work and nice utilities I never had time for. I don’t really build in public sadly, but it very much is a force multiplier and makes me hate my job less sometimes (everyone has a horrible brownfield codebase or two).
Note: the study used sonnet-3.5 and sonnet-3.7; there weren’t any agents, deep research or similar tools available. I’d like to see this study done again with:
1. juniors ans mid-level engineers
2. opus-4.6 high and codex-5.2 xhigh
3. Tasks that require upfront research
4. Tasks that require stakeholder communication, which can be facilitated by AI
I’d be thrilled if that AI could finally make one of our most annoying stakeholders test the changes they were so eager to fast track, but hey, I might be surprised.
Of course, all of that can be done by humans, too. But this discussion is about average speed of a developer, and there’s a reason many companies employ product owners for the stakeholder communication.
The workflow that seems more perilous is the one where the developer fires up gas town with a vague prompt like "here's my crypto wallet please make me more money". We should be wielding these tools like high end anime mech suits. Serialized execution and human fully in the loop can be so much faster even if it consumes tokens more slowly.
I have like 15 personalized apps now, mostly chrome extensions
But yes, I usually constrain my plans to one function, or one feature. Too much and it goes haywire.
I think a side benefit is that I think more about the problem itself, rather than the mechanisms of coding.
Not sure why we'd want a tool that generates so much of this for us.
Which frankly describes pretty much all real world commercial software projects I've been on, too.
Software engineering hasn't happened yet. Agents produce big balls of mud because we do, too.
Maybe they need to start handing out copies of the mythical man month again because people seem to be oblivious to insights we already had a few decades ago
The technology is accelerating. Hard projects from early last year are now trivial for me. Even AI related tools we are using internally are being made redundant from open source models and new frameworks (eg. OpenClaw).
It feels like we are in the AI version of "Don't look up". Everyone is on borrowed time, you should be looking at how to position yourself in an AI world before everyone realises.
- AI creating un-opinionated summaries of PRs to help me get started reviewing
- AI being an interactive tutor while I’ll still do the hard work of learning something new [1]
- AI challenging my design proposal QA style, making me defend it
- boilerplate and clear refactorings, while I’ll build the abstractions
[1] https://www.dev-log.me/jokes_on_you_ai_llms_for_learning/
But I think this only works is because I have a decade of experience in basically every field in the programming space and I had to learn it all without AI. I know exactly what I want from AI where opus 4.6 and codex 5.3 understands that and executes on it faster than I could ever write.
The real value isn't in "vibe coding" everything from scratch - it's in having the right architecture and patterns that AI can work within. That's why I built VCSK (Vibe Coding Starter Kit) - it gives you a solid NextJS foundation with proven patterns, then you can vibe code features on top of that foundation.
The difference is architectural discipline. When you start with proper auth, DB setup, deployment pipeline, and component structure, AI becomes incredibly productive for building features. When you ask AI to architect everything from scratch, you get the dead-end architectures you mentioned.
I still write the critical business logic by hand, still review every PR, and still understand the codebase deeply. But for UI components, API endpoints that follow established patterns, and data transformations? AI is genuinely faster than manual coding now.
The key insight: "vibe coding" isn't a replacement for engineering judgment - it's a force multiplier when you have the foundation right.
Fortunately, I've retired so I'm going focus on flooding the zone with my crazy ideas made manifest in books.
She unfortunately lost me at the first sentence. I cannot get business value out of that, so I don't do that at work.
What I can do is design a situation where I can get business value out of having the AI agent do one thing where the details are too annoying/boring for me to do. Then I read the PR and I decide if the parts about where the data comes from, how it is massaged, etc are right. Then I scan over the HTML and CSS templating garbage I have no interest in and then I post the PR for review.
This is exactly the opposite of what she's talking about, and it's working great for me and my colleagues.
Likewise those people are guaranteeing "AI"s obsolesence. The parrots need humans to feed them.
When someone vibe-codes a project, they typically pin whatever dependency versions the LLM happened to know about during training. Six months later, those pinned versions have known CVEs, are approaching end-of-life, or have breaking changes queued up. The person who built it doesn't understand the dependency tree because they never chose those dependencies deliberately — the LLM did. Now upgrading is harder than building from scratch because nobody understands why specific libraries were chosen or what assumptions the code makes about their behavior.
This is already happening at scale. I work on tooling that tracks version health across ecosystems and the pattern is unmistakable: projects with high AI-generation signals (cookie-cutter structure, inconsistent coding style within the same file, dependencies that were trendy 6 months ago but have since been superseded) correlate strongly with stale dependency trees and unpatched vulnerabilities.
The "flow" part makes it worse — the developer feels productive because they shipped features fast. But they're building on a foundation they can't maintain, and the real cost shows up on a delay. It's technical debt with an unusually long fuse.
I wonder if there's something similar going on here.
By not going through this process, you loose intent, familiarity, and opinions.
It's the exact same as vibe-coding.
Was this actually a failed prediction? A article claiming with 0 proof that it failed is not good enough for me. With so many people generating 100% of their code using AI. It seems true to me.
HN often bring up that quote pretty quickly whenever author of an article is perceived to be bias one way or another. I’m surprised it hasn’t been mentioned in the comments here.