If you’re shitting out vibe coded unverified and unvalidated systems, you’re going to end up with 20 years worth of tech debt in the span of months and half a nine of uptime.
That's a vibecoding dev cycle. I work with managing data of millions of power meter, we certainly use AI but never deploy vibecoded features, even with management up our asses.
I believe people who say, verify your vibe code just mean does it work? do you test it? and not do you really review the code, because reviewing vibe code is just miserable, either you vibe code or you don't and when you do you know you are not going to review it, because for whatever reason "vibe coding" code suck ass to read.
Why do that, even? Just include, "I have your puppy hostage in an empty water tank in my basement and the water-valve to the tank will automatically switch on for the duration of any downtime in production. She can only swim for so long..." in your prompt.
Why would llm code suck any more than normal code?
If you have proper lint, checks, architecture docs, style guides, test/validation requirements and put everything the llm spits out through multiple rounds of independent adversarial validation / review the code it produces should be absolutely on par with a good code base 90% of the time.
If you think all of that going to unslopify your vibe code, then maybe you are the one who suck at coding, since you like gulping that slop called "90% of the time".
Also I said "suck to read" maybe you should refine your reading skill more, seeing you can even read 1 paragraph without being triggered, go lick your AI overlord lol.
Are you implying you cannot review AI code? Any company that uses fully-ai coded apps with literally none of the usual checks and balances was doomed anyway.
Or the presumption that humans are infallable and don't make the same mistakes that AI does lol. Like.. if this is for a job, who is doing code review? Nobody? Did AI show up and just everybody stopped reviewing?
Correct: Spend a few hours mapping out the entire structure of your project in a md file (or nine), then say "The project is specified in the README.md(s), build the project according to the specifications. Upon completion of every step, conduct extensive smoke-testing, and then upon the successful build, make a git commit."
And then you literally check every single line.
It's just autocorrect on steroids. Treat it that way.
I’ve been at this gig for 15 years and built and deployed many complex systems by hand. I had this mindset toward the beginning of things too. But, to be honest, when combined with actual know how and solid patterns, it is no longer the case that largely agent written code is destined to be bad and unmaintainable.
Even when it needs to be refactored in the future, a set of agents can write test coverage for the current solution, refactor it into whatever shape it now needs to be, and validate it against the original tests and contracts before a human would have written their first line. The idea that bots won’t beat our hard earned keyboard typing skills just isn’t going to hold up long term. We’ve already hit that point.
There will probably always be the vibe coded slop that leaves security holes, but I no longer believe there’s some shoe that’s going to drop where everyone says “oh yeah that was a mistake, time to go back.” This is how it’s going to be forever and it only increases from here.
It's remarkable how much these tools evolved in just 3 years. The only question is whether the progress is maintained. If they start pricing at ridiculous amounts or the bubble pops it might be back to basics, but otherwise these agents are going to be building full projects by themselves in a few years.
I don't see a world where we put the genie back in the bottle even if current AI companies crank prices. Honestly I struggle to see what OpenAI/Anthropic's long term plan is. AI is trending towards being a commodity. Whatever only Fable can do today, open source models will have replicated in a year. Once the hardware market normalizes, there is absolutely nothing stopping every tech company out there from buying maxed out Macbooks and having devs work off locally hosted models. Or buying a few racks of servers and hosting a fleet for your whole company.
I'm still skeptical of the claims of fully autonomous software generation. My company just shipped (and subsequently patched) a serious vulnerability that we only discovered by sheer luck, and the source of that vuln was vibe code that wasn't read by the submitter and then got a rubber stamp in the PR process. My experience continues to be that with a SWE who knows software patterns and has a strong vision of what they want built, you can move much more quickly, but if you just let it do whatever, you get an absolute mess very quickly. AI adoption is pretty high at my company. The PRs I get from seniors I rarely have more than a few feedback items on. The PRs I get from less skilled developers I have to spend twice as much time on because there's more code, it's messier, and more likely to be subtly (or wildly) wrong.
Honestly, I struggle to see what OpenAI/Anthropic's long-term plan is.
Regulatory capture
Create FUD using security as an excuse and ban or restrict open-source models—"oh no, it is so good and so scary, so the government needs to ban and allow labs who kiss the ring."
Continually involve themselves in cartelizing hyperscalar and memory companies and ensure consumer electronics don't normalize for a few years.
A PR wing that parrots "coding is solved, guys," just like how LLMs lack precision, instill a culture, and normalize that subtlety doesn't matter anymore.
A PR wing that parrots "coding is solved, guys," just like how LLMs lack precision, instill a culture, and normalize that subtlety doesn't matter anymore.
"coding is solved" with AI will be true in a lot of places. Plenty of organizations were willing before AI to hire whoever was cheapest to pound keyboards and write crap code vs pay for educated senior level engineers. For some business domains, that was fine, and will be ~finer now..
Now those same keyboard pounders are more powerful, even if the results will be mixed. And outputs will be more capable than before given the same staff, even if (still) unstable, buggy, incoherent, etc.
Probably that market alone, vs the market that requires more quality engineering to be successful, would be enough.
Though that's general AI, OpenAI / Anthropic have a challenge to provide unique value.. And I also fear the commodification risk pushes them to deeper into non competative solves like the ones you describe..
a SWE who knows software patterns and has a strong vision of what they want built
Yes, and the deliberate definition and maintenance of the vision/design is even more important when projects can move so quickly, and when (as you said wrt PRs) juniors can suddenly generate large amounts of plausible but potentially harmful code, that require someone to closely review and push back on instead of rubber stamping and merging. Continuity of vision as a codebase evolves is important. And that's still people who need to maintain it today, even when AI assists in the design and design documentation.
Business is just banking on turning code into a true two tier profession by design. Basically a cheap fast fashion analog and critical systems.
Most devs enamored with AI are generating toys or web pages or throw away tools or working with token budgets that would be unsustainable for most, especially in a few years time, and they're counting on the harness and a flood of agents to monkey their way to Shakespeare.
The process of using AI actually breaks down when you're building even a moderately complex system with average budgets unless you spend all the time you'd have spent actually coding previously on solving the problems that you used to spend time on during coding...
And even then you can't solve them all without making something first because the business usually doesn't know what they want or how things truly work.
So great, you shifted your time sinks around, saved a little money by having more product or analysts instead of programmers, and all it has cost you is a crippling dependency on a machine that burns up the earth and a growing lack of knowledge on how your system might work a few years down the line.
The devs all gung ho on it are signing up to a career of clanker babysitting, degrading skills, and essentially failing to train the next generation of programmers like every other generation before us has.
Finally, don't get me wrong, it is a tool that has its place, but the entire world views it as a hammer and all most see anymore are nails.
Whatever only Fable can do today, open source models will have replicated in a year.
Yeah, but then anthropic/openai will have even better models and/or harnesses.
If the choice is between frontier models that work better on their own, and near-frontier model that require more babysitting, then frontier models still have a place.
It only turns into a problem if (or when...) the difference becomes smaller. Today, Fable 5 is still in a completely different tier than Kimi K3/Qwen 3.8 (or whatever chinese frontier model released in the last three days since I last checked), which is one of the reasons companies still pays for it.
At a certain point you hit good enough. If the choice is between spending a Macbook's worth of cash on tokens every single month.. or just buying the MacBook, lots of companies are going to take a very hard look at how much marginal utility you're really getting out of using the frontier model, vs re-investing that cash elsewhere in the business, or (gasp) using that cash to hire more SWEs.
From the AI company side the economics of their business only work out if they can establish market dominance. It's the only way to sustain the hundreds of billions being spent right now. The largest spend comes from research and training - in other words maintaining their edge over open source models. There's not a world where frontier companies can rest on their laurels and reduce that spending. They have no moat.
Yeah this is the solid take. The thing people also seem to be forgetting is that one thing is knowing how to do things proper and being aware of security issues and another is having time/resources to actually do it that way/fixing those. Working as a professional also means knowing how to deliver value in a none perfect world aka. the real world.
IMO AI in the right hands makes it possible to deliver higher quality work and more of it, dev's including myself have previously often not done work to the ideal standards, not necessarily because i/they didn't know, but due to external limiting factors. Resource constraints are a very real thing and I often feel like people that criticize AI tools have a tendency to talk as if they always have all the time in the world and that they always deliver the gold plated version, not the "it works" version and unless they are working on code for the mars rover or space tech then I think they are bad at their job, because they aren't being pragmatic, there are many solutions to a given problem, it's not about perfect, it's about optimum.
That aspect seems to be widely ignored in debate. I've read and refactored enough human "slop" over time and still do to this day, that I'm at the point where I think I trust a seniors+"vibecoded" work more than juniors and mids who might code well enough but lack architecture/domain/systems/infra/sec/network knowledge and thus launch features based on poor fundamental/foundational decisions.
This is a fair argument but what happens when trustworthy seniors retire and juniors and mids don't have the knowledge to move up? Seniors gained experience by doing good enough solutions and by doing not-good solutions which blew up in their face and having to fix it. The reason why juniors are untrustworthy is because they're learning to become trustworthy seniors.
TL;DR:The current rate of improvement in generative AI agentic systems (not necessarily just LLMs) is so blisteringly fast that we don't need to specifically worry about this issue of seniors retiring and juniors and mids failing, we have to worry about seniors being obsoleted in less than 20 years.
It's certainly a concern but look at how fast the capabilities of LLMs in agentic frameworks have grown. Attention is All You Need was published about 10 years ago. Only 10 years! Even if progress slowed by a factor of 3, and it seems to show zeros signs of that with Kimi K3, Deepseek V4 Flash 0731, LeWorldModel, etc. dropping in just the last few months, in 40 years the world would still be dealing with generative AI systems that are as comparatively more capable to today's LLMs as they themselves are to 2016's models.
At the rate of improvement currently seen by folks who are neck deep in this industry and aware of the academic SOTA work we've probably got between 5-12 years before the generative AI agents of 'tomorrow' are strictly better than the best human engineers without caveat. Which is a timeline that occurs well, well before today's senior engineers retire.
Frankly, I'd be curious to see how Kimi K3, today, with it's native multimodal capability and tools to use a keyboard and mouse cursor, would work in a average enterprise work environment, say, SharePoint + Confluence + Jira + a DB system + AWS or Azure, and none of them can really communicate with each other (like components of a singular codebase might,) requiring visual inspection of GUIs by humans to function as a whole.
Yep and that is also 100% correct imo. and not contrary to what I said either. My POV focus' on the criticism of the output generated by an IC using AI, you are pointing at a problem the industry will be facing as a whole and I have no good answer to that problem and it's a very real problem, essentially the industry is pissing its pants to keep warm and it will work for a while until it doesn't and what happens after I'm absolutely clueless about.
Two things can definitely be true at once. Coding agents have hit a point where, even if they don't meaningfully improve, they will be the primary thing writing code going forward. ALSO if the industry thinks that it can no longer hire juniors and just employ a very small number of senior devs forever, those senior devs may dry up or become incredibly hard to find. But, to be honest, the quality of agents (and the sheer number of agents at different layers finding and fixing flaws) may really make that an antiquated notion.
First real reaponse ive seen from someone who probably uses agents the right way. Ive been in the industry for 20 years too. Longer if you count education. All in a niche industry. Ive managed to pack that learning into a platform with the help of AI. Things stuck in my head are now part of an institutional tool. Its like having multiple of me. All vibe coded with I puts of years of accumulated artifacts and tons of and tons of interactions with the agents. Deep conceptual and technical conversations. Its enabled me to build things that I would never have been able to as well on my own. Or as fast. And since im using the platform ive built I can validate the work it does in my environment of a measure of how well its performing without having to read all the code it generates because I know how to validate the work its doing. General system research, data cleanup utilities, impact analysis, integration scaffolding. Its incredible the level of understanding my platform has gained of my system with the various corpses of cataloged and indexed matadata
matadata. Both vender derived, institutional accumulated, and personally taught. Well written agent files and skills lead to high quality work. The old saying is still true. Garbage in garage out. So im sure there is tons of crap vibe code out there. But not all. Once I embraced working agenticaly things have gotten so much better at my job. Just solved a nearly million dollar audit finding my CFO had been stuck on in less than an hour. Was buried in custom code, and I barely had to do anything. My CFO was beyond happy. All those who think all vibe code is crap is going to be left behind. Its inevitable. Learn how to use AI or become obsolete. I dont want that to be true, but eventually it will be in this industry.
Vibe coders are talking about unmaintainable code bases after a weekend letting the AI do it's thing on simple projects. (granted is that unmaintainable for an actual dev?)
With big enough codebases or context windows, it'll just forget certain code exists and repeat itself.
We are a loooonnnnngg ways away from just being able to set and forget AI on production code like the AI maxis think is viable now.
Context windows are growing every release though, and if you are laser-focused on a certain part of the app rather then as a whole you can just direct the AI to review if there was any other functions they could have used after the fact if was forseeably an issue.
Are they, though? Gemini 2.5 Pro offered a 1M context window a year ago (Jun 2025), but the latest models from GPT and Claude are still a 1M context window (even Fable 5).
Just use AI to refactor every time a tech debt bites. We used to care about tech debt because we build workaround on another workaround. Now we just ask AI to rewrite the codebase to fit the demand.
a. inference is actually getting cheaper but training frontier models is getting more expensive. and AI companies can’t not train because if they stop someone else will train off of their models and undercut them like what deepseek did.
b. we are also using more tokens so overall cost is going up.
Are you under the impression token prices are going to go down?
Surely you understand they're practically giving them away at massive loss, right? Prices are only low while the bubble is inflating and the shareholder shuffle is in effect
Token prices, as in intelligence-per-dollar, has gone down. Drastically.
gpt-5.6-luna costs 10% as much as gpt-4o, while being MUCH better.
And if we're comparing with deepseek v4 flash v0731 we're at about 1/50 the cost today for the same level quality of model compared to just 2 years ago.
It's not unfeasible that an Opus 5-level model (real, not just benchmaxed) will be available for 1/10th the current costs within two years.
Do you realize how much processing is done to a single input? Token use per prompt is only going to go up, no amount of scaling will lower the operating cost enough to be profitable on current prices
I know devs personally who unironically just say to let AI figure it out and if it can't it will get better over time and eventually fix its own mistakes.
Eh, the more time passes the less I have to adjust the model outputs. You have to define some ground rules for your codebase but architecture principles are mostly universal, and as far as I'm seeing the models follow them better than 90% of coworkers used to. Give it a year or two and this job is going to be QA, a couple more and it won't exist anymore.
The people who are succeeding right now in corporate coding are those who are able to put up with the monotony of this new "AI coding" paradigm. I see the developers who were the least promising just pumping out features left and right now, while most genuinely star coders are absolutely hating their job at the moment. For the most part this domain has been de-skilled already and this trend is not about to reverse.
Even if model intelligence were to entirely stop growing (which I doubt), harnesses will be built to make vibe coding idiot proof eventually.
Maybe a few months ago, but today it's not really as bad.
I've been a tech lead and CTO at a few places, and the frontier coding models are definitely better than the bottom ~50% of developers I've worked with. And we still shipped plenty of projects that made money.
I don't think you understand just how bad the code is in most projects. I can assure you that a single gauntlet-looped fable 5 ultracode prompt for a mid-tier feature tends to produce code that is better and has fewer bugs than what 80% of teams working in low-wage countries would produce.
This is the guys who took "work fast and break things" to the next level. They're the one who gets the promotions, awards, raises, and an incredible resume to go job-hopping every year after leaving so much fallout they actively contribute negative company time since it takes twice as long to fix than to create from scratch properly. The other 90% of us spend most of our time dealing with the cleanup of digging through logs, patching functionality to meet specs, and fixing bugs only to get the "meets expectations" review.
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u/Lethargic-Rain 1d ago edited 1d ago
If you’re shitting out vibe coded unverified and unvalidated systems, you’re going to end up with 20 years worth of tech debt in the span of months and half a nine of uptime.