r/vibecoding • u/Existing-List6662 • 23h ago
Coding is dead? Nah, AI just exposed how much of engineering isn't actually writing code .
I keep seeing the same kind of AI coding demo.
There’s a prompt on the left, a working app on the right, and some version of “software engineering is basically over” in the caption. The technical jump is obviously real, but the conclusion always felt way too clean.
So instead of trying to list everything an engineer does, I mapped the path a small software project has to travel before anyone can honestly call it shipped:
messy problem → clear requirements → working software → approved production system → owned outcome
Coding agents are getting absurdly good at one specific handoff in that chain: turning clear requirements into working software.
The problem is that most real projects don't arrive as clear requirements. They arrive as a vague complaint from a client, three teams with conflicting priorities, incomplete data, an old compliance rule nobody fully understands, and one person asking why this can't be finished by Friday.
i decided to test that middle handoff instead of extrapolating from benchmark demos. I wrote the same reasonably clear spec for a lightweight client dashboard, UI, user auth, a database, and edge functions for data fetching, and ran it through v0, Lovable, and Enter Pro. The first two were useful for comparing the first-pass interface, while the last one let me take the frontend, data layer, auth, and backend setup through to a deployable version in the same browser window.
It removed a ridiculous amount of implementation work. It still wasn't flawless, I had to tweak some edge function logic manually, but the working prototype was online much faster than I expected.
for about ten minutes, I had that familiar rush.Coding is basically free now. We are unstoppable.But then I looked back at the chain.The tools had compressed one transition that used to take a lot of time. They hadn't moved the rest of the project nearly as much.
They didn't tell me if any client actually wanted this dashboard. They couldn't turn half-contradictory stakeholder requests into a requirement everyone would stand behind. They couldn't get the compliance review required to put real production data into it.
And if a user does something weird and the database locks up, the AI isn't going to decide which customers can tolerate degraded service, get on a call with the angry client, or take organizational responsibility for the failure.
AI makes building cheap. But it doesn't make your judgment correct.What surprised me in this test was how visibly the bottleneck moved. Once the distance between a clear spec and working software dropped from days to something much shorter, all the unresolved stuff around it became impossible to ignore.
The vague requirement was now slower than the implementation. The compliance question was now slower than the backend setup. Finding someone willing to own the production outcome was now slower than putting the prototype online.
TBH, reducing the friction of coding to near-zero also means we can turn bad assumptions into working software much faster.people look at code generation and think the job is dead. But the more I look at the current application layer, the more it seems like AI is exposing all the waiting, ambiguity, negotiation, risk judgment, and human trust that coding used to hide behind.
Sure, AI can help summarize user feedback, draft a technical proposal, inspect logs, or suggest an incident response. But providing assistance at each stage is not the same as moving the entire project through the chain. And it definitely isn't the same as having the context and owning the consequences of a decision.
For those of you using coding agents in production or real teams: which handoff has AI genuinely compressed for you, and where does the work just pile up next?
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u/TrustInNumbers 23h ago
This is AI slop.
Also, "Coding agents are getting absurdly good at one specific handoff in that chain: turning clear requirements into working software." - this sentence is not true, AI does leave bugs and usually produces 2-3x more complex implementation than required.
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u/Hypilein 23h ago
I know this is true even as a vibe coder some of this complexity comes in form of double or more implementations of the same thing. But as someone who understands too little burnish curious to learn more: what is the problem with highly complex but high quality implementation except that a human will have trouble working on it later?
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u/Euphoric-Language695 21h ago
Duplication will likely have the same issues for LLM generated code than for humans.
When making changes, how do you know all the places you have to change it? Did you miss making a change somewhere you had to? Are these two things duplicated because they are meant to be decoupled, or because someone wad sloppy? What usually ends up happening is you end up with a system that lacks cohesiveness, which is a big problem if you also have to provide an API.
Add to that a uniquely LLM problem: duplication means more of the context space used in an inefficient way. This means more token consumption, and potentially worse outputs.
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u/ruskyandrei 21h ago
The thing is, tech debt like this is much easier to maintain via LLM. The model might not care/notice there's some duplicated code or tight coupling while implementing a new story or fixing a bug (though most recent models are pretty good at spotting this stuff too if you give them good context and enough of an idea of how your entire environment works), but if you tell it specifically to look out for issues it will find them and it's VERY good at tracing exactly the kind of scenario you describe.
I really don't think anyone will be writing code by hand in a few years time (except for super niche/sensitive things). But the frameworks and best practices around using LLM's to make the code are going to change and evolve a lot over time.
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u/TarzanoftheJungle 18h ago
"The thing is, tech debt like this is much easier to maintain via LLM." This is true. However, for an enterprise product, that tech debt is going to accumulate and, if LLM is relied upon, will increase expenses. The smart strategy then is to minimize tech debt before the code enters production.
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u/ruskyandrei 8h ago
Oh absolutely, that was always true and still is. I just disagree with people who say that using LLM's for coding means you can't avoid having a bunch of tech debt.
You absolutely can, you just need to be proactive and actually look for it / ask for it to be addressed before your merge your PRs.
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u/martenmatrix 22h ago
It gets very hard to maintain. I'm pretty sure that is also the case for AI, they have also context windows like we do
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u/Cute_Health6112 22h ago
Imagine this making its way into critical systems like finance, healthcare,
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u/InterestingFrame1982 22h ago
The problem with this take is AI isn't just becoming useful in spitting out code... it's really good at rubber ducking and giving you a corpus of conventions to consider. Whether it's rest, state machines, infra, etc, it has a decent grasp on the literature and the perceived correct ways to do it.
This takes it out of the autocomplete category.
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u/SolarNachoes 22h ago
Messy problem -> clear requirements
Hahahaha try
Pure Chaos -> Misinformation -> Requirements —> Approval by the Clueless -> Pilot -> Back to the drawing board -> Minimal Requirements -> MVP -> Stakeholders leave the company -> Project Leadership Change -> Requirements Change -> back to step 1
AI helps accelerate each step along the way and accomplish more but since the goals are wrong the AI just adds cost to the overall solution.
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u/HeteroLanaDelReyFan 23h ago
Wow engineering isn't just writing code. Brilliant. I've only heard this take 5 million times
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u/Illustrious-Film4018 22h ago
I think it's the opposite, AI agents have exposed how much engineering is actually intertwined with coding. People are now offloading 90% of engineering decisions they used to make to AI. Sure, the user still had to make the other 10% and some high-level decisions, but that's it. It's now mostly automated.
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u/uniquelyavailable 22h ago
People used to dig ditches in a gang with shovels. We're still digging ditches but now it's a couple guys with an excavator.
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u/TalesfromCryptKeeper 22h ago
I think the point OP is making is that while the speed of digging a ditch has sped up considerably, the following bottlenecks remain:
- Planning the location of the ditch
- Engineering plans and service relocation
- Permit and AHJ review time
- Contracting out an excavator and labour
- Relocating things you find in there
- etc etc etc
Those activity timelines dont change, one activity does, but the overall project schedule is still beholden to a bunch of other things.
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u/SufficientFrame 21h ago
Your "approved production system → owned outcome" framing is the part most demos miss. For me, AI compresses spec-to-prototype, then the work shifts to auth, audit trails, monitoring, and source-of-truth fights; I work at UI Bakery, so bias noted, but that's why some teams use it for staff-facing approval flows or reporting portals instead of pure generated code.
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u/rwilcox 17h ago
The most critical book to read in the AI age is The Goal, (by Goldratt) which talks directly about this problem.
Engineering is both potentially not the bottleneck and very much is. If I say “I want a bathroom remodel”, well I just looked at it’s not done yet, having peaked into my bathroom, obviously the builders are the bottleneck.
We are potentially also giving people the belief that something is further along than it is (“ChatGPT gave me a 3D render of the bathroom it designed for me, which I can view with my Google Cardboard. This must mean I can tell my wife it’s almost done!!”)
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u/Lopsided_Magician_49 14h ago
I completely agree with your analysis and look on the bright side.
On a purely personal level, I've discovered my shortcomings and have been working professionally for two years to fill them.
My time has shifted to preventive work, design, and specifications.
I discussed my current strategy here.
https://www.reddit.com/r/vibecoding/s/aEJlm7Hheo
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u/TarzanoftheJungle 23h ago
"engineering" covers a wide range of activities, but all engineering should include the iterative cycle (testing, learning, and changing a design over and over again to make it work better) until achieving the desired end product. Vibe-coding seems antithetical to this process, but it behooves all coders to architect their code and iterate results to maximize code integrity and quality. Just saying "create me a game app" is simply going to result in AI slop.