The link in the last paragraph provides some data to back up the claim. https://mikelovesrobots.substack.com/p/wheres-the-shovelware... - If the goal is to increase the rate of software production, there isn't much evidence that AI has moved the needle.

Sure, code gen is faster now. And the industry might finally be waking up to the fact that writing code is a small part of producing software. Getting infinitely faster at one step doesn't speed up the overall process. In fact, there's good evidence it that rapid code gen actually slows down other steps in the process like code review and QA.

I'm not sure if people are playing innocent or are really this naive. AI can generate apps that are similar to what people have built multiple times. But it's still a far cry for an AI to generate a brand-new system that has new similar predecessors that the AI has seen before. Besides, a service as sophisticated as Spotify has at least thousands of design points that may require thousands of pages to lay out the detailed spec. Yet we expect that a person magically builds the app with a few paragraphs of prompts? Of course, it's possible for someone to use AI as a helper to generate the the code incrementally, but I'd assume that's not what the author mean by "AI-Generated Apps", correct? If that is exactly what the author meant, the real question is why: what's the incentive behind the probably multi-personal-year effort to replicate an existing system?

The premise is extremely flawed. If users are able to generate their own apps instead of having to buy them, it shrinks the TAM for those apps. If a meatpacker makes its own CRM, it's not going to put it on an app store or try to sell it!

Building software and publishing software are fundamentally two different activities. If AI tilts the build vs. buy equation too far into the build column, we should see a collapse in the published software market.

The canary will be a collapse in the outsourced development / consulting market, since they'd theoretically be undercut by internal teams with AI first -- they're expensive and there's no economy of scale when they're building custom software for you.

davydm nails it. The gap isn't in generating code - it's in everything else that makes software actually work.

I've been building accounting tools for years. AI can generate a function to parse a bank statement CSV pretty well. But can it handle the Barclays CSV that has a random blank row on line 47? Or the HSBC format that changed last month? Or the edge case where someone exports from their mobile app vs desktop?

That's not even touching the hard stuff - OAuth token refresh failures at 3am, database migrations when you change your mind about a schema, figuring out why Xero's API returns different JSON on Tuesdays.

The real paradox: AI makes starting easier but finishing harder. You get to 80% fast, then spend longer on the last 20% than you would have building from scratch - because now you're debugging code you don't fully understand.

Perhaps because, amongst the other solid reasons already posted:

Demand is the real bottleneck: New tools expand who can ship, but they don’t expand how many problems are worth solving or audiences worth serving. Adoption tends to concentrate among "lead users" and not "everyone".

App store markets are power-law distributed (no citations sorry, it's just my belief): A tiny slice of publishers captures the most downloads/revenue. That’s discoverability, not "lack of builders".

Attention and distribution are winner-take-most: Even if creation is cheap, attention is scarce.

The hidden (recurring) cost as other commenters point out is maintenance: Tooling slashes first release cost but not lifecycle cost.

Problem-finding outweighs problem-solving: If the value of your app depends on users or data network effects, you still face the "cold start problem".

"Ease" can change the meaning of the signal: If anyone can press a button and "ship an app" the act of shipping stops signaling much. Paradoxically, effort can increase personal valuation (the IKEA effect), and a lower cost to the creator as seen from the outside kills the (Zahavi) signal.

And finally, maybe people just don't actually want to use and/or make apps that much? That's not to say that good apps aren't valuable, but the ubiquity of various platforms app stores implies that there's some huge demand, but if most app usage is concentrated amongst a small number of genuine day-to-day problem solving tools that already have heavy hitters that have been around for years, an influx of new things perhaps isn't that interesting.

But this is how disruptive innovation works. I recall that even around 2005, after digital camera sales overtook the sales of film cameras, people were still asking "If digital is so good, why aren't the professional photographers using them?" and concluding that digital photography is just a toy that will never really replace print.

Most of ours are internal-only because we don't need or want to release them to the public. Sometimes there isn't much of an UI - they're one-off vibe-coded apps for specialized functions within our organization meant for a small number of people. Beginning to think of the vibe-coded apps akin to spreadsheets with lots of macros.

>> Where is everybody?

The AI businesses are busy selling AI to each other. Non-tech businesses are busy spending their AI budgets on useless projects. Everybody is clueless, and like - let's jump in just like we did for blockchain, because we don't want to lose out or be questioned on our tech adaption.

I guess the author doesn't hang out on Reddit much. A lot of the tech hobbyist subs I used to enjoy are now nothing but a flood of self-promotional marketing posts for vibe coded apps.

This paradox almost doesn't matter.

Non-technical business stakeholders who own requirements for line-of-business apps can generate *working* (to them) end-to-end prototypes just by typing good-enough English into a text box.

Not just apps, too! Anything! Spreadsheets, fancy reports, customer service, you name it --- type what you need into the box and wait for it to vend what took an entire team days/weeks to do. Add "ultra-think really hard" to the prompt to trigger the big boy models and get the "really good" stuff. It sounds a little 1984, but whatever, it works.

Design? Engineering? QA? All of those teams are just speed bumps now. Sales, Legal, Core business functions, and a few really experienced nerds is all you need. Which has always been the dream for many business owners.

It doesn't matter that LLMs provide 60% of the solution. 60% > 0%, and that's enough to justify offshoring everything and everyone in the delivery pipeline to cheaper labor (or use that as a threat to suppress wages and claw back workers' rights), including senior engineers who are being increasingly pressured to adopt these tools.

A quick jaunt through /r/cscareerquestions on Reddit is enough to see that this train blasted off from its station with a full tank of fuel for the long-haul.

There's always the possibility that several really bad things happen that makes the entire industry remember that software engineering is an actual discipline and that treating employees well is generally a good thing. For now, all of this feels permanent, and all of it sucks.

I stumble upon AI-generated websites and apps quite frequently. They look like crap, but they're there.

AI code generation has something important in common with 3D printed houses. Both optimize the easy part.

3D printed houses can only manage the underlying structure of the house. It doesn't cover finishing work which is much harder on the nerves and pocketbook.

Likewise, AI code generation is only useful for the actual implementation. That's maybe 20% of the work. Coordination with stakeholders, architecture, etc are much harder on the nerves and pocketbook.

A lot of discussions around vibe AI coding flaws: awful architecture, performance problems, security holes, lack of maintainability, bugs, and low code quality. All correct, but none of those is matter if:

- you create small utility that covers only features needed only for you. As many researches show that any individual uses only less than 20% of software functionality, your tool covers only 10-20% that matters for you

- it only runs locally, on user computer or phone, and never has more than one customer. Performance, security, compliances do not matter

- the code lies next to application, and small enough to fix any bug instantly, in a single AI agent run

- as a single user, you don't care about design, UX, or marketing. Do the job is only matter

It means, majority of vibe coded applications run under radar, used only by a few individuals. I can see it myself: I have a bunch of vibe code utilities that never intended for a broad auditory . And, many of my friend and customers, mention the same: "I vibe coded utility that does ... for me". This means a big consequences for software development: the area for commercial development shrinks, nothing that can be replaced by the small local utility has a market value.

One of my coworkers (not a developer, but familiar enough with technology) asked about a new “low code” app and wondered if our engineering team used that. My response, I think, provides something of an answer to this paradox: it’s not writing the code that’s time consuming; it’s the requirements gathering. It’s not enough to generate code or even have the LLM build or deploy the software. It’s all the knowledge and experience around crafting an app that’s likely preventing the shovelware. Turns out code is only a part of our job.

Also, why is this the “Gorman” Paradox? He literally links to the article that I remember as first proposing this paradox (though I don’t think the original author called it a paradox). This author just summarizes that article. It should be the Judge Paradox, after the original author.

Vibe coded CRMs do exist. They are not open to the public though, as they are just used by the company that built them tailored to their needs is all.

Same with more complex systems: entire shop systems with payment integration, ERP and all – heavily supported by LLM code tools with a 3-10x productivity boost, just done by the CTO and no additional developers needed. They exist, the shop greets it’s customers and all you see is Vue and Tailwind as the tech stack where Shopify could’ve been. It’s now completely owned by the company selling things (they just don't sell the software).

I would expect that most apps generated today contain at least some Ai generated code, whether through chat completion or agentic use. But, I think such tools currently mostly support people who already are able to create apps.

As others have said, I think a lot of the difficulty in creating an app lies in making the numerous choices to make the app function, not necessarily in coding. You need "taste" and the ability to push through uncertainty and complexity, which are orthogonal to using Ai in many cases.

I work on deeply embedded software that doesn't have what you'd commonly think of as a "UI". So, unless there are bugs or we ship faster or something like that, users will never have any idea how much of our code is AI generated.

But it's happening.

With the help of AI, I made EACL (Enterprise Access ControL), a situated ReBAC authorization library based on SpiceDB, built in Clojure and backed by Datomic: https://github.com/theronic/eacl

EACL replaced SpiceDB at work for us, and would not have been possible without AI.

Already-established top-of-the-market apps will not be dethroned overnight.

For them, it will be a slow death (e.g. similar to how Figma unseated Adobe in the digital design art space).

As for new app markets, you will surely see (compared to past generations) smaller organizations being able to achieve market dominance, often against much more endowed competitors. And this will be the new normal.

I think the answers are fairly simple.

If you're talking about internally developed software: AI generated apps suffer from the same pitfalls.

If you're talking about third-party alternatives: AI generated apps suffer from the same pitfalls.

Bonus reasons: advertising your product as AI generated will likely be seen as a liability. It tends to be promoted as a means of developing software more rapidly or for eliminating costly developers. There is relatively little talk about the quality of software quality, and most of the talk we do see is from developers who have a lot to lose from the shift to AI generated software. (I'm not saying they're wrong, just that they are the loudest because they have the most to lose.)

I've been experimenting with "vibe coding" recently, and it's been interesting.

I was playing around with v0, and was able to very quickly get 'kinda sorta close-ish' to an application I've been wanting to build for a while, it was quite impressive.

But then the progress slowed right down, I experienced that familiar thing many others have where, once you get past a certain level of complexity, it's breaking things, removing features, re-introducing bugs all while burning through your credits.

It was at this point I remembered I'm actually a software engineer, so pushed the code it had written to github and pulled it down.

Total mess. Massive files, duplicated code all over the place, just a shitshow. I spent a day refactoring it so I could actually work with it, and am continuing to make progress on the base it built for me.

I think you can vibe code the basis of something really quickly, but the AI starts to get confused and trip over it's own shitty code. A human would take a step back and realise they need to do some refactoring, but AI just keeps adding to the pile.

It has saved me a good few months of work on this project, but to really get to a finished product it's going to be a few more months of my own work.

I think a lot of non-technical people are going to vibe-code themselves to ~60-70% of the way there and then hit a wall when the AI starts going around in circles, and they have no idea how to work with the generated code themselves.

Oh, AI could hurt the concepts of "apps" badly and still be good for people.

Strong chance that the push in innovation in this space doesn't get reflected in "apps sold or downloaded," and in fact hurts this metric (and perhaps people buying and selling code in general) -- but still results of "people and organizations solving their own problems with code."

I'd be wary about interpreting a simple trend of App Store / Google Play apps without other context. Both are walled gardens, with developer fees and review processes managed by gatekeepers with an incentive and an ability to artificially control the rate of new apps. I would ask: What is the trend of app store review waiting times? What is the trend of rejections? What is the trend of delistings?

I think there is a different way to look at it. My personal experience is that enterprises that are at the forefront of adopting new ways of working, are now much more comfortable taking risks with building applications and insourcing SaaS functionality. The amount of custom software build is actually increasing and the codebase are getting more complex. Is there a price to pay down the road? Maybe.

Another conspicuous thing is the lack of vibe-coded PRs on mature open source projects. Maybe it's because these projects have erected policies limiting AI contributions, but given the high scores on SWEBench, you'd expect _something_ to come of it?

And yet in real world use you get stuff like https://github.com/scikit-learn/scikit-learn/pull/32101 (not to pick on that particular author since I pulled it completely at random. But it's notable that this also was not a fly-by-night PR by a complete newb. The author seems to have reasonable credentials and programming experience, and this is a fairly "self-contained" PR limited to just one function. Yet despite those advantages, the LLM output couldn't pass muster.)

Meta spends enormous amounts of money hiring AI experts instead of just using AI to improve its AI. AI just isn't there yet.

But when AI can be used to improve itself, that's when things get interesting.

Gemini-CLI made me linux GCODE viewer totally on its own. It could view the result itself, so no feedback needed. I only provided test gcode-file.

https://github.com/timonoko/Plotters/blob/main/showgcode_inc...

Steve Jobs once came to speak at my company when he was running NeXT. Almost nobody came to the talk, in the company cafeteria. The CEO of our company had to make an announcement on the PA encouraging folks to come. Finally, about 20 people (out of ~750) showed up.

He started talking aobut Objective-C and how it was 10x more productive than other programming languages and how easy it is to write good applications quickly with it. Someone shouted out the question: "If it's so easy and fast to write applications, where are all the NeXT killer apps?" There was no good answer....

The most AI gens Ive seen was in r/selfhosted, with some ppl from the sub complaining about the trend (with mods deleting the complaints).

https://tools.simonwillison.net/

If it is so easy to make a product, then why would you go to the trouble of marketing it? A competitor could wipe out your market in as little time as you spent yourself.

My bet is that we will see much more software, but more customized, and focused precisely on certain needs. That type of software will mostly be used privately.

Also, don't underestimate how long it will take for the masses to pick up new tools. There are still people, even here on Hacker News, proclaiming that AI coding assistants do not offer value.

They are out there. I bet many are just trials (people testing it out) and many more are fixing the bugs in the AI-generated code.

Not only is this wrong on multiple levels (there are lots of new ai-slop apps flooding the internet, and marketplaces e.g. steam has ~10k games marked as using ai), but it's always cringe when someone names something after themselves like this.

I generally agree. AI is far from being good enough to ship entire apps on its own. So for now it's more like a kind of "power templating assistant" for devs who already know what they're doing

that said, the article says "Why buy a CRM solution or a ERM system when “AI” can generate one for you in hours or even minutes"

and I'd say that it's also wrong to see it that way, because the issue with "rolling your own" is not so much shipping the initial version, than maintaining and developing and securing it over time. This is what takes a lot of manpower, and that's why you usually want to rely on an external vendor, who focuses exclusively on maintaining and updating that component, so you don't have to do it on your own

If somebody is going to have the conceit to name an argument after themselves - and imply some sort of equivalence with a great physicist they could at least make some effort with the details.

How do they know there aren't apps and services with significant AI contributions? Any closed source app is by definition a black box in regard to the composition.

We decry AI slop but this article shows no AI is needed for that.

I built and grew a SaaS from $0 to $200k ARR in the last year. Putting aside that AI is at the core of the product (we replace some low-skill human labor in a super niche market), I never could have built this product in this timeframe without AI. So when I got laid off from my big tech job six months ago, I'd have just gone to look for another job.

I also know multiple non-technical people who have built little apps for themselves or for their company to use internally that previously would have purchased some off the shelf software.

I dunno, I'm working on a TUI for a Claude Code like UI in Clojure right now, and after examining all of the available libraries and tools, just had Opus work with me to generate a new library (with tests) that does what I want.

Works great, and it's easy to add features to (as evidenced by the dozen or stuff I've added already).

Is that a "new library"? It certainly doesn't show up in any statistic the OP could point to.

Where we might see AI-generated code having a bigger impact is in internal tools, which usually just need to "work" but not be production grade. CRUD type apps that replace spreadsheets and that sort of thing.

Even if we solve coding part by replacing it entirely with AI there are still many other factors involved making software. AI alone simply is not native to the procedure we have to release software

Oh perhaps the same reason we're not seeing AI impact across the entire economy? Because everything is demand limited, and AI can only increase supply.

The big problem for demand is wealth concentration, ie. the problem is money. Lots of people not having money, specifically. So unless AI actually becomes a way to transfer money to a great deal of people, it won't move the needle much. And if it becomes an actual reason to do layoffs (as opposed to an excuse, like it is now) then it will actually have negative impact.

Invisible. Trust me.

At this point, the question we should all be worried about is what is going to happen once the biggest investors see and internalize these articles? Will the economy withstand the collapse of the AI industry and temporary damage to adjacent tech sectors or will this combined with the dodgy loans taken by Meta/Amazon/Alphabet pull the wider economy into a recession?

Simply put, the tools are not at the level the grifters want you to believe.

You'll always find someone claiming to have made a thing with ai alone, and some of these may even work to an extent, but the reality is that the models have zero understanding, so if you're looking to replicate something that already exists (or exists in well defined, open source parts), you're going to get further than if you're thinking truly outside the box.

Then there's library and framework churn. AI models aren't good with this (as evidenced by the hours I wanted trying to get any model to help me through a webpack4 to webpack5 upgrade. There was no retained context and no understanding, so it kept telling me to ado webpack4 things that don't work in 5).

So if you're going to make something that's easily replicated in a well-dpcunented framework with lots of stack overflow answers, you might get somewhere. Of course, you could have gotten then yourself with those same inputs, and, as a bonus, you'd actually understand what was done and be able to fix the inevitable issues, as well as extend with your own functionality. If you're asking for something more niche, you have to bring a lot more to the table, and you need a fantastic bullshit detector as the model will confidently lead you down the wrong path.

Simply put, ai is not the silver bullet it's sold as, and the lack of app explosion is just more evidence on that pile.

This is the biggest red flag for me. In contrast, as Ruby and Rust were adopted they generated many tools, libraries, frameworks, and full applications which answered unmet needs in clever and interesting ways. What has generative LLMs produced? Apparently it is nearly all slop or minor iterations. In some cases there are significant bug fixes accumulating which is about as good as it gets. Still many powerful tools coming from the generative LLM wave, but probably worth waiting for best practices to emerge and pricing to stabilize.

every couple months I try my luck with a very sophisticated prompt akin to: "make me a web application that generates $1k of profit a month. do not hallucinate, it's ok - family friendly, ultrathink or go to jail"

they are all around us, polluting our world with as many fake videos, lies, scams, and bs as it can be buggy-whipped into generating.

The industrial age was plagued by smog. And so shall be the Information Age.

I can't help but notice (yeah, I know, lots of stupid takes start with this) that many articles about vibe coding and AI enablement describe how you can write an agent which will help you vibe-code an application which is wrapping some model to generate even more applications that largely do more of the same.

Nobody is writing a tutorial on how to use ML to make a robot to fold laundry. Instead, it's you spend tokens to spend more tokens to spend even more tokens. At this point, the word "token" starts bearing unwelcome connotations with "tokens" from NFTs and Brave's "BAT" fads.

Hmm. I noted this paradox here several weeks ago.

Can people spot them?

https://countrx.app/ is something I vibed in a month. Can people here tell? Sure the typical gradiënt page is something to spot, but native apps i think are harder. I would love to see app store and Google Play Store stats to see how many new apps are onboarded.

Looking at distribution channels like Google Play, they added significant harder thresholds to be able to publish an app to reduce low quality new apps. Presumably due to gen ai?

Edit: Jesus guys, the point I'm trying to make is that there are probably a lot more out there that are not visible... Im not claiming i developed the holy grail vibe coding.