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- Hacker News
- I think of it this way. If AI could build the product alone, then why would it ever be worth more than the tokens you spent on it? I think this would hold true 99% of the time. It's literally like asking someone "Can you pay me $1m for new gadget? I hired this other dude for $20p/h and he made it in a week". It simply doesn't add up.
The 80/20 rule still stands.
by Philip-J-Fry - The is the crux of why I don't get the vision that with AI everyone will be entrepreneurs. If everyone can start a successful business simply because of AI, why wouldn't their potential customers just do it themselves?by _heimdall
- Airlines capture almost none of the value they enable. The business man taking a business class plane trip to land a business deal doesn't pay more or less for the flight based on how big the deal is. If the tokens enable software worth a million dollars or zero dollars, it's all just tokens to the seller.by fragmede
- I prefer the article's own "The Prototype Isn't the Product" title, since that seems to better capture what's being discussed.
With AI, it can be ridiculously quick to vibe code throwaway prototypes and personal use apps where "seems to mostly work" is the quality criteria, but what remains harder and slower is software engineering - building things where quality and maintainability are critical.
There is much less discussion of this - we mostly see people wanting to show off some flashy demo they just vibe coded, not discussion of how useful AI was, or wasn't, in working on some large legacy codebase or whatever non-sexy non-trivial task their day-to-day professional work consists of.
You are not going to get a 6am sunday call at home because your vibe-coded 3-D game prototype stopped working, but you surely will if there's a bug in the 24x7 production system that was just updated.
- The fear in most people is not of losing the job, but of losing their value in the market as ai raises the floor of capabilities of other people competing for the same job.by chii
- Because losing their value in the market means they'll lose their job.by amelius
- I'm far above that floor so I'm not worried.
What worries me is the job is transforming into something that a different type of person than me enjoys.
by zulban - I think you've misidentified the fear.
People worry about losing their jobs in a market that hires fewer people (because AI makes doing more work easier) but not about losing their jobs because they can't keep up.
The whole point of AI is that you can't meaningfully fall behind. If you ever did, you could ask AI to get you up to speed again. The tech that automates work and effort is not something you need work and effort to use.
Similarly, I don't currently have a bread machine. I am not falling behind in baking skills to people who do, because I could always, at literally any point, get a bread machine and hit the exact same productivity level.
by Planktonne - I think the main fear is that the (assumed) increase of productivity means that employers may require less employees, resulting to less positions in the market. But I don't think that expertise is no longer relevant or we see getting more people hired with less expertise because AI. If anything, there is this talk about companies not hiring junior engineers as much. Moreover, as getting the expertise could become harder, it could become more of a moat.by freehorse
- What I actually fear is more subtle: I already do a fair bit of project management and technical leadership. I could do more. Sure, I'd miss the coding, but I also enjoy a lot of the stuff around it.
But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time.
Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9.
By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business.
Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs.
And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?
by ekidd - I think we have played this game long time ago. If products were a question of a single request then outsourcing companies would dominate over product ones.
I think a lot of product development happens in the itearations after the intial prototype/MVP and so on. It is not only the technical aspect to it, you need to spend time on a problem deeply understand what are the root causes of pains and address them in your product, both from UX and also from technical perspective.
People were able to "prompt" a product even before to an outsourcing company, but they'd rather pay the fee to a product company because of the expertese they have gained through out the years and all the users they've spoken to.
by imilev - I think this is intuitively true, and I've noticed this even as someone who doesn’t have an engineering background.
In my LLM coding experience, I’ve had this happen numerous times, for instance when building a card game.
The LLM does a great job for a while, but adding special event cards becomes impossible because it imported a standard 52 card deck library instead of having a flexible data model with card objects.
I suppose this is knowable and you could tell an LLM to do this from the beginning. It's just that when you view making a game (or any piece of software) as just a throwaway thing and don't start with deep thought about the implementation as an experienced engineer might, you don't think about this.
I think part of the issue with AI-generated writing, code, or anything else, is that it robs us of the thinking that used to be baked into the process of making stuff.
For my game, if I had to build it from scratch, I surely would've sketched out the kind of data model it requires to not waste my time.
I do think some of this stuff is overblown though. It's true that LLMs don't create production-grade stuff, but not all software needs to be scalable, fast, and easily maintainable.
This might be required for infrastructure or apps designed to (hopefully eventually) be used by millions.
But it's not true for a minor utility, like if I want to build a meal-planning app for my family. I don't care if the app is the fastest it can be or if the data model could eventually support a feature to configure dietary preferences and allergies or to plan the meals for Google's tens of thousands of employees.
There was a viral article a few years ago around how software could be like a home-cooked meal. And AI enables exactly that. A home-cooked meal doesn't require culinary perfection, but to feed the family and, maybe more importantly, be a gift of labor to the other person.
by FinnLobsien - I find the base logic of this post flawed - it can make a prototype, but what about everything after the prototype? Well, um, then you work on those things too? It seems like the premise of articles like this assume that using AI means whatever you can one-shot from a four line prompt. There are obviously a lot of issues with it, but this just sounds like uncritical self-justification rather than any fundamental insight.by kouunji
- That's funny, I found the article to align exactly with my own personal experience of working with LLMs in cobdebases with poor test coverage, uncertain requirements and non standard production deployments. As soon as you are off the happy path, they start making really bad assumptions that break in production. Even the smartest LLMs haven't come close to what I'd consider good architecture for those environments.by mortalapeman
- There's going to be plenty of work helping other companies make some sense of their vibe coded efforts. The value of individual projects might decrease, but there will be a lot more of them. And without help they won't actually work out all that well.
I talked to a company that does not employ software engineers that were doing some things with Claude Code a few weeks ago. Insightful comment: I want that person to do what I hired them to do, not mess around with code. What they were trying to do was a bit out of their comfort zone and they were smart enough to realize it.
There is going to be a lot more of this. What's very real is that companies selling one size fits all products to others are going to have a much harder time selling because everybody is going to expect a thing tailored to them because they now can. Delivering those things is still going to be work that needs to be done. A lot of work actually. People with experience building things with their own hands have an advantage. And if those people also understand the domain in which they are trying to do stuff, that's a double advantage.
Like always, most people haven't got a clue about what they actually need. Figuring out what people need (consulting) and then delivering it has always been the job. But you might be able to take on a few more customers now. There won't be a shortage of those once people figure out software just got cheaper.
- > Insightful comment: I want that person to do what I hired them to do, not mess around with code.
What does that mean? Who is that person who should not mess with the code?
by watwut - The litmus test is this: do you enjoy consuming AI-generated stuff?
I don’t. Whether it’s written text, or video, audio, restaurant menus, clothing pictures, documentation, airport control, ads…
I do think there’s value in LLMs but as a sort of better search engines and q/a machines.
by sdevonoes - You enjoy good AI all the time you just don’t know it.
Nobody enjoys bad 1-shot AI.
by cyanregiment - To be fair. I hate slop as much as any other person. But for me the issue is poor quality rather than just the fact that it's ai.
I love good ai stuff even if it's obvious it's ai
by kuboble - People are spending billions of dollars a month in aggregate because yes, they do like it.by zulban
- I consider AI generated stuff as a raw material , never as a product ready to be consumed by any public clientsby melezhik
- > do you enjoy consuming AI-generated stuff?
you might be in the minority, because a lot of people don't care for low quality, as long as it "works". The same effect happened with consumer appliances/products, software and fast foods.
There's a minimum standard/threshold that must be met, but sooner or later, the AI generating these output can meet them (even if it doesnt right now). Therefore, at some point in the future, ai output will dominate man made ones, whether you want to or not. You will still have a chance at purchasing manually created product/service, just like you could today with handcrafted goods (at exorbitant prices compared to the cheap manufactured goods available).
by chii - I have two different experiences with LLMs.
First one is that I have vibe coded two different projects for work, one is a slack plugin which basically pushes alerts to a channel based on a people roaster and another one is a gmeet plugin to add a talking timer for each participant in the meeting. I used Opuse 4.6 and both were written with a node backend and I actually don't have a good understanding on how it works, but both works without any issue and are deployed in gcp. However, it was not a one shot prompt, but a very detailed step by step plan created for both apps and then executed including adding tests. Both of these apps are used for as internal team tools and we used to pay 20$ per person (back then the team was 5 people, but now it has grown) for the slack plugin before and after we built our own version of it, now it's just 0.07$ per month and that's the gcp infra cost.
Second one in using LLMs more like a coding monkey for work. I design the architecture, discuss it with my teammates, we nitpick and refine the approach and agree on how it should be implemented and then we create detailed JIRA tickets and feed these tickets to LLM (in this case again Opus). However, again first it must create a detailed implementation plan and only after it was reviewed and approved by an engineer it is being implemented.
If someone wants a quick working MVP, then I think the first option is the best to even test if it's possible to have a proof of concept. However, if someone wants to build a long lasting product, then throwaway that MVP and do a proper plan thinking about scalability and clean architecture from the start and use LLM as a code monkey. In my experience LLMs are still pretty bad at making better architecture and clean code decisions that is maintainable by humans in the long run.
- This mirrors my experience. Vibe-coding one-off, low-risk apps, it's been fantastic. Trying to take the same approach when implementing features in massive legacy codebases has been an utter nightmare.by rideontime
- When you have built your working product try this prompt:
- Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
Then cry as the ai reveals that it didn't actually do anything close to what it said it did. I call this my million dollar prompt, as in it teaches you just how much you are being fooled.
by tim-projects - I get better result when I say $1 billion dollars. /sby xnx
- > Then cry as the ai reveals that it didn't actually do anything close to what it said it did.
If using AI to generate code, you told it generate some code, so it did. No amount of "You are an expert developer" or "Make no mistakes" will change the fact that it just generates tokens and has a limited thinking budget.
Adversarial review loops of N parallel agents looking at whatever characteristics you care about will make it better, even if it will Nx the tokens you need to achieve something, though in general it will be cheaper than N human reviewers (which you might not have).
Obviously you shouldn't forget about traditional tooling for formatting and linting, as well as static code analysis and having test coverage that approaches 100%. It might be annoying to do manually, but AI has no issues with refactoring code to make it more testable and eventually will catch some issues that way. It's never going to be perfect in the 1st attempt.
> Review the codebase is it production ready? I'm selling it for $1million dollars can it meet that standard.
This is far too vague though and will never be good, even sans AI. When it comes to AI, it will nitpick the fuck out of the codebase if you ask it to do and sometimes jump around between different approaches because neither is actually a good fit for the problem space (there might not be a good fit at all, just various tradeoffs). If you still ask it to find issues and there's nothing obvious, it will just make shit up in pursuit of being useful (RLHF).
When it comes to people, you will get various standards, from "It looks like Java, ship it" to "You should rework a quarter of your codebase because I read about this one approach in an authoritatively written book that you should also follow because I view it as dogma and will hold back your merge until it all works exactly like I want it to." (you get all sorts of people and personalities).
In my experience other people are no panacea either, nor is writing code all by myself. Fuck it, I'll take anything and everything to help me ship stuff that's good enough and on time (even if some/most? deadlines within the industry are made up). I'd argue that producing something that would pass most critique and could be considered "good code" (not "good enough") or even more broadly a "good product" might take about an order of magnitude more effort than most people and organizations actually can, or can budget for.
by KronisLV - OpenClaw sold for how much?...by sschueller
- How many millions of dollars should Oracle database sell for?
- Then ask it to fix it. When “fixed”, ask the same question again and you’ll get a similar response again!by geraneum