Note: nothing against fluid.sh, I am struggling to figure out something to build.
So the lifecycle of an app would be:
1) Create your game/quiz/whatever app.
2) Pay a successful app $x per install, and get a bunch of app installs.
3) Put all sorts of scammy "get extra in game perks if you refer your friends" to try to become viral.
4) Hope to become big enough that people start finding you without having to pay for ads.
5) Sell ads to other facebook app startups to generate installs for them.
It was a completely circular economy. There was not product or income source other than the next layer of the pyramid.
It didn't last long.
full circle, not full stop
Fast forward 18 years, and the company is going strong with millions of subscribers and distributing Oscar winning films such as Demi Moore’s The Substance.
In a complex project the hard parts about software are harder than the hard parts about the domain.
I've seen the type of code electrical engineers write (at least as hard a domain as software). They can write code, but it isn't good.
The ability to acquire domain knowledge quickly however, isn't exactly the same as the ability to develop complex software.
The best part, of course, is that this mostly works, most of the time, for most busineses.
Now, the same domain experts -who still cannot code- will do the exact same thing, but AI will make the spreadsheet more stable (actual data modelling), more resilient (backup infra), more powerful (connect from/to anything), more ergonomic (actual views/UI), and generally more easy to iterate upon (constructive yet adversarial approach to conflicting change requests).
Hallucinations sure make spreadsheets nice and stable.
From the view you describe, it seems AI just lets you experiment faster, when all you want to do is experiment. You find product market fit easier, you empower designers more, etc. Much easier to iterate and find easy wins from alternative designs - as long as your fundamentals work!
Only problem is that you are experimenting in public, so the massive wave of new AI generated features come to the public from everywhere at once. Hence the widespread backlash.
Not to mention, the core job function when you are experimenting is different from what defines a lot of hard technical progress: creating new technologies, or foundational work that others build on, is naturally harder and slower than building e.g. CRUD services on top of an existing stack. Deep domain expertise matters for selling, deep programming expertise matters for stability. I don't know, curious where the line will end up getting drawn.
There’s a requisite curiosity necessary to cross the discomfort boundary into how the sausage is made.
Programming is not something you can teach to people who are not interested in it in the first place. This is why campaigns like "Learn to code" are doomed to fail.
Whereas (good) programmers strive to understand the domain of whatever problem they're solving. They're comfortable with the unknown, and know how to ask the right questions and gather requirements. They might not become domain experts, but can certainly learn enough to write software within that domain.
Generative "AI" tools can now certainly help domain experts turn their requirements into software without learning how to program, but the tech is not there yet to make them entirely self-sufficient.
So we'll continue to need both roles collaborating as they always have for quite a while still.
My belief is that engineers should be the prime candidates to be learning the domain, because it can positively influence product development. There’s too many layers between engineers and the the domain IME
The beauty of LLMs is that they can quickly gather and distill the knowledge on both sides of that relationship.
Web dev is low entry barrier and most web devs don’t need a very deep knowledge base.
Embedded, low level language, using optimizations of the OS / hardware require MUCH more specialized knowledge. Most of the 4 year undergraduate program for Computer Science self selects for mathematics inclined students who then learn how to read and learn advanced mathematics / programming concepts.
There’s nothing that is a hard limit to prevent domain expert autodidacts from picking up programming, but the deeper the programming knowledge, the more the distribution curves of programmers / non-programmers will be able to succeed.
Non programmers are more likely to be flexible to find less programming-specific methods to solve the overall problem, which I very much welcome. But I think LLM-based app development mostly just democratizes the entry into programming.
"Are there more or less examples of successful companies in a given domain that leverage software to increase productivity than software companies which find success in said domain?"
But an answer to your question would be Capital One.
In what domains have you had experience taking non programmers with domain knowledge and making them programmers?
Sure, I could go and create an accounting app - or a clinical trial recruitment app - as a basic clone of what I've already created. And I might even make it better for some niche. But even if I know what that product system needs, I still need to find someone with the relationships to get in the door.
The trick is - you don't need an idea man for a non-technical founder. You really need someone with a rolodex and a problem.
My codebase is full of one-offs that slowly but surely converge towards cohesive/well-defined/reusable capabilities based on ‘real’ needs.
I’m now starting to pitch consulting to a niche to see what sticks. If the dynamic from the office holds (as I help them, capabilities compound) then I’ll eventually find something to call ‘a product’.
He kept ranting about what a b*tch of a problem that was, every time we went out drinking, and one day, something got into me, and thought there must be some software that can help with this.
Surely there was, and I set up a server with an online web UI where every employee could put in when they were able to work, and the software figured out how to assign timeslots to cover requirements.
I thought it was a nice exercise for me in learning to admininster a linux server, but when I showed it to my friend, he looked me in the eye and told me I a saved him a day of work every week, and called me a wizard :D
It occured to me, how naturally part of the programming profession is to make things in fixed amounts of time, that turn difficult and time consuming tasks a human needed to do into something that essentially just happens on its own.
I mean it in terms of owning the solution to a problem, being accountable/responsible for something working e2e not just the software or even the product - the service/experience of the customer that makes them want to give you money. Once you put on another hat - guess what - you'd probably be the star of some operations team or a great supervisor of some department. You would automate everything around you to a point others think you're the most capable person they've ever seen in that role.
I am now so deep into the rabbit hole that I have made a version that runs entirely in the browser and an ESP32 version. I have now also taken the printer apart to find that the built in BLE is an external module and I could interface directly with the printer by replacing it with my own custom PCB...
[1] https://sschueller.github.io/posts/making-a-label-printer-wo...
There are an infinite amount of problems to solve.
Deciding whether they’re worth solving is the hard part.
It’s really liberating. Instead of saying “gosh I wish there was an app that…” I just make the app and use it and move on.
Or maybe ask yourself what do you like to do outside of work? maybe build an app or claude skill to help with that.
If you like to cook, maybe try building a recipe manager for yourself. I set up a repo to store all of my recipes in cooklang (similar to markdown), and set up claude skills to find/create/evaluate new recipes.
Building the toy apps might help you come up with ideas for larger things too.
Don’t get me wrong, I have found uses for various AI tools. But nothing consistent and daily yet, aside from AI audio repair tools and that’s not really the same thing.
I think we see an aspect of this here, a lot of things we took for granted are changing, shared assumptions are being challenged and it's a period we're all relearning new things. To some extent spending too much time diving on the current iteration of AI tooling might be for nothing if gets invalidated by another sudden jump.
With all these new tools people are building, I can't help but feel they are building foundations on moving soil.
After the war the US created extra demand in the form of consumerism.
China is creating extra demand for infrastructure overcapacity with its belt and road initiative.
I wouldnt underestimate the abililty of the country to creatively create demand to counter oversupply.
Outside of tech companies, I think this is extremely common.
Also what does society need? Smart workers and people who believe in the system... so where does that leave us? We need to make something that would better enable children to want to grow up in the world and participate. Otherwise were doing nothing of value and in a death spiral
To summarize: Everyone wants to automate stuff. Most people do not want to touch boring, large problems.
This is not even AI - it's pre-AI, and everyone has continued to try to create things that other people can use as a dependency, just on a much higher pace.
I've found writing simulations that my childhood brain would have LOVED to see run fun and fulfilling.
AI is a product in search of a killer feature
First AGI was anyday going to come. Gpt5 had showed intelligence apparently
Then got started adult chat with paying customers
They'll work for hours and end up with $4 of gold
selling it is the hard part, nothing new there
These are the pets.com of the current bubble, and we'll be flooded by them before the damn thing finally pops.