What is your theory of when AI gets to 100%. PMs and business analysts build all the software? Or just like a 700 or so 1-founder companies in the world and everyone else is without work? The matrix?
See my issue with these comparisons is that it always compare AI to 100%.
When 100% does not exist. Most software out there has issues, bugs, compliance problems, security weaknesses, scaling, redundancy, availability issues...etc. A lot of this is not actually related to how good or bad software engineers are. It's about costs and time to ROI. Greed is an issue too.
So people seem to have this idea that software created by humans is perfect (its not). And that deterministic (human created software with if/then) is alway going to be better than probabilistic (LLMs). Which in a perfect world is the case, but we live in a capitalistic world where this is not the case.
> Or just like a 700 or so 1-founder companies in the world and everyone else is without work?
This. But instead of 700 it's more likely that everyone will be a founder (more or less). It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
In my own (admittedly limited) experience, 2 employees in my company (that had no programming knowledge or experience) have vibe coded apps that simplify their daily roles. The apps basically automate a flowchart of steps where multiple people need to submit certain pieces of info and as they do, a "project" moves through stages and the employees get notified on Telegram.
The app really is just several simple forms with some if/else logic, but claude code allowed them to get the app up and running and deployed on vercel's free tier, and it's Good Enough™ to save them an hour or so each day lost in messaging and chasing up things.
I don't think anyone would ever have targeted an app for sale to them, and it would have been hard to twist some sort of flow management app and integrate it with Zapier or something to handle external api calls. With claude code they could just tell it what they wanted and solve their very niche issue. That's why I think that even though LLM coding has improved so much you might not see more software for sale - it's easier for people to just...make their own software.
The best part of this workflow - which I see often - is that by having someone build custom software to automate some process they often step back away from the process being their job. That eventually translates into them understanding that some (or sometimes most or all) of that process is not needed. There are so many corporate processes that were implemented and then become the way... and if there are people who identify that process as being their job those people resist attempts to optimize that process.
I have seem several people use AI to write apps to automate a process and along they way finally ask the question 'do we even need this process?'.
Don’t get me wrong, :) that’s pretty cool! I’ve also made highly personalized mini apps for my own personal life. Currently working on an iOS one to log mood and correlate it with HealthKit data since the native health app does a bad job of it.
That said, I meant more like production grade apps that have to serve N>1, which is IME where the hard part LLMs suck at comes in. I saw a tweet somewhere along the lines of “CEOs/execs are so divorced from the last mile effort that they are uniquely susceptible to believing AI can replace engineers end to end”
> It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
No it isn’t. The things that were hard are now harder. The things that were comparatively easy are now easier. But if you build another piece of vibe-coded crap in a world awash in vibe-coded crap, you will not stand out. Nobody cares about your unpolished, one-shot prototype, so cranking them out faster is not really helpful.
Differentiation is always a problem of effort and care, and this isn’t going to change.
When 100% does not exist. Most software out there has issues, bugs, compliance problems, security weaknesses, scaling, redundancy, availability issues...etc. A lot of this is not actually related to how good or bad software engineers are. It's about costs and time to ROI. Greed is an issue too.
So people seem to have this idea that software created by humans is perfect (its not). And that deterministic (human created software with if/then) is alway going to be better than probabilistic (LLMs). Which in a perfect world is the case, but we live in a capitalistic world where this is not the case.