Comment by marcyb5st
16 days ago
I am more afraid if AI will actually deliver what CEOs are touting. People that are now working will be unemployable and will have to pivot to something else, overcrowding these other sectors and driving wages down.
If that comes to pass you will work the same or more for less money than now.
Basically jump back to a true plutocracy since only a few people will syphon the wealth generated by AI and that wealth will give them substantial temporal power.
This is basically just a standard cliche doomer prediction about any new development.
I mean, I just dont see any evidence of that happening. TBF I'm a SWE so I can only speak to that segment, but its literally worse than useless for working with anything software related thats non-trivial...
I see that sentiment here all the time and I don't understand what you must be doing; our projects are far from non trivial and we get a lot of benefit from it in the SWE teams. Our software infra was alway (almost 30 years) made to work well with outsourcing teams, so maybe that is it, but I cannot understand how you can have quite that bad results.
> our projects are far from non trivial
Well, there you go; as they say, it's okay for trivial stuff.
Butting in here but as I have the same sentiment as monkaiju: I'm working on a legacy (I can't emphasize this enough) Java 8 app that's doing all sorts of weird things with class loaders and dynamic entities which, among others, is holding it in Java 8. It has over ten years of development cruft all over it, code coverage of maybe 30-40% depending on when you measure it in the 6+ years I've been working with it.
This shit was legacy when I was a wee new hire.
Github Copilot has been great in getting that code coverage up marginally but ass otherwise. I could write you a litany of my grievances with it but the main one is how it keeps inventing methods when writing feature code. For example, in a given context, it might suggest `customer.getDeliveryAddress()` when it should be `customer.getOrderInfo().getDeliveryInfo().getDeliveryAddress()`. It's basically a dice roll if it will remember this the next time I need a delivery address (but perhaps no surprises there). I noticed if I needed a different address in the interim (like a billing address), it's more likely to get confused between getting a delivery address and a billing address. Sometimes it would even think the address is in the request arguments (so it would suggest something like `req.getParam('deliveryAddress')`) and this happens even when the request is properly typed!
I can't believe I'm saying this but IntelliSense is loads better at completing my code for me as I don't have to backtrack what it generated to correct it. I could type `CustomerAddress deliveryAddress = customer` let it hang there for a while and in a couple of seconds it would suggest to `.getOrderInfo()` and then `.getDeliveryInfo()` until we get to `.getDeliveryAddress()`. And it would get the right suggestions if I name the variable `billingAddress` too.
"Of course you have to provide it with the correct context/just use a larger context window" If I knew the exact context Copilot would need to generate working code, that eliminates more than half of what I need an AI copilot in this project for. Also if I have to add more than three or four class files as context for a given prompt, that's not really more convenient than figuring it out by myself.
Our AI guy recently suggested a tool that would take in the whole repository as context. Kind of like sourcebot---maybe it was sourcebot(?)---but the exact name escapes me atm. Because it failed. Either there were still too many tokens to process or, more likely, the project was too complex for it still. The thing with this project is although it's a monorepo, it still relies on a whole fleet of external services and libraries to do some things. Some of these services we have the source code for but most not so even in the best case "hunting for files to add in the context window" just becomes "hunting for repos to add in the context window". Scaling!
As an aside, I tried to greenfield some apps with LLMs. I asked Codex to develop a minimal single-page app for a simple internal lookup tool. I emphasized minimalism and code clarity in my prompt. I told it not to use external libraries and rely on standard web APIs.
What it spewed forth is the most polished single-page internal tool I have ever seen. It is, frankly, impressive. But it only managed to do so because it basically spat out the most common Bootstrap classes and recreated the W3Schools AJAX tutorial and put it all in one HTML file. I have no words and I don't know if I must scream. It would be interesting to see how token costs evolve over time for a 100% vibe-coded project.
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What products and what models have you tried?
I couldn't tell you because I've kept it at arms length but over the last year our most enthusiastic "AI guy" (as well as another AI-user on the team) has churned through quite a few, usually saying something like "$NEW_MODEL is much better!" before littering garbage PRs all over the project.