StrongDM is attempting that. The code they produced is not inspiring confidence on a relatively small scale [0], and based on what I saw with a cursory inspection I very much doubt I wouldn't find much deeper issues if I took the time to really dig into their code.

Don't get me wrong, "sort of works if you squint at it" is downright miraculous by the standards of five years ago, but current models and harnesses are not sufficient to replace developers at this scale.

[0] https://news.ycombinator.com/item?id=46927737

That's really cool, I can see the utility in it but why on this earth would anyone want this for anything other than doing internal integration tests.

I can see a company who spends upwards of $2000 a month on just slack, but can you justify building, deploying and maintaining your own personal slack clone even if you can get an AI to do all of that. What happens when, inevitably, there are issues that the AI gets stumped on? You will need some level of humans intervention or at least review the issue before letting the autonomous loop run. Even if you ignore all of that, if the loop doesn't catch the bug but a human does, they're still the ones left reporting it.

You're shifting this entire process of owning a product to an AI, which even in the best case leaves you paying a bill probably larger than a $2000/mo. You're 100% better off just deploying an OSS alternative, maybe sponsoring them for some feature requests than managing an AI loop to build, deploy and maintain.

Maybe at the scale of paying $20,000/mo or $50,000/mo you could start to justify it in some way but when you're able to pay that much for a productivity enhancement service what really are you going to do better than them. Their incentive is to earn and yours is to save, theirs is much stronger to deliver than yours. The argument that SaaS is dead is very poorly focused. I get your specific workplace pays for a service that they only use 2-3 features of, that's not the case for major SaaS products. Something like dynamics 365 takes month for process engineers to implement business processes, there is 0 coding involved just interviewing, documenting and configuring. The actual act of programming might take a single digit percentage of the total cost to deliver a complete deployment.

Ignoring all the business talk, I don't think anyone denies LLMs ability to bring huge productivity boost. I can wholeheartedly support the argument that companies no longer have an excuse to significantly over hire and micro delegate tasks over hundreds of engineers because it's more efficient to let fewer engineers have higher productivity.

I'm part of a small team that's embraced AI for code assistance right at ChatGPT 3.5. There are still cases where when AI doesn't have enough examples about a problem in it's training data, for no matter how long you let it ruminate, debug, harness etc. would it be able to solve those certain issues. If you had let it run for long enough it may have completely reimplemented the library back on it's own, failed then attempted to reimplement the entire saas product as a MVP replacement.

Even if we setup the AI could fully autonomously implement, test, debug and deploy features fully autonomously the case where there it runs into an issue, services fail to deploy, new business use cases to implement can possibly lead to huge costs/lost revenue which isn't worth not having software engineers for. If your company's business needs require 30 servers with 300-500 instances of different services deployed across them, and you keep software engineers for that worst case scenario you can't keep just 2-3 because a human can only push their cognitive load so far. The number of software engineers needed to maintain and manage will remain more or less the same, the only difference will be they'll have more time to contribute to more important parts of the business. Maybe even manage that Slack clone.