I'm a software engineer, completing 10 years of professional experience this year. I started my career as a web frontend engineer (it was easier for me to de...
A good chunk of good patterns are to make sure humans understand it sure. But a good chunk of patterns exist to make individual components reusable and make sure you’re encapsulating requirements and testing them correctly.
A lot of LLMs take the “easy” way out and duplicate code, suppress listing, etc to make a prompt work. It works at that point in time but when you suddenly have a bunch of spaghetti and repeated code littered across multiple services suddenly making changes without causing massive regressions becomes a headache.
Companies are going to pay for this mess in several months as token prices go up and the codebase is a massive pile of slop.
That’s going to be the bubble. When AI has to be able to actually pay for itself, no one is going to be able to afford it, and if you happen to be one of the companies that went all in any used AI to build your codebase and fire not devs and front line workers, you’re going to be the hardest hit. Possibly the only hope is that they saved enough from partial and didn’t pass any savings on to the customer (because of course they wouldn’t) that they can almost survive the actual unsubsidized token costs. But then you will be in direct competition with everyone else who can write a prompt with likely literally no differentiator outside of maybe name recognition in an industry.
I think there’s a fundamental misunderstanding here.
All of the qualities that make a codebase easier to read, maintain, and consume by humans do the same things for LLMs.
A codebase designed for humans is a codebase that is designed for LLMs. It’s just that most teams don’t even know how to design a codebase for humans. And those same teams just kind of accept LLM and Agent Slop as “Designed for LLMs”. When it most definitely is not.
Patternization
Structural consistency
Naming conventions
Style Opinionation
Organizational conventions
Safety and Quality Standards
…etc
All these things matter just as much for humans as they do for LLMs. And like I said previously, most human developers don’t understand these things and do not optimize for them anyways. Which means that most human developers are ill-equipped to create codebases that are not degrading rapidly under the use of agents.
This is a bit of a rant of mine… because I’ve spent the last decade learning how to optimize software engineering to best fit the needs of humans. Now that LLMs are crashing onto the scene, teams that already were writing slop by hand can now write slop at twenty times the rate. And then seem to think that all the things that make for good software no longer apply to them
That will come back to bite them in the arse, mark my words.
It’s also patently false.
A good chunk of good patterns are to make sure humans understand it sure. But a good chunk of patterns exist to make individual components reusable and make sure you’re encapsulating requirements and testing them correctly.
A lot of LLMs take the “easy” way out and duplicate code, suppress listing, etc to make a prompt work. It works at that point in time but when you suddenly have a bunch of spaghetti and repeated code littered across multiple services suddenly making changes without causing massive regressions becomes a headache.
Companies are going to pay for this mess in several months as token prices go up and the codebase is a massive pile of slop.
That’s going to be the bubble. When AI has to be able to actually pay for itself, no one is going to be able to afford it, and if you happen to be one of the companies that went all in any used AI to build your codebase and fire not devs and front line workers, you’re going to be the hardest hit. Possibly the only hope is that they saved enough from partial and didn’t pass any savings on to the customer (because of course they wouldn’t) that they can almost survive the actual unsubsidized token costs. But then you will be in direct competition with everyone else who can write a prompt with likely literally no differentiator outside of maybe name recognition in an industry.
ya, but in the mean time i still need to fund my landlords retirement/pay rent
I think there’s a fundamental misunderstanding here.
All of the qualities that make a codebase easier to read, maintain, and consume by humans do the same things for LLMs.
A codebase designed for humans is a codebase that is designed for LLMs. It’s just that most teams don’t even know how to design a codebase for humans. And those same teams just kind of accept LLM and Agent Slop as “Designed for LLMs”. When it most definitely is not.
All these things matter just as much for humans as they do for LLMs. And like I said previously, most human developers don’t understand these things and do not optimize for them anyways. Which means that most human developers are ill-equipped to create codebases that are not degrading rapidly under the use of agents.
This is a bit of a rant of mine… because I’ve spent the last decade learning how to optimize software engineering to best fit the needs of humans. Now that LLMs are crashing onto the scene, teams that already were writing slop by hand can now write slop at twenty times the rate. And then seem to think that all the things that make for good software no longer apply to them
_
Nothing ever bites them in the arse. It always runs downhill.