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.
If the only problem is that your code is slop and nobody can work with it without AI, then it’s probably not that bad. Text models I can run locally on my five year old Macbook are maybe a year behind in terms of coding assistance. So AI for coding is probably never going away. The worst case for someone in this scenario is just that it gets a bit slower and dumber and that they have to hire more engineers again. It’ll suck but I think it’s survivable. Someone would have to make a new Stackoverflow though if we’re going to google stuff again.
Now if you integrated multiple AI services into all your business workflows and into the products you sell, on the other hand, that might be a different story. In a way the risk is the same as with cloud providers. You get locked into a stack and then your product literally dies if the provider decides you’re not paying enough, because you have no feasible way out. Tbh I would much prefer working at a post-bubble era software company fixing the codebase to working at a random company now extracting their IT from a hyperscale cloud. But in reality, most companies that bet on AI are in this scenario. Nobody only installed Claude and called it a day.
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
Some of these are arguably much more important for LLMs because of limited context sizes. The more of the code in the context window follows good practice, the more likely the LLM is to align with it. Any nonsense in the context window will multiply and beat that one document with the style guide that the LLM might not even see.
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.
If the only problem is that your code is slop and nobody can work with it without AI, then it’s probably not that bad. Text models I can run locally on my five year old Macbook are maybe a year behind in terms of coding assistance. So AI for coding is probably never going away. The worst case for someone in this scenario is just that it gets a bit slower and dumber and that they have to hire more engineers again. It’ll suck but I think it’s survivable. Someone would have to make a new Stackoverflow though if we’re going to google stuff again.
Now if you integrated multiple AI services into all your business workflows and into the products you sell, on the other hand, that might be a different story. In a way the risk is the same as with cloud providers. You get locked into a stack and then your product literally dies if the provider decides you’re not paying enough, because you have no feasible way out. Tbh I would much prefer working at a post-bubble era software company fixing the codebase to working at a random company now extracting their IT from a hyperscale cloud. But in reality, most companies that bet on AI are in this scenario. Nobody only installed Claude and called it a day.
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
Some of these are arguably much more important for LLMs because of limited context sizes. The more of the code in the context window follows good practice, the more likely the LLM is to align with it. Any nonsense in the context window will multiply and beat that one document with the style guide that the LLM might not even see.
ya, but in the mean time i still need to fund my landlords retirement/pay rent
Nothing ever bites them in the arse. It always runs downhill.
_