We might not even need libraries after all, since APIs, libraries and abstractions in general exist for humans to grasp complexities. Abstractions have their merits, but they have downsides too, and AI might be a way to solve them. The interesting part for me is where that ends, because it's systems all the way down, and even on a higher level abstractions exist to allow humans to make sense of the world. Services, products, companies, political parties, what if in the future we don't need any of it anymore because the abstraction is obsolete?
Libraries do a LOT more than that. Abstractions also make LLMs more efficient, and lets some problems be handed off to much cheaper algorithms. They also mean that the way data is handled can be standardized which means you don't have to design an entire protocol from scratch every time you want to send data somewhere.
Every advantage libraries have given us still exists in the world of AI.
What about readability? Are people going to give up on reading and understanding the code? Maybe the abstractions aren't only necessary for writing the code?
I think the answer to this is yes, it'll be just like "reading and understanding your assembly". We just don't check what instructions your code compiles down to anymore, and I think the same will be for code.
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
And here I sit with my own native cross-platform GUI library, made with my own Lisp-To-Rust programming language... Tell me more about what we all are not doing :)
> And here I sit with my own native cross-platform GUI library, made with my own Lisp-To-Rust programming language...
Currently working on something like this. Do you have a writeup somewhere? Specifically how you solved cross platform rendering equivalence? Do you use an intermediate representation or just pure vis-a-vis splatting via native APIs?
But who cares if they gain traction? I don't need it to gain traction for me to be able to make use of it correctly and efficiently. In fact, I probably need the opposite, for it to not gain traction, so I can focus on using it and improving it for myself only.
gist was certainly not implying that it was not possible to do it or that some people won't continue doing it but the point was rather that the pool of such people or such projects will significantly decrease because software has now (mostly) reached the abundance state where all of these technical details start to matter less and less for general consumption.
> you might find the quality subpar, but in terms of cost ratio, it is commercially good enough. Business will accept 99.99 at fraction of cost of 99.999.
Personally, based on over a decade of professional software engineering experience at all sort of organisations, from small startups, Government, big tech, financial services of all scale.
In terms of literature, you can look into "enshiftication" as a starting point into research around cost savings and value extraction in business.
Also, about 7 years ago, I had some ideas about how AI will pan out, and here is the releveant excerpt from it:
> [S]oftware development industry, the very workhorse of automation and tools of efficiency is eating itself away. The outward image may look very different with the software development becoming an integral part of more and more industries, yet, the number of developers required for a project of given complexity is reducing at unimaginable rate.
Tricky to make predictions based on costs or quality when the costlier parts, training and inference, are totally disconnected from reality thanks to VC money and when total cost of ownership over time is radically different.
True intelligence should not have problems adapting to new programming languages. Especially with good TTD and other harnesses, current SOTA models are likely very capable of writing code in esoteric languages
> Software Engineering as science will be largely dedicated to AI development
Which will require fewer people.
Simple software engineers who work on CRUDs and are not PhD-s and stuff will go away. Most of software is like this. The few percent who work on kernels, AI models, etc. will still have work. The rest won't, or rather much less people will be needed to simply use AI to do that work.
But right now AI assisted development isn't really making you more productive by an order of magnitude. It's easily a game of whack-a-mole right now. I estimate that an excellent programmer equipped with Claude Code only sees a 30-40% improvement in speed right now
What you speak of may happen, but I doubt it will happen in the next 10 years.
Software engineering has been getting more efficient for decades and over that time there’s been a massive increase in the number of software engineers.
The Mythical Man-Month describes teams of ten engineers operating before the Internet. Today (pre-LLMs) that’s probably 1-2 people.
Sometimes I feel the same but then I sit in a refinement meeting and have to handhold the stakeholders through the process of finding out what they actually want, what they don't want, which trade offs they find acceptable and which they don't. And while AI is infinitely patient, my feeling is that being good at this is a super individual people skill and I know that more often than not the people I am talking to are kind of happy they are talking to a person instead of a machine. However when it comes to actually implementing the stuff, AI reliance is now necessary with the timelines given.
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
Eh, you just don’t get how people work. You might be right overall, or maybe not.
People will write new UI frameworks and languages if the problem seems interesting to them. There are loads of people who just like working on interesting problems, and that’s very unique to each person. They may use AI, they may not, but as long as people get deeply sucked into interesting problems, we’ll still see new ideas and projects.
Or, look at react. It was invented because Facebook was running into issues writing big web UIs. Many companies still have issues writing these, and if they have issues, it’ll be a way they can differentiate and compete. What happens when someone turns an AI on the optimization problem and invents a bespoke in-house tool that’s actually fantastic? That’s another way you could get new tools.
1. If majority of developers use LLMs for coding, and then if someone developed a better React, then no one can really use it because the models does not know how to write idiomatic code in it. It might be easier to make the LLM implement workaround for the issue with the current React, than making it use an unfamiliar library or paradigm.
3. Without some critical adoption rate, the new library won't have enough training material, and this becomes a chicken and egg problem. This can only be broken by AI companies by somehow training the models on the new React by code explicitly written, but again, without critical adoption, there is no incentive for them to do it.
Imagine we are living in the time of jquery and all the LLMs are proficient in it. I don't think in such a world, no one would want Angular/React, because LLMs will happily handle all the complexity that using jquery results in.
Exactly. And you can already see the direction things are going with most Web and React Native UI libraries converging on tailwind, since it is extremely genai friendly.
"People will write new UI frameworks and languages if the problem seems interesting to them."
I would think even with AI assistance industrial quality solutions (which is what we are talking here I think ) still require corporate size sponsor to thrive.
Yes, anyone will create whatever suits their fancy. But there is huge gap between hobby project and industrial adoption.
> This might sound obvious, but it is worth putting it down, Software Development going forward will be largely done by AIs, you might find the quality subpar, but in terms of cost ratio, it is commercially good enough. Business will accept 99.99 at fraction of cost of 99.999. It is all about general consumer expectations, which will shift.
This is correct but a misunderstanding of "who does software development". It's no different to claiming "compilers will do software development from now on". LLMs are simply a metaprogramming tool.
> But most of all, not just that we are not going back, we are also not going anywhere. Software Engineering as science will be largely dedicated to AI development and outside of this discipline, it will slow down to a grinding halt.
Wrong. In fact due to the advances in LLMs we finally have the tools to optimize the underlying foundations. Historically, writing something from scratch or messing around with low-level implementations was out of the question for anyone but the most massive tech firms. Now a small(ish) team can experiment with building a custom VCS and CI/CD pipeline from scratch, without relying on Git. In fact, I'd argue that most current software is _no where close to the optimum yet_.
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
Dead wrong, on all fronts. And it shows that the author doesn't even understand what LLMs do. I wrote a custom DSL Lisp like language (entirely custom forms and relatively custom syntax) that the LLM understands perfectly through BNF forms + examples. And the domains it's used in (robotics) gives absurd results. Literally outperforming the competition by miles. Pre-LLM this wouldn't be doable without sinking years of dev time.
> Yes, it will be easier for people to build new libraries and languages, but they won't gain traction. This might be different for large corporations who can afford to train and finetune models on their new fangled technology, but that will be the exception, and likely struggle with building a community and talent pool outside of this developing organisation as other people may not fancy using or even have access to their internal models.
Irrelevant. In fact, custom building specific purpose code is far more attractive than it ever was. Why do I care how widely used a library is if it serves my use case perfectly? Historically, again, you had to wrestle with library conventions and styles just to get something to work. Now you can have a microoptimized library built _exactly_ for your use case. " This might be different for large corporations who can afford to train and finetune models on their new fangled technology" -- wrong, you don't need to retrain models at all to understand custom libraries. Again, I have a near entirely in-house written stack (proprietary, never seen the light of day) and LLMs understand it _perfectly_. In fact they can even write perfectly idiomatic code in them, despite it being obscure as all hell, example
ox::app app;
app.add_resource<users_view>(ox::sv{ "/users" });
app.add_resource<user_view>(ox::sv{ "/users/:id" });
auto admin = app.group(ox::sv{ "/admin" });
admin.use(ox::basic_auth(ox::sv{ "admin" }, [](ox::sv u, ox::sv p) { return u == ox::sv{ "root" } && ox::ct_equal(p, ox::sv{ "toor" }); }));
admin.get(ox::sv{ "/stats" }, [](context &c) {
micron::string b{};
b.append("hello ", 6);
const ox::local_value *who = c.get_local(ox::sv{ "user" });
if ( who && who->is<micron::string>() ) {
const micron::string &s = who->cast<micron::string>();
b.append(s.c_str(), s.size());
}
b.append(", here are the admin stats\n", 27);
c.text(200, ox::sv{ b.c_str(), b.size() });
});
N years down the road, 3 year old children can just tell AI to generate a custom game or cartoon for them on the spot.
What's left for humans to do?
Live on UBI, explore this wonderful world, colonize other planets..
And along the way, while anyone can make anything they can think of, it'll come down to who has the better idea, we might enter an Economy of Ideas, and maybe the Idea Guys™ will finally have their day :)
We might not even need libraries after all, since APIs, libraries and abstractions in general exist for humans to grasp complexities. Abstractions have their merits, but they have downsides too, and AI might be a way to solve them. The interesting part for me is where that ends, because it's systems all the way down, and even on a higher level abstractions exist to allow humans to make sense of the world. Services, products, companies, political parties, what if in the future we don't need any of it anymore because the abstraction is obsolete?
Libraries do a LOT more than that. Abstractions also make LLMs more efficient, and lets some problems be handed off to much cheaper algorithms. They also mean that the way data is handled can be standardized which means you don't have to design an entire protocol from scratch every time you want to send data somewhere.
Every advantage libraries have given us still exists in the world of AI.
They also help AI to manage complexity, in just the same way.
What about readability? Are people going to give up on reading and understanding the code? Maybe the abstractions aren't only necessary for writing the code?
I think the answer to this is yes, it'll be just like "reading and understanding your assembly". We just don't check what instructions your code compiles down to anymore, and I think the same will be for code.
Where we're going, we won't even need programming languages or libraries at all. AI's will just generate pure binaries full of magic, right? /s
I mean, you already can, the AI will just do “natural language” to a programming language, and then call a compiler for the next step. Done.
> we won't even need programming languages
> just do “natural language” to a programming language
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
And here I sit with my own native cross-platform GUI library, made with my own Lisp-To-Rust programming language... Tell me more about what we all are not doing :)
> And here I sit with my own native cross-platform GUI library, made with my own Lisp-To-Rust programming language...
Currently working on something like this. Do you have a writeup somewhere? Specifically how you solved cross platform rendering equivalence? Do you use an intermediate representation or just pure vis-a-vis splatting via native APIs?
Yes, it will be easier for people to build new libraries and languages, but they won't gain traction.
But who cares if they gain traction? I don't need it to gain traction for me to be able to make use of it correctly and efficiently. In fact, I probably need the opposite, for it to not gain traction, so I can focus on using it and improving it for myself only.
This is a general comment; it might not apply to your personal use-case.
I guess I should have prefixed my initial comment with "This is my personal use-case, not a general comment"?
gist was certainly not implying that it was not possible to do it or that some people won't continue doing it but the point was rather that the pool of such people or such projects will significantly decrease because software has now (mostly) reached the abundance state where all of these technical details start to matter less and less for general consumption.
> you might find the quality subpar, but in terms of cost ratio, it is commercially good enough. Business will accept 99.99 at fraction of cost of 99.999.
What is this based on?
Personally, based on over a decade of professional software engineering experience at all sort of organisations, from small startups, Government, big tech, financial services of all scale.
In terms of literature, you can look into "enshiftication" as a starting point into research around cost savings and value extraction in business.
Also, about 7 years ago, I had some ideas about how AI will pan out, and here is the releveant excerpt from it:
> [S]oftware development industry, the very workhorse of automation and tools of efficiency is eating itself away. The outward image may look very different with the software development becoming an integral part of more and more industries, yet, the number of developers required for a project of given complexity is reducing at unimaginable rate.
https://gist.github.com/omeid/9a180f9acccc8409d6c5f3e7fa1a77...
Nothing, this is pure speculation. Nobody knows how this is going to pan out - definitely not this guy.
It might turn out AI does all programming in future. But it also might turn out that it's incapable of long term maintenance. We don't know yet.
Tricky to make predictions based on costs or quality when the costlier parts, training and inference, are totally disconnected from reality thanks to VC money and when total cost of ownership over time is radically different.
They aren't totally disconnected from reality. There are plenty of providers who don't have a ton of VC money selling tokens at basically cost price.
Not to be that one guy asking why this is on the front page of HN, but why is this on the front page of HN?
Although I don‘t feel the same level of despair as the author, it struck a nerve reading this. I can imagine that many other had a similar reaction.
True intelligence should not have problems adapting to new programming languages. Especially with good TTD and other harnesses, current SOTA models are likely very capable of writing code in esoteric languages
They are capable but at a context and token expense handicap. Which means it is pointless and uneconomical.
This seems like a pretty naive take. We "went places" in the past for one set of reasons, we'll "go places" in the future for another set.
No point in going anywhere for its own sake. When there's a need to go somewhere, we'll try to get there. (But maybe with a different mode of travel.)
This. If AI can do most of the things why do we need any specific place to go? Software engineering is not a jobs program.
> Software Engineering as science will be largely dedicated to AI development
Which will require fewer people.
Simple software engineers who work on CRUDs and are not PhD-s and stuff will go away. Most of software is like this. The few percent who work on kernels, AI models, etc. will still have work. The rest won't, or rather much less people will be needed to simply use AI to do that work.
But right now AI assisted development isn't really making you more productive by an order of magnitude. It's easily a game of whack-a-mole right now. I estimate that an excellent programmer equipped with Claude Code only sees a 30-40% improvement in speed right now
What you speak of may happen, but I doubt it will happen in the next 10 years.
Software engineering has been getting more efficient for decades and over that time there’s been a massive increase in the number of software engineers.
The Mythical Man-Month describes teams of ten engineers operating before the Internet. Today (pre-LLMs) that’s probably 1-2 people.
Sometimes I feel the same but then I sit in a refinement meeting and have to handhold the stakeholders through the process of finding out what they actually want, what they don't want, which trade offs they find acceptable and which they don't. And while AI is infinitely patient, my feeling is that being good at this is a super individual people skill and I know that more often than not the people I am talking to are kind of happy they are talking to a person instead of a machine. However when it comes to actually implementing the stuff, AI reliance is now necessary with the timelines given.
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
Eh, you just don’t get how people work. You might be right overall, or maybe not.
People will write new UI frameworks and languages if the problem seems interesting to them. There are loads of people who just like working on interesting problems, and that’s very unique to each person. They may use AI, they may not, but as long as people get deeply sucked into interesting problems, we’ll still see new ideas and projects.
Or, look at react. It was invented because Facebook was running into issues writing big web UIs. Many companies still have issues writing these, and if they have issues, it’ll be a way they can differentiate and compete. What happens when someone turns an AI on the optimization problem and invents a bespoke in-house tool that’s actually fantastic? That’s another way you could get new tools.
Couple of aspects you are not considering.
1. If majority of developers use LLMs for coding, and then if someone developed a better React, then no one can really use it because the models does not know how to write idiomatic code in it. It might be easier to make the LLM implement workaround for the issue with the current React, than making it use an unfamiliar library or paradigm.
3. Without some critical adoption rate, the new library won't have enough training material, and this becomes a chicken and egg problem. This can only be broken by AI companies by somehow training the models on the new React by code explicitly written, but again, without critical adoption, there is no incentive for them to do it.
Imagine we are living in the time of jquery and all the LLMs are proficient in it. I don't think in such a world, no one would want Angular/React, because LLMs will happily handle all the complexity that using jquery results in.
We would have stuck with JQuery forever..
Yesterday, I wrote down a list of UI libraries for myself.
There are many, for every web design framework (for React/etc or PHP).
So yes, people write new libraries. The question is how they’re going to write them in the next 5 years.
Exactly. And you can already see the direction things are going with most Web and React Native UI libraries converging on tailwind, since it is extremely genai friendly.
"People will write new UI frameworks and languages if the problem seems interesting to them."
I would think even with AI assistance industrial quality solutions (which is what we are talking here I think ) still require corporate size sponsor to thrive.
Yes, anyone will create whatever suits their fancy. But there is huge gap between hobby project and industrial adoption.
Lots to criticize here.
> This might sound obvious, but it is worth putting it down, Software Development going forward will be largely done by AIs, you might find the quality subpar, but in terms of cost ratio, it is commercially good enough. Business will accept 99.99 at fraction of cost of 99.999. It is all about general consumer expectations, which will shift.
This is correct but a misunderstanding of "who does software development". It's no different to claiming "compilers will do software development from now on". LLMs are simply a metaprogramming tool.
> But most of all, not just that we are not going back, we are also not going anywhere. Software Engineering as science will be largely dedicated to AI development and outside of this discipline, it will slow down to a grinding halt.
Wrong. In fact due to the advances in LLMs we finally have the tools to optimize the underlying foundations. Historically, writing something from scratch or messing around with low-level implementations was out of the question for anyone but the most massive tech firms. Now a small(ish) team can experiment with building a custom VCS and CI/CD pipeline from scratch, without relying on Git. In fact, I'd argue that most current software is _no where close to the optimum yet_.
> No one is going to write new UI libraries if SOTA models know React best, no one is going to bother with new languages if SOTA models know Python, Go, JavaScript, and so on the best.
Dead wrong, on all fronts. And it shows that the author doesn't even understand what LLMs do. I wrote a custom DSL Lisp like language (entirely custom forms and relatively custom syntax) that the LLM understands perfectly through BNF forms + examples. And the domains it's used in (robotics) gives absurd results. Literally outperforming the competition by miles. Pre-LLM this wouldn't be doable without sinking years of dev time.
> Yes, it will be easier for people to build new libraries and languages, but they won't gain traction. This might be different for large corporations who can afford to train and finetune models on their new fangled technology, but that will be the exception, and likely struggle with building a community and talent pool outside of this developing organisation as other people may not fancy using or even have access to their internal models.
Irrelevant. In fact, custom building specific purpose code is far more attractive than it ever was. Why do I care how widely used a library is if it serves my use case perfectly? Historically, again, you had to wrestle with library conventions and styles just to get something to work. Now you can have a microoptimized library built _exactly_ for your use case. " This might be different for large corporations who can afford to train and finetune models on their new fangled technology" -- wrong, you don't need to retrain models at all to understand custom libraries. Again, I have a near entirely in-house written stack (proprietary, never seen the light of day) and LLMs understand it _perfectly_. In fact they can even write perfectly idiomatic code in them, despite it being obscure as all hell, example
Let's imagine the ideal endgame:
AI becomes godlike and does everything for us.
N years down the road, 3 year old children can just tell AI to generate a custom game or cartoon for them on the spot.
What's left for humans to do?
Live on UBI, explore this wonderful world, colonize other planets..
And along the way, while anyone can make anything they can think of, it'll come down to who has the better idea, we might enter an Economy of Ideas, and maybe the Idea Guys™ will finally have their day :)
>AI becomes godlike and does everything for us.
As soon as AI can tell original hilarious jokes, I am going to shut my doors and laugh myself to death.