I think the thing that drives me nuts is that, while most people think the result of programming is a program, I disagree. The result of programming is one or more people who have a deep understanding of a problem space. Codegen models still require a human in the loop; that human has to be a software expert. You only become a software expert by writing software.
I'm in my mid-40's now. I taught myself C when I was 15. I have no desire to use LLMs to pump out code.
I take comfort in re-reading much of the 70's and 80's literature which focuses the possibilities of user experience. We still haven't fully explored all the dreams of half a century ago.
If AI forces the business case that "code is cheap", I can only hope we re-double our efforts at creating new interfaces and capabilities for computer systems. The Meta glasses, Apple Vision, and the like are small steps in this direction.
>I'm in my mid-40's now. I taught myself C when I was 15. I have no desire to use LLMs to pump out code.
For me, it's the other way around: I'm glad that AI can write code for me. A few months ago, I moved from an engineering role to a researcher role exactly because I got tired of writing code. Probably 95% of code/features (at least the kind you get paid for) is just boring CRUD stuff where you move bytes from one place to another and then show them in the UI (plus some access rules and a few invariants here and there).
All of it was actually interesting in the first couple of years. But when you do it over and over again for 20 years in a row... Yeah. Sometimes there are interesting projects from time to time, but usually it's the same stuff you've done countless times. Deja vu.
In my current researcher role, my task is to explore novel ideas, productize research papers, etc., and LLMs allow me to quickly write prototypes and demos, play with various ideas, without having to spend a lot of time manually moving bytes from one place to another. It’s fun again.
I'm in a similar age and career point and have similar feelings about my SE job atm. Can you share more about your transition to the researcher role? What field are you in, how did you get there, what problems did you encounter, etc.
But what happens when the bill comes in? That's my biggest fear. I heard on a recent podcast that it is a great time to be a micro-entrepreneur, and I think that's true right now because AI is so cheap. But AI companies are hemorraghing money. What happens to those micro-entrepreneurs when the price goes up? Are we going to live in a world where only large, rich corporations can afford to competitively develop things? Maybe so, but it is depressing to think about.
For the plebeians (like me), I think hand-coding skills will always be relevant and necessary.
It's unclear whether the price increases we saw for ride sharing (for example) will come to AI. There's plenty of competition and not much in the way of lock-in. And by the time the bubble is over, the underlying costs may have dropped due to improved algorithms.
It might be more like personal computing in the 80's, when Moore's law resulted in both more usage and cheaper prices as the tech improved.
There’s also the other way around. Semantic AI is a good chunk of meat, but it can only be useful as it’s harnessed properly with a nice set of bones. I think that symbolic AI will make a come back eventually. Not as an accelerator of what’s already been done in the Industry, but as the actual revolution.
And don’t be naive to think that there aren’t sophisticated symbolic handling mechanisms being implemented in the training of the models by Big Tech. Not even baby soap is truly neutral.
As a possible example of this, I was kind of baffled how quickly we're all now throwing the sophisticated AST/program analysis and refactoring methods over board we already had before AI. Just look at the refactoring methods of Eclipse or IntelliJ.
I think those should be very useful, especially with AI: Either as a tool for the agents themselves - why spend heaps of tokens completely rewriting a code file, if you could do most of it by calling some global refactoring operations on the IDE's AST/symbol database?
Or side-by-side with it, to give human users better insight what the AI did.
Instead it seems to be all VSCode (if at all) + grep + AI agents, and nothing else.
"Code Mode", where the AI writes a little program or script to do the AST/symbol transformations sounds like the win. As you point out, less tokens, and gives the humans insight.
More like architects, in the civil engineering sense. Even if you don't pour concrete or drive nails yourself, you have to have a lot of technical understanding, including the low level, to be able to design a building competently. And, very certainly, you have to be able to inspect how the nails are driven and how the bricks are laid, whether your contractors are humans or robots.
> And, very certainly, you have to be able to inspect how the nails are driven and how the bricks are laid, whether your contractors are humans or robots.
As someone coming from non-software engineering (mechanical), I always believed that good engineer is capable, and sometimes does these things himself. This guarantees he actually understands his domain in all aspects.
Or it could be an awful lot of productivity. We have to think bigger. What would the world look like if every programmer were a 10x (or whatever) programmer?
I self-taught myself coding at a young age but I haven't had any identity crisis due to AI. I always saw myself as a software architect, not a coder.
When I was a junior learning to code, I would feel proud of myself because I could remember 100 lines of Windows API code needed to create a new window... But it's been decades since I understood that the real value is not in the code. It's in the architecture. As the author alludes to; the intuition behind the code is what counts.
I think highly competent engineers are often underappreciated because the really clever stuff they do doesn't appear clever at all; it looks deceptively simple. I think what people don't understand is that maintaining simplicity whilst requirements are becoming more complex, is very difficult.
I think the thing that drives me nuts is that, while most people think the result of programming is a program, I disagree. The result of programming is one or more people who have a deep understanding of a problem space. Codegen models still require a human in the loop; that human has to be a software expert. You only become a software expert by writing software.
I'm in my mid-40's now. I taught myself C when I was 15. I have no desire to use LLMs to pump out code.
I take comfort in re-reading much of the 70's and 80's literature which focuses the possibilities of user experience. We still haven't fully explored all the dreams of half a century ago.
If AI forces the business case that "code is cheap", I can only hope we re-double our efforts at creating new interfaces and capabilities for computer systems. The Meta glasses, Apple Vision, and the like are small steps in this direction.
>I'm in my mid-40's now. I taught myself C when I was 15. I have no desire to use LLMs to pump out code.
For me, it's the other way around: I'm glad that AI can write code for me. A few months ago, I moved from an engineering role to a researcher role exactly because I got tired of writing code. Probably 95% of code/features (at least the kind you get paid for) is just boring CRUD stuff where you move bytes from one place to another and then show them in the UI (plus some access rules and a few invariants here and there).
All of it was actually interesting in the first couple of years. But when you do it over and over again for 20 years in a row... Yeah. Sometimes there are interesting projects from time to time, but usually it's the same stuff you've done countless times. Deja vu.
In my current researcher role, my task is to explore novel ideas, productize research papers, etc., and LLMs allow me to quickly write prototypes and demos, play with various ideas, without having to spend a lot of time manually moving bytes from one place to another. It’s fun again.
I'm in a similar age and career point and have similar feelings about my SE job atm. Can you share more about your transition to the researcher role? What field are you in, how did you get there, what problems did you encounter, etc.
Interesting! Could you give some examples of these unexplored fields/applications ?
But what happens when the bill comes in? That's my biggest fear. I heard on a recent podcast that it is a great time to be a micro-entrepreneur, and I think that's true right now because AI is so cheap. But AI companies are hemorraghing money. What happens to those micro-entrepreneurs when the price goes up? Are we going to live in a world where only large, rich corporations can afford to competitively develop things? Maybe so, but it is depressing to think about.
For the plebeians (like me), I think hand-coding skills will always be relevant and necessary.
It's unclear whether the price increases we saw for ride sharing (for example) will come to AI. There's plenty of competition and not much in the way of lock-in. And by the time the bubble is over, the underlying costs may have dropped due to improved algorithms.
It might be more like personal computing in the 80's, when Moore's law resulted in both more usage and cheaper prices as the tech improved.
A lot of this really resonates with me, but, as much as I empathize with the Deep Blue analogy I don't fully subscribe to it.
Maybe that's just me being naïve! Feel like humans have always been able to do more with more, not certain why now would be any different.
Does not mean the role won't change and evolve! Building the airplane while flying is part of the game, and to me is what makes this field exciting.
There’s also the other way around. Semantic AI is a good chunk of meat, but it can only be useful as it’s harnessed properly with a nice set of bones. I think that symbolic AI will make a come back eventually. Not as an accelerator of what’s already been done in the Industry, but as the actual revolution.
And don’t be naive to think that there aren’t sophisticated symbolic handling mechanisms being implemented in the training of the models by Big Tech. Not even baby soap is truly neutral.
As a possible example of this, I was kind of baffled how quickly we're all now throwing the sophisticated AST/program analysis and refactoring methods over board we already had before AI. Just look at the refactoring methods of Eclipse or IntelliJ.
I think those should be very useful, especially with AI: Either as a tool for the agents themselves - why spend heaps of tokens completely rewriting a code file, if you could do most of it by calling some global refactoring operations on the IDE's AST/symbol database?
Or side-by-side with it, to give human users better insight what the AI did.
Instead it seems to be all VSCode (if at all) + grep + AI agents, and nothing else.
This is an interesting idea! I searched around and it looks like there's [ast-grep](https://ast-grep.github.io/), an AST-aware CLI that can search and refactor code -- and you can expose it to your AI agent using a skill (https://github.com/ast-grep/agent-skill).
Not exactly symbolic AI, but pretty cool nonetheless.
> especially with AI
Yeah, the middle path sounds promising.
"Code Mode", where the AI writes a little program or script to do the AST/symbol transformations sounds like the win. As you point out, less tokens, and gives the humans insight.
This isn't exactly the same application of a "code mode" as before, but in my view it's a broad philosophy. AI for building machines, instead of doing the work directly. It also allows for easier updates/retries too. https://blog.cloudflare.com/code-mode/ https://news.ycombinator.com/item?id=45399204 https://blog.cloudflare.com/code-mode-mcp/ https://news.ycombinator.com/item?id=47089505
So...software engineers become product managers? All of us? That's going to be an awful lot of product managers.
More like architects, in the civil engineering sense. Even if you don't pour concrete or drive nails yourself, you have to have a lot of technical understanding, including the low level, to be able to design a building competently. And, very certainly, you have to be able to inspect how the nails are driven and how the bricks are laid, whether your contractors are humans or robots.
> And, very certainly, you have to be able to inspect how the nails are driven and how the bricks are laid, whether your contractors are humans or robots.
As someone coming from non-software engineering (mechanical), I always believed that good engineer is capable, and sometimes does these things himself. This guarantees he actually understands his domain in all aspects.
I create a lot of applications with AI. I don't inspect any of the cod.
Maybe you should, something fishy may be going on in it.
Or it could be an awful lot of productivity. We have to think bigger. What would the world look like if every programmer were a 10x (or whatever) programmer?
Sure. I'm with you. I'm just puzzled by the article.
I self-taught myself coding at a young age but I haven't had any identity crisis due to AI. I always saw myself as a software architect, not a coder.
When I was a junior learning to code, I would feel proud of myself because I could remember 100 lines of Windows API code needed to create a new window... But it's been decades since I understood that the real value is not in the code. It's in the architecture. As the author alludes to; the intuition behind the code is what counts.
I think highly competent engineers are often underappreciated because the really clever stuff they do doesn't appear clever at all; it looks deceptively simple. I think what people don't understand is that maintaining simplicity whilst requirements are becoming more complex, is very difficult.
That maybe true, value is producing simple solutions as requirements get complex, but will companies realize and value this?