The argument against relying on AI for everything is that the humans who curate and architect systems learned what they did through their experience at lower levels.
Overutilization of AI is pulling the ladder up and preventing the next generation of software architects and engineers from learning through experience.
I think the whole AI vs non. AI debate is a bit besides the point. Engineers are stuck in the old paradigm of "perfect" algorithms.
I think the image you post at the beginning basically sums it up for me: ChatGPT o3/5 Thinking can one-shot 75% of most reasonably sized tasks I give it without breaking a sweat, but struggles with tweaks to get it to 100%. So I make those tweaks myself and I have cut my code writing task in half or one third of the time.
ChatGPT also knows more idioms and useful libraries than I do so I generally end up with cleaner code this way.
Ferrari's are still hand assembled but Ford's assembly line and machines help save up human labor even if the quality of a mass-produced item is less than a hand-crafted one. But if everything was hand-crafted, we would have no computers at all to program.
Programming and writing will become niche and humans will still be used where a quality higher than what AI can produce is needed. But most code will be done by minotaur human-ai teams, where the human has a minimal but necessary contribution to keep the AI on track... I mean, it already is.
> Programming and writing will become niche and humans will still be used where a quality higher than what AI can produce is needed. But most code will be done by minotaur human-ai teams, where the human has a minimal but necessary contribution to keep the AI on track... I mean, it already is.
Or alternatively, we will build bigger and better things with the power of AI. Everyone talks about it replacing us, but we aren't running out of things to build. It's not like we're gonna run out of ways to improve the world, and compared to other things the digital world is unconstrained.
It would be good if there were a mode where AI trained the human operator as it worked, to reduce future reliance. Instead of just writing a document or editing a document, it would explain in a good amount of detail what it was doing, and tailor the information to the understanding level of the operator. It might even quiz the operator to assure understanding.
This would take more time in the short run, but in the long run it would result in more well-rounded humans.
When there are power/internet/LLM outages, some people are going to be rendered completely helpless, and others will be more modestly impacted — but will still be able to get some work done.
We should aim to have more people in the latter camp.
I have gone from using Claude Code all day long since the day it was launched to only using the separate Claude app. In my mind that is a nice balance of using it, but not too much, not too fast.
there is the temptation to just let these things run in our codebases, which I think for some projects is totally fine. For most websites I think this would usually be fine. This is for two reasons: 1) these models have been trained on more websites than probably anything else and 2) if a div/text is off by a little bit then usually there will be no huge problems.
But if you're building something that is mission critical, unless you go super slowly, which again is hard to do because these agents are tempting to go super fast. That is sort of the allure of them: to be able to write sofware super fast.
But as we all know, in some programs you cannot have a single char wrong or the whole program may not work or have value. At least that is how the one I am working on is.
I found that I lost the mental map of the codebase I am working on. Claude Code had done too much too fast.
I found a function this morning to validate futures/stocks/FUT-OPT/STK-OPT symbols where the validation was super basic and terrible that it had written. We had implemented some very strong actual symbol data validation a week or two ago. But that wasn't fully implemented everywhere. So now I need to go back and do this.
Anyways, I think finding where certain code is written would be helpful for sure and suggesting various ways to solve problems. But the separate GUI apps can do that for us.
So for now I am going to keep just using the separate LLM apps. I will also save lots of money in the meantime (which I would gladly spend for a higher quality Claude Code ish setup).
It used to be reading and writing were skills. People would strive to get better, usually by steeping themselves in the work of better writers so some rubs off.
Now, the llm summarizes the email, so you only have to be so literate to understand bullet points. The llm takes your bullet points and turns them into long form writing, because you can’t write. They say this is necessary because they aren’t good writers, but this creates a self fulfilling prophecy if they just avoid writing entirely.
A sad time when people are allowing their ability to read and write fall by the wayside. These used to be skills people valued and took pride in improving for themselves. Now people shut their brain off.
Tools like Claude Code and OpenAI’s Codex CLI have boosted my productivity massively. They already handle about 90% of the coding work, and I just step in to finish the last 10%. Every month they get better, maybe in a year it’s 95%, in two years 97%, in three years 98%. We can all see where this is going.
AI can churn out code that works, but usable, secure code is written for humans first; clear, predictable, and safe by design. Until AI can reason like a team lead and think like an attacker, it’s still just guessing. Usability and security aren’t side effects, they’re intentions, and AI doesn’t have those yet. Code without security is useless[0].
[0] https://www.npr.org/2025/08/02/nx-s1-5483886/tea-app-breach-...
AI is using the well known Embrace Extend Extinguish strategy, coined by Microsoft. Today corporations push us to embrace AI by all means possible, and use where it's useful and where it's not useful. Tomorrow AI will extend our ways of thinking and working in such a ways that we'll have to rely on it, like a medicine that you'd rather not use, but got dependent on. And then AI will extinguish us, for its main selling point is it replaces employees, makes them unnecessary. That's the plan, at least.
The replacement of reading books and writing letters with television/video streaming and phone/texting has done far more to depress average human cognitive skills than LLM code and text generation could ever hope to achieve.
As far as code generation, there are some intriguing workflows out there. Eg start by having the LLM generate a git repo for your project. Use the DAG for the repo as the guide for more code generation. Embed the code files into a vector database, differentianting between static and dynamic files and re-embed on commits. Use all those data structures in compilers: ASTs, call graphs, dependency graphs - as guides for the AI along with the git repo's DAG. Then if its proprietary code development, run an open source LLM locally to avoid leaking at the embedding and code generation stages. Now, run an 'overnight build' using your local LLM for code generation. Come in next day, review and test all the code the LLM generated. End of the day, commit all the changes that look good, rinse and repeat.
The thing here is you are actively involved at every stage - and the same strategies work for any non-coding writing task. Eg structure your prompts carefully like well-designed short essays. Read the LLM output with an eye out for errors and inconsistencies, copy and paste those into the next prompt and demand a critical review. Once the context window gets too big, boil it all down into a vector database for future reference, generate a new summary prompt from that database, rinse and repeat.
I'd suggest thinking of yourself as the conductor of an orchestra, but one who knows what the capabilities of every instrument is, so you're actually thinking harder and working harder than you were before you had access to these AI tools. That at least will keep your cognitive skills in tune.
P.S. I tend to get much better critical analysis from the LLM if I start all chats with:
> "Preliminary instructions: do not engage in flattery or enthusiastic validation or praise of any kind. Do not offer suggestions for improvement at the end of output. If this is clear, respond with "yes, this is clear"."
Don't let the LLM give you roses and lead you down the garden path, instead think of it as a politely-adversarial low-trust relationship.
I've been learning how to build houses as my escape hatch. A lot of people like to talk about how AI isn't capable of this and that - the ONLY thing you will be complaining about in 5 years is the Basic Income programs needing to expand quickly and reform so we people can keep their 1.8M mortgages with $2000/mo taco bell income.
I share much of the same ideas about this as the author.
For a long time, to make coding more natural (before and after LLMs) and not having to think about certain keywords or syntax, I would
create little Anki decks (10-20 cards) with basic exercises for a particular language or tool I was using. One to two weeks of 5-10 minutes/day of doing these exercises (like, how to redirect both stout and strrr into a file, how to read from a channel in go, etc) and I was working without having to pause.
Writing code became less disruptive and much easier to get my ideas into a text editor.
I’m also the creator of typequicker.com (disclaimer) and we added Code mode as a typing exercise.
At first I thought folks wouldn’t be interested. I was pleasantly surprised though; many people are using it specifically for the same reason that the author is talking about.
I think we’ll need to rethink what it means to be “skilled.” Fewer people will write every line of code themselves, but more will need to understand systems end-to-end, ask the right questions, and guide AIs to meaningful outcomes.
AI isn’t replacing thinking, it’s changing what we think about. Coding skill won’t disappear; it’ll just evolve.
Yes, it will replace human thinking. Thats quite literally the explicit goal of every AI company.
Historically every technological recolution serves to replace some facet of human labor (usually with the incentive of squeezing profits as technology gets cheaper over time, but wages do not).
Industrial revolution == automate non dexterous manual labor
Information age == automate "computational"/numerical thinking
I think what we see is the final struggle between the Anglo-Saxon concept of Education ( Locke, Dewy) and the German concept of “Bildung” (Humboldt, Herder).
The former puts more emphasis on the pragmatic transmission of knowledge and socialisation, while the latter focuses on self formation via the gaining of knowledge.
People will always be educated, acquiring skills and being part of a society does not go out of fashion.
I am not sure the ideal of the “gebildeter Mensch” will survive. Self discovery through deep engagement with intellectual tasks requires exactly those skills AI tries to automatise.
The thing is, this is already occurring and seems to be OK as long as there exists a class of individuals who provide the intellectual horsepower to keep society moving forward.
But I feel very uncomfortable in the fact that people (who for whatever reason) can't make rational and responsible choices, are not being protected and free to exploitation.
why would the human controlled AI replace the thinking of remote controlled humans thinking they are thinking? Manual enshittification is still a case of enshittification.
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[ 4.2 ms ] story [ 45.4 ms ] threadI wonder if some of the proponents know where the line is in the art. I suspect not.
Overutilization of AI is pulling the ladder up and preventing the next generation of software architects and engineers from learning through experience.
I think the image you post at the beginning basically sums it up for me: ChatGPT o3/5 Thinking can one-shot 75% of most reasonably sized tasks I give it without breaking a sweat, but struggles with tweaks to get it to 100%. So I make those tweaks myself and I have cut my code writing task in half or one third of the time.
ChatGPT also knows more idioms and useful libraries than I do so I generally end up with cleaner code this way.
Ferrari's are still hand assembled but Ford's assembly line and machines help save up human labor even if the quality of a mass-produced item is less than a hand-crafted one. But if everything was hand-crafted, we would have no computers at all to program.
Programming and writing will become niche and humans will still be used where a quality higher than what AI can produce is needed. But most code will be done by minotaur human-ai teams, where the human has a minimal but necessary contribution to keep the AI on track... I mean, it already is.
Or alternatively, we will build bigger and better things with the power of AI. Everyone talks about it replacing us, but we aren't running out of things to build. It's not like we're gonna run out of ways to improve the world, and compared to other things the digital world is unconstrained.
This would take more time in the short run, but in the long run it would result in more well-rounded humans.
When there are power/internet/LLM outages, some people are going to be rendered completely helpless, and others will be more modestly impacted — but will still be able to get some work done.
We should aim to have more people in the latter camp.
there is the temptation to just let these things run in our codebases, which I think for some projects is totally fine. For most websites I think this would usually be fine. This is for two reasons: 1) these models have been trained on more websites than probably anything else and 2) if a div/text is off by a little bit then usually there will be no huge problems.
But if you're building something that is mission critical, unless you go super slowly, which again is hard to do because these agents are tempting to go super fast. That is sort of the allure of them: to be able to write sofware super fast.
But as we all know, in some programs you cannot have a single char wrong or the whole program may not work or have value. At least that is how the one I am working on is.
I found that I lost the mental map of the codebase I am working on. Claude Code had done too much too fast.
I found a function this morning to validate futures/stocks/FUT-OPT/STK-OPT symbols where the validation was super basic and terrible that it had written. We had implemented some very strong actual symbol data validation a week or two ago. But that wasn't fully implemented everywhere. So now I need to go back and do this.
Anyways, I think finding where certain code is written would be helpful for sure and suggesting various ways to solve problems. But the separate GUI apps can do that for us.
So for now I am going to keep just using the separate LLM apps. I will also save lots of money in the meantime (which I would gladly spend for a higher quality Claude Code ish setup).
Now, the llm summarizes the email, so you only have to be so literate to understand bullet points. The llm takes your bullet points and turns them into long form writing, because you can’t write. They say this is necessary because they aren’t good writers, but this creates a self fulfilling prophecy if they just avoid writing entirely.
A sad time when people are allowing their ability to read and write fall by the wayside. These used to be skills people valued and took pride in improving for themselves. Now people shut their brain off.
It's like asking -- "will robots be good for building things?"
Sure, some things. What things?
Personally, I'm hoping for the revival of the idea that Hypercard was intended for; yes, let us enable EVERYONE to build little tools for themselves.
As far as code generation, there are some intriguing workflows out there. Eg start by having the LLM generate a git repo for your project. Use the DAG for the repo as the guide for more code generation. Embed the code files into a vector database, differentianting between static and dynamic files and re-embed on commits. Use all those data structures in compilers: ASTs, call graphs, dependency graphs - as guides for the AI along with the git repo's DAG. Then if its proprietary code development, run an open source LLM locally to avoid leaking at the embedding and code generation stages. Now, run an 'overnight build' using your local LLM for code generation. Come in next day, review and test all the code the LLM generated. End of the day, commit all the changes that look good, rinse and repeat.
The thing here is you are actively involved at every stage - and the same strategies work for any non-coding writing task. Eg structure your prompts carefully like well-designed short essays. Read the LLM output with an eye out for errors and inconsistencies, copy and paste those into the next prompt and demand a critical review. Once the context window gets too big, boil it all down into a vector database for future reference, generate a new summary prompt from that database, rinse and repeat.
I'd suggest thinking of yourself as the conductor of an orchestra, but one who knows what the capabilities of every instrument is, so you're actually thinking harder and working harder than you were before you had access to these AI tools. That at least will keep your cognitive skills in tune.
P.S. I tend to get much better critical analysis from the LLM if I start all chats with:
> "Preliminary instructions: do not engage in flattery or enthusiastic validation or praise of any kind. Do not offer suggestions for improvement at the end of output. If this is clear, respond with "yes, this is clear"."
Don't let the LLM give you roses and lead you down the garden path, instead think of it as a politely-adversarial low-trust relationship.
That paper has far too many graphs which are totally made up.
It's been less than three years since ChatGPT launched. What's this going to be be like in a decade or two?
I've been learning how to build houses as my escape hatch. A lot of people like to talk about how AI isn't capable of this and that - the ONLY thing you will be complaining about in 5 years is the Basic Income programs needing to expand quickly and reform so we people can keep their 1.8M mortgages with $2000/mo taco bell income.
I share much of the same ideas about this as the author.
For a long time, to make coding more natural (before and after LLMs) and not having to think about certain keywords or syntax, I would create little Anki decks (10-20 cards) with basic exercises for a particular language or tool I was using. One to two weeks of 5-10 minutes/day of doing these exercises (like, how to redirect both stout and strrr into a file, how to read from a channel in go, etc) and I was working without having to pause.
Writing code became less disruptive and much easier to get my ideas into a text editor.
I’m also the creator of typequicker.com (disclaimer) and we added Code mode as a typing exercise.
At first I thought folks wouldn’t be interested. I was pleasantly surprised though; many people are using it specifically for the same reason that the author is talking about.
AI isn’t replacing thinking, it’s changing what we think about. Coding skill won’t disappear; it’ll just evolve.
Historically every technological recolution serves to replace some facet of human labor (usually with the incentive of squeezing profits as technology gets cheaper over time, but wages do not).
Industrial revolution == automate non dexterous manual labor
Information age == automate "computational"/numerical thinking
AI == automate thinking
Robotics + AI == automate dexterous manual labor
The former puts more emphasis on the pragmatic transmission of knowledge and socialisation, while the latter focuses on self formation via the gaining of knowledge.
People will always be educated, acquiring skills and being part of a society does not go out of fashion.
I am not sure the ideal of the “gebildeter Mensch” will survive. Self discovery through deep engagement with intellectual tasks requires exactly those skills AI tries to automatise.
But I feel very uncomfortable in the fact that people (who for whatever reason) can't make rational and responsible choices, are not being protected and free to exploitation.
if you need gen ai to be an efficient dev then you are bad at your job.