The real benefactors of AI in software development are senior devs who’ve had enough of boilerplate, framework switching, and other tedious low-value tasks. You cut down on the former laborious tradition of picking through StackOverflow for glimmers of hope.
Here's something I assert to know about how AI and software engineering intersect in 2025: It makes certain classes of tasks 150% faster, while making other classes of tasks 300% slower. What I rarely know upfront is whether a given task will fall into the former class or the latter.
What about graphic, web and app design? I just do not see a long term career going forward (been meaning to research how many UX jobs are being listed in 2025 to previous years). UX Research I do as you are interacting with users and AI is not a person (yet).
While I read this article, Claude code was fixing a bug for me.
I agree with Cal that we basically don’t know what happens next. But I do know that the world needs a lot more good software and expanding the scope of what good software professionals can do for companies is positive.
Big nothingburger which is particularly surprising given the quality of Newport's other work. tl;dr don't believe the extreme takes while we are in a time of high uncertainty.
There are different groups with vested interests that color a lot of AI discourse
You have Tech CEOs that want work done cheaper and AI companies willing to sell it to them. They will give you crazy alarming narratives around AI replacing all developers, etc.
Then you have tech employees who want to believe they’re irreplaceable. It’s easy to want to keep working how we’ve always worked with hope of getting back to pre 2022 levels of software hiring and income. AI stands in the way of that.
I don’t think people are doing this intentionally all the time. But there is so much money and social stature from all these groups on the line, there are very few able to give a neutral, disinterested perspective on a topic like AI coding.
And to add to that, reasoned, boring, thoughtful middle of the road takes are just naturally going to get fewer eyeballs than extreme points of view.
The value of AI is easy to see personally, concretely. But there is always a gap between concrete value in your hands and how that plays out in larger systems. The ability to work remotely could intuitively project to outsourcing of almost all knowledge work to cheaper labor markets, and yet that has only happened at the margins. The world is complex and complicated, reserve a measure of doubt.
AI more of a force muliplier than a replacement. If you rated programmers from 0 to 100. AI can take you from 0 to 80, but can't take you from 98 to 99.
I'd love to record these AI CEOs statements about what's going to happen in the next 24 months and look back at that time -- see how "transformed" the world is then.
I’m of the opinion that LLM assisted coding tools have a few general effects in the domain of software development:
1. Increases in productivity for experienced software engineers.
2. Raised industry expectations around productivity of individual software engineers.
3. Lowered ratio of human engineers to other parters in the development process (customers, stakeholders, etc.)
I’ve found the net result is an environment where we can churn out code easily, but producing the right code becomes harder. Fewer eyes on the production of a product means most of the validation later in the development process.
How much an issue this is remains to be seen. Engineers will presumably have more time to review and debug things. Debugging will be easier. That said, I have no idea if any of that makes up for retroactively realizing design assumptions were bad or that some subtle constraints about your software were violated.
I'm running about a dozen sockpuppet Twitter accounts that post LLM-generated gaslighting posts about software engineering no longer being a viable career choice. I'd recommend other senior engineers do the same - it only takes about 15 minutes each day.
The key is to look at the long term structural changes the industry is going through, and whether or not AI helps, or hinders that goal
In general, the industry has been making huge efforts to push errors from runtime, to compile time. If you imagine points where we can catch errors being laid out from left to right, we have the following:
The industry is trying to push errors leftwards. Rust, heavier review, safety in general - its all about cutting down costs by eliminating expensive errors earlier in the production chain. Every industry does this, its much less costly to catch a defective oxygen mask in the factory, than when it sets a plane on fire. Its also better to catch a defective component in the design phase, than when you're doing tests on it
AI is all about trying to push these errors rightwards. The only way that it can save in engineer time is if it goes through inadequate testing, validation, and review. 90% of the complexity of programming is building a mental model of what you're doing, and ensuring that it meets the spec of what you want to do. A lot of that work is currently pure mental work with no physical component - we try and offload it increasingly to compilers in safe languages, and add tests and review to minimise the slippage. But even in a safe language, it still requires a very high amount of mental work to be done to make sure that everything is correct. Tests and review are a stop gap to try and cover the fallibility of the human brain
So if you chop down on that critical mental work by using something probabilistically correct, you're introducing errors that will be more costly down the line. It'll be fine in the short term, but in the long term it'll cost you more money. That's the primary reason why I don't think AI will catch on - its short termist thinking from people who don't understand what makes software complex to build, or how to actually produce software that's cheap in the long term. Its also exactly the same reason that Boeing is getting its ass absolutely handed to it in the aviation world. Use AI if you want to go bankrupt in 5 years but be rich now
> I expected to find vastly differing views of what future developments might look like, but I was surprised at just how much our alums differed in their assessment of where things are today.
> We found at least three factors that help explain this discrepancy. First was the duration, depth, and recency of experience with LLMs; the less people had worked with them and the longer ago they had done so, the more likely they were to see little value in them (to be clear, “long ago” here may mean a matter of just a few months). But this certainly didn’t explain all of the discrepancy: The second factor was the type of programming work people cared about. By this we mean things like the ergonomics of your language, whether the task you’re doing is represented in model training data, and the amount of boilerplate involved. Programmers working on web apps, data visualization, and scripts in Python, TypeScript, and Go were much more likely to see significant value in LLMs, while others doing systems programming in C, working on carbon capture, or doing novel ML research were less likely to find them helpful. The third factor was whether people were doing smaller, more greenfield work (either alone or on small teams), or on large existing codebases (especially at large organizations). People were much more likely to see utility in today’s models for the former than the latter.
There is a great article floating around on the economics of AI and how parasitic the current market is between the Fab Five.
We are 27-ish months since the claim that all software engineers would be replaced within six months by some of these CEOs. It is their job to analyze the market and determine what the next big thing is, but they can be wrong - no one has a crystal ball here.
The difficulty for me is how disconnected a lot of the takes are (or even flat out manipulative) that are being pushed out. I am an early adopter of AI tools. I utilize them on a day-to-day basis, but there is no way that I see AI taking SW jobs right now.
You have others claiming that these tools will just get exponentially better now, time will tell, but as of right now there is still too much value in human coders any anyone that is actively pushing for replacing SWE with "Agents" is either betting big on the future (that is unproven) or attempting to entice/manipulate the larger market.
AI research is like the topic of sex during adolescence: of everyone say they are doing it, few are; and the few that are in fact doing it are probably doing it wrong.
I´m doing the work of 3 dudes alone (dev, qa, devops). My delivery is 100% faster and my development practice allows me to reduce the bugs dramatically.
Maybe my stack is easier or the product is not too complex but if I take my own experience as a truth, my truth, (we) Engineers are going to suffer for a long time.
Everybody will have different experiences but my guess not all developers are working in frontier projects so their jobs will be the first to suffer the change. At least for me this is going to happen.
I really blame the Sam Altman hype machine for all of this dystopian nonsense. He really is like a Ryan holiday you can’t trust anything he says. He's the one who started all this "employees are going away" stuff with this $20,000 AI employee. that's not when he started it, he started it long before that with all his basic income bullshit.
I don’t think you can become a talented enough software developer to benefit from using AI by using AI. Google Maps causes us to lose the ability to navigate without it. I would not be surprised in the slightest if the same is true with AI.
So. A little boost now. But at the risk of not knowing how to get where you’re going unless someone is holding your hand.
And if that’s the case, how can we possibly get to where we’ve never been before?
Wouldn't the be reacting differently if the cost and speed of development in the economy's highest ROI industries was really expected to improve by an OOM in the near future?
The people who are saying AI will replace everyone are people who don't actively deploy code anymore. People like CEOs and VPs.
People who are actively deploying code are well aware of the limitations of AI.
A good prompt with some custom context will get you maybe 80% of the way there. Then iterating with the AI will get you about 90% of the way, assuming you're a senior engineer with enough experience to know what to ask. But you will still need to do some work at the end to get it over the line.
And then you end up with code that works but is definitely not optimal.
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[ 3.4 ms ] story [ 53.4 ms ] threadI agree with Cal that we basically don’t know what happens next. But I do know that the world needs a lot more good software and expanding the scope of what good software professionals can do for companies is positive.
You have Tech CEOs that want work done cheaper and AI companies willing to sell it to them. They will give you crazy alarming narratives around AI replacing all developers, etc.
Then you have tech employees who want to believe they’re irreplaceable. It’s easy to want to keep working how we’ve always worked with hope of getting back to pre 2022 levels of software hiring and income. AI stands in the way of that.
I don’t think people are doing this intentionally all the time. But there is so much money and social stature from all these groups on the line, there are very few able to give a neutral, disinterested perspective on a topic like AI coding.
And to add to that, reasoned, boring, thoughtful middle of the road takes are just naturally going to get fewer eyeballs than extreme points of view.
One would usually show some kind of evidence after making such a statement. Claims from CEOs of AI companies don't count.
I don't know a single person with a bit of seniority, using Claude Code, who wants to go back to any IDE from 5 years ago.
I'd love to record these AI CEOs statements about what's going to happen in the next 24 months and look back at that time -- see how "transformed" the world is then.
1. Increases in productivity for experienced software engineers.
2. Raised industry expectations around productivity of individual software engineers.
3. Lowered ratio of human engineers to other parters in the development process (customers, stakeholders, etc.)
I’ve found the net result is an environment where we can churn out code easily, but producing the right code becomes harder. Fewer eyes on the production of a product means most of the validation later in the development process.
How much an issue this is remains to be seen. Engineers will presumably have more time to review and debug things. Debugging will be easier. That said, I have no idea if any of that makes up for retroactively realizing design assumptions were bad or that some subtle constraints about your software were violated.
In general, the industry has been making huge efforts to push errors from runtime, to compile time. If you imagine points where we can catch errors being laid out from left to right, we have the following:
Caught by: Compiler -> code review -> tests -> runtime checks -> 'caught' in prod
The industry is trying to push errors leftwards. Rust, heavier review, safety in general - its all about cutting down costs by eliminating expensive errors earlier in the production chain. Every industry does this, its much less costly to catch a defective oxygen mask in the factory, than when it sets a plane on fire. Its also better to catch a defective component in the design phase, than when you're doing tests on it
AI is all about trying to push these errors rightwards. The only way that it can save in engineer time is if it goes through inadequate testing, validation, and review. 90% of the complexity of programming is building a mental model of what you're doing, and ensuring that it meets the spec of what you want to do. A lot of that work is currently pure mental work with no physical component - we try and offload it increasingly to compilers in safe languages, and add tests and review to minimise the slippage. But even in a safe language, it still requires a very high amount of mental work to be done to make sure that everything is correct. Tests and review are a stop gap to try and cover the fallibility of the human brain
So if you chop down on that critical mental work by using something probabilistically correct, you're introducing errors that will be more costly down the line. It'll be fine in the short term, but in the long term it'll cost you more money. That's the primary reason why I don't think AI will catch on - its short termist thinking from people who don't understand what makes software complex to build, or how to actually produce software that's cheap in the long term. Its also exactly the same reason that Boeing is getting its ass absolutely handed to it in the aviation world. Use AI if you want to go bankrupt in 5 years but be rich now
> I expected to find vastly differing views of what future developments might look like, but I was surprised at just how much our alums differed in their assessment of where things are today.
> We found at least three factors that help explain this discrepancy. First was the duration, depth, and recency of experience with LLMs; the less people had worked with them and the longer ago they had done so, the more likely they were to see little value in them (to be clear, “long ago” here may mean a matter of just a few months). But this certainly didn’t explain all of the discrepancy: The second factor was the type of programming work people cared about. By this we mean things like the ergonomics of your language, whether the task you’re doing is represented in model training data, and the amount of boilerplate involved. Programmers working on web apps, data visualization, and scripts in Python, TypeScript, and Go were much more likely to see significant value in LLMs, while others doing systems programming in C, working on carbon capture, or doing novel ML research were less likely to find them helpful. The third factor was whether people were doing smaller, more greenfield work (either alone or on small teams), or on large existing codebases (especially at large organizations). People were much more likely to see utility in today’s models for the former than the latter.
— https://www.recurse.com/blog/191-developing-our-position-on-...
We are 27-ish months since the claim that all software engineers would be replaced within six months by some of these CEOs. It is their job to analyze the market and determine what the next big thing is, but they can be wrong - no one has a crystal ball here.
The difficulty for me is how disconnected a lot of the takes are (or even flat out manipulative) that are being pushed out. I am an early adopter of AI tools. I utilize them on a day-to-day basis, but there is no way that I see AI taking SW jobs right now.
You have others claiming that these tools will just get exponentially better now, time will tell, but as of right now there is still too much value in human coders any anyone that is actively pushing for replacing SWE with "Agents" is either betting big on the future (that is unproven) or attempting to entice/manipulate the larger market.
Maybe my stack is easier or the product is not too complex but if I take my own experience as a truth, my truth, (we) Engineers are going to suffer for a long time.
Everybody will have different experiences but my guess not all developers are working in frontier projects so their jobs will be the first to suffer the change. At least for me this is going to happen.
So. A little boost now. But at the risk of not knowing how to get where you’re going unless someone is holding your hand.
And if that’s the case, how can we possibly get to where we’ve never been before?
We know there's a lot of lying, fakery, dishonest, and hype in AI.
AI is communists playing the Wizard in The Wizard of Oz.
It's about power, intimidation, and psychology.
People who are actively deploying code are well aware of the limitations of AI.
A good prompt with some custom context will get you maybe 80% of the way there. Then iterating with the AI will get you about 90% of the way, assuming you're a senior engineer with enough experience to know what to ask. But you will still need to do some work at the end to get it over the line.
And then you end up with code that works but is definitely not optimal.