> "With so many companies worldwide faced with upheaval due to AI in software development, I believe this program will become a template for rapidly leveling up new developers across the industry."
This seems cognitively dissonant with the AI upheaval resulting in the industry needing fewer developers. Maybe they want cheaper developers?
Based on posts by people involved like Scott Hanselman and Mark Russinovich, this initiative is a direct response to the long-term threat of AI completely replacing junior developers, and thus cutting off the pipeline of experience that turns junior developers into senior developers, who are still needed to leverage AI effectively.
There is of course the possibility that AI gets good enough that even senior engineers won’t be needed, but at this point it seems unlikely. My reasoning is that even expert humans make mistakes, which is why we need other humans to collaborate and check each others’ work (which, if you think about it, is a source of a lot of the processes and bureaucracy at companies; trivial example: code reviews.)
So even if AI gets to the level of human experts, this dynamic will not change... unless we hit ASI, where the AI far exceeds human capabilities. At which point we’ve hit the singularity and all bets are off.
Short of that, however, human experts will be needed, which is why programs like this are needed: to future proof against the short term economic pressures to minimize junior workers which will impact the long term pipeline that produces senior experts.
OK, so let me get this straight: Microsoft replaces junior SWEs with AI... and this presumably doesn't really let the company execute any faster, because bottlenecks at MSFT are mostly unrelated to kLOC/hour. But okay. And then they establish a parallel program for people who are kept on Microsoft payroll... while coding in an academic setting... so that they can progress through the rungs of the SWE ladder and eventually be the ones supervising the AI.
This is really what we're doing? This makes sense? Am I taking crazy pills?
The bigger issue is that undergrad students learning in the era of AI is really a terrible environment for actually learning the fundamental theory that gives someone a long and productive career. And the workplace has never been a good place to learn that stuff. The talent pipeline from colleges has always been a steady trickle despite eras when signups into computer science was a flood. With AI, that trickle has become a scant drip.
Yes, I believe programs like this aim to address this issue too. The traditional ways of learning might no longer be sufficient or reliable enough to produce appropriately skilled workers in the era of AI. Maybe something like this program or other apprenticeship programs would bridge that gap by focusing on juniors who are truly interested in the field.
The traditional pedagogy still works for those students who could be disciplined enough to not use AI on problem sets and projects. But that's not a realistic expectation. You can learn how to use AI on the job. It is very rare someone gains the baseline knowledge of algorithms on the job.
Programs are swapping capstone projects and courses for comprehensive oral examinations and that's partly from input from industry that there's been a skill drop. But that feels like a panic stopgap and we really are going to struggle with this for a while.
Yes, that dynamic is essentially what this is proposing to solve. Microsoft is acknowledging that junior engineers will be less in demand due to AI (and presumably it is experiencing that itself) because economics, i.e. when supervised by senior engineers, AI is way cheaper and faster than junior engineers.
These economics would choke off the pipeline that turns junior engineers into senior engineers, which would still presumably needed to supervise the AI.
I don't see the irrationality here?
And yes, bottlenecks at MSFT (or any large corporation) are unrelated to kLOC/hour, which I would argue are primarily because of human factors... which also AI threatens to obviate: https://news.ycombinator.com/item?id=50121996
You're replacing juniors with AI to save money (because again, I doubt there are velocity gains to be had at MSFT), and then you're spending money to keep junior SWEs on payroll and train them in a parallel program.
There's no way this is cheaper than just letting some junior SWEs actually do the coding on the actual projects you're building, right? And gain experience that way?
It makes perfect sense when you accept that the LLM hype was mostly delusional. Nobody invested in that lie can admit it just yet because there's still some juice left. Never mind all the "I told you so"s ever since 2022 market dip.
If we freeze AI models at their current stage of development, then yes. If not, given the rate at which models are improving (compare Claude today to Claude 1 year ago), in 3 to 5 years software development could be fully automated.
Possibly. But even if not, how many AI model developers do we need? We will likely need a nonzero number, but it could be very few compared to the total number of software developers we have today.
Let's see, my grandparents were about 160cm when they were 35. My parents were about 170cm when they were 35 and I am about 180cm around 35. You are saying I can expect my grandchildren to be around 200cm at that age and their grandchildren 220cm?
Or we could assume since new data is increasingly AI generated then the next generation of models are inherently born out of incestuous relationships so I would imagine Claude's grandson is going to be a dwarf.
Of course it is possible also that Claude will marry a chinese model (since it's asian odds are it is also smarter) bringing some diversity into the "gene" pool. Or better yet, a Russian supermodel.
Are you sure that AI model progression and the height of your family members are 35 is related? It seems unlikely but I guess you'd know better than I.
"We're running out of data, so LLM progress is hitting a wall" was a prominent late-2024 narrative. Back then many experts including Ilya used the word "plateau" when describing LLM progress. No one is using that word now, and there is no evidence we are about to reach a plateau, even though we ran out of data long ago.
If we didn't have any humans before your grandparents, and these were the only data points we have to extrapolate from, then, yes, this would be a very reasonable prediction.
Some of that is long pre-existing, where too many companies would go: "Hey, let someone else hire juniors and train them up, we'll them away after that."
Not that the new LLM-as-junior trend doesn't make it worse.
Is there evidence that overall software developer positions are declining relative to other jobs? It wouldn't surprise me if Jevons paradox applied here.
There is mixed evidence on the impact of AI on developer jobs, but on the balance it seems that to the extent AI is impacting dev jobs, it is mostly the junior roles that are being affected.
There is even some evidence that suggests senior roles are more in demand (and getting correspondingly higher pay too) which gels with the common observation that senior workers are more effective at managing the AI which is doing the work of (and hence displacing) junior workers.
Asking a chatbot or Google AI overviews should surface relevant studies.
Thanks! I've heard numerous examples of this. E.g. paint rollers, and later paint sprayers, made painting so much more efficient and cheaper that people wanted their houses and apartments painted more often, ultimately increasing house painter employment. But I didn't realize the phenomenon had a name.
The broader topic of marginal utility is worth looking into if this is interesting to you.
The reason why AI induces more demand is more interesting than simply observing a correlation. At the root of the paradox is the dissatisfaction with AI as it raises the bar for human work. In that sense, AI functions like training wheels more than actually doing anything useful on its own. More people can ride the bike well as a result.
This is the "law of diminishing marginal utility". In other words, LLMs are an NLP-powered google search 2.0. That sums up pretty well why AI hit a plateau a couple of years ago. There's only so much training data and humans become more efficient and creative once exposed to the exact information they were always looking for, but could never find in the past.
It’s really hard to get a good read on it because of the state of the post-COVID economy and job market.
Like the mass layoffs the last couple of years seemed to be primarily driven by the goofy hiring spree every company went on during the pandemic and the AI reasoning was only partly true and mostly a coverup for their mistakes in hiring in previous years.
On the other hand, the amount of garbage level devs that were being churned out of bootcamps and such in the years leading up to COVID was also due for some kind of correction and AI may be the catalyst for that correction.
I’ve been around devs making an easy 6 figures that did nothing but copy/paste code from the internet or other parts of the codebase and spew jank into the repo because management needs the work done yesterday.
Your comment completely misses the point. AI is fully capable of replacing shitty developers that cannot program and cannot write original code of their own. This is not a technology problem, missing skills problem, or even an AI problem.
It’s a problem of missing training and missing mentorship coupled with weak leaders who would rather throw their people to the wolves armed with crayons and coloring books until they just figure it out.
Before AI was a thing once a large organization hired too many shitty developers you could occasionally clean house by laying some off and ordering the rest to own more responsibilities. Once AI starts replacing the weakest among us the scapegoats are gone. Then what? Eventually the finger pointers can only point at themselves once there is nobody else to point at.
AI will make developers infinitely more productive. You will need one or two guys to do the work that requires a whole team before. That means cheaper software.
The demand for software is huge, the number of manual processes that can be automated in any organisation, small or large, is humongous. The Jevons paradox will play at full, as software gets cheaper we will consume even more of it, and I think there will paradoxically be more software developer jobs.
52 open software engineering positions in a company of over 100k. Most will likely to be filled by internal folks getting downsized. Who are they kidding?
Im sorry for being so negative but MS sold software engineers (and US high school and college students) down the river so long ago, the nerve of them now even pretending to care is just so offensive. This business has gotten so rotten, the best thing I can tell anyone is to find something rewarding to do with your life.
Curious how realistic the skills learned in such a program are still applicable in 1-2 years. This industry is moving very fast, it feels like the self motivated, self starter, self learner has been thriving. Probably a good thing to have such a program, but I have doubts.
The YC meta model is probably a better school than school now. I'm sure YC knows that.
If you need to be told what to do, it's a short step to your AI replacement being told what to do; money will ensure it. If your job is to discover and capture new things in an exploding possibility space, that's a career.
Schools should teach hacking, but it doesn't seem that's what this program is doing.
"software engineer" is likely a career long gone. "Product engineer" and architect is probably the only software-adjacent title that will have consistent demand for awhile.
Basically still just a prompt jockey but knows the difference between outputs that are good and bad.
Yep. If you think your job involves "writing code" as the core part of your responsibilities, write yourself off immediately. That career is dead and dusted. Sadly most companies still haven't realized this, given how few Architect openings exist (and those that do, most are asking for 20+ years of exp lol)
In my professional experience most people I encountered that held the architect title didnt really know anything about proper software engineering and just knew how to talk well and hold meetings with many different stakeholders - which tbf is not a trivial skill, but I can’t imagine everyone becoming architects and no one doing actual work they understand.
Definition of what the role actually means can change as well over time, in the same way that a civil engineer also isn’t physically doing the construction.
Most companies aren't gonna need an architect, but if you can tell and demonstrate a good story around quality/risk/stability they might be interested in that.
I was a huge AI skeptic from the beginning through ~2025. But once Opus came out, it's pretty obvious writing code by hand is an obsolete skill on its own. You still need to know how to read code, how to know what good/bad code looks like, and if it matches what you're truly asking for. But there's really no reason to be typing code with your own fingers in 2026 and beyond.
2024 me would be shocked at current me saying this.
> writing code by hand is an obsolete skill on its own. You still need to know how to read code, how to know what good/bad code looks like,
It's my impression that the best way to be able to read code and distinguish good from bad is to write code. Get burned a few times and you know why certain patterns are a bad idea. You don't internalize that equally well from a powerpoint slide.
Compare this to math. Every successful mathematician has gone through the motions of proving certain basic theorems in math. Like Euclid's proof that there are infinitely many prime numbers. And then they went through related exercises. This was all learned with sweat. The statement above is akin to saying there is no point in doing any proofs by hand anymore, just read one and you will have sufficient understanding of mathematics and become a good mathematician. I don't think that's true. (And I also believe that many sw eng people would be better at their job had they groked the proof of the halting problem.)
The assumption here is that senior engineers are still needed to review AI-generated code. That assumption might not be valid for very long. I find myself reviewing AI-generated code less and less because the code quality is very good -- and getting better every month. Soon reviewing AI-generated code will be like manually verifying the results of a calculator--it is a waste of time. At that point all you need is an architect. I predict senior software engineers will be obsolete within a year.
As a principal, I still catch people submitting shitty AI code on a weekly basis. Their brains are rotting to the point that they can't tell if the code is doing the correct thing or not.
Just left my programming job due to this. Stopped thinking, stopped caring. AI destroyed my coding joy, so I’m finished with that. Leadership challenges next - hope to use my brain a bit more.
I've found myself engaging more with AI writing code. There is no easy time during the work day. I'm talking to product about what they want, communicating with AI to write it, and verifying that it covers what was asked for. And the better I get at it, the more of the product I need to learn at a faster pace. If I find some downtime, I work on additional features at the same time, something I wouldn't do on my own.
Quite simply, I'm more mentally drained at the end of a day working with AI than I am a normal day of coding. There is still a lot of energy and thought that needs to go into what needs to be written.
Anecdotally, I find the flow-vs-drained state is more about who uses the LLM and whether I'm in a position to know the difference between artifact and intent.
1. [Good] I know what I wanted, I have a concept of what "correct" looks like. I ask the LLM to do the thing, and I check if it's the thing I intended. Even if it's annoyingly wrong, I can fix it up.
2. [Bad] Someone else had a concept once. They put it through an LLM to generate something that reaches me, a ticket, PR, or review comment. However now I have to constantly guess which parts of the thing are "real" and which are nonsense the human never actually intended or even noticed.
The latter is worsened by how reading means inferring/simulate the mind of the writer, but LLM output is not from a mind we typically simulate. Subtle persistent wrongness is exhausting.
The issue is the higher ups don't even seem to care. You just turn into the guy that's yelling at the clouds. I don't know what the future of this industry is.
As of now it is insufficient to outline the end-user requirements, AI will ask you a bunch of questions that requires you to make architectural decisions. I think within 3 to 5 years that might change as well.
If you say "yeah go with your own recommendation" every time you'll get something that'll at least kinda work sooner or later. Not sure you need an "architect."
But is that workflow enough in a competitive world anyway, or will there be an edge for people who do more than "outline the end-user requirements" like it's the waterfall 70s?
> "With so many companies worldwide faced with upheaval due to AI in software development, I believe this program will become a template for rapidly leveling up new developers across the industry."
This seems cognitively dissonant with the AI upheaval resulting in the industry needing fewer developers. Maybe they want cheaper developers?
Based on posts by people involved like Scott Hanselman and Mark Russinovich, this initiative is a direct response to the long-term threat of AI completely replacing junior developers, and thus cutting off the pipeline of experience that turns junior developers into senior developers, who are still needed to leverage AI effectively.
There is of course the possibility that AI gets good enough that even senior engineers won’t be needed, but at this point it seems unlikely. My reasoning is that even expert humans make mistakes, which is why we need other humans to collaborate and check each others’ work (which, if you think about it, is a source of a lot of the processes and bureaucracy at companies; trivial example: code reviews.)
So even if AI gets to the level of human experts, this dynamic will not change... unless we hit ASI, where the AI far exceeds human capabilities. At which point we’ve hit the singularity and all bets are off.
Short of that, however, human experts will be needed, which is why programs like this are needed: to future proof against the short term economic pressures to minimize junior workers which will impact the long term pipeline that produces senior experts.
OK, so let me get this straight: Microsoft replaces junior SWEs with AI... and this presumably doesn't really let the company execute any faster, because bottlenecks at MSFT are mostly unrelated to kLOC/hour. But okay. And then they establish a parallel program for people who are kept on Microsoft payroll... while coding in an academic setting... so that they can progress through the rungs of the SWE ladder and eventually be the ones supervising the AI.
This is really what we're doing? This makes sense? Am I taking crazy pills?
The bigger issue is that undergrad students learning in the era of AI is really a terrible environment for actually learning the fundamental theory that gives someone a long and productive career. And the workplace has never been a good place to learn that stuff. The talent pipeline from colleges has always been a steady trickle despite eras when signups into computer science was a flood. With AI, that trickle has become a scant drip.
Yes, I believe programs like this aim to address this issue too. The traditional ways of learning might no longer be sufficient or reliable enough to produce appropriately skilled workers in the era of AI. Maybe something like this program or other apprenticeship programs would bridge that gap by focusing on juniors who are truly interested in the field.
The traditional pedagogy still works for those students who could be disciplined enough to not use AI on problem sets and projects. But that's not a realistic expectation. You can learn how to use AI on the job. It is very rare someone gains the baseline knowledge of algorithms on the job.
Programs are swapping capstone projects and courses for comprehensive oral examinations and that's partly from input from industry that there's been a skill drop. But that feels like a panic stopgap and we really are going to struggle with this for a while.
Yes, that dynamic is essentially what this is proposing to solve. Microsoft is acknowledging that junior engineers will be less in demand due to AI (and presumably it is experiencing that itself) because economics, i.e. when supervised by senior engineers, AI is way cheaper and faster than junior engineers.
These economics would choke off the pipeline that turns junior engineers into senior engineers, which would still presumably needed to supervise the AI.
I don't see the irrationality here?
And yes, bottlenecks at MSFT (or any large corporation) are unrelated to kLOC/hour, which I would argue are primarily because of human factors... which also AI threatens to obviate: https://news.ycombinator.com/item?id=50121996
> I don't see the irrationality here?
You're replacing juniors with AI to save money (because again, I doubt there are velocity gains to be had at MSFT), and then you're spending money to keep junior SWEs on payroll and train them in a parallel program.
There's no way this is cheaper than just letting some junior SWEs actually do the coding on the actual projects you're building, right? And gain experience that way?
[dead]
It makes perfect sense when you accept that the LLM hype was mostly delusional. Nobody invested in that lie can admit it just yet because there's still some juice left. Never mind all the "I told you so"s ever since 2022 market dip.
This guy has been sitting in cash for 4 years.
It feels like it's all senior developers now and nobody new. At some point they will want someone new and won't be able to find them.
> At some point they will want someone new
If we freeze AI models at their current stage of development, then yes. If not, given the rate at which models are improving (compare Claude today to Claude 1 year ago), in 3 to 5 years software development could be fully automated.
Do you have anything to back up this 3-5 years claim or is this another hollow 'full self driving by the end of next year' promise?
we dont' need to "freeze" models if next versions aren't a huge improvement over previous ones. That seems to be case now.
Really? Only last month OpenAI declared the "AGI era" is here.
Do you keep a lot of Tesla and SpaceX stocks in your portfolio?
OpenAI and Anthropic say a lot of shit to keep investments flowing in while they try to find an avenue for profitability.
Including development of AI models?
Possibly. But even if not, how many AI model developers do we need? We will likely need a nonzero number, but it could be very few compared to the total number of software developers we have today.
Called "recursive self-improvement"
Well, if you automate everything, the bad parts will still be there, then you need real engineers to fix them
Yes, however like in modern factories, very few of them.
Are you sure that's how this works?
Let's see, my grandparents were about 160cm when they were 35. My parents were about 170cm when they were 35 and I am about 180cm around 35. You are saying I can expect my grandchildren to be around 200cm at that age and their grandchildren 220cm?
Or we could assume since new data is increasingly AI generated then the next generation of models are inherently born out of incestuous relationships so I would imagine Claude's grandson is going to be a dwarf.
Of course it is possible also that Claude will marry a chinese model (since it's asian odds are it is also smarter) bringing some diversity into the "gene" pool. Or better yet, a Russian supermodel.
Are you sure that AI model progression and the height of your family members are 35 is related? It seems unlikely but I guess you'd know better than I.
That's their point, we can't assume model capabilities will go up linearly forever just because they have increased quickly in the past
"We're running out of data, so LLM progress is hitting a wall" was a prominent late-2024 narrative. Back then many experts including Ilya used the word "plateau" when describing LLM progress. No one is using that word now, and there is no evidence we are about to reach a plateau, even though we ran out of data long ago.
The way Claude writes code comments there will be no shortage of data.
If we didn't have any humans before your grandparents, and these were the only data points we have to extrapolate from, then, yes, this would be a very reasonable prediction.
Some of that is long pre-existing, where too many companies would go: "Hey, let someone else hire juniors and train them up, we'll them away after that."
Not that the new LLM-as-junior trend doesn't make it worse.
Is there evidence that overall software developer positions are declining relative to other jobs? It wouldn't surprise me if Jevons paradox applied here.
There is mixed evidence on the impact of AI on developer jobs, but on the balance it seems that to the extent AI is impacting dev jobs, it is mostly the junior roles that are being affected.
There is even some evidence that suggests senior roles are more in demand (and getting correspondingly higher pay too) which gels with the common observation that senior workers are more effective at managing the AI which is doing the work of (and hence displacing) junior workers.
Asking a chatbot or Google AI overviews should surface relevant studies.
I have been feeling like “mostly junior roles being affected” has been a false indicator or false narrative.
Junior roles are always affected when industries have high unemployment rates.
I don’t think AI has eliminated the need for junior engineers, nor will it, it’s just that companies have their pick of available experienced hires.
I would think that junior engineers would make more sense in an era where AI can fix and catch rookie mistakes.
> Jevons paradox
Thanks! I've heard numerous examples of this. E.g. paint rollers, and later paint sprayers, made painting so much more efficient and cheaper that people wanted their houses and apartments painted more often, ultimately increasing house painter employment. But I didn't realize the phenomenon had a name.
The broader topic of marginal utility is worth looking into if this is interesting to you.
The reason why AI induces more demand is more interesting than simply observing a correlation. At the root of the paradox is the dissatisfaction with AI as it raises the bar for human work. In that sense, AI functions like training wheels more than actually doing anything useful on its own. More people can ride the bike well as a result.
This is the "law of diminishing marginal utility". In other words, LLMs are an NLP-powered google search 2.0. That sums up pretty well why AI hit a plateau a couple of years ago. There's only so much training data and humans become more efficient and creative once exposed to the exact information they were always looking for, but could never find in the past.
https://en.wikipedia.org/wiki/Marginal_utility
Exactly. At least in my part of the world (hardware) we are hiring more software folks these days and fewer pure hardware
It’s really hard to get a good read on it because of the state of the post-COVID economy and job market.
Like the mass layoffs the last couple of years seemed to be primarily driven by the goofy hiring spree every company went on during the pandemic and the AI reasoning was only partly true and mostly a coverup for their mistakes in hiring in previous years.
On the other hand, the amount of garbage level devs that were being churned out of bootcamps and such in the years leading up to COVID was also due for some kind of correction and AI may be the catalyst for that correction.
I’ve been around devs making an easy 6 figures that did nothing but copy/paste code from the internet or other parts of the codebase and spew jank into the repo because management needs the work done yesterday.
Your comment completely misses the point. AI is fully capable of replacing shitty developers that cannot program and cannot write original code of their own. This is not a technology problem, missing skills problem, or even an AI problem.
It’s a problem of missing training and missing mentorship coupled with weak leaders who would rather throw their people to the wolves armed with crayons and coloring books until they just figure it out.
Before AI was a thing once a large organization hired too many shitty developers you could occasionally clean house by laying some off and ordering the rest to own more responsibilities. Once AI starts replacing the weakest among us the scapegoats are gone. Then what? Eventually the finger pointers can only point at themselves once there is nobody else to point at.
> Maybe they want cheaper developers?
This. I imagine they are showcasing a template to hire a new breed of cheap highly replaceable/fireable devs.
AI will make developers infinitely more productive. You will need one or two guys to do the work that requires a whole team before. That means cheaper software.
The demand for software is huge, the number of manual processes that can be automated in any organisation, small or large, is humongous. The Jevons paradox will play at full, as software gets cheaper we will consume even more of it, and I think there will paradoxically be more software developer jobs.
52 open software engineering positions in a company of over 100k. Most will likely to be filled by internal folks getting downsized. Who are they kidding?
https://apply.careers.microsoft.com/careers?start=0&pid=1970...
I see 517 openings on 52 pages of results.
Entry + intern = 23 jobs
It's PR to soften the blow of reality
> Who are they kidding?
Themselves.
Usually the government too.
Im sorry for being so negative but MS sold software engineers (and US high school and college students) down the river so long ago, the nerve of them now even pretending to care is just so offensive. This business has gotten so rotten, the best thing I can tell anyone is to find something rewarding to do with your life.
Curious how realistic the skills learned in such a program are still applicable in 1-2 years. This industry is moving very fast, it feels like the self motivated, self starter, self learner has been thriving. Probably a good thing to have such a program, but I have doubts.
The industry feels like it’s moving at lightspeed but I feel the fundamentals that were true 30-50 years ago still hold up for most real life work
The YC meta model is probably a better school than school now. I'm sure YC knows that.
If you need to be told what to do, it's a short step to your AI replacement being told what to do; money will ensure it. If your job is to discover and capture new things in an exploding possibility space, that's a career.
Schools should teach hacking, but it doesn't seem that's what this program is doing.
Dude we have enough hacks in this industry. We need more people actually curious about understanding problems, and not just "shipping".
Microsoft? Maybe they’re hopeful the next admin will make OPT cheap labor again.
Developers developers developers https://www.youtube.com/watch?v=Vhh_GeBPOhs
Wasn't Carnegie Mellon once a respected university, at least in mny times? When did it go downhill like this?
Now that you have learnt Software Engineering, hunting for a job is easy just make 10^10^10^10 job applications.
Start your own business.
how?
Just slop them, bro
At this point AI is learning faster than I've ever seen a human learn ...
"software engineer" is likely a career long gone. "Product engineer" and architect is probably the only software-adjacent title that will have consistent demand for awhile.
Basically still just a prompt jockey but knows the difference between outputs that are good and bad.
Yep. If you think your job involves "writing code" as the core part of your responsibilities, write yourself off immediately. That career is dead and dusted. Sadly most companies still haven't realized this, given how few Architect openings exist (and those that do, most are asking for 20+ years of exp lol)
In my professional experience most people I encountered that held the architect title didnt really know anything about proper software engineering and just knew how to talk well and hold meetings with many different stakeholders - which tbf is not a trivial skill, but I can’t imagine everyone becoming architects and no one doing actual work they understand.
Definition of what the role actually means can change as well over time, in the same way that a civil engineer also isn’t physically doing the construction.
Most companies aren't gonna need an architect, but if you can tell and demonstrate a good story around quality/risk/stability they might be interested in that.
[dead]
I was a huge AI skeptic from the beginning through ~2025. But once Opus came out, it's pretty obvious writing code by hand is an obsolete skill on its own. You still need to know how to read code, how to know what good/bad code looks like, and if it matches what you're truly asking for. But there's really no reason to be typing code with your own fingers in 2026 and beyond.
2024 me would be shocked at current me saying this.
> writing code by hand is an obsolete skill on its own. You still need to know how to read code, how to know what good/bad code looks like,
It's my impression that the best way to be able to read code and distinguish good from bad is to write code. Get burned a few times and you know why certain patterns are a bad idea. You don't internalize that equally well from a powerpoint slide.
Compare this to math. Every successful mathematician has gone through the motions of proving certain basic theorems in math. Like Euclid's proof that there are infinitely many prime numbers. And then they went through related exercises. This was all learned with sweat. The statement above is akin to saying there is no point in doing any proofs by hand anymore, just read one and you will have sufficient understanding of mathematics and become a good mathematician. I don't think that's true. (And I also believe that many sw eng people would be better at their job had they groked the proof of the halting problem.)
>how to know what good/bad code looks like, and if it matches what you're truly asking for
And how you would know to do that, if you're not writing code?
The same way many of us ask for the Assembly output of our compiler, while never having writing Assembly in decades.
Maybe you think there’s no reason for you to be writing code by hand anymore, but it doesn’t follow that the entire skill is obsolete.
Same. "Writing Code" is dead. Reading code, understanding code, and knowing how to manage products and services is still very much alive.
> students will take DevOps: Engineering for Secure Development and Deployment
... in Github Actions? What a great way to discourage them from entering the industry.
Can anyone share syllabi for these?
It’s a glorified PR campaign disguised as a training program.
“So you type your request into this chat window…”
Training is never a problem. The feedback is the essential requirement of human learning process, and AI cut it off.
[dead]
The assumption here is that senior engineers are still needed to review AI-generated code. That assumption might not be valid for very long. I find myself reviewing AI-generated code less and less because the code quality is very good -- and getting better every month. Soon reviewing AI-generated code will be like manually verifying the results of a calculator--it is a waste of time. At that point all you need is an architect. I predict senior software engineers will be obsolete within a year.
As a principal, I still catch people submitting shitty AI code on a weekly basis. Their brains are rotting to the point that they can't tell if the code is doing the correct thing or not.
Just left my programming job due to this. Stopped thinking, stopped caring. AI destroyed my coding joy, so I’m finished with that. Leadership challenges next - hope to use my brain a bit more.
You won’t, leadership requires even less brain.
Good leadership requires brains. Average leadership is attainable without brains.
Management surveys have shown that 90% of managers rate themselves as being in the top 10% of management.
I have no evidence but suspect most of the actual top 10% of managers are in the other 10%.
Well what kind of leader could you be if you didn't even have self belief?
A good one.
Brains, found in other people's bodies.
I've found myself engaging more with AI writing code. There is no easy time during the work day. I'm talking to product about what they want, communicating with AI to write it, and verifying that it covers what was asked for. And the better I get at it, the more of the product I need to learn at a faster pace. If I find some downtime, I work on additional features at the same time, something I wouldn't do on my own.
Quite simply, I'm more mentally drained at the end of a day working with AI than I am a normal day of coding. There is still a lot of energy and thought that needs to go into what needs to be written.
> mentally drained
Anecdotally, I find the flow-vs-drained state is more about who uses the LLM and whether I'm in a position to know the difference between artifact and intent.
1. [Good] I know what I wanted, I have a concept of what "correct" looks like. I ask the LLM to do the thing, and I check if it's the thing I intended. Even if it's annoyingly wrong, I can fix it up.
2. [Bad] Someone else had a concept once. They put it through an LLM to generate something that reaches me, a ticket, PR, or review comment. However now I have to constantly guess which parts of the thing are "real" and which are nonsense the human never actually intended or even noticed.
The latter is worsened by how reading means inferring/simulate the mind of the writer, but LLM output is not from a mind we typically simulate. Subtle persistent wrongness is exhausting.
The issue is the higher ups don't even seem to care. You just turn into the guy that's yelling at the clouds. I don't know what the future of this industry is.
> At that point all you need is an architect.
What value would an architect provide that a SOTA LLM can't?
As of now it is insufficient to outline the end-user requirements, AI will ask you a bunch of questions that requires you to make architectural decisions. I think within 3 to 5 years that might change as well.
If you say "yeah go with your own recommendation" every time you'll get something that'll at least kinda work sooner or later. Not sure you need an "architect."
But is that workflow enough in a competitive world anyway, or will there be an edge for people who do more than "outline the end-user requirements" like it's the waterfall 70s?
The value of knowing when to say no.
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