> "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?
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.
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?
Is there evidence that overall software developer positions are declining relative to other jobs? It wouldn't surprise me if Jevons paradox applied here.
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.
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.
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.
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.
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 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?
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 my final mood often depends on whether I know the origin and trustworthiness of the stuff in front of me, ex:
1. [Good] I know what I wanted, I have a concept of what "correct" looks like. I use the LLM to generate an output, and I check it. Even if it's annoyingly wrong, I can fix it up.
2. [Bad] Someone else had a concept, and they put it through an LLM to generate something (Ticket, PR, review comment.) However the provenance is lost, I have to guess which parts of the thing are "real" and which are nonsense the human never actually intended or even noticed.
I think some of the latter drain comes from how reading means inferring/simulate the mind of the writer, but LLM output is wrong enough that little warning flags never stop popping up.
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.
This seems cognitively dissonant with the AI upheaval resulting in the industry needing fewer developers. Maybe they want cheaper developers?
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.
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?
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.
Basically still just a prompt jockey but knows the difference between outputs that are good and bad.
2024 me would be shocked at current me saying this.
What value would an architect provide that a SOTA LLM can't?
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?
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 my final mood often depends on whether I know the origin and trustworthiness of the stuff in front of me, ex:
1. [Good] I know what I wanted, I have a concept of what "correct" looks like. I use the LLM to generate an output, and I check it. Even if it's annoyingly wrong, I can fix it up.
2. [Bad] Someone else had a concept, and they put it through an LLM to generate something (Ticket, PR, review comment.) However the provenance is lost, I have to guess which parts of the thing are "real" and which are nonsense the human never actually intended or even noticed.
I think some of the latter drain comes from how reading means inferring/simulate the mind of the writer, but LLM output is wrong enough that little warning flags never stop popping up.